rafmacalaba/datause-encoder-data huggingface dataset:
exit rates for the treatment group as compared to the control group. Fortunately, we have estimates of the relevant exit rates since our panel data track both groups of firms beyond 2004 to 2006, and we observe exit over these two years. The two-year survival rates of 14 percent and 13 percent for the control and treatment groups, respectively, correspond to annual exit rates of 7.5 and 6.5 percent which are broadly similar to the Alvarez and Vergara estimates. Thus, program participation appears to inhibit exit of treated firms by about 1 percent per annum relative to the control group. Since the mean time since program entry is 6 years in our panel data, one possible strategy for addressing this potential bias is to estimate program impacts after trimming the bottom 5 percent of the treatment group in each outcome, assuming that the least well-performing treated firms would have exited in the absence of the program. As a robustness check, we also estimate treatment effects when we trim the bottom 10 percent of the treatment group.

We implemented this sensitivity analysis by sorting and dropping the bottom 5 percent (or 10 percent) of the treatment group’s distribution of each outcome variable. The results of estimating the treatment effects with trimming are reported in Table 10, panels A and B for the 5 and 10 percent exclusions, respectively. Several points emerge from comparing the treatment effects of any program participation estimated with trimming and the original estimates reported in Table 7, panel B. First, the program impacts on sales and output are broadly similar – averaging between 8 and 10 percent as compared to the original estimates of 9 percent. Second, the treatment effects for employment are now positive and marginally significant whereas the original estimates showed no statistically significant impact. Third, wage effects are essentially the same at 7 percent as compared to 8 percent estimated previously. Finally, the biggest change is seen in the effects on labor productivity, which at 8 percent are now just half as large as the 16 percent originally estimated.

Table 10 also reports the treatment effects estimated with trimming by type of program. The broad patterns observed in Table 7 of differential impacts on outcomes by program type are repeated here when outcomes are trimmed. The principal difference is that trimming the bottom 5 percent appears to reduce the treatment effects of BDS programs (FAT) and increase the estimated treatment effects for the cluster programs (PROFO and PDP); trimming the bottom 10 percent further magnifies these differential impacts on the two types of programs. The essential point to take away from this sensitivity analysis is that the direction and magnitudes of treatment effects are broadly robust to controls for potential biases from firm exit.

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~ government, _x_ 1, _t_ includes the real T-bill rate, the lagged stock market index and the lagged real industrial production.

The cusum of squares statistic is larger than the interval confidence since 2005, so the parameters’ null of constancy and hence the equilibrium assumption are rejected. The cusum test gives a different result: the null of equilibrium is not rejected. But, this result is not robust since the recursive estimates of the parameters (Figure 13) show that not all the coeffcients are constant over time.

**Figure 12: CUSUM Tests**

<!-- Start of picture text -->
1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>-0.4<br>03 04 05 06 07 08 09 10 11<br>CUSUM of Squares 5% Significance<br>Source: Constructed by the authors.<br>20<br>10<br>0<br>-10<br>-20<br>03 04 05 06 07 08 09 10 11<br>CUSUM 5% Significance<br><!-- End of picture text -->

33
Policy Research Working Paper 8982

# **Abstract**

Turkey has moved rapidly from a current account that was relatively in balance up to the turn of the millennia, to sustaining relatively large current account deficits over the past 15 years. Using annual data from 1986 to 2017 and a jackknife model-averaging estimator, the paper estimates the relationship between the current account balance and a set of determinants that are broadly consistent with the cross-country literature. These determinants include private sector credit, public expenditure, real exchange rate changes, gross domestic product growth relative to the rest of the world, trade openness, international oil prices, foreign direct investment levels, past net foreign assets, inflation volatility, and global levels of uncertainty. The analysis then decomposes the predicted current account balance for five-year periods to illustrate the factors that have driven the current account over time. Over 2003–07, a large current account deficit became established in Turkey, driven by an expansion of credit to households and rapid gross domestic product growth, coupled with improved macroeconomic stability that supported higher spending and therefore imports. Since then, the negative effect of household credit has abated, but was replaced in 2008–17 by an expansion of credit to the corporate sector as a driver of the current account deficit. The current account balance in Turkey is also found to be less persistent than.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aportugalperez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The end-use energy demand projected by the TIMES model and energy price indices provided by the CGE model are then used in TIMES to produce a mix of energy supply to meet the projected demands ensuring that the optimal energy supply mix represents the least cost scenario to deliver end-use energy services. Besides energy demands and prices the supply module of TIMES requires other input data including operational characteristics of energy supply technologies (e.g., power plant technologies, resource availability, such as solar irradiation in a given location for a solar power plant), and cost data (e.g., capital costs of photovoltaic cells, current fuel prices).

The projections of energy supply-mix produced by the TIMES model are supplied to the CGE model, where necessary parameters (e.g., share parameters, autonomous energy efficiency parameters) are adjusted in a way that the energy supply mix of the TIMES model is reflected in the CGE model. It implies that the growth rates of primary energy commodity (coal, oil, natural gas) in the CGE model are the same as in the TIMES model. The same should have been done for primary electricity. However, the CGE model has only one electricity commodity and does not explicitly represent the primary electricity. Therefore, growth rates of primary electricity in the TIMES model are not reflected in the CGE model. Moreover, new and renewable energy (solar, wind), which are explicit in the TIMES model, are embedded in the total electricity in the CGE model.

One would argue that why not disaggregate the electricity sector in the CGE model to various technologies in the same way several CGE models (e.g., Timilsina et al. 2018; Timilsina and Landis, 2014; Timilsina and Shretha, 2006) have done. However, the existing studies do not follow linking the CGE model with a bottom-up model. Instead, they refine their electricity sector modeling by disaggregating the electricity sector in their SAMs to various technologies (a kind of hard linking, as discussed earlier). Doing so partially brings some technological characteristics of electricity generation technologies into the CGE model, it still does not represent one of the main features of electricity sector operation, load dispatching.<sup>4</sup> Moreover, even if we disaggregate the electricity sector to various technologies, it is not possible to disaggregate other energy/emission-intensive sectors, such as cement, chemicals, iron and steel. The detailed disaggregation is for hard linking of a top-down model with bottom-

> 4 Load dispatching refers to operating of electricity generation plants to meet the demand in a given time based on their operating costs (mainly fuel costs), it is also called merit order dispatching in electricity economics literature.

6
# 1. **Introduction**

Do incumbents use social programs to get themselves re-elected? Much theoretical and empirical work on public expenditure patterns in democratic regimes argues that politicians manipulate the timing, composition, and geographic distribution of expenditures to maximize the likelihood of remaining in office. Increases in public expenditures are timed to coincide with upcoming elections; budgetary tradeoffs are made to satisfy important constituencies; and expenditures are directed towards areas of the country that are considered critical for an incumbent's re-election bid.

This paper discusses the relationship between elections and the expenditures made by the Peruvian Social Fund (FONCODES) during President Alberto Fujimori's first tern in office (1990-1995). FONCODES was created in 1991, during a severe recession which followed the adoption of stringent stabilization and structural adjustment measures by the Fujimori government. Its stated objectives were to generate employment, help alleviate poverty, and improve access to social services (World Bank 1998). By 1993, the Peruvian economy had begun a remarkable recovery, and FONCODES was being hailed by govermnent officials and foreign donors alike as an important component of a medium-term strategy to reduce poverty.

Scholars in Peru and abroad have generally been more guarded in their praise. Critics argue that social funds like FONCODES undermine line ministries and local governments. And questions about the "politicization" of FONCODES have bedeviled the program from its creation (Graham 1994, Kay 1995, Ball6n and Beaumont 1996). Yet there are no satisfactory studies of the impact of elections on FONCODES expenditures.

This paper attempts to fill this gap using highly disaggregated data on expenditures, socioeconomic indicators, and electoral outcomes. I use econometric techniques to analyze the timing and geographic distribution of FONCODES expenditures, and reach two main conclusions. First, FONCODES expenditures increased significantly prior to national elections. Second, FONCODES projects were directed to provinces in which the marginal political impact of expenditures was likely to be largest. The results are robust to a large number of specifications and controls. The Peruvian data thus support predictions made in the literature on "political business cycles" (Nordhaus 1975, Rogoff 1990) as well as the literature on political influences on the allocation of discretionary funds (Wright 1974, Cox and McCubbins 1986, Lindbeck and Weibull 1987, Dixit and Londregan 1996, Case 1997).

The rest of the paper proceeds as follows. Section 2 provides a brief description of Peruvian politics between 1990 and 1995, and FONCODES. Section 3 describes data requirements and sources for the paper. Section 4 estimates the effect of the electoral calendar on the _timing_ of FONCODES I Moncada (1996) and Graham and Kane (1998) discuss the timing and distribution of FONCODES expenditures, respectively. As I discuss below, however, both papers have serious limitations.

2
Bhattarai, A. K. 1989. Social Security Programmes in India. International Social Security Review: pp. 479-488.

Bureau International du Travail. 1991. Madagascar. Rapport au gouvemement sur la situation actuelle et les perspectives d'evolution de la protection sociale. Geneve.

Boskin, M., Kotlikoff, L., Puffert, D. and Shoven, J. 1986. "Social Security: A Financial Appraisal Across and Within Generations." NBER Working Paper Series (1891).

Boskin, Michael; Kotlikoff, L.; Puffert, D.; Shoven, J. 1987. "Social Security: A Financial Appraisal Across and Within Generations." _National Tax Journal_ 40(1): 19-34.

Brancato, Carolyn K. 1994. "The Brancato Report on Institutional Investment." The conference Board: Seminar on Trends in U.S. Institutional Investment and Implications for Corporate Governance. New York.

Brenes. 1983. _Investment of Social Security Funds in Six countries in Latin America._ Geneva.

Bulatao, Rodolfo. 1979. "On the Nature of the Transition in the Value of Children." East-West Center Population Institute Paper 60-A. East-West Center. Honolulu.

Bulatao, Rodolfo and Eduard Bos. 1989. _Projecting Mortality for All Countries._ Washington D.C.: World Bank.

Bulatao, Rodolfo, Eduard Bos, Patience Stephens, and My T. Vu. 1989. _Europe, Middle East, and Africa (EMN) Region Population Projections. 1989-1990 Edition._ Washington D.C.: World Bank.

Burkina Faso National Social Security Scheme. 1985. _Annuaire Statistique de la Securite Sociale._ Burkina Faso.

Caisse de Pension des Employes Prives. 1991. Luxembourg. Comptes Rendus. Exercice 1990. Luxembourg.

Caisse Sociale du Rwanda. 1987. Annuaire Statistique de la Securite Sociale au Rwanda 1981-1985 and 1989. Rwanda.

The Canada Pension Plan Advisory Board. Report 1991.

Caja Costarricense del Seguro Social. Memorias de la CCSS. Costa Rica: CCSS; 1970, 1977, 1982, 1988.

Caja Costarricense de Seguro Social. 1992. _Anuario Estadistico._ Costa Rica.

Caja de Seguro Social Republica de Panama. 1990. _Memoria 1989-1990._ Panama: Panamanian Government.

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# _3.3 Main results_

Table 3 presents our main results for the Lerner index (Panel A) and the net interest margin (Panel B). Following Bertrand, Duflo, and Mullainathan (2004), we cluster heteroskedasticity-adjusted standard errors on the country level to allow for serial correlation in the error terms. All regressions are performed on annual data, and we drop countries with multiple crises (Russia and Ukraine), although the results are not affected when these countries are included.

# [TABLE 3: The effect of government interventions on banking competition]

All coefficients that capture policy responses exhibit a negative sign. In Panel A, liquidity support and recapitalizations assume significance at conventional levels, suggesting the provision of liquidity and capital injections increase competition. Panel B confirms the competition-increasing effects for liquidity support, capital injections, and nationalizations.<sup>16</sup> For the Lerner index, we also illustrate the effects in terms of their economic magnitude using Indonesia, a country where liquidity support was provided and banks were recapitalized. Indonesia is located at the 15<sup>th</sup> percentile of the distribution of the Lerner index in 1997 (0.138). Having had liquidity support, the banking systems experienced an increase in competition to the level of Haiti, located at the 8<sup>th</sup> percentile with a Lerner index of 0.103.

Blanket guarantees, mostly used in the South East Asian Crisis, remain insignificant. Guarantees that are not accompanied by other measures may not be credible, and foreign creditors tend to ignore them. Further, some countries introduced tax policies that undermined blanket guarantees at the time the guarantee was announced, e.g., Ecuador. The absence of an effect may also reflect the smaller number of blanket guarantees. Moreover, in some countries, such as Ireland, blanket guarantees exceed GDP, questioning the sovereigns’ ability to service such commitments.

# _3.4 Alternative explanations_

Next, we confront alternative explanations. A common shock, i.e., a banking crisis, rather than interventions can affect competition. However, some crisis countries did not revert to these government actions, and we show in Figure 3 that our data satisfy the key identification assumption of parallel trends between treatment and control groups. Further, not all coefficients display the same effect. If

> 16 Additional tests include a dummy that takes on the value of one if asset management companies or restructuring agencies assume nonperforming assets. These tests leave our inferences unchanged. We also replicate in unreported tests the regressions without the control variables, and we additionally use the PanzarRosse (1987) H-Statistic, an alternative competition measure at the system level, as dependent variable. Using WLS to account for the fact that the H-Statistic is obtained from a regression, we observe again competitionincreasing effects for the key variables of interest.

12
A stratified sample of 154 and 184 households was selected from Ambositra and Vatomandry respectively for HAP monitoring. Concentrations of PM2.5 and CO were monitored in the kitchen three times in every household during February-March 2009, April 2010 and August 2010 using UCB Particle Monitors and GasBadge Pro Single Gas Monitors. In each occasion monitoring periods lasted 24 hours. Wherever household air was monitored, findings suggest that air pollution is dangerously high in the kitchens. An average concentration of PM2.5 of 0.776 mg/m<sup>3</sup> has been recorded, whereas the WHO Air Quality Guidelines for typical indoor exposures recommend the 24-hour average of 0.010 mg/ m3 PM2.5 as the lowest levels at which total cardiopulmonary and lung cancer mortality have been shown to increase with more than 95% confidence (WHO, 2005). Similarly, while the WHO recommends 6.1 ppm of CO for typical indoor exposures (assuming that the exposure occurs when the people are awake and alert but not exercising), a concentration of 28 ppm CO is common in Madagascar, implying a serious health hazard (WHO, 2010).

We conducted fixed-effects panel regression analysis to investigate the roles of several basic determinants, such as fuels (charcoal, wood, ethanol), stoves (traditional, improved, ethanol), kitchen size, kitchen ambience (open, airy), building materials (permeable roof, permeable wall) of the kitchen, and ambient environment. Our econometric findings strongly suggest that variations in cooking fuel, stove types and kitchen size produced large differences in HAP. As expected, in terms of both PM2.5 and CO, ethanol is significantly cleaner than biomass fuels, and for both parameters a larger kitchen makes a significant difference to the quality of household air. While we found no significant impact of improved charcoal stoves on air quality compared to traditional charcoal stoves, the improved wood stove (incorporating a chimney) was effective in reducing concentrations of CO in the kitchen. Circulation of wind also provides significant benefits to reduce the concentration of CO.

These results highlight household level adjustments that can significantly mitigate HAP exposure; first, switching to clean fuels (for example ethanol) is desirable. Second, if cooking with clean fuels is not possible, use of an improved wood stove with a chimney can make a significant difference for household concentrations of CO. Finally, a spacious kitchen and providing ventilation in cooking areas will yield a better household health environment. In short, clean fuels, improved stoves and good ventilation are effective means of reducing HAP.

17
bers of business associations) are affected by corruption and by uncertainty in the production climate.

We test this implication at the firm level using data from the _Investment Climate Assessments (ICA)_ of the World Bank, which survey more than 28,000 firms in 58 countries between 2000 and 2004, and provide information on firms’ characteristics, firms’ productivity, and the investment climate. Because not all questions are asked in each country, our final sample consists of 19,000 firms spread over 49 countries. The survey is not nationally representative, but is representative of specific sectors that are consistently surveyed across countries: in the sample, firms in the manufacturing sector represent 72% of the sample, firms in services 21%, and the remaining 7% of the sample consists of firms operating in agriculture and construction.

As a proxy for connectedness we use the variable _MEMBER_ , which indicates whether a firm is a member of a business association. Memberships to business associations are rarely compulsory, and there are usually few binding contractual obligations arising from membership. Therefore, business associations can be considered informal institutions that “connect” their members by mitigating information asymmetries, providing a basis for solving disputes with trade partners, and easing the transmission of information. This role of business associations is strongly supported by a recent study of Pyle (2005).

To be sure, a positive association between corruption, uncertainty, and membership to business associations could potentially also stem from bad governance, leading in the long term to low quality services, and firms’ increased incentives to lobby. Although with existing data we are not able to entirely exclude this hypothesis, we apply the following safeguards to avoid capturing such a relationship. First, we give the value one to the variable _MEMBER_ only if the firm identifies the ability to resolve disputes, to provide information on domestic product markets, or to accredit standards, as important functions of the business association: these functions should capture the ability of a business association to spread information about other firms, and to coordinate punitive actions, and give less importance to lobbying intentions. Moreover, our analysis is at the firm level, so that the macroeconomic relationship between corruption and lobbying should be captured by the country fixed effect. Finally, we construct an uncertainty index from variables that have (at least in the short run) little relationship with governance and lobbying. Specifically, we construct two uncertainty indexes using the following variables: 11