rafmacalaba/datause-encoder-data huggingface dataset:
studies. Yet, on average, only 5% of studies relate to educational outputs that measure student achievement **and** school efficiency. Tr.is is surprising considering that the fundamental output of the education system is student learning. One fifth of all studies examine education and its relation to the labor market; this category also includes all tracer studies, as well as manpower and specific vocational training development studies. It is also interesting to note that a number of studies relate to statistical and information system development.

_Table 5: Regional comparison of distribwion of studies by type, 1982-1989, as a percent of all studies withir the ?egion._

||Educat-<br>ional<br>Inputs|<br>Educat-<br>ional<br>Outputs|Labor<br>Market<br>Studies|Feasib-<br>ibility<br>Studies|Cost<br> <br>and<br>Finance|MIS<br>and<br>Stats|Other<br>|
|---|---|---|---|---|---|---|---|
|**Allregions**|24.8%|5.6%|21.21|8.8%|15.1%|12.2%|12.4%|
|Africa|29.2|5.0|13.3|8.3|17.5|14.2|12.5|
|Asia|23.6|8.9|23.6|9.8|12.2|8.9|13.0|
|EMENA|19.5|3.5|27.6|13.8|11.5|12.6|<br>11.5|
|LAC|25.9|3.7|22.7|2.5|19.7|13.6|12.3|

21 In general, for all regions, studies related to educational inputs were the most frequent, while those related to educational outputs received least attention. There were no between-region differences in the distribution of types of studies, _x(_ 18, N=436) = 22.4, P > .05.

22. We attempted to classify the studies in Tan (1982), using the descriptions of studies found in Annexes II-VII of her paper, so that comparisons of changes in study type over the two time periods, 1972-1982 and 1982-1989 could be made. However, these descriptions were not specific ernough for consistent classification, and studies listed were typically tracer study or broad institution and program evaluation. In the 1982-1989 period classification, broad evaluations were listed as "other.' For tl: .9721982 period, where possible, such broad evaluations were classified by the areas that were being evalua6A . Therefore, extreme caution should be taken in interpreting changes in the types of studies funded between the two time periods.

23. Table 6 compares the distribution of study types for 1972-1982 and 19821990. There was a statistically significant difference between the type of studies planned between 1972-1982 and those planned between 1982-1989 **(X2** (5, N= 708) = 46.14, P < S
ment information is available in CRS. Moreover, because _DAC_ 2 _a_<sup>_agg_</sup> _RY_<sup>is complete, it corrects for the incomplete</sup> nature of the CRS data in a simple manner.

This method assumes that the sectoral distribution of incomplete CRS commitments is a good guide to the actual distribution of total disbursements across sectors. This assumption may not hold if, for instance, a donor’s propensity to report disaggregated aid to the CRS database varies by sector, or if donors that report a good deal of their aid to CRS have different sectoral preferences than donors that largely fail to report disaggregated aid. As a result, equation (5) may yield highly imperfect measures of sectoral disbursements, especially if CRS coverage is low, such that the sectoral distribution of CRS commitments that is used to allocate aggregate DAC2a disbursements across sectors is based on only a small subset of the total aid committed to a recipient.

To address these problems, I first restrict the analysis to the 1990-2004 period, for which CRS disbursement information is available. More importantly, I construct more complete data on earmarked education and health aid disbursements by accounting for additional information available in DAC Table 2a and DAC Table 5. Because the method is described in detail in the supplemental appendix, available at http://wber.oxfordjournals. org/, I provide only a brief summary here.

I begin with aggregate and sectoral gross CRS disbursements in a recipient-donor-year (RDY) format, labeled _CRSRDY_<sup>_agg_and</sup><sup>_CRS_</sup> _RDY_<sup>_s_(for</sup><sup>_s_=1</sup><sup>_, . . . , S_),respectively.For each RDY observation,the amount of</sup> aid that is absent from CRS is calculated as the difference between DAC2a and CRS disbursements: The aim is to allocate this total residual ( _RESRDY_<sup>_agg_)across sectors,therebygenerating sectoralresiduals that</sup> can be added to the CRS sectoral disbursements to compensate for the incomplete nature of the latter.

To achieve this goal, I use data from DAC Table 5. DAC5 comprises aggregate aid and its sectoral distribution but organizes information only by donor and not by recipient ( _DAC_ 5<sup>_agg_</sup> _DY_<sup>and</sup><sup>_DAC_5</sup> _DY_<sup>_s_, respectively).</sup> However, DAC5 has an advantage in that these data contain more complete information than CRS.<sup>11</sup> By converting the CRS data into the same donor-year (DY) format, I can calculate the amount of sectoral aid that is absent from CRS in each DY ( _RESDY_<sup>_s_) for each sector.As a result, for each DY and sector, I can compute the</sup> share of the sectoral residual in the total residual: This donor- and year-specific allocation of the total residual across sectors is then applied to the total residual 7
because of the hidden costs of foregone earnings and implementation costs, based on the analysis of India’s Employment Guarantee Scheme. The technical difficulty in addressing these questions lies in the absence of a counterfactual, because an individual may have worked in the local labor market had opportunities for workfare not existed.

This paper investigates the largest safety net program in Bangladesh, the Employment Generation Program for the Poorest (EGPP). Bangladesh has been implementing various safety net programs and more than 140 safety net programs operate particularly in poor rural areas (See World Bank, 2016). As these programs tend to have been introduced in response to crises and natural disasters over time, many of them are added to existing programs without a systematic process of targeting, identification, enrollment, or payment. The Ministry of Disaster Management and Relief (MoDMR) implements many of the large social assistance programs (food aid for humanitarian relief and workfare) which have long been operational in rural Bangladesh.<sup>2</sup> In 2009, in response to the global food crisis, the Ministry added a modern workfare program, EGPP, to its portfolio. Compared to other social assistance programs, EGPP is a modern system in that it has pre-determined cycles with work schedules with pre-determined rules including the number of days and wage rates, and cash payments are directly transferred to individual bank accounts.

To understand the impact of EGPP on poor households in rural areas, we acquired an administrative roster of households that have been receiving benefits from existing social assistance programs administered by MoDMR for the past 12 months. This is based on the observation that a large majority of EGPP beneficiaries have been already exposed to various social assistance programs that have been operating for a long time. The MoDMR roster revealed that a sub-sample of the beneficiaries of traditional social assistance programs have also participated in EGPP. Therefore, we examine the impact of EGPP participation among the beneficiaries of the traditional social assistance programs.

Given that EGPP participation is self-selected, we use an instrumental variable to identify the impacts of the program. Considering that the program’s cash payment operates through bank accounts, we use accessibility to the closest bank in each _Union_<sup>3</sup> as an instrument. Our results suggest that EGPP helped households increase their overall consumption, mostly due to increases

> 2 Under this umbrella, there are two humanitarian aid programs, the Vulnerable Group Feeding and the Gratuitous Relief, and two public works programs: Test Relief and Food for Work. 3 Township. The smallest local administrative unit in rural Bangladesh.

3
|Urban Project(02)|Charles K. Boakye|
|---|---|
|Urban Project(02)|Charles K. Boakye|
|Urban Project(02)|Charles K. Boakye|
|Urban Project(02)|Charles K. Boakye|
|Urban Project(02)|Charles K. Boakye|
|Urban Project(02)|Charles K. Boakye|
|Urban Project(02)|Charles K. Boakye|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|Financial Sector Adjustment Credit Project(02) (FINSAC 02)|Gerard A. Byam|
|AGRIC SECAL|Gotz A. Schreiber|
|AGRIC SECAL|Gotz A. Schreiber|
|AGRIC SECAL|Gotz A. Schreiber|
|AGRIC SECAL|Gotz A. Schreiber|
|AGRIC SECAL|Gotz A. Schreiber|
|Program to Promote Private Investment and Sustained Developme|n|
|Program to Promote Private Investment and Sustained Developme|n|
|Program to Promote Private Investment and Sustained Developme|n|
|Program to Promote Private Investment and Sustained Developme|n<br>|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|ADULT LITERACY|Irene S. Xenakis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Agricultural Diversification Project|Solomon Bekure Woldegiorgis|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|Private Enterprise and Export Development Project|Marilyn Swann Manalo|
|CommunityWater and Sanitation Project|Jennifer J. Sara|
|CommunityWater and Sanitation Project|Jennifer J. Sara|
solar panels, hydraulic and hybrid systems). In all cases the desired end product is the supply of 220 volts AC at 50 Hz frequency, 24 hours a day.

Central government publicizes the Rural Electrification Program (REP) at the national, regional and borough levels. It also hires consultants to work with regional governments in designing electrification strategies. The procedure for applying to the REP is as follows: the inhabitants of a locality without electricity supply organize themselves, prepare a list of potential users, and request the municipality in which they live to undertake an electrification project, alternatively the municipality itself may detect the corresponding need. The municipality, in turn, requests a technical project and budget, at no charge, from the distribution company, or else occasionally hires a consultant for this purpose. Municipalities collect the information needed to compute the project's private and social NPV, according to a standard methodology developed by Mideplan. The regional government then revises this evaluation.

By way of example we describe the methodology used to measure the benefits to residential consumers. It is measured in terms of consumer surplus, in other words as the area under the demand curve. The no-project price-consumption pair _(Po, Qo)_ is estimated as follows. The execution of rural electrification projects enables the population to cut down on consumption of candles, paraffin, batteries or gas, and also reduce the time spent purchasing such items. Estimates of these figures are converted into their equivalents in kWh per month (the consumption _Qo)_ and Ch$ per month (current spending on energy of households G), respectively. The price _P0_ is obtained dividing expenditure (G) by consumption _(Qo)._ 20
Lees (2022), and Angelov and Waldenström (2021) highlight the role tax administrative data can play in measuring economic activity in the private sector. Data is collected continuously, often at high frequencies, and typically covers a high share of formal firms. Similar to the aforementioned research based on surveys, analysis of tax administrative data has highlighted the pandemic’s negative impacts on firms’ sales and employment levels. Many firms have struggled to stay profitable, and at times have exited the market completely. For example, Mascagni and Lees (2022) use VAT returns to outline the severe repercussions of COVID-19 lock downs in Rwanda. Despite reporting low numbers of COVID-19 cases, VAT-registered firms in Rwanda experienced a 30 percent drop in aggregate sales over the lockdown period. Over this period, losses in sales amounted to 5.2 percent of GDP, with smaller firms suffering the most in proportionate terms. Mascagni and Lees (2022) also show that firms belonging to the accommodation, food, and transport sectors have struggled in particular since the onset of the pandemic.

There is also a relevant strand of the literature that goes beyond measuring the crippling impacts of COVID-19 by examining what characteristics of firms are associated with their recovery from crises. Jin et al. (2018) studied the recovery of firms’ performance in the aftermath of the 2007–08 global financial crisis. They found that firms with stronger financial constraints experienced a more sluggish recovery from the crisis relative to firms with weaker constraints. Additionally, well-developed financial institutions and structures were able to contribute significantly to firms’ recovery by easing their financial constraints. Amin and Viganola (2021) corroborate these findings: firms with better access to finance experience a less significant drop in sales, albeit results are highly heterogeneous. The reduction in likelihood of a decline in sales is much higher for firms that have a stronger and longstanding relationship with prominent stakeholders such as skilled workers and input suppliers. Cowling et al. (2018) also analyzed the 2008 global financial crisis and found that firms that had been established for longer periods of time were in a better position to stave off negative impacts from the crisis compared to young firms. Furthermore, the severity of the crisis meant that previous entrepreneur experiences did not have any substantive effects on small 5
O.A.M.D.G.

Although the data are obviously limited, we can use it to attempt a sample selection correction. We can model the selection process according to Heckman and calculate the IMRs. This will allow us to correct the linear approaches and linear approximations in our work. However, we cannot control for selection in the non-linear specifications. This is because we cannot calculate the stratification weights in the original population which are needed for the approaches for Boyes et al (1989) and Greene (1992). This is because the variables in the SISBEN sample are not the same as in Familias, and very rudimentary.

We estimate the equation for z in the basic Heckman model explained above, taking ‘migration out of the SISBEN register’ as dependent variable. Given the limitations of the SISBEN register’s data, we are somewhat restricted in the choice of possible outmigration determinants. Including household level variables would reduce the sample too much, so that we have to rely on pre-2000 municipality level characteristics alone. We include the number of guerrilla fighters, landmine incidents, kidnappings and the homicide rate as right hand side variables. We also include the sum of all bank assets per head as an attempt to proxy for wealth at the municipality level. We include a squared term of each variable in order to be less restrictive in the functional form. The results are given below.
Foroutan does not control for other factors that may have induced countries to behave as they did, making it impossible to disentangle the effect of trade agreements from other global, regional or sectoral trends.

Using a detailed cross-industry dataset on Argentina for 1992, 1993 and 1996, Bohara, Gawande and Sanguinetti (2004) examine the influence of imports from Mercosur’s partner Brazil on Argentina’s external tariffs. They find that increased preferential imports vis-à-vis the value added of the domestic industry led to lower external tariffs in Argentina, especially in industries that experienced trade diversion. The study of Bohara et al. concentrates on the effects of increases in preferential imports, however, and does not address the direct effect of preferential tariffs. Additional concerns are that their dataset begins in 1992, a year after the establishment of Mercosur, and that they do not account for the effect of Mercosur becoming a customs union in 1995.

Interestingly, results from Limao (2006) and Karacaovali and Limao (2008) are opposite of those in Bohara et al. (2004), making it difficult to draw broad conclusions. One important distinction between the two sets of studies is the country sample: while the former studies the two majors, the latter analyses a developing country. It may be that the incentives generated by preferences are very different in these contexts. In particular, since developing country tariffs are usually much higher than U.S. or E.U. tariffs, the cost of trade diversion and the resulting incentive to liberalize vis-à-vis outsiders can be far greater in the former. A second important difference is the type of liberalization: Limao (2006) and Karacaovali and Limao (2008) examine external liberalization resulting from a multilateral trade round, while Bohara et al. (2004) examine unilateral external liberalization. A final but not less important distinction is the purpose of the preferences. As Limao highlights, industrial countries often use preferences to extract 8
Table 3. Effect of a 1 Percentage Point Increase in the Income Share Held by the 4<sup>th</sup> Quintile on Log GDP per capita

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|1.12|1.75|
|Cambodia|-0.46|0.46|
|Indonesia|-0.65|0.69|
|Laos|-0.68|0.55|
|Malaysia|-0.20|1.16|
|Philippines|-0.22|0.62|
|Singapore|0.30|1.73|
|Thailand|-0.22|0.94|
|Vietnam|-0.69|0.57|
|ASEAN Average|-0.19|0.94|
|ASEAN Median|-0.22|0.69|

Note: The table reports effects on log GDP per capita of a 1 percentage point increase in the income share held by the 4<sup>th</sup> quintile. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015), see there column (5) of Table 1, with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015), see there column (5) of Table 1, with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is ln(GDP per capita)ct = -263*Q4ct + 40*Q4ct*ln(initial GDP per capita)c.

17
# **5. Conclusions**

This paper has analyzed the relation between energy consumption and income levels, and other characteristics, in Mexican households. To the extent that subsidies are currently applied to energy sources for general consumption, or that a social tariff has to be designed, this issue becomes relevant for better targeting of public mechanisms. Previous studies linking energy consumption and income have only focused on the extreme poor who receive conditional cash transfer schemes (i.e. Gertler et al., 2009).

We used microdata at the household level from the Income-Expenditure Surveys (ENIGH) 2010 for Mexico. The surveys record energy expenditure, income, and socioeconomic characteristics, among other things. We first calculated energy consumption standardized in KgOE, so that all results can be directly compared to each other. We then calculated relations between income and energy expenditure through Engel curves, then applied an econometric model to relate specific and total energy consumption to household characteristics and income levels through Tobit and OLS models. The preliminary relations show an s-shaped curve with slight variations for urban and rural households. For rural areas, the relation always increases.

The results from the econometric models show that, in general, there is an increasing relation between income levels and energy demand, with some exceptions, and that supports the hypothesis for this analysis. For total energy consumption, in urban areas there is a constant increasing and significant effect, while for rural areas the relation resembles an elongated s-shaped curve, with significant effects starting from the middle deciles and spreading to the upper ones. Rural electricity consumption shows a larger divergent trend from the other energy sources, since the model shows only the effects at the upper income levels. Gasoline consumption clearly increases with income, and urban households consume three times the amount of rural households. In addition, it should be noted that household characteristics are also relevant for targeting households that need to increase energy consumption but lack the means to do it on their own. The geographical area and indigenous status, as well 28