rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Effect of SNAP-benefit changes on prices

|Specifcation|(1)<br>OLS|(2)<br>IV|(3)<br>IV|(4)<br>IV|(5)<br>IV|(6)<br>IV|(7)<br>OLS|(8)<br>IV|
|---|---|---|---|---|---|---|---|---|
|VARIABLES||||Lo|g price index||||
|Log benefts per population|0.00397|0.232|0.157***|0.148***|0.118***|0.118***|0.0211**|0.0820***|
|Log housing price|(0.0177)|(0.165)|(0.0323)<br>0.126***|(0.0267)<br>0.118***|(0.0300)<br>0.106***|(0.0295)<br>0.107***|(0.00951)<br>0.0474***|(0.0234)<br>0.0752***|
||||(0.0214)|(0.0190)|(0.0171)|(0.0172)|(0.0115)|(0.0124)|
|Log unemployment rate||||-0.00802|-0.00518|-0.00493|0.0000128|-0.00383|
|||||(0.00619)|(0.00599)|(0.00573)|(0.00244)|(0.00355)|
|Log population||||-0.146**|-0.136**|-0.134**|-0.132***|-0.144***|
|||||(0.0648)|(0.0627)|(0.0618)|(0.0383)|(0.0391)|
|Log average wage||||0.00755|0.00830|0.00852|0.00930*|0.00748|
|Log tobacco tax||||(0.00559)|(0.00544)<br>0.00741***|(0.00540)<br>0.00740***|(0.00552)<br>0.00826***|(0.00555)<br>0.00519***|
||||||(0.00268)|(0.00265)|(0.00158)|(0.00180)|
|Log SNAP average gross income||||||0.00382|0.00133|0.00634|
|||||||(0.0139)|(0.00472)|(0.00851)|
|Log SNAP average rent||||||-0.00512|-0.00348|-0.00490*|
|||||||(0.00369)|(0.00269)|(0.00291)|
|Observations|382560|382560|382560|382560|382560|382560|382524|382524|
|R-squared|0.884|0.771|0.868|0.874|0.884|0.884|0.904|0.899|
|Prob _>_ F|0.824|0.010|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|7970|7970|7970|7970|7970|7970|7970|7970|
|Number of clusters|48|48|48|48|48|48|48|48|
|First stage F-stat||2.578|13.996|17.857|20.655|20.682||20.194|
|Energy controls|||||||X|X|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Store and period fixed effects are included. Energy controls refer to annual state-level total energy, electricity, LPG, and natural gas quantities consumed and prices for the residential sector.

39
|**Ta**|**ble 2. Th**<br>|**e impact**<br>|**of remitt**<br>|**ances on**<br>|**the real e**<br>|**xchange**<br>|**rate**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
||(I)<br>FE|(II)<br>FE|(III)<br>FE/IV1|(IV)<br>FE/IV1|(V)<br>FE/IV2|(VI)<br>FE|(VII)<br>FE|(VIII)<br>FE/IV1|(IX)<br>FE/IV1|(X)<br>FE/IV2|
|Change Remittances|2.44|2.42|17.78|18.04|24.06|2.04|2.06|21.43|20.07|29.39|
|(% of GDP)|_3.26_|_3.22_|_2.93_|_2.85_|_2.35_|_1.95_|_1.96_|_2.31_|_2.31_|_1.96_|
|Change Remittances x LAC||||||0.83|0.75|-7.87|-4.78|-10.53|
|(% of GDP)||||||_0.55_|_0.49_|_-0.54_|_-0.34_|_-0.52_|
|Interest Rates|0.01|0.01|0.02|0.02|0.02|0.01|0.01|0.01|0.01|0.01|
|(US 6-month rate)|_1.27_|_1.24_|_1.01_|_1.00_|_1.00_|_1.32_|_1.29_|_0.43_|_0.56_|_0.44_|
|Terms of Trade|0.28|0.27|0.24|0.24|0.33|0.28|0.27|0.28|0.26|0.39|
|(Change, %)|_2.96_|_2.81_|_1.38_|_1.37_|_1.32_|_2.91_|_2.77_|_1.42_|_1.39_|_1.34_|
|Government consumption|0.62|0.69|1.32|1.31|1.03|0.61|0.68|1.26|1.29|0.93|
|(% of GDP)|_1.43_|_1.54_|_1.52_|_1.50_|_0.85_|_1.40_|_1.51_|_1.38_|_1.44_|_0.71_|
|Growth||0.10||-0.03|-1.00||0.09||0.00|-1.07|
|(%)||_0.86_||_-0.13_|_-1.03_||_0.83_||_0.02_|_-1.01_|
|Number of observations|250|250|221|221|221|250|250|221|221|221|
|Anderson canon. Correlation (p-val)|||0.05|0.07|0.17|||0.07|0.05|0.1|
|Sargan test of overidentification(p-val)|||0.47|0.48|0.85|||0.37|0.31|0.89|

The table reports the results of regressing the changes in the logged real effective exchange rate on the variables in the first column, allowing for fixed effects. IV1 Indicates that remittances are treated as endogenous; IV2 indicates that remittances and growth are treated as endogenous. Instruments are in both cases the level and first differences of (logged) output per capita of the host countries of migrant workers weighted by (i) distance between sending and receiving country and (ii) share of migrants of the receiving country in the sending country. t-statistics in italics. Source: Author’s calculations.

17
My econometric analysis reveals that individuals’ trust towards both the president and the municipal government is positively associated with a higher level of income at the municipality level. I also document that municipality-specific business cycle is a strong predictor of trust in mass media and, to a lesser extent, of trust in the national legislature and confidence in the judicial system but does not affect trust in the Catholic Church. By performing a heterogeneity analysis, I also show that the relationship between income and trust towards the president and municipalities is affected by a rural urban divide and differences in municipal state capacity. That is, most of the positive association between income and trust in the Salvadoran president is explained by individuals living in rural areas and municipalities with low levels of state capacity (as proxied by the share of households with access to electricity and water services), whereas most of the positive association between income and trust in the municipal government comes from individuals living in urban areas.

Additionally, views of fairness in income distribution, as well as preferences for democracy, are positively shaped by municipality-specific business cycles, while neither generalized trust nor satisfaction with democracy is empirically associated with variations in income across Salvadoran municipalities.

The rest of this paper is organized as follows. In Section 2, I discuss the data used in this paper, including a validation of nighttime light density as proxy for income at the municipality level, as well as recent trends in attitudes toward public institutions in El Salvador. Section 3 presents the main empirical strategy to study the link between attitudes and municipality-specific business cycle. Section 4 presents and discusses the main empirical results whereas section 5 concludes.

# **2 Data and Recent Evolution in El Salvador**

In this section I describe the data used and provide a brief description of the recent evolution in attitudes toward a set of public institutions in El Salvador. I also discuss the relevance of Nighttime Light Density as proxy for income across Salvadoran municipalities.

## _2.1 Attitudinal Data from Opinion Surveys_

In order to study Salvadorans’ attitudes towards public institutions over the 2004-2018 period, I use several waves of two opinion surveys, namely, LAPOP (9 waves) and Latinobar ́ometro (13 waves). Both surveys are implemented in El Salvador since the 1990s and focus primarily on citizen’s perceptions and attitudes toward different institutional actors. LAPOP is conducted every two years and interviews around 1,500 individuals in each wave whereas Latinobar ́ometro interviews around 1,000 Salvadorans every year. Both surveys provide information on a small set 4
local production costs, it is possible to calculate shares of subsidized prices for each energy source. Box 1 summarizes the methodology used to calculate such shares.

# **Box 1: Estimating Shares of Subsidized Prices**

For energy products consumed by the household, namely electricity, LPG, gasoline and diesel, a subsidy level “S” for each product is estimated using the price gap approach. According to this approach, a first price is calculated by adding to the international reference price (IP) all local taxes and domestic distribution costs. The resulting price is assumed to reflect the cost of efficient market supply, given the conditions and regulations of a given country as well as international prices. This price is called the “non-subsidized price” (NP). Subsidies (S) are calculated as the difference between the estimated NP and the observed domestic sale price, or market price (DP): Si = NPi – DPi, where _i_ refers to each energy source for residential consumption. The subsidy rate SRi for source _i_ is the ratio of Si to NPi. In the case of Tunisia, domestic prices used in this analysis are from the Ministry of Finance, while the IPs were obtained from the Ministry of Industries (Direction Générale de l'Énergie) for electricity, LPG, gasoline and diesel, respectively.

_Source:_ Authors based on Araar and Verme (2012) and World Bank (2013).

Table 3 presents the rate of subsidized energy prices with respect to the observed market prices since May 2014. The rate of subsidized LPG prices is estimated at 68% of the nonsubsidized price. In other words, for every liter of LPG consumed at a final price of TND 0.570, some TND 1.220 have been subsidized from the estimated price of TND 1.790 (reflecting international reference prices). Likewise, a similar calculation shows shares of subsidized prices of 10% for gasoline and 21% for diesel. In the case of electricity, the subsidized rates for each block decrease with consumption. This is the case for the two volume tiers. In fact, the two top consumption blocks of the high-volume tier—consumers of more than 300 kWh per month— receive negative subsidies. That is, they are net contributors to the subsidies of consumers of lower-volume consumption. Consumers from the two levels of highest consumption end up paying a higher price than the international reference price plus taxes and distribution costs.

10
**Figure 7. Incidence of main components of tax benefit system by deciles, share of final income, percent.**

<!-- Start of picture text --> 110<br>90<br>70<br>50<br>30<br>10<br>-10<br>-30<br>-50<br>poorest 2 3 4 5 6 7 8 9 richest total<br>SSC Direct Taxes Pensions Direct Transfers<br>Indirect Taxes In-kind Health In-kind Education Total<br>Note: deciles are defined based on market income.<br>Source: authors’ calculations using HBS-2017 data.<br><!-- End of picture text --> If we examine the incidence of main components of the tax benefit system separately for some representative household types, there are three categories of net payers, while other four benefit from the system (Table 10, Figure 8). The three categories of net payers are households with no children, and no pensioners, and families that consist of two working age adults and one or two children. They do benefit from the system through direct transfers, inkind health and education, but the amount of their contributions through direct and indirect taxes is higher than the benefits they receive. On net they contribute 4-12 percent of their final incomes. These types of families are among the least vulnerable.

The main beneficiaries are retirees-only households that get 69 percent of their final income as net benefits from the system, mainly through pensions (73 percent) and other direct transfers (8 percent). The households in this category are also the main beneficiaries of the health system in relative terms (9 percent of their final income is coming from this source). However, for obvious reasons, they do not benefit from the education system. Families without children, but with pensioners and working adults are also among net beneficiaries, receiving on average 33 percent of their final income mainly through pensions.

Another important category of beneficiaries includes families with three or more children and incomplete families with one adult and children (Table 10, Figure 8). These two vulnerable groups are benefiting 16 and 4 percent of their final income respectively. It mostly comes in the form of in-kind education and to a less extend direct transfers. In-kind education transfers account for 18-24 percent of their final income, 5-11 percent coming through direct transfers and 3 percent through in-kind health services.

**Table 10. Incidence of main components of tax benefit system by types of households, share of final income,** **<u>percent.</u>**

||SSC|Direct Taxes|Pensions|Direct<br>Transfers|Indirect<br>Taxes|In-kind<br>Health|In-kind<br>Education|<sup>**Total**</sup>|
|---|---|---|---|---|---|---|---|---|
|two adults(or more)and 1 child|-19.0|-4.7|7.2|4.1|-11.5|4.5|9.6|**-9.9**|
|two adults(or more)and 2 children|-17.4|-4.8|4.7|6.4|-11.0|4.4|14.1|**-3.6**|
|two adults(or more)and 3+ children|-10.3|-4.1|4.0|11.2|-11.7|3.3|23.8|**16.2**|
|one adult and child(ren)|-9.0|-3.0|2.2|5.2|-12.5|3.1|18.4|**4.4**|
|onlyworkingage adults|-18.4|-4.7|16.3|3.4|-13.3|3.6|1.2|**-11.8**|
|only pensioners|-2.5|-1.0|72.8|8.1|-17.3|8.6|0.0|**68.7**|
|mixed adults,no children|-10.0|-2.7|47.3|6.9|-15.5|6.2|1.2|**33.4**|

Source: authors’ calculations using HBS-2017 data.

20
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE subsidy. While it is typical for utility subsidies to be regressive in African countries, the distributional performance of Senegal’s subsidies is average in the case of power, and relatively good in the case of water (figure 25).

**Figure 25. Electricity and water subsidies that reach the poor**

<!-- Start of picture text --> a. Electricity b. Water<br>Nigeria Senegal<br>Gabon CAR<br>Congo Gabon<br>Côte d'ivoire Congo<br>Nigeria Kaduna<br>Cape Verde<br>Togo<br>Togo<br>DRC<br>Sao Tome<br>Nigeria FCT<br>Senegal Cameroon<br>Cameroon Côte d'ivoire<br>Mozambique Niger<br>Ghana Chad<br>CAR Cape Verde<br>Guinea Burundi<br>Burundi Malawi Blantyre<br>Ghana<br>Burkina<br>Guinea<br>Chad<br>Uganda<br>Malawi<br>Malawi Lilongwe<br>Uganda Burkina<br>Rwanda Rwanda<br>0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1<br>Omega Omega<br><!-- End of picture text --> _Source:_ Banerjee and others 2008b.

_Note:_ Omega is a measure of distributional incidence that measures the share of subsidies received by the poor as a percentage of their share in the population. The higher the value of omega, the better the distributional performance of the subsidy. Values of omega below one denote a regressive subsidy; and values above one denote a progressive subsidy. CAR = Central African Republic; DRC = Democratic Republic of the Congo.

How expensive would utility bills become if cost-reflective tariffs were applied? With a power costrecovery tariff of $0.29 per kWh and a monthly subsistence consumption of 50 kWh, the associated utility bill would come to $14.5 per month. Based on the distribution of household budgets in Senegal, monthly utility bills at these levels would be affordable by close to 100 percent of the population (figure 26). Indeed, the share of the population that could afford the service is much higher than the share of the population that already has the service, suggesting that Senegal has scope to increase coverage before affordability could become a serious impediment. A more limited level of subsistence consumption of 25 kWh/month for power and 4 m<sup>3</sup> /month for water—which is capable of meeting the most basic needs— would cost $7.3 and $4.5 per month, respectively, and would be affordable to most of the population.

40
# **I. INTRODUCTION**

For almost 10 years, the World Bank and the IMF have been undertaking the Financial Sector Assessment Programs (FSAP) across the developed and developing world, with the objective of evaluating the stability and efficiency of financial systems. Some of the most significant potential benefits of FSAP-type studies of financial systems derive from viewing a country’s performance in the light of international experience. Financial development benchmarking can give policy makers a better sense of how different components of their financial system are performing relative to other countries. This can help countries identify lagging areas possibly in need of policy reform. Inversely, good performance may be the result of sound and creative policies that other countries might wish to learn from and emulate.

Yet, the use of quantitative benchmarks on financial development has been thus far generally limited and of uneven quality and usefulness. Most often, the country is compared to a limited and arbitrary set of comparator countries that may be at very different stages of economic development or have very different structural characteristics. Hence, the policy implications of cross-country differences are unclear. Moreover, benchmarking is neither systematic across sectors or dimensions of financial development nor supported by a consistent and uniform set of good quality data that facilitates comparisons.

These shortcomings result only in part from data limitations on financial development indicators (FDIs). While important gaps no doubt remain in FDI data coverage (for example as regards bank outreach, non-bank institutions, or bond market indicators), the availability and sophistication of financial sector data, both from official sources and private sources, has increased abruptly in recent years. Substantial efforts have been made, by official agencies (including the Fund and the World Bank) as well as private organizations, to collect and produce financial indicators of increasing variety across large samples of countries through a number of primary or secondary databases.<sup>2</sup> This paper intends therefore to capitalize on these initiatives and help fill in some of the benchmarking gap by:

- proposing a standard methodology for financial development benchmarking;

- narrowing down the set of possible FDIs, based on current data availability, to ten core indicators;

- identifying some of the main remaining information and analytical gaps and possible ways to fill these up looking ahead.

The paper starts by discussing the rationale for using outcome indicators that measure features such as size, efficiency and outreach. The use of such “intermediate” indicators

> 2 In particular, the World Bank has made, through the interconnected DECRG, FSDI and Financing Growth databases, important progress in putting together a comprehensive set of financial indicators across different sectors and dimensions of development.

2
**Table 4. Correlations between Scores on** **_Ser Bachiller_ and** **_Ser Maestro_ Assessments at the School** **<u>Level – Language and Mathematics (UGB)</u>**

||**Lang**<br>|**uage**<br>|**Mathe**<br>|**matics**<br>|
|---|---|---|---|---|
|Lo of score on Ser Maestro|Log score<br>_Ser Bachiller_<br>0.186***|Log score<br>_Ser Bachiller_<br>0.176***|Log score<br>_Ser Bachiller_<br>0.156***|Log score<br>_Ser Bachiller_<br>0.152***|
|g|[0.017]|[0.018]|[0.014]|[0.014]|
|Shl’ ttl tdt||0.000||-0.000**|
|coos oa suens<br>School’s total mathematics teachers||[0.000]||[0.000]<br>0.007***|
|||||[0.002]|
|School’s total language teachers||0.003*<br>[0.002]|||
|Ctt|0.824***|0.877***|0.995***|1.007***|
|onsan|[0.113]|[0.116]|[0.089]|[0.089]|
|Observations|1.271|1.271|1.164|1.164|
|Adjusted R-squared|0.081|0.086|0.097|0.107|

_Source:_ Author using _Ser Maestro_ 2016 and _Ser Bachiller_ 2017/2018 datasets. _Note:_ *** = P ≤ 0.001. Standard errors in brackets.

# _Improving Program Targeting_

In-service training opportunities in Ecuador under the SIPROFE program are made available to teachers on a first-come, first-served basis. Once these training opportunities become available, program administrators contact teachers by e-mail or through local campaigns organized by the MINEDUC in coordination with school district authorities. Teachers who want to participate in the available programs sign up until all slots are filled.

The combined information from the _Ser Bachiller_ and the _Ser Maestro_ assessments constitutes a powerful tool to target interventions in schools (and school districts) with the largest needs. Figure 4 plots school-level overall results of the _Ser Maestro_ (y-axis) and _Ser Bachiller_ (xaxis) assessments. The dotted lines in the figure represent test scores that are one standard deviation above and below the average scores. Each dot in the figure represents a school. The color of the dots represents the poverty rates of the parishes (the smallest administrative territorial disaggregation) where schools are located. The blue dots represent the schools located in areas with the highest poverty rates, and the black dots represent the schools located in areas with the lowest poverty rates.<sup>7</sup> Figure 4 illustrates that schools with the worst-performing teachers also have the worstperforming students (bottom left panel of the chart). While this relationship does not necessarily imply a causality, it contributes to identifying the worst-performing institutions, presumably those

> 7 Poverty is measured by the (census-based) Unsatisfied Basic Needs Index produced in 2010 by Ecuador’s National Office of Census and Statistics.

16
Table 2: Correlation between COVID-19 shock and pre-pandemic observables

||Mom illiterate|Father completed<br>at least class 8|General Caste|
|---|---|---|---|
||(1)|(2)|(3)|
|COVID shock|-0.094|0.105|0.010|
||[0.098]|[0.104]|[0.113]|
|Outcome mean|0.31|0.70|0.28|
|Observations|3,180|3,136|3,180|

_Notes:_ This table presents the coefficient estimates from a regression of the respective pre-pandemic observable on our “leave-one-out” COVID-19 shock measure. Each column represents a separate regression. Robust standard errors clustered by ITI district are in brackets. Variable definitions are in the online Appendix. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01.

COVID-19 exposure variable. We also control for a vector of respondent characteristics ( **X** _i_ ) that are likely to affect her labor market aspirations and expectations including her age, a categorical variable for her ITI program (or “trade”), and indicators for belonging to a Scheduled caste or tribe, an Other Backward Class, having ever married, living in a rural area, completing at least class 12 before joining the ITI, having an illiterate mother, and having ever worked before joining the ITI.<sup>5</sup> Moreover, we control for whether a respondent’s district of residence differs from the district where the ITI is located since this may be relevant for their COVID-19 exposure.<sup>6</sup> Finally, we include fixed effects for district of ITI location, _δd_ , to control for time-invariant district-specific unobservables.<sup>7</sup> We estimate robust standard errors and cluster them at the ITI level.

Our coefficient of interest, _β_ , has a causal interpretation under the assumption that, conditional on socioeconomic characteristics and district-fixed effects, the variation in our “leave-one-out” COVID-19 exposure variable is exogenous to our outcomes of interest. To assess the validity of this assumption, in Table 2, we show that our shock measure is uncorrelated with respondents’ pre-pandemic socioeconomic characteristics, such as parents’ education and caste, that could impact our outcomes. We lack data on other pre-pandemic measures of socioeconomic status, e.g., household income. Moreover, as previously mentioned, our COVID-19 exposure variable is significantly correlated with the district-level COVID-19 death rate, and, as we show later, our results are robust to using this more

> 5 Although we have information on household asset ownership, we do not control for it because it may have been directly affected by the pandemic.

> 6Roughly a quarter of the respondents live in a district different from the district where the ITI is located.

> 7Our results are similar if we instead include fixed effects for district of residence. These results are available upon request.

6