rafmacalaba/datause-encoder-data huggingface dataset:
Speci...cally, the project aimed at: (i) providing communities with baseline information on the status of service delivery, both in absolute terms and relative to other providers, and the government standard for health service delivery at the dispensary level; and (ii) encouraging people to develop a plan that identi...ed areas of concerns in health provision, and how to address them within the existing resource envelope. These components are discussed next. A time-line and a schematic view of the intervention and expected outcomes are depicted in ...gures 1 and 2.

# 5.1 Data Collection and Report Cards

Data collection was governed by two objectives. First, data were required to assemble report cards on how the community at large views the quality and e¢ cacy of service delivery. We also wanted to contrast the citizens’view with that of the health unit sta¤. Second, data were required to rigorously evaluate impact. To meet these objectives, two surveys were implemented: a survey of health care providers and a survey of health care users. Both surveys were implemented prior to the intervention (data from these pre-intervention surveys formed the basis for the report cards) and one year after the project had been initiated.

A quantitative service delivery survey (QSDS) was used to collect data from the health service providers. Since agents in the service delivery system may have a strong incentive to misreport (or not report) key data, the data were obtained directly from the records kept by facilities for their own need (i.e. daily patient registers, stock cards, etc.) rather than from administrative records submitted to the district-level government. The former, often available in a highly disaggregate format, were considered to su¤er the least from any incentive problems in record-keeping.

The household survey collected data on both households’ health outcomes and health facility performance, including performance parameters such as usage, availability, access, reliability, quality and satisfaction. To the extent that it was possible, household responses were supported by patient records, i.e., patient exercise books and immunization cards. These records helped the household recall details about its visits to the health facility and also minimized problems of misreporting. The postintervention household survey also included a shorter module on health outcomes. Speci...cally, data on under-...ve mortality were collected and we measured the weight of all infants in the surveyed households.

A strati...ed random sample of households within the catchment area of the facility were surveyed. In total, roughly 5,000 households have been surveyed in each round. The design and implementation of the surveys are explained in more detail in the appendix.

The data from the two pre-intervention surveys were analyzed and a smaller subset of the ...ndings were assembled in report cards for the treatment localities.<sup>9</sup> The data

> 9Thus, the design and size of the surveys were largely driven by the second objective –to evaluate impact.

9
<!-- Start of picture text --> HE (9.35,10.01] \<br>WW (8.69,9.35]<br>1) (8.02,8.69]<br>(7.36,8.02]<br>(6.70,7.36]<br>(6.04,6.70]<br>(5.37,6.04]<br>1 (4.71,5.37] /<br> (4.05,4.71]<br>GM (3.39,4.05]<br>WB (2.72,3.39] SP<br>|<br><!-- End of picture text -->
**Table 1 Dynamic Performance of the** **_Maquila_ Sector in Honduras, 1990–2006**

|_Measure_|_1990_|_1995_|_2000_|_2001_|_2002_|_2003_|_2004_|_2005_|_2006_|
|---|---|---|---|---|---|---|---|---|---|
|Number of firms|24|135|218|230|252|273|294|306|313|
|Number of employees<br>(thousands)|9.0|55.0|106.5|94.4|105.5|114.2|119.9|125.2|130.1|
|Average annual wage (current<br>US dollars)|656|1,456|3,142|3,210|3,041|3,358|3,447|3,669|3,829|
|Value added (current US<br>dollars,in millions)|16.2|162.7|575.4|560.0|612.8|710.0|815.3|969.2|1,062.2|

_Source_ : Authors, based on data from Banco Central de Honduras.

_Maquila_ has become the single most important export activity in Honduras. In 2006 it represented 27 percent of total exports of goods and services, up from virtually zero in 1990. Between 1990 and 2006, the value added of exports by the sector rose from $16.2 million to $1,062.2 million, growing at an impressive average annual rate of 33 percent (table 1). The percentage of total exports accounted for by the _maquila_ sector rose from 1.5 percent in 1990 to 26 percent in 2006 (figure 2). During the same period, the share of traditional export crops, such as coffee and bananas, declined from 51 percent to 16 percent. In contrast to the sluggish evolution of coffee and bananas, exports of other nontraditional products— particularly farmed shrimp, minerals, palm oil, and other agroindustries—expanded.<sup>4</sup>

**Figure 2 Composition of Exports in Honduras, 1990–2006**

<!-- Start of picture text --> 1990 1995 2000 2005<br>maquila (value added) coffee and bananas<br>other<br>80<br>60<br>40<br>percent of total exports 20<br>0<br><!-- End of picture text --> _Source_ : Authors, based on data from the Banco Central de Honduras The unilateral trade preference conceded by the United States under the Caribbean Basin Initiative, established in 1983, and a variety of other factors (such as logistics, abundant and low

> 4 For an estimation of the ex-ante poverty effects of trade liberalization in Honduras focusing on the agricultural sector, see República de Honduras (2005).

4
focusing on the effects of income levels. Our empirical hypothesis is that energy consumption increases with income but that the starting point differs for rural and urban households. In addition, we want to test whether household characteristics play a role in determining energy consumption, and when they do, they can be taken into account when designing a more targeted subsidy scheme.

To test our hypotheses, we first compare income with energy expenditure, and then apply both OLS with log of consumption and a Tobit model to calculate significant factors affecting energy expenditure and to separate the models for urban and rural households. Here, we are using microdata at the household level from the National Income Expenditure Surveys (ENIGH), which include sociodemographic characteristics of the households, as well as data for all energy consumption, including those for different energy sources (electricity, gas, LPG, gasoline, etc.). These surveys also includes income that is used for the official measures of poverty in Mexico.

The main contribution of the paper is to shed light on a topic that has been overlooked by Mexican public policies: the relation between income levels, some other characteristics, and the different forms of energy consumption. The paper addresses issues such as inequality in the use of energy and explores mechanisms that would allow better access to energy and determine optimal prices—a discussion that starts with the identification of characteristics at different income levels of the energy consumption in both urban and rural areas.

The paper will, first, review the corresponding literature relating household characteristics to demand for energy level; second, it will explain the data and the models for the analysis; third, it will present the results, and finally, it will offer conclusions and the implications of the results.

4
Geographical masking methods work by modifying the geographic coordinates linked to each household or community. Either random perturbations or affine transformations can be used. If the relationship of sample points to one and other is important while the relationship to another data layer (say, a base map or a road network) is not, then simply moving all points by some given distance and direction or rotating them about a chosen point may preserve confidentiality and not greatly degrade usability of the data.<sup>20</sup> Normally however, point locations obtained with a GPS are merged onto other data layers so these affine transformations will introduce error that reduces the usability of the GPS data. Another option is to introduce random perturbation around the original point with the radius of the perturbation circle chosen by the data custodian, possibly weighting the size of the circle by population density at each point to take into account the effect of population density on the risk of disclosure (Kwan, Casa and Schmitz, 2004). For example, many recent DHS surveys include tests for HIV infection and because of confidentiality issues related to HIV status, up to two kilometers of random error in any direction is added to cluster locations in urban areas, and up to five kilometers of random error is added to cluster locations in rural areas. Additionally, one point in each survey with HIV testing is displaced up to 12 km in any direction.<sup>21</sup> Only limited research has been conducted on the effect of random perturbation on either disclosure risk or the accuracy of results. Kwan et al (2004) show that as the size of the perturbation circle increases the accuracy of results diminishes. Zimmerman and Pavlik (2006) show that releasing metadata about the perturbation methods and having different masked versions of the same dataset (e.g., a spatially aggregated dataset and a randomly perturbed dataset) can considerably increase disclosure risk.

# **CONCLUSIONS**

The removal of selective availability and falling costs of GPS receivers have made the collection of GPS information increasingly feasible in household surveys – yet many household surveys still do not use this technology. This paper argues that the collection of GPS coordinates should become a routine part of household survey collection, since doing so can lead to better economics and better policy advice. In particular, we have shown how GPS is being used to help

> 20 This is the approach used by the Rural Investment Climate Survey in Indonesia.

> 21 For more details see: <u>http://www.measuredhs.com/topics/gis/methodology.cfm.</u>

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# **Figure 11 – In Many Countries, Employment of Insiders Still Very Protected**

Employment protection legislation in ECA, 2007 <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: Institute for the Study of Labor (IZA), OECD Note: Higher numbers denote stricter EPL regimes. All sub-indexes are normalized in the 0–6 range (OECD Standard) Just as important as being able to adjust the size of the workforce, the business environment needs to be open for entrepreneurs to start new businesses. Enabling private sector growth requires a regulatory environment where new entrants with drive and good ideas, regardless of their gender or ethnic origin, can get started in business and where firms can invest and grow, generating more jobs. Economic activity requires good rules—rules that establish and clarify property rights and reduce the cost of resolving disputes; rules that increase the predictability of economic interactions and provide contractual partners with certainty and protection against abuse. The objective is regulations designed to be efficient, accessible to all and simple in their implementation<sup>22</sup> . This is another area where countries in the region have made significant reforms. However, in some countries there are still important restrictions and high costs to doing business (Table 7).

Labor taxes are another important determinant of the cost and the ability of firms to hire workers. They are composed of both social insurance contributions by employers and employees and the personal income tax of employees as a share of total labor costs. Critically, labor taxes in ECA are relatively higher at lower earnings levels. A comparison with other EU, OECD and neighboring countries shows that the tax wedge on labor at lower wage levels (33 percent of average wages) tend to be relatively high. The tax wedge measures the difference between labor costs and take-home pay of workers. For a single person with no children who receives a gross wage 33 percent of the average wage, only few EU-15 countries— like Sweden, Germany, Belgium, and Finland—charge higher taxes than most of the EU’s new member states (Figure 12) (Koettl and Weber, 2012).

> 22 World Bank (2012a)

22
omitted variable bias in the model. The IV approach also helps correct for potential measurement error in the education variable.

# **Identification strategy**

I construct an instrumental variable for years of schooling, which exploits SDIP, the school construction program carried out during the 1970s, following Duflo (2000; 2001).

An individual’s exposure to the school construction program was determined by (i) her age at the time the school program was launched in 1974 and (ii) the number of schools built in her region of birth. Since Indonesian children normally attend primary school between the ages of 7 and 12, all children who were 12 years or older in 1974 (i.e. born in 1962 or earlier) would have already completed primary school by the time the program started and would not have benefited from it. Grade repetition and delayed school entry mean that a few of the children born prior to 1962 could still have been exposed to the program. However, analysis of IFLS data shows that less than 7% of all women born between 1950 and 1962 were still in primary school in 1974. For children born after 1962, exposure to the program is an increasing function of their date of birth. A second dimension of variation in children’s exposure to the program is their region of birth. Program intensity in different regions across the country was based on enrollment rates in each region in 1972. Children born in regions where enrollment rates were relatively low in 1971 would have been exposed to a higher level of SDIP intensity than those in regions with higher initial enrollment rates.

24
# **1 Introduction**

Over the past three decades, a decline in import tariffs has coincided with a rise in quality regulations (Figure 1). Quality regulations specify minimum standards a product must conform with in order to be sold in the market, and apply to domestic as well as foreign firms.<sup>1</sup> Quality regulations can affect firms’ capabilities, defined by Sutton (2012) as their cost of production, which shifts supply, and their product quality, which shifts demand. For example, conforming with food safety certification could increase firms’ fixed cost, given the investment required to bring the production process into conformity. Yet once consumers are assured by the certification that the food product is safe, demand could increase. How quality regulations affect the allocation of trade has important implications for economic development and inequality between countries. As more importing countries adopt quality regulations, lower-income exporting countries could benefit if such regulations reassure customers that their products are of high quality. At the same time, quality regulation could be a barrier to entry for exporting firms from lower-income countries, if their competitors in higher-income countries have a lower compliance cost due to their prior experience with these regulation in their home markets.

This paper quantifies the heterogeneous effects of quality regulations and other non-tariff measures (NTMs) on trade, focusing on how they shape the allocation of trade between lower- and higher-income economies and the channels through which they achieve these effects. We overcome two key empirical challenges to provide the most comprehensive evidence yet on these important policies. A first empirical challenge concerns measurement: NTMs comprise a highly diverse set of policies that are difficult to measure accurately, especially over time. To overcome this challenge, we assemble a new panel dataset including (1) all NTMs at a disaggregated product level imposed by 16 middle-income Latin American countries from 1996 to 2017; (2) product-level exports to these countries from each of their trading partners; and (3) firm-level exports to these countries from a subset of their trading partners. These middle-income countries in Latin America are a unique laboratory to study the impact of NTMs as they have engaged in substantial adoption of new quality regulations and standards in recent decades, unlike high-income countries, which have already adopted these regulations, and low-income countries, which have yet to adopt them. We leverage rich panel variation in the timing of the adoption of these NTMs to identify their treatment effect on trade.

> 1Quality regulations can be set locally by a country or can be based on international standards. An example of international standards are those for food safety developed by the Codex Alimentarius Commission funded by the Food and Agriculture Organization of the United Nations and the World Health Organization.

2
The impact analysis carried out by Galasso and Ravallion (2004) is based on the October 2001 and October 2002 rounds of the _Encuesta Permanente de Hogares_ survey, which covers urban areas. With respect to leakage, the study finds that one-third of those receiving the program were ineligible and that 80% of individuals who were eligible did not receive the program. In particular, more than half of the program participants were women who were probably not heads of households. Despite the problems in imposing eligibility criteria, however, the program was fairly well targeted at poor households. About half of program participants came from the poorest fifth and 80% came from the poorest 20% of the population.

Galasso and Ravallion (2004) evaluate program impacts using cross-sectional and differencein-difference propensity score matching approaches. The treatment group includes those who applied and were admitted into the program, and the comparison group includes persons who applied for the program but had not yet joined. Galasso and Ravallion (2004) find it difficult to predict participation status among program applicants, and the predictive power of the propensity score model is not very high, raising some concerns as to whether the observables included in the propensity score model adequately control for differences between the treatment and comparison groups.

The matching analysis reveals that program participants experienced a smaller drop in real income on average than the comparison group, suggesting net gains on average between half and two-thirds of the gross wage, depending on the estimator used. Galasso and Ravallion (2004) argue that, given the level of income support, the observed income gains should have been in the range (0,150) and that negative estimates or estimates that exceed 150 should therefore be excluded. On these grounds, they prefer the impact estimates derived from the cross-sectional estimator, which indicate that 26% of _Jefes_ participants would have been unemployed were it not for the program and 23% would have been inactive (primarily women).<sup>8</sup> On the whole, Galasso and Ravallion (2004) find that the program reduced Argentina‟s aggregate unemployment rate by about 2.5% and contributed to social protection during the economic crisis by supplementing the income of poor families.

> 8 Ruling out negative estimates or estimates that exceed 150 is potentially problematic. Income support could have been used for productive purposes, such as a small business, making gains in excess of 150 possible. See, for example, the discussion of the Kaboski and Townsend (2007) study in section 4. Also, negative income gains are also feasible, if, given the transfer, some women might have withdrawn from the labor force.

15
Table 28

|Dependent variable: Fi|scal adjust|ment whe|n needed b|ased on Esc|olano et al|.(2014)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.005*|0.005*|0.006|0.003|0.011|0.047***|-0.034*|0.002|
|(% of GDP)|(0.003)|(0.003)|(0.005)|(0.006)|(0.009)|(0.018)|(0.017)|(0.024)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.454||-0.966|-0.575|-1.118|-1.086|-0.968|
|||(1.115)||(1.359)|(1.278)|(1.294)|(1.261)|(1.356)|
|Inflation (t-1)||-0.131||0.751|1.583|0.953|1.678*|0.744|
|||(0.654)||(0.890)|(0.973)|(0.837)|(0.938)|(0.896)|
|U.S. Interest rate||-0.034||-0.051|-0.070**|-0.074**|-0.088**|-0.051|
|||(0.028)||(0.036)|(0.034)|(0.032)|(0.040)|(0.036)|
|**Political factors**|||||||||
|Gini|||0.008**|0.008**|0.009***|0.040***|0.008**|0.008*|
||||(0.004)|(0.004)|(0.003)|(0.011)|(0.004)|(0.004)|
|Rule of law|||0.063|0.113***|0.088*|0.115***|-0.147|0.113***|
||||(0.042)|(0.041)|(0.051)|(0.036)|(0.136)|(0.043)|
|Margin of Majority|||0.061|0.075|0.157|0.136|0.075|0.033|
||||(0.246)|(0.348)|(0.348)|(0.335)|(0.324)|(1.040)|
|Election's year|||-0.094*|-0.102*|-0.085|-0.089*|-0.081|-0.102*|
||||(0.052)|(0.055)|(0.059)|(0.053)|(0.059)|(0.055)|
|**Interactions**|||||||||
|EMEs|||||0.408<br>(0.335)||||
|EMEs * Rigidity|||||-0.024<br>(0.015)||||
|Gini * Rigidity||||||-0.001***<br>(0.000)|||
|Rule of law * Rigidity|||||||0.009**<br>(0.004)||
|Majority * Rigidity||||||||0.002<br>(0.042)|
|Observations|185|183|165|163|163|163|163|163|
|Country FE|No|No|No|No|No|No|No|No|
|r2_p<br>Robust standard error<br>*** p<0.01, ** p<0.05|0.0183<br>s clustered<br>, * p<0.1|0.0338<br>by country|0.0718<br>in parenth|0.110<br>eses|0.156|0.146|0.149|0.110|

- 57 -
The household-level matching findings reported in this section are consistent across different matching estimators as well as different caliper widths/bandwidths used for each matching estimator. Only the results using the nearest-five neighbors matching estimator are reported in table 9. Results using the nearest neighbor and kernel estimator are very similar and available upon request. The results in Table 9 show differences in welfare between comparable NE and old-age pension transfer recipient and non-recipient households. Table 9 also shows the results when the urban and rural samples are considered separately. At the very least, antipoverty program administrators would like to see no difference in average expenditure outcomes across treatment and comparison groups. The fact that the average value of real per capita monthly expenditure for the treated units is 1,037 Lek _less_ than the figure for the matched comparators is worrisome.<sup>23, 24</sup> The matching analysis shows that while NE participants record lower figures in all expenditure categories, the difference in overall consumption across the treatment and comparison groups appears to be driven by the trends in food and nonfood expenditures. Specifically, the mean differences in the category of real per capita food and nonfood expenditures are both estimated at -423 Lek. These estimates are statistically significant at the 1 percent level.

NE households are also less likely to (i) be satisfied with the current financial situation or life, (ii) think that the financial situation or life has improved in the past 3 years, (iii) believe that the current financial situation or life will improve in the next 12 months, and (iv) find overall and food consumption to be adequate. Exploring the heterogeneity of program impact by urban/rural

> 23The 2002 average difference in per capita consumption between NE participant and non-participant households was -3200 Lek, as reported in Table 5. In absolute terms, this figure is surely an overestimate of the actual program impact recovered from the PSM analysis.

> 24A confounding problem for the household-level matching estimates may be that income- or consumption-related poverty, which is the main outcome of interest, is also incorporated in the decision of the social administrator in order to determine the eligibility of an applicant household. However, the extensive set of variables included in the program participation regression should be adequate enough to reflect the welfare of sample households, rendering the potential problem irrelevant.

26