The impact of the one-stop shop for business registration in the Dominican Republic

Bobic, Vida; Delgado, Lucía; Gerardino, María Paula; Hennessey, Michael; MartinezCarrasco, José Working Paper The impact of the one-stop shop for business registration in the Dominican Republic IDB Working Paper Series, No. IDB-WP-01415 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Bobic, Vida; Delgado, Lucía; Gerardino, María Paula; Hennessey, Michael; Martinez-Carrasco, José (2023) : The impact of the one-stop shop for business registration in the Dominican Republic, IDB Working Paper Series, No. IDB-WP-01415, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0005089 This Version is available at: https://hdl.handle.net/10419/289933 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode The Impact of the One-Stop Shop for Business Registration in the Dominican Republic Vida Bobic Lucía Delgado María Paula Gerardino Michael Hennessey José Martinez-Carrasco IDB WORKING PAPER SERIES Nº IDB-WP-01415 May 2023 Office of Strategic Planning and Development Effectiveness Inter-American Development Bank May 2023 The Impact of the One-Stop Shop for Business Registration in the Dominican Republic Vida Bobic Lucía Delgado María Paula Gerardino Michael Hennessey José Martinez-Carrasco Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library The impact of the one-stop shop for business registration in the Dominican Republic / Vida Bobic, Lucía Delgado, María Paula Gerardino, Michael Hennessey, José Martinez-Carrasco. p. cm. — (IDB Working Paper Series; 1415) Includes bibliographic references. 1. Small business-Dominican Republic. 2. Informal sector (Economics)-Dominican Republic. 3. Self-employed-Dominican Republic. 4. Gender mainstreaming-Dominican Republic. I. Bobic, Vida. II. Delgado, Lucía. III. Gerardino, María. IV. Hennessey, Michael. V. Martinez, José. VI. Inter-American Development Bank. Office of Strategic Planning and Development Effectiveness. VII. Inter-American Development Bank. Institutions for Development Sector. VIII. Series. IDB-WP-1415 Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. 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The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. http://www.iadb.org 2023 The Impact of the One-Stop Shop for Business Registration in the Dominican Republic Vida Bobic, Lucía Delgado, María Paula Gerardino, Michael Hennessey, and José Martinez-Carrasco* May 2023 Abstract Digital one-stop shops for firm registration can significantly reduce costs and increase access to information for firms entering the formal sector. This paper examines the impact of a nationwide program with a one-stop registration shop and lower registration fees. In addition to analyzing its impact on the number of firms registering in the formal sector, this study explores how the program reshapes the labor market for women and men. The empirical setting, the Dominican Republic, is characterized by high levels of firm and labor informality. The government launched the digital one-stop shop called Formalízate in 2013. To analyze its impact, this paper takes advantage of the sequential rollout of the program across provinces in the country. Results show that the launch of the program in a province is associated with a greater number of micro firms entering the formal market. Interestingly, these firms are concentrated in sectors in which informality was high prior to rollout of the program, especially the commerce and tourism sector. In addition, the results show that women’s participation in the labor forced is impacted by the program, but men’s participation is not. More specifically, the presence of Formalízate increased women’s participation in the labor market as self-employed entrepreneurs. JEL Classifications: O17; O12; J40 Keywords: formalization, entry regulation, impact evaluation, micro firms, self-employment ______________ *Lucía Delgado ([email protected]), María Paula Gerardino (m[email protected]g), Michael Hennessey ([email protected]), and José Martinez-Carrasco ([email protected]) are with the Inter-American Development Bank (IDB); Vida Bobic (v[email protected]) is with the International Finance Corporation. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the IDB, its Board of Directors, or the countries they represent. The authors thank Paloma Corporán, Antonio María Giraldi, Ledys Feliz for helpful comments and discussions. Particular thanks go to Bymayri de Leon for support and extensive conversations on the design and implementation of the program. The authors are also are grateful to the Ministry of Industry, Commerce and SMEs and the General Directorate of Internal Taxes of the Dominican Republic for their excellent collaboration. 2 1. Introduction In many countries, economic activity occurs in the informal sector, where micro and small firms are typically the main participants. For these firms, formality is a choice that depends on several factors, including the costs of registration in the formal sector and the regulation burden, financial market development, and the quality of the legal system. High levels of informality are a key characteristic of lowand middle-income countries. There are several consequences of informality that are of interest to policymakers, including the reduction of the expected tax base, which negatively impacts the provision of public goods for society; the lack of coverage and protection of workers 1 (i.e., pension systems and health insurance); and the potential inefficient allocation of resources, as formal and informal firms compete in the same market but have different marginal costs. Informal firms have lower regulatory costs than formal firms (i.e., by not paying taxes and having lower labor costs thanks to their reliance on informal labor), resulting in a misallocation of resources in the economy (Hsieh and Klenow 2009). By proposing to bring informal firms into the formal sector, the government does not just aim to increase its tax base, but also tries to provide a range of benefits to these firms, including access to formal financial sector services, and the ability to take advantage of various government services, become a supplier to the public sector, and export their products. These benefits can improve firm productivity and facilitate growth. Governments of many developing countries have undertaken significant efforts to increase formalization rates through programs and policies focused on either firms or workers. Among the former, programs have typically focused on incentivizing firm formalization through tax breaks, information campaigns, simplified registration procedures or cost reductions, reductions of payroll taxes, and interventions enforcing formalization (Bruhn and McKenzie 2014). This paper examines the impact of Formalízate, the one-stop registration shop in the Dominican Republic introduced in October 2013. The goal of the shop is to increase firms’ incentives to be part of the formal sector by reducing the time and costs required to register and by providing information about the different processes involved. By exploiting the fact that the introduction of Formalízate took place at different times and in different provinces of the country, the analysis uses a staggered difference-in-differences model to estimate the impact on the number of firms operating formally at the provincial level. The focus is on examining the effects of the program on firm registration and exploring its heterogeneous effects on employment. The present study finds a positive and significant impact of Formalízate on the number of micro firms entering the formal sector, accounting for a nearly 30 percent increase relative to pretreatment average values. These firms are concentrated in sectors where informality was high before the program (i.e., services, commerce, and tourism); in these sectors most firms are very small and female representation in the labor force is high. Furthermore, significant impacts were found on female labor force participation, with women entering the labor market as self-employed entrepreneurs once the one-stop shop is in place. Results show that a significant reduction in time and registration costs is associated with a higher number of micro firms entering the formal market each year. This follows from the fact that 1 Please note that in the Spanish version of this document, non-inclusive masculine grammar is used regardless of the sex of the persons referred to. 3 the most impacted firms were micro firms for which the one-time cost reduction is more likely to make a difference. This result is in line with other studies such as Klapper and Love (2010), who used a cross-country analysis to establish that fairly large regulatory changes are needed to generate any meaningful effects in terms of formality rates. According to their results, costs need to decrease by at least 40 percent to have an impact, and reforms should ideally target multiple aspects of the problem at the same time. In the case of Formalízate, the reduction in the registration cost was very large, going from US$1,000 to US$150. However, it is important to mention that even with reforms of that scope, some studies that establish statistically significant impacts find that the size of such impacts is fairly modest. For instance, in a study of a Portuguese policy reform that decreased firm registration fees by 80 percent and the time needed to register from months to hours, Branstetter et al. (2013) found that almost 5,000, mostly smaller, firms were created as a result over the course of two years. The empirical findings of this paper contribute to the literature by studying the effects of reducing the costs and improving the access to information for firms to formalize in a context characterized by having very high firm and labor informality. By exploring heterogeneous effects and looking at impacts on the labor market by gender, it is possible to determine that some of the mechanisms that could generate an increase in business registration induced by the program include the formalization of existing microenterprises in sectors with high informality and the increase of labor force participation as self-employed entrepreneurs. This latter result is particularly important for women, who traditionally face barriers to accessing formal employment and may be turning to formal entrepreneurship as a way to generate income and achieve economic autonomy while being able to access Social Security. The remainder of the paper is organized as follows. Section 2 provides a literature review, and Section 3 describes the background of the Formalízate Program. Section 4 presents data sources, Section 5 specifies the empirical methodology, Section 6 presents the main results, and Section 7 reports robustness checks. The final section, Section 8, presents the conclusions from the analysis. 2. Literature Review In a recent quantitative analysis of the literature on formalization programs in lowand middleincome countries, Jessen and Kluve (2021) found mixed evidence regarding their effectiveness. However, the literature does suggest that some types of interventions are less likely to be successful than others. For instance, evidence is fairly consistent regarding interventions that focus on information campaigns to address the potential lack of knowledge regarding the process and benefits of formalization. In an experimental study in Bangladesh, De Giorgi and Rahman (2013) found that providing information to firms regarding the procedures and benefits of registration had no effect on formality. Neither were information campaigns successful in raising formality rates in Sri Lanka (De Mel, McKenzie, and Woodruff 2013) or Brazil (de Andrade, Bruhn, and McKenzie 2013), despite the fact that the campaigns were effective in increasing knowledge. This suggests that addressing the lack of knowledge is not sufficient, and that informal firms need an additional incentive to formalize. That incentive may come in the form of increased enforcement (i.e., potential fines) or cash payments to firms that register. For instance, a ”stick” 4 approach of the threat of tax or municipal authority fines does appear to have motivated firms in Bangladesh (De Giorgi, Ploenzke, and Rahman 2018) and Brazil (de Andrade, Bruhn, and McKenzie 2013) to register, though the size of the effect was quite small. De Mel, McKenzie, and Woodruff (2013) found that monetary incentives in the form of cash payments to informal firms also worked to raise formality rates, though it is unclear if such a policy would be scalable. Interventions that focus on simplifying the registration process and reducing the cost of formalization have met with somewhat greater success. For example, Bruhn (2011) found that Mexico’s SARE Program, which aimed to simplify the process of firm registration, had significant positive impacts: first, on formality (5 percentage points), with most of the increase coming from newly formed firms; and second, on employment and firm income. Using a different dataset, Kaplan, Piedra, and Seira (2011) found somewhat more modest benefits of the same program in both size and duration. Their results also suggest that most of the temporary increase in formalization was due to the registration of existing informal firms, rather than an increase in business formation. Positive effects on formality were also found by Branstetter et al. (2013) in a study of reforms in Portugal. On the other hand, in their experimental work in Brazil, de Andrade, Bruhn, and McKenzie (2013) found no effect of reforms based on simplification and cost reduction, which is similar to the results found in the non-experimental studies from Indonesia (Rothenberg et al. 2016), and Brazil (Rocha, Ulyssea, and Rachter 2018). For effects that go beyond formalization rates – outcomes such as revenues, profits, or employment rates – evidence is scarce, and positive impacts appear to often be limited to specific segments of the population – for instance, a small number of high-performing firms (De Mel, McKenzie, and Woodruff 2013) or medium-sized firms (McKenzie and Sakho 2010). A possible explanation for the limited impact of these programs taken together is a lack of complementary reforms, as the costs to firms of high taxes and other fees may offset any benefits of formalizing, especially for smaller firms. For example, firms in Brazil with more than two employees are required to use an accountant, which significantly increases the ongoing costs of formality and in fact is found to deter registration (de Andrade, Bruhn, and McKenzie 2013). Campos, Goldstein, and McKenzie (2018) found that informal firms in Malawi had little interest in registering with the tax authorities, as opposed to just obtaining a business registration certificate, which suggests that the cost of taxes tends to outweigh the perceived benefits of formalization, even with reduced costs associated with the latter. This is confirmed by Rocha, Ulyssea, and Rachter (2018), who found no effect of the reduction in the costs of entry into the formal sector but did find significant impacts when the ongoing costs of formality (i.e., taxes) are reduced. In fact, simplifying and reducing the tax burden of firms may also improve firm performance (Fajnzylber, Maloney, and Montes-Rojas 2011). 3. Background In 2014, the Dominican Republic was going through an economic boom. In fact, while the Latin America and Caribbean region was growing that year at 1.1 percent on average, Dominican GDP grew at 7.1 percent. At the same time, however, much of the job growth was concentrated in lowproductivity sectors, such as services or wholesale and retail trade. In this context, the 5 government decided to decisively tackle one of the most relevant challenges that characterize the economy, and those sectors in particular: the high rate of business and labor informality. The informal economy accounted for 44 percent of GDP in the Dominican Republic in 2013 (MICM-RD 2014), with business informality concentrated among micro businesses. 2 While almost all (97.2 percent) small and medium-sized firms were properly registered in 2013 as formal businesses, only 10.2 percent of micro firms were registered. Thus, to reduce informality, it was important to focus on the barriers and incentives those firms face to formally register their operations. This is particularly relevant given that micro firms are the most important source of employment in the country. In 2013, they represented 97.7 percent of all businesses in the Dominican Republic and accounted for 75.9 percent of all employment. 3 , 4 Therefore, it was plausible that an impact on formal registration of firms might also have an indirect effect on formal employment because of its requirement for payroll registration. 3.1 Formalízate: A One-Stop Shop for the Registration of Businesses The government of the Dominican Republic and the Inter-American Development Bank (IDB) designed the two-part programmatic policy-based loan series DR-L1072 and DR-L1121 to address the issues faced by the economy in terms of lagging productivity. 5 To tackle the specific problem of informality, one component of the program included launching a formalization web portal known as Formalízate to serve as a one-stop shop for the registration of businesses, including registration with the Chamber of Commerce, Internal Revenue Office, Social Security, National Industrial Property Office, and the Ministry of Labor’s employer registry. While the existing system required an applicant to fill out different sets of forms for each relevant institution or authority, Formalízate simplified this process by integrating all the forms, thus requiring significantly less paperwork. The goal of the program was to increase firms’ incentives to formalize their operations by significantly reducing the time and costs necessary to do so. In this, the program was quite successful. Prior to the launch of the program, registering a firm took on average 20 business days, at least seven in-person visits to various offices, and a cost of around US$1,000 (including the cost of intermediaries). 6 With Formalízate, only one in-person visit is needed, 7 the cost (US$150) is only a fraction of the previous amount, and the entire process can be completed in seven business days. 2 A business is considered formal when it complies with government registration requirements, which include registering the trade name, a tax identification number, and a payroll (MICM-RD 2014). 3 The estimates are based on the Report of the Module of Characterization of Micro, Small and Medium Enterprises in the 2013 National Household Survey of Multiple Purposes (ONE, 2015). 4 In terms of value added, micro businesses only represented 19.1 percent of GDP in 2013 (ONE, 2015). 5 For more details of the two programs, see www.iadb.org/en/project/DR-L1072 and www.iadb.org/en/project/DRL1121. 6 This is an average cost that includes payments to a law firm to manage the registration of the firm. It is important to note that the registration cost depends on the amount of social capital of the firm. 7 This is only the case if the payment is done online. If the payment cannot be completed online, a second visit is required. 12 entry of microenterprises, particularly those of smaller size. Consequently, it is expected that if there were any effect on formal employment, it would be concentrated on the generation of formal self-employment through the registration of these businesses. Although it is possible to observe formality for the self-employed population, the results suggest that the program induces an increase in self-employment among women. 13 Table 1. Staggered Difference-in-Differences Results Impact on Firms Entering and Exiting the Formal Sector at the Provincial Level Panel A. Micro Firms Firm Entry Firm Exit Firms Entering the Formal Sector by Economic Activity Firms Entering the Formal Sector Entry Rate Firms Exiting the Formal Sector in t+1 Exit Rate Agriculture Commerce and Tourism Services Other No controls ATT 45.23** 0.0117 7.542** 0.00567*** 1.975** 34.45** 4.522 4.485 (18.72) (0.0170) (3.535) (0.00218) (0.953) (15.33) (3.314) (2.882) N 288 288 288 288 288 288 288 288 P-value 0.016 0.49 0.033 0.009 0.038 0.025 0.172 0.12 Controls ATT 32.99* 0.00490 4.066 0.00410** 5.838*** 28.99* -2.018 0.512 (18.25) (0.0222) (3.088) (0.00209) (2.234) (16.84) (4.540) (4.077) N 272 272 272 272 272 272 272 272 P-value 0.071 0.825 0.188 0.05 0.009 0.085 0.657 0.9 Pre-treatment means 155.254 0.080 10.003 0.003 1.53 71.09 44.54 38.09 Panel B. Micro Firms with Annual Income below RD$5 million Firm Entry Firm Exit Firms Entering the Formal Sector by Economic Activity Firms Entering the Formal Sector Entry Rate Firms Exiting the Formal Sector in t+1 Exit Rate Agriculture Commerce and Tourism Services Other No controls ATT 41.45** 0.0113 7.365** 0.00566*** 1.964** 32.51** 3.443 3.722 (16.65) (0.0172) (3.444) (0.00218) (0.951) (13.95) (3.191) (2.786) Number 288 288 288 288 288 288 288 288 P-value 0.013 0.511 0.032 0.009 0.039 0.02 0.281 0.182 Controls ATT 29.67* 0.00769 4.007 0.00409** 5.823*** 27.87* -3.131 -0.566 (16.22) (0.0217) (3.087) (0.00209) (2.235) (15.25) (4.840) (4.054) Number 272 272 272 272 272 272 272 272 P-value 0.067 0.723 0.194 0.05 0.009 0.068 0.518 0.889 Pre-treatment means 130.348 0.068 8.739 0.003 1.25 57.97 39.08 32.06 Source: Prepared by the authors. Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Matching method to incorporate covariates: inverse probability weighting. Covariates at the provincial level: pre-treatment trend for population, pre-treatment trend for number of formal firms, and pre-treatment mean of average years of schooling. The pre-treatment mean is computed using values for 2014, the year prior to implementation of the Formalízate Program. Entry and exit rates are computed using the average number of firms registered in the formal sector in the pre-treatment period as denominator. ATT: average treatment effect on the treated. 14 Table 2. Staggered Difference in Difference Results Impact on Employment Rates of the Working-age Population at the Provincial Level Working Population Working Population Employed Population with Access to Social Security Self-Employed Population Employed Population All Men Women All Men Women All Men Women All Men Women No controls ATT 0.0103 - 0.00166 0.0220 0.0192 0.0159 0.0204* 0.00137 -0.0108 0.0164 0.00577 -0.00299 0.0170 (0.0136 ) (0.0193) (0.0191) (0.00982) (0.0229) (0.0120) (0.00997) (0.0125) (0.0133) (0.00685) (0.00926) (0.00882) Number 288 288 288 288 288 288 288 288 288 288 288 288 P-value .448 .931 .248 .05 .487 .088 .891 .388 .216 .4 .747 .054 Controls ATT 0.0045 3 0.00490 0.0146 0.0177 0.0180 0.0248** -0.00367 0.00226 - 0.00663 -0.00635 -0.0120 0.00156 (0.0106 ) (0.0132) (0.0115) (0.0112) (0.0165) (0.0110) (0.00938) (0.0119) (0.0130) (0.00998) (0.0112) (0.0110) Number 272 272 272 272 272 272 272 272 272 272 272 272 P-value .668 .71 .202 .114 .274 .024 .696 .85 .609 .525 .285 .887 Pre-treatment means 0.62 0.83 0.41 0.31 0.48 0.13 0.28 0.30 0.26 0.19 0.22 0.15 Source: Prepared by the authors. Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Matching method to incorporate covariates: inverse probability weighting. Covariates at the provincial level: pre-treatment trend for population, pre-treatment trend for number of formal firms, and pre-treatment mean of average years of schooling. Workingage population is defined as individuals between 14 and 60 years old. For the analysis time frame, we are not able to observe access to Social Security among selfemployed population. Pre-treatment mean is computed using values for 2014, the year prior to implementation of the Formalízate Program. ATT: average treatment effect on the treated. 15 7. Robustness Checks 7.1 Parallel Trends Placebo Test To test for deviations from the pre-treatment parallel trend assumption, results are estimated using a placebo treatment on the pre-treatment period. Only observations for the period prior to the implementation of Formalízate (2010–2013) are used. Provinces where Formalízate was carried out in 2014 and 1015 received a placebo treatment in 2011, and provinces treated after 2015 received a placebo treatment in 2012. The main conclusions are robust to this test. Table 3 shows results for estimated placebo treatment effects on firm entry and employment rates, respectively. No positive or significant placebo treatment effects are found for firm entry, firm exit, or self-employment rates. This supports the assumption of parallel trends between treated and not-yet-treated provinces in the pre-treatment period. Statistically significant placebo effects on firm entry are found when disaggregating by economic sector. However, for firms in the most relevant sector, services, the estimated placebo effect is negative. Finally, for firms in the agricultural sector, the placebo treatment is positive and significant. This is considered when deriving conclusions and the increase in the number of formalized firms is not attributed to the program for firms in the agricultural sector. 16 Table 3. Placebo Test for Staggered Difference-in-Differences Results Firms Entering and Exiting the Formal Sector at the Provincial Level Panel A. Micro Firms Working Population Firm Entry Firm Exit Self-Employed Population Firms Entering the Formal Sector Entry Rate Firms Exiting the Formal Sector in t+1 Exit Rate Women Main results including controls ATT 32.99* 0.00490 4.066 0.00410** -0.00663 (18.25) (0.0222) (3.088) (0.00209) (0.0130) Number 272 272 272 272 272 P-value .071 .825 .188 .05 .609 Placebo treatment on pre-treatment period ATT placebo 2.916 -0.0127 -3.234 -0.00644 -0.0214 (5.555) (0.00866) (1.987) (0.00410) (0.0144) Number 128 128 128 128 -0.0214 P-value .6 .142 .104 .116 (0.0144) Panel B. Micro Firms with Annual Income Below RD$5 million Firm Entry Firm Exit Firms Entering the Formal Sector Entry Rate Firms Exiting the Formal Sector in t+1 Exit Rate Main results including controls ATT 29.67* 0.00769 4.007 0.00409** (16.22) (0.0217) (3.087) (0.00209) Number 272 272 272 272 P-value .067 .723 .194 .05 Placebo treatment on pre-treatment period ATT placebo 2.179 -0.00981 -3.196 -0.00644 (4.820) (0.00923) (1.986) (0.00411) Number 128 128 128 128 P-value .651 .288 .108 .117 Source: Prepared by the authors. Note: Standard errors in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Matching method to incorporate covariates: inverse probability weighting. Covariates at the provincial level: pre-treatment trend for population, pre-treatment trend for number of formal firms, and pre-treatment mean of average years of schooling. Analysis period for placebo tests: 2010–2013; Placebo treatment in 2011: Groups 2014 and 2015; Placebo treatment in 2012: Groups 2016, 2017, and 2018. Entry and exit rates are computed using the average number of firms registered in the formal sector in the pre-treatment period as denominator. ATT: average treatment effect on the treated. 17 7.2 Randomization Inference Randomization inference p-values are also consistent with the main results. A Monte Carlo simulation was run for this exercise. At each iteration, the year in which Formalízate was implemented in each province was randomized, and the staggered difference-in-differences model was estimated in the same way that it was done for the main results. The estimated ATTs were to construct the distribution of estimated effects under the sharp null hypothesis. Randomization inference p-values were obtained by comparing the ATT from the main results (original assignment) to the distribution of estimated effects under the sharp null hypothesis (hypothetical random assignments). Randomization inference p-values are then interpreted as the probability of observing a treatment effect of similar size to that estimated under different hypothetical random assignments for the rollout of Formalízate. Figure 4 shows the distribution of the estimated effects under the strong null hypothesis, as well as the estimated impact under the original assignment, for the main outcomes of this analysis. Table 4 presents the randomization inference p-values for the main results. Figure 4. Distribution of Estimated Impacts under the Sharp-Null Hypothesis A. Firms Entering the Formal Sector B. Self-Employed Women Source: Prepared by the authors. Note: Distribution computed with 10,000 iterations. The vertical line represents the estimated impact under the original assignment. 18 Table 4. Randomization Inference P-values Staggered Difference-in-Differences Results Firms Entering and Exiting the Formal Sector at the Provincial Level Micro Firms Working Population Firm Entry Firm Exit Disaggregation of Firms Entering the Formal Sector by Economic Activity Self-Employed Population Firms Entering the Formal Sector Entry Rate Firms Exiting the Formal Sector in t+1 Exit Rate Agriculture Commerce and Tourism Services Other All Men Women Main Results including controls ATT 32.99* 0.00490 4.066 0.00410** 5.838*** 28.99* -2.018 0.512 0.0177 0.0180 0.0248** (18.25) (0.0222) (3.088) (0.00209) (2.234) (16.84) (4.540) (4.077) (0.0112) (0.0165) (0.0110) P-value Statistical inference .071 .825 .188 .05 .009 .085 .657 .9 .114 .274 .024 Randomization inference 0.092 0.4304 0.408 0.084 0.0002 0.0381 0.779 0.572 0.086 0.249 0.035 Source: Prepared by the authors. Note: Standard errors in parentheses. * p<0.1 **, p<0.05, *** p<0.01. Matching method to incorporate covariates: inverse probability weighting. Covariates at the provincial level: pretreatment trend for population, pre-treatment trend for number of formal firms, and pre-treatment mean of average years of schooling. Working-age population is defined as individuals between 14 and 60 years old. Randomization inference p-values computed for 10,000 Monte Carlo simulations. Entry and exit rates are computed using the average number of firms registered in the formal sector in the pre-treatment period as denominator. ATT: average treatment effect on the treated. 19 8. Conclusions This paper has examined the impact of Formalízate, a one-stop registration shop to register businesses in the Dominican Republic. The program was introduced in 2013 with the goal of increasing firms’ incentives to formalize their operations by reducing the costs of registration. The program also contributed to making information about the different processes involved in registration more accessible to owners of micro firms. Formalízate was implemented at different times in different provinces of the country. This allowed the use of a staggered difference-in-differences model to estimate the impact of the program. The analysis focused on its effects on firm registration and also explored heterogeneous effects by economic sector. To better understand the mechanisms driving the main results, the impact on employment rates was also examined disaggregating by type of occupation and gender. Results show that the implementation of a one-stop shop for firm registration is associated with a higher number of micro firms entering the formal market. Formalízate induced an increase of nearly 30 percent in the number of micro firms entering the formal sector relative to pretreatment values in the average province. This effect is mainly driven by firms in the services, commerce, and tourism sectors. These sectors are characterized by high levels of labor informality; they consist mostly of very small businesses and have a high female representation in the workforce. Furthermore, the analysis found a positive and significant impact of the program on female labor force participation as entrepreneurs (self-employed), suggesting that firms whose registration in the formal sector was induced by the program are led largely by women. The empirical findings described in this paper contribute to the literature by studying the effects of reducing costs and improving access to information on firm formal registration in a context characterized by high levels of firm and labor informality. By exploring heterogeneous effects and impacts on the labor market, it is possible to determine that some of the mechanisms that could be driving the increase in firm registration produced by the program include formalizing existing microenterprises in sectors with high informality and increasing labor participation as selfemployed entrepreneurs. This latter result is especially important for women, who traditionally have faced barriers to accessing formal employment and may be turning to formal entrepreneurship as a way to generate income and achieve economic autonomy while enabling them to access Social Security benefits. The findings also provide useful information for policymakers by generating evidence on the effectiveness of the program overall, as well as on those population groups that could derive more significant benefits, in the context of the Dominican Republic. 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Note: MSME: micro, small, and medium-size enterprise. a) Less than 1 year b) 1 to 5 years c) 5 to 10 years d) More than 10 years e) 5 to 10 years I don't have this information 0% 10% 20% 30% 40% Years of experience of the owner(s) of the business in this sector Azúa Baoruco Barahona Dajabón Distrito Nacional Duarte Elías Pina El Seibo Espaillat Hato Mayor Independencia La Altagracia La Romana La Vega María Trinidad… Monseñor Nouel Monte Cristi Monte Plata Pedernales Peravia Puerto Plata Salcedo Samana Sánchez Ramírez San Cristóbal San Jose de Ocoa San Juan San Pedro de Macorís Santiago Santiago Rodríguez Santo Domingo Valverde 0% 25% 50% Province Local Regional National International 0% 20% 40% 60% 80% The market for your product or service is: (check all that apply) 0% 20% 40% 60% 80% Owner Manager Employee Intermediary Other Please indicate your position or relationship with the company: