Reconciliation once and for all: Economic impact evaluation and social cost benefit analysis

Banerjee, Onil; Cicowiez, Martin; Moreda, Adela Working Paper Reconciliation once and for all: Economic impact evaluation and social cost benefit analysis IDB Working Paper Series, No. IDB-WP-835 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Banerjee, Onil; Cicowiez, Martin; Moreda, Adela (2017) : Reconciliation once and for all: Economic impact evaluation and social cost benefit analysis, IDB Working Paper Series, No. IDB-WP-835, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0000823 This Version is available at: https://hdl.handle.net/10419/173888 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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. http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode Reconciliation Once and for All: Economic Impact Evaluation and Social Cost Benefit Analysis Onil Banerjeea, Martin Cicowiezb and Adela Moredac a Corresponding author Inter-American Development Bank Environment, Rural Development, Environment and Disaster Risk Management Division 1300 New York Avenue N.W. Washington, D.C., 20577, USA +1 202 942 8128 [email protected] b Universidad Nacional de la Plata Facultad de Ciencias Económicas Calle 6 entre 47 y 48, 3er piso, oficina 312 1900 La Plata, Argentina c Inter-American Development Bank Environment, Rural Development, Environment and Disaster Risk Management Division 1300 New York Avenue N.W. Washington, D.C., 20577, USA 2 Abstract There is a debate in the literature on the appropriate methods and metrics for evaluating the economic impacts of tourism investments. Available analytical techniques include input-output modelling, computable general equilibrium modelling, cost benefit analysis, expenditure-based methods, and others. Metrics of benefits often include indicators such as gross regional product, household income and measures of welfare, while the choice of appropriate metrics will in part be conditioned by from whose perspective the analysis is undertaken. In this paper, we capitalize on the strengths of general equilibrium and cost benefit analytical techniques and develop an integrated approach to evaluating public investments in tourism. We apply the approach to the evaluation of a US$6.25 million tourism investment in Uruguay from the perspective of a multilateral development bank and the beneficiary government. The approach developed here is powerful in that it captures first and subsequent rounds of investment impacts both on the benefits and costs side; resource diversion and constraints are accounted for, and; the estimation of benefits is consistent with the welfare economics underpinnings of cost benefit analysis. JEL Codes: Z3 Tourism Economics; C68 Computable General Equilibrium Models; D61 Allocative Efficiency • Cost–Benefit Analysis; O1 Economic Development; O2 Development Planning and Policy; O5 Economywide Country Studies. Keywords: ex-ante economic impact analysis; cost benefit analysis; dynamic computable general equilibrium model; welfare economics; tourism investment analysis; tourism development; Uruguay. 3 Table of Contents 1.0. Introduction .............................................................................................................................. 4 2.0. Dynamic Computable General Equilibrium Analysis ............................................................. 5 3.0. Cost Benefit Analysis .............................................................................................................. 7 4.0. Integration of DCGE Estimates and CBA ............................................................................... 9 5.0. Integration of DCGE and CBA: An Application to Uruguay ................................................ 11 5.1. Scenario Design.................................................................................................................. 15 5.2. DCGE Model Results ......................................................................................................... 17 5.3. Cost-Benefit Analysis ........................................................................................................ 19 6.0. Conclusions ............................................................................................................................ 20 4 1.0. Introduction There is a debate in the literature on the appropriate methods and metrics for evaluating the economic impacts of tourism investments (Abelson, 2011; Blake, 2005; Burgan, 2001; Dwyer et al., 2004; Dwyer et al., 2016; Layman, 2004). Carefully defined public investment objectives are critical for determining the appropriate choice of method and metric. The analytical techniques available include input-output modelling, computable general equilibrium modelling (CGE), cost benefit analysis (CBA), expenditure-based methods and benefit scoring, among others. The metrics used to represent benefits include gross domestic or gross regional product, net household income or consumption, employment and welfare measures such as consumer/producer surplus and equivalent variation. The strengths and limitations of these methods and the indicators used in evaluating tourism investments have been discussed elsewhere (Abelson, 2011; Dwyer et al. 2005; Dwyer et al. 2006; Dwyer et al., 2016; Layman, 2004). This paper contributes to the literature on tourism investment impact analysis in two ways. First, we capitalize on the strengths of two well-established analytical approaches, CGE and CBA, and develop a rigorous and integrated approach to evaluating public investments in tourism. This analysis may be undertaken from the perspective of a multilateral development bank and from the perspective of a beneficiary government. Second, in considering the beneficiary government’s perspective, we build-in the repayment of a loan to finance the investment in a temporally dynamic modelling framework and estimate the net present value and internal rate of return of the investment. To illustrate the approach, we estimate the economic and welfare impacts of a US$6.25 million public investment in tourism in the Uruguay River corridor from both the multilateral development bank and beneficiary’s perspectives. This paper is organized as follows: section 2 provides an overview of CGE analysis, followed by a description of the main principles of CBA in section 3. Section 4 presents key considerations for integrating CGE with a CBA approach. Section 5 illustrates the approach in application into a US$6.25 million public investment in tourism. Section 6 concludes the paper with a discussion of key findings. 5 2.0. Dynamic Computable General Equilibrium Analysis In the analysis of large public investments or policies that are expected to impact multiple sectors and actors in an economy with dynamic effects, a dynamic computable general equilibrium (DCGE) approach is powerful. DCGE analysis captures important inter-sectoral and backward and forward linkages, and the direct, indirect and induced benefits of an investment (Cattaneo, 2002, Dwyer et al., 2006, Dwyer et al., 2003, Dixon and Rimmer, 2002, Banerjee et al., 2015). Pearce et al. (2006) suggest that where projects are large and complex, partial equilibrium frameworks are seldom sufficient and that the analytical framework should be capable of considering a wide range of impacts on all sectors that may be impacted. All project spillovers, and indirect costs and benefits should be accounted for. As Pearce et al. (2006) emphasize, a core strength of the DCGE approach is its meticulous detail in appraising spillovers of an intervention. Ex-ante economic impact analysis with DCGE models has been undertaken for public investments in the forestry (Banerjee et al., 2016a) and tourism sectors (Banerjee et al., 2015, Banerjee et al., 2016b, Taylor, 2010, Taylor and Filipski, 2014). Indeed, DCGE analysis can be applied across a broad range of economic sectors where large public investments are concerned and inter-sectoral linkages are important. Beyond consideration of economic impacts of large public investments, DCGE models have a long history in applied policy analysis, from fiscal to trade to environmental policy analysis, with DCGE models distinguishing themselves as the ‘workhorse’ of policy analysis (Jones, 1965, Dixon and Jorgenson, 2012, Dixon et al., 1992). Indeed, as Nobel Economist Kenneth J. Arrow stated: “…in all cases where the repercussions of proposed policies are widespread, there is no real alternative to CGE” (Arrow, 2005). DCGE models are mathematical models that consist of systems of equations which describe the relationships between sectors, agents and other accounts in the underlying Social Accounting Matrix (SAM). DCGE models are based on SAMs for a country, region, or for all countries linked together through trade as in the Global Trade Analysis Project (GTAP) database (Aguiar et al., 2016). A SAM provides a snapshot of an economy describing all monetary transactions between economic sectors and its agents, including households, government and enterprises, and the relationships between the modelled economy and other countries or regions of the world (King, 1985). 12 net social benefits accruing to the borrowing country’s citizens. Based on the discussion above and since we are concerned with changes in welfare at original prices (i.e. or before intervention/pre-simulation prices), equivalent variation is the appropriate measure of welfare. The DCGE model developed in Banerjee et al. (2016, 2015) is calibrated with a new SAM for Uruguay with a base year of 2013 (Cicowiez, 2016). The DCGE is applied to the ex-ante economic analysis of a US$6.25 million public investment in tourism 2 . This investment is supporting tourism development in the Uruguay River corridor to create employment and income in emerging destinations, and consolidate tourism opportunities to improve regional equity. The three main objectives of the investment are to: (i) create and consolidate tourism infrastructure (US$3.555 million); (ii) catalyze private sector investment in the corridor (US$950,000, and; (iii) strengthen regional tourism governance (US$900,000). Operations and maintenance of new infrastructure is estimated at an annual cost of 3% of the value of this infrastructure while the management costs of the tourism program are equal to US$845,000 annually. Figure 2 in section 5 describes the distribution of the investment and operations and maintenance costs until 2045 which is the time horizon used in this analysis. A SAM for 2013 was developed for Uruguay which is the most recent year for which complete national accounts data were available (Cicowiez, 2016). This SAM was extended to disaggregate foreign tourism demand/expenditure. Table 1 describes the accounts in the Uruguay SAM. 2 The US$6.25 investment is composed of a US$5 million loan from the Inter-American Development Bank with US$1.25 million in counterpart funding. 13 Table 1. Main accounts in the Uruguay SAM. Source: Authors’ own elaboration; Uruguay SAM. According to the SAM, Uruguay’s GDP reached 1,140,989 million pesos in 2013. Uruguay imported 75,958 million pesos more than it exported, while foreign tourism demand directly contributed to almost 3.4% of GDP (table 2). Category Item Category Item Sectors Agriculture, forestry and fishing Factors Land 12 Processed food continued Timber resources Manufacturing Fisheries resources Utilities Mining resources Mining, petroleum, chemicals Institutions Households Construction 3Government Commerce Rest of the world Hotel and restaurant Taxes Unskilled labor factor tax Transportation 9Skilled labor factor tax Communications Capital factor tax Public administration Natural resources factor tax Other services Import and export duties Factors Salaried labor, low skill Direct taxes 11 Salaried labor, mid skill Activity taxes Salaried labor, high skill Other taxes Non-salaried labor, low skill Social security contributions Non-salaried labor, mid skill Investment Private investment Non-salaried labor, high skill 3Government transport infra investment Capital Other government investment 14 Table 2. Uruguay, 2013, total supply and demand. Source: Authors’ own elaboration; Uruguay SAM. The sectoral structure of Uruguay’s economy is depicted in Figure 1. The Other services sector is the largest sector accounting for 38% of the economy’s value added. Commerce is a far second followed by Construction, and then Agriculture, forestry and fisheries. While not shown here, Processed food and Agriculture, forestry and fisheries lead Uruguay’s exports (35% and 28%, respectively) while Manufacturing and Mining, petroleum and chemicals account for the greatest share of imports. Item Millions of pesos Demand Private consumption 751,198$ Government consumption 157,987$ Fixed investment 261,421$ Exports 235,238$ Tourism demand 38,642$ Total demand 1,444,487$ Supply GDP 1,140,989$ Imports 311,197$ Stock change (7,698)$ Total supply 1,444,487$ 15 Figure 1. Sector structure in 2013, value added shares. Source: author’s own elaboration. 5.1. Scenario Design This section presents the simulations, results and analysis. The following five scenarios were undertaken: (i) the baseline scenario, which is the without investment scenario; (ii) the investment scenario where the government investment in tourism infrastructure, institutional strengthening, and capacity building is implemented; (iii) the demand scenario which simulates the projected increase in foreign overnight leisure tourism expenditure arising from the investment; (iv) a combination scenario where scenarios (ii) and (iii) are implemented jointly, and; (v) a combination scenario which internalizes the repayment of the US$6.25 investment in the DCGE simulation. Details of each scenario follow: Baseline scenario: this first simulation assumes that average past trends will continue from 2014 to 2045. The non-base simulations that follow only deviate from the baseline scenario beginning in 2017. Invest scenario: this simulation imposes increased government investment in tourism infrastructure, institutional strengthening and capacity building financed through a multilateral loan. The structure and sequencing of the investment are shown in figure 2. The year 2017 is the first year of the investment which continues until the year 2021, inclusive. Public administration 6% Agriculture, forestry and fisheries 9% Commerce 11% Communications 3% Construction 10% Hotel and restaurant 4% Manufacturing 3% Mining, petroleum and chemicals 3% Other services 38% Processed food 6% Transportation 5% Utilities 2% 16 Demand scenario: in this simulation, foreign leisure tourist overnight arrivals and expenditure are projected to increase as a result of the increased tourism opportunities created by the investment. With program tourism demand was estimated in Eugenio-Martin and InchaustiSintes (2016) with econometric regression analysis. In this regression, the economic value of the presence of an additional tourism attraction was estimated using tourism expenditure as the independent variable (Eugenio-Martin and Inchausti-Sintes, 2016). The three attractions considered were nautical, ecotourism and cultural tourism attractions. Based on the characteristics and number of new attractions to be developed through the investment, the total additional tourism expenditure was estimated at US$5,894,561. This increased tourism demand was distributed according to a logistical functional form over a 10year period, such that 2.5% of the increase was applied in the first year, 6% in the second year, 14% in the third year, 28% in the fourth year, 50%, in the fifth year, 72% in the sixth year, 87% in the seventh year, 94% in the eighth year, 98% in the ninth year and 100% in the tenth year (figure 2). Figure 2. Distribution of investment costs and projected tourism demand increase. Source: Authors’ own elaboration. Combi scenario: this scenario models the invest and demand scenarios combined. $0 $10 $20 $30 $40 $50 $60 $70 $80 Millions of Uruguayan pesos Costs New tourism demand 17 Combi-pay scenario: this scenario models the invest and demand scenarios combined, and; internalizes the repayment of the US$6.25 million investment in the DCGE model. According to conditions applied to similar multilateral loans, repayment begins after a grace period in year 7, which is year 2023 in this analysis. Interest owing and the principle payment are made annually with the final payment made in 2039. The interest rate used is 1.58% and is based on the US Dollar LIBOR 3 . The value of the repayment is held constant over the period and is equivalent to 11.85 million Uruguayan pesos or US$419,539 annually. To finance repayment of the loan, direct tax rates are adjusted to generate the necessary funds. 5.2. DCGE Model Results Figure 3 illustrates impacts on EV, the measure of household welfare, in millions of pesos. This represents the change in household income at current prices that a change in prices would have on household welfare if income were held constant. In other words, where an intervention does not occur, EV is the amount of income an individual would have to be compensated with to make them as well off if the intervention were to have taken place. In the invest scenario, EV spikes with the disbursements of the loan, declining back to baseline levels at around 2023 and then growing more quickly than baseline thereafter once the investment’s medium-run positive impacts on capital stocks begin to materialize. The impact on EV in the demand scenario naturally follows the increase in projected demand arising from the creation of new tourism attractions and opportunities. While not reported here, the DCGE model also reports results related to employment levels, sectoral output, exports and imports, among other indicators, all of which are considered when calculating EV. The combi scenario represents essentially the sum of the invest and demand scenarios, reaching over an additional 60 million pesos by 2045 compared with the baseline. Finally, the combi-pay scenario follows a similar trend as the combi scenario, though the combi-pay trend is between 5 and 15 million pesos lower than the combi scenario during the loan repayment period. There is also an upward jump in household welfare in 2039 once the loan is repaid; at this point, the impact on EV rises close to the level of the demand scenario in 2045. The rate of growth from 3 LIBOR rate retrieved on October 28, 2016. 18 2039 forward in the combi-pay scenario follows the rate of growth of the combi scenario. In 2045, the difference between the combi and the combi-pay scenario is 5.3 million pesos. The cumulative difference between the combi and combi-pay scenario by 2045 is almost 289 million pesos. Figure 3. Impact on equivalent variation, deviation from baseline; millions of pesos. Source: Authors’ own elaboration. Table 3 provides an overview of key macro-indicators and their deviation from their baseline values in year 2021 (the final year of the investment), 2030, and 2045. Both exports and imports decline in all scenarios. The trend with fixed investment in the case of the demand, combi and combi-pay scenarios is to decline, generally in later years of the time horizon. GDP impacts are positive in all scenarios and years. The government consumes more goods and services in all scenarios, except for the demand scenario which is a function of its allocation of resources toward the development of new tourism attractions. Private consumption generally follows GDP trends, while private investment tends toward decline. This is a characteristic outcome of a sudden increase in government investment, as it tends to temporarily crowd out private investment during the period of accelerated public investment (Banerjee et al., 2016b, Dwyer et al., 2006). -10 0 10 20 30 40 50 60 70 Deviation from baseline in millions of Uruguayan Pesos Invest Demand Combi Combi-pay 19 Table 3. Key macro-indicators, difference from baseline for select years; pesos. Source: Authors’ own elaboration. 5.3. Cost-Benefit Analysis In this section, the investment is considered from the perspective of a multilateral development bank, and; from the perspective of the beneficiary government. From the perspective of the lender, the NPV of the investment is calculated by: (i) calculating the EV; (ii) comparing this deviation from baseline in EV alongside the cost of the loan as it is disbursed in the first 5 years of project implementation. In this case, all costs are assessed in the first 5 years which has significant implications for the NPV of the investment, particularly if the discount rate is high. We use the standard discount rate of 12% used by the multilateral lender, the Inter-American Development Bank, in this analysis, and; (iii) NPV is then calculated as indicated in equation 1. From the perspective of the beneficiary government, the government only begins incurring the direct costs of the investment once repayment begins in year 2023. Loan repayments occur annually until the entire investment is repaid in 2039. Table 4. Net present value and internal rate of return from the multilateral lender and beneficiary’s perspective; pesos. Source: Owners’ own elaboration. Table 4 shows the results of the analysis from both the multilateral lender and the beneficiary’s perspective. With all direct costs incurred in the first 5 years of the period of analysis, the NPV from the lender’s perspective is $182.9 million pesos. This is lower than the NPV of $251.6 million pesos estimated from the beneficiary’s perspective. While the analysis undertaken from the beneficiary’s perspective results in a higher NPV than from the lender’s perspective, it does consider follow-on costs that may arise from the repayment of the loan. Specifically, modelled in this way, just as the DCGE model accounts for first, second and subsequent round impacts of Invest Demand Combi Combi-pay 2021 2030 2045 2021 2030 2045 2021 2030 2045 2021 2030 2045 Absorption 23,776,888$ 22,544,796$ 1,511,127$ 6,123,212$ 1,097,552$ 16,441,985$ 51,949,193$ 51,990,462$ 24,874,406$ 38,986,637$ 53,459,075$ 58,111,859$ Private consumption 12,335,328$ 9,874,528$ (128,827)$ 3,825,857$ 1,064,894$ 16,014,234$ 52,094,158$ 57,909,887$ 13,400,210$ 25,888,854$ 51,964,250$ 61,734,233$ Government consumption 10,379,263$ 11,436,345$ 1,174,536$ 1,174,536$ -$ -$ -$ -$ 10,379,263$ 11,436,345$ 1,174,536$ 1,174,536$ GDP market prices 5,983,982$ 2,608,372$ 1,482,741$ 6,261,819$ 1,016,903$ 15,271,118$ 49,053,846$ 50,736,926$ 7,000,722$ 17,877,840$ 50,535,886$ 56,997,094$ Tourism demand -$ -$ -$ -$ 4,311,381$ 67,829,186$ 275,874,934$ 432,689,976$ 4,311,381$ 67,829,186$ 275,874,934$ 432,689,976$ Exports (11,209,913)$ (14,915,115)$ (356,782)$ 1,350,535$ (2,204,359)$ (35,233,022)$ (152,497,564)$ (252,644,471)$ (13,414,305)$ (50,148,185)$ (152,854,491)$ (251,294,414)$ Imports (6,582,993)$ (5,021,309)$ 328,395$ (1,211,927)$ (2,187,670)$ (33,767,032)$ (126,272,717)$ (181,299,040)$ (8,770,761)$ (38,789,798)$ (125,943,632)$ (182,510,326)$ Fixed investment 1,062,298$ 1,233,923$ 465,419$ 1,122,819$ 32,658$ 427,751$ (144,965)$ (5,919,426)$ 1,094,933$ 1,661,438$ 320,289$ (4,796,910)$ Private fixed investment (6,906,731)$ (7,795,026)$ (712,270)$ (54,870)$ 32,658$ 427,751$ (144,965)$ (5,919,426)$ (6,874,095)$ (7,367,511)$ (857,400)$ (5,974,599)$ Government fixed investment 7,969,029$ 9,028,949$ 1,177,689$ 1,177,689$ -$ -$ -$ -$ 7,969,029$ 9,028,949$ 1,177,689$ 1,177,689$ Scenario NPV IRR Combi Development Bank 182,904,636$ 40% Combi-pay Beneficiary 251,592,563$ N/A 20 increased economic activity, this approach also considers first, second and subsequent rounds of impacts of costs incurred and the forgone economic activity due to resource allocation toward the repayment of the loan. From the multilateral lender’s perspective, the investment results in an internal rate of return (IRR) of 40%. From the beneficiary’s perspective, the absence of a negative cash flow renders it impossible to calculate an IRR for the investment. The reason for this is that since no costs are incurred until 2023, there is no negative cash flow in the initial years of the investment, in contrast to the first approach where the investment is assessed from the lender’s perspective. Even after 2023, the benefits outweigh the annual repayment costs. This may not be an issue, however, since in practice, once an investment loan has been formulated, the CBA is often used to validate the economic viability of the loan rather than compare among investment opportunities which is a core application of the IRR. 6.0. Conclusions In this paper, we draw on the strengths of CBA and DCGE modelling and present a rigorous and integrated approach to evaluating public investments in tourism. We undertake this analysis from the perspective of a multi-lateral development bank, and from the perspective of the beneficiary government. A new feature in our approach, is that in considering the beneficiary government’s perspective, we build-in the repayment of the public investment into the DCGE and then estimate the NPV of the investment. A significant advantage of this approach is that just as first, second and subsequent round impacts of increased economic activity are considered in the analysis, so are these multiple rounds of impacts considered on the cost side, and as such, any forgone economic activity due to resource allocation toward the repayment of the loan. For compatibility with the welfare economics foundations of CBA and the characteristics of public investment in tourism where the target beneficiary is frequently the household, EV estimated with a DCGE is the appropriate measure of welfare. There are several strengths of the DCGE approach for estimating benefits. First is its ability to capture first and subsequent round investment impacts on household welfare, on both the benefit and cost side. Second, a general equilibrium framework estimates overall net benefits robustly where resource diversion and 21 constraints is an important consideration. Third, a DCGE model’s internally consistent accounting framework renders double counting of benefits (and costs) impossible. The analysis of a US$6.25 million tourism investment in Uruguay is undertaken from the perspective of a multilateral development bank and the beneficiary government. Viewed from the perspective of the multilateral lender, with the cost to the lender incurring in the first 5 years, the NPV is lower than when compared with the NPV estimated from the perspective of the beneficiary government. This result is explained by the fact that costs incurred by the beneficiary government are only incurred following the grace period, with repayment beginning in 2023. It is the distribution of these costs and the discounting of net benefits than results in the lower NPV from the perspective of the multilateral development bank. Internalizing the repayment of the investment as undertaken in the analysis from the beneficiary’s perspective is arguably more defensible than considering investment costs outside of the modelling framework. In this way, benefits estimated with the model are treated the same as costs, enabling the consideration of any dynamic, second and subsequent round impacts of both costs and benefits to be accounted for. Certainly, resources allocated to repayment of a debt have implications for current year expenditure and thus have an opportunity cost which is accounted for in this approach. As we have seen, despite this consideration of opportunity cost, the NPV of the investment will tend to be higher when considered from the beneficiary government’s perspective where there is a grace period or costs are incurred by the beneficiary further in the future than when considered from the lender’s perspective. One potential drawback of the approach is that, given the repayment schedule of the investment examined in this study, it was not possible to calculate an IRR. This of course is a function of the repayment schedule and magnitude of benefits. If there is no negative cash flow as is the case with the Uruguayan example, it is not possible to calculate an IRR. This would also be the case from the multilateral bank’s perspective, if the magnitude of benefits generated were to outweigh costs in all years of the analysis. This, however, should only be a real issue if the CBA is used to compare alternative investments, rather than explore, enhance transparency and demonstrate the economic viability of a specific investment.