rafmacalaba/datause-encoder-data huggingface dataset:
raising their (short-term) wages, as local road construction and improvement initiatives in India serve as employment programs for the landless poor. Education, which one expects to have the most profound effect on poverty over the medium and long-term, seems least affected by decentralization.

New evidence from India specifically on decentralization of education services further illustrates this point (Banerjee et al, 2007). A central plank of public policy for improving primary education services in India is the participation of Village Education Committees (VECs) which were created in the 1990s, consisting of the head of the elected village government, parents, and public school teachers. In a survey of 280 villages in India’s most populous and educationally challenged state, Uttar Pradesh, in March 2005, at least 10 years after the formal creation of this agency, Banerjee et al (2006) find that parents do not know that a VEC exists, sometimes even when they are supposed to be members of it; VEC members are unaware of even key roles they are empowered to play in education services; public participation in improving education is negligible, and correspondingly, people’s ranking of education on a list of village priorities is low. Large numbers of children in the villages have not acquired basic competency in reading, writing, and arithmetic. Yet, parents, teachers, and VEC members seem not to be fully aware of the scale of the problem, and seem not to have given much thought to the role of public agencies in improving outcomes. That is, school failures coexist with local apathy to improving it through local public action.

Between September and December 2005, an Indian NGO, Pratham, intervened in 195 of the 280 villages surveyed with different types of information and advocacy campaigns that communicated to village citizens the status of learning among their children, and the potential role that VECs and local governments could play in improving learning. The basic format of the interventions was to organize a village meeting on education, with the attendance of the head of the local village government and the head teacher of the village public school, the key members of the VEC, from whom the village community is urged to ask and receive basic information about local agencies in primary education. The issue raised most frequently in the village meetings, and about which people were most animated, was a government scholarship program intended to provide cash assistance to students from SC/ST groups. SC/ST parents complained that they were 24
Respondent households provide individual-level details regarding employment, income and consumption particulars. Our analysis focuses on reported occupational categories of workingage (25 to 60 year-old) women and men in both rural and urban areas of the country. In particular, we exploit the variation in individual-level responses to a question on one’s “usual principal activity,” which can take the following categorical values:

- Household enterprise/self-employed: own account worker, own account employer, helper in household enterprise;

- Salaried or wage employees (no subfields);

- Casual wage labor: in public works, in other types of work;

- Unemployed: did not work but was seeking and/or available for work;

- Other categories of workers outside the labor force: attended to domestic duties only, attended to domestic duties and was also engaged in collection of goods for household use, attended an educational institution, and other miscellaneous categories outside the labor force.

For our empirical analysis, we do not consider individuals reporting a currently-enrolled status or in the other miscellaneous categories of activity. Due to changes in administrative boundaries over time we concord district and state definitions to be consistent over time. Our analysis covers data from 418 unique consistently-defined districts across 32 states/UTs (pre-2001 definitions). Using the sample weights provided with the data, we construct population-level estimates of workers in each of the subcategories by rural/urban district areas in each period. MGNREGS and Panchayati Raj information is then matched in using geographical location data available in NSS. This data set becomes the basis for our analyses to follow. We also use data on the fraction of district population with access to various public goods from the 2001 Population Census.

Table 1a characterizes national trends in men’s and women’s labor force participation across rural and urban areas for our main period of study (1999-2009). These highlight, in particular, how women’s LFP has shown an interesting downward trend in recent years, as well as sharp differences (both in level and in trends) in urban and rural areas of the country. We note that much of this downward trend is in rural areas, with hardly any change in urban areas over this period.

9
versions of the ISCO classification or even their own codes (e.g. Argentina). In order to have a unique classification we converted the occupation codes of each country to the 2-digit ISCO 08 using official crosswalks.

Once we have the occupations in the two data sets (HFPS and NHS) grouped following the same classification (2-digit ISCO 08), we proceed to compare the resulting structure of occupations. In particular, we compare the structure for the pre-pandemic occupation question in the HFPS with the structure in the 2019 NHS. Figure 1 shows the results for the 1-digit grouping. The shares of workers in each group are similar across data sets, with two noticeable exceptions. Compared to the NHS, there is an over-representation of professionals and an under-representation of workers in skilled agricultural and elementary occupations in the HFPS. Figure 2 shows that the under-representation in the latter group occurs mainly among the agricultural, forestry and fishery laborers.<sup>7</sup> These biases are consistent with the findings of Mejia-Mantilla et al. (2021) regarding the bias of the HFPS sample towards more educated and urban households: a typical limitation in phone surveys given the difficulties in reaching rural workers. Naturally, these biases should be kept in mind when looking at aggregate statistics, but they are not necessarily a significant problem when we carry out the analysis at the individual level, as in our regression analysis.

# **3.4 Job Losses**

Our main variable of interest is job losses during the COVID-19 pandemic. We focus on permanent job losses, defined as situations where the respondent was working before the pandemic but is not working at the time of the interview and does not have a job to return to.

For simplicity, in this section we construct a binary indicator for “any job loss” that equals one if the worker suffered a job loss in any of the 3 waves compared to the prepandemic situation. For those who had a job prior to the pandemic, Figure 3 shows the percentage who lost their job in waves 1, 2, or 3 of the HFPS. Job losses were very large; the share of workers who lost their job in this very short time window was around 30 percent on average in the region. There are heterogeneities across countries, from 18 percent in Argentina to 47 percent in Colombia.

We are particularly interested in the heterogeneities of the impact of the crisis across socioeconomic characteristics, such as gender. Columns 1 and 2 of Table 3 show that job losses were larger for women than for men in all countries, and the gender gaps were as high as 22 percentage points in Paraguay. Also, job losses were larger for the youth (columns 3 and 4 of Table 3) and for the unskilled (columns 5, 6 and 7 of Table 3) in all the countries. Taking into account the pre-pandemic occupation, non-salaried workers experienced larger job losses in most countries (columns 8 and 9 of Table 3).

> 7Of course, the severity of the problem varies across countries. In Colombia, for example, there is very little difference between the share of elementary workers that arises from both sources.

8
<!-- Start of picture text --> 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Year Year<br>Note:<br>a/ Simple averages of number of exporters to OECD at the product (SITC4) level.<br>b/ Import-weighted averages (weights = shares of each SITC4 product in OECD imports in given<br>year)<br>60 120<br>50 100<br>40<br>80<br>wav_nber<br># suppliers, simple av. 30<br>60<br>20<br>40<br><!-- End of picture text --> The extensive margin as measured by the average number of source countries does not seem to show the same kind of trend reversal that we observed in the concentration indices, which pick up action at both the extensive and intensive margins. Simple averages of the average number of OECD suppliers by SITC4 product category are rising monotonically over time. Import-weighted averages are leveling out after 2000, but this is not very surprising. The numbers on the vertical axis show that on a trade-weighted basis, the average number of suppliers per product was over 100. For many products, this is likely to exhaust the pool of potential exporters, so a leveling off is to be expected.

Table 3 reports the results of pooled and fixed-effects regressions of the number of exporters to the OECD on time, its square, and a specific time trend for the post-2000 period.

In the latter period, as expected from Figure 4, there is a decline in the rate of increase in the number of exporters to the OECD. This is reflected by the negative coefficient on Post 2000. This inflexion is however not strong enough to reverse the trend. The observed re-concentration of OECD imports thus seems to be entirely caused by action at the intensive margin.

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their prospects of receiving a loan, how much more discouraged were they likely to be in the midst of a banking crisis?

A comparison of the reasons that firms did not apply for loans provides additional clues about why the financial constraints reported by small firms changed little during the early phase of the crisis. Among large firms that did not apply for loans in 2005, 32.3% reported that they had no need of external finance (Table 6). In the 2008-9 survey, that figure rose to 83.3%. In other words, nearly the only reason that large firms did not apply for loans in the early phase of the crisis was because they were financially self-sufficient. Among small firms, the percentage of non-applicants that reported no need for external finance rose, but only from 38.4% in 2005 to 56.9% in 2008-9. This too is consistent with the idea that lingering impediments to obtaining credit for small firms made them more reluctant to turn to banks for loans during the crisis.

Further evidence that large firms were more likely to change their status regarding financial constraints than small ones comes for a transition matrix similar to the one in Brown et al. (2011). Firms in the 2005 BEEPS were sorted into three categories in the left-hand column of Table 7: (1) had a loan; (2) did not apply for a loan because the firm reported no need for external finance; or (3) did not apply for a loan because firm owner was discouraged from doing so. Potential reasons for being discouraged were: application procedures for loans or lines of credit are complex; interest rates are not favorable; collateral requirements are too high; size and maturity of loan are insufficient; and firm owner did not think it would be approved. Because a sizable share of firm owners that did not apply for loans chose not to answer these questions, our sample is too small to present results for specific reasons. We therefore lump all non-loan applicants that did not respond that they had no need for external finance into a single category, ―Discouraged.‖ In the 2005 survey, small firms were divided relatively evenly between the three categories: had loan, discouraged from applying, and had no need for a loan. By contrast, nearly sixty percent of large firms had loans, while a third of them reported no need. Less than ten percent of large firms were discouraged from applying for any of the reasons listed above. The pattern confirms much better access to credit for the large firms in our sample as was found in 29
Groh, M., N. Krishnan, McKenzie, D. and T. Vishwanath (2012). “Soft skills or hard cash ? the impact of training and wage subsidy programs on female youth employment in Jordan”, Policy Research Working Paper No. 6141, World Bank, Washington D.C.

- Ibarraran, P., and D. Rosas. (2008). “Evaluating the Impact of Job Training Programs in Latin America: Evidence from IDB Funded Operations.” Inter-American Development Bank, Washington D.C.

- Karlan, D., and M. Valdivia. (2011). “Teaching Entrepreneurship: Impact of Business Training on Microfinance Institutions and Clients.” _Review of Economics and Statistics_ , 93 (2).

- Klinger, B., and M. Schündeln. (2011) “Can Entrepreneurial Activity be Taught? QuasiExperimental Evidence from Central America.” _World Development_ 39 (9).

- Kluve, J., F. Rother, and M. L. Sánchez-Puerta. (2010). “Training Programs for the Unemployed, Low Income and Low Skilled Workers.” In _Skills Development Strategies to Improve Employability and Productivity: Taking Stock and Looking Ahead_ by R. Almeida, J. Behrman and D. Robalino (Editors) World Bank, Washington D.C.

- McKenzie, D. and Christopher Woodruff. (2012). What are we learning from business training and entrepreneurship evaluations around the developing world? Policy Research Working Paper No. 6202, World Bank, Washington D.C.

- MFPE and World Bank. (2009): “Dynamique de l’emploi et adéquation de la formation parmi les diplômés universitaires. Analyse comparative des résultats de deux enquêtes (2005 et 2007).” Washington, D.C., World Bank.

- Oosterbeek, H., Mirjam van Praag and Auke Ijsselstein. (2008). “The Impact of Entrepreneurship Education on Entrepreneurship Competencies and Intentions: An Evaluation of the Junior Achievement Student Mini-Company Program.” IZA Discussion Paper No. 3641, Bonn.

World Bank. 2012a. _World Development Report 2013: Jobs._ World Bank, Washington DC.

World Bank. 2012b. _Jobs in the Middle East and North Africa_ . World Bank, Washington DC.

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**ness** of male and famale **_staff_** or the gender of farm contacts.

> 2 'VTh*re are also numerous more specialized tasks .n the delivery of health care where less highly trained (and less costly) parmedics who are dedicated to performing a fey functions can do these activities better than, or at least **as veil as,** doctors. In Thailand midwives **were trained** to _insert_ IUDs. They were then carefully evaluated against doctors providing **the same** sorvice. The midwives **vere** more successful in delivering this single service than were the doctors. Performance was measured in term of the incidence of a variety of objective complications and in terms of patient satisfaction and frequency of complaints (Population Council, 1972).
||OLs<br>|DOLS<br>||OLs<br>||IV<br>|
|---|---|---|---|---|---|---|
||PullSample|Full Sample|LowIncome|MiddleIncome|—HighIncome|Full Sample|
|Elasticities|||||||
|o|0.0251**|0.0636*|0.00960|0.0314**|0.0380"|O.O7+|
|Be (CAPO)|0.0414|0.0455|0.190"|0.0717+|-0.0402|0.43+|
|Be (CA<0)|0.138"|0.1306|0.0609+|0.0845"|0.0728+||
|Bs|-0.324*|-0.343°"|-1.016"*|-0.247+|-0.0232||
|Br|-0.35 0"|-0.225"|0.133|-0.301"|0.0115||
|Be|-1.846""*|-1.3244"|-0.180|-1.836""|-1.440+||
|Vr (CA>O)|0.0733|0.0739|0.112|0.0541|0.118+||
|Vr (CA<0)|-0.102*|-0.0978*|-0.00433|-0.178|-0.112"||
|Ma|0.208"|0.185%|0.136|0.146"|0.241||
|Va|0.263%"|0.148"|0.290+|0.270|0.154||
|Vie|1.117|2.082"|0.872|0.984|2.018+|4.96%"|
|é|3.181|2.1840|2257*|5 eb aaa|1555|5.15*|
|CriticalConditions|||||||
|<br>Bet Pevr (CA>0)|0.0566|0.0592|0. agRae|0.0796|-0.0119|0.43+|
|Bet Pepr (CA<0)|0.1174|0.112%|0.0603+|0.0584"|0.0457||
|ad|o.o7ag|0.139%|0.0217|0.106"|0.0590+|0.3605+|

=e 90.01, = p<0.05, * p<01, + p<02 Notes Statistical apruficance is reported for country dustenng. The standard deviations for the IV estimation are calculated using the suest and nlcom Stata routines on the direct ratios. The same routes are used for the standard demations of the entical conditions.
_Fiscal Crisis in Central Europe_ 17

||Evol|Pol<br>ution of Def<br>(percentag|<br>and, Hu~ig<br>icit - An Al<br>e of GDP, a|Table 3.2<br>ary and Czec<br>temative D<br>verage over|hoslovakia:<br>ecompositio<br>specified pe|n, 1986-199<br>riods)|2.|||
|---|---|---|---|---|---|---|---|---|---|
|||Poland|||Hungary||Cz|echoslova|kia|
||1985-87|1991-92|Change|1986-88|1991-92|Change|1986-88|1991-92|Change|
|General Government<br>Balance|-0.4|_-5.5_|-5.0|-2.2|-5.3|-3.0|-1.7|-2.8|-I.|
|Net Revenues|41.1|40.6|-0.5|51.6|50.0|-1.6|49.5|46.4|-3.|
|Net profit taxes|4.0|5.1|1.1|0.7|0.7|0.0|3.5|6.4|2.|
|Tumover Taxes|11.5|8.2|-3.3|17.3|13.4|-4.0|15.8|12.7|-3.|
|Net Expenditures|41.5|45.9|4.5|53.8|55.2|1.4|51.3|49.2|-2.|
|Transfers to H'holds|9.9|19,7|9.9|13.7|24.4|10.7|11.8|16.3|4.|
|Consumer Subsidies|9.1|2.9|-6.3|5.8|2.3|.3.5|4.1|0.0|-4.|
|Interest Payments|0.9|2.2|1.3|1.8|3.9|2.1|0.0|0.8|0.|
|Capital Expenditures|5.3|3.0|-2.3|8.0|6.3|-1.7|2.0|4.9|2.9|

Sources: Ministries of Finance; Authors' estimates The decline in net revenues common to the three countries, however, is due to altogether different reasons than the ones seen in table 3.1. The most important fact emphasized by table 3.2 is in fact that the _net_ contribution of the enterprise sector to the fiscal budget has _increased_ during transition, by 1. I percent of GDP in Poland and by almost 3 percent in Czechoslovakia and has not changed substantially in Hungary. Despite the shrinkage in the tax base due to the recession, therefore, the transition to the market economy appears to have benefitted general government finances, at least in their direct relation with the enterprise sector.

The main factor underlying the decline in revenues, on the other hand, was provided - in all countries - by the relative decline of turnover taxes, which shrunk by 3-5 percent of GDP. In Poland, such decrease is partially due to the fact that the prices of the goods which account for most of the tax revenue - spirits, petroleum product and tobacco - have not kept pace with the
assumptions. While this approach can be used for analyzing interval censored data, it only focuses on estimating conditional quantile regressions, requiring specialized software that are not readily available. In contrast, the method we propose can be applied not only for the estimation of conditional quantile regressions, but also for the estimation of unconditional distribution statistics.

Other studies, like the one proposed by Han et al. (2020), construct new measures of the income distribution and estimate poverty in the U.S. using data from the monthly Current Population Survey (CPS). They address the problem of censored income data using draws from the empirical income distribution observed in the last March supplement. A similar method is proposed by Parolin and Wimer (2020), who produce monthly updates of the Supplemental Poverty Measure (SPM) rates with demographic data from the CPS and poverty data from the previous March supplement of the CPS. However, these studies seek to obtain income estimates using the uncensored distribution of previous years, which is not always available with other data sources, like the ones analyzed in this paper.

Buutner and Rassler (2008) propose a multiple imputation approach, similar to ours, to analyze wages from the German Institute of Employment Research (IAB) employment survey. While their method focuses on the analysis top coded data, we expand the approach to analyze data with a more generalized censoring structure.

The paper is organized as follows. Section 2 introduces the model and the econometric issues associated with the imputation method; Section 3 provides a Monte Carlo simulation exercise to analyze the performance of the methodology; Section 4 uses the methodology to analyze labor income distribution changes in Grenada using the 2013-2020 series of the Labor Force Survey. Section 5 concludes.

# 2. Methodology

To address the problem of interval-censored data, we propose a multiple imputation approach based on a heteroskedastic interval-regression model. An interval-regression model is a generalization of the Tobit model that allows using a mixture of censored and completely observed data, even if the censoring thresholds are unique to each individual. The goal of the model is to
different budget scenarios as shown in Figure 6. By drawing a vertical line in correspondence of the maximum distance between coverage and leakage rates, we can derive the optimal budget. In the example proposed, this budget is equivalent to 58.7% of the budget necessary for universal coverage. This is the budget that simultaneously maximizes coverage and minimizes leakage.

**Figure 6 - Coverage rate and Leakage rate for different budget scenarios**

Source: Authors’ estimations based on Jordan UNHCR data.

On the contrary, a decision maker may prefer to fix a priori the percentage of coverage. This is shown in Table 3 where, for a given coverage rate, the corresponding percentage of the universal budget, as well as the leakage rate and the cut point are given. For example, in order to reach 80% of coverage, one needs to use 62.25% of the universal budget, which corresponds to 22% of leakage and to a probability threshold equal to 66%.

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