rafmacalaba/datause-encoder-data huggingface dataset:
The paper makes three contributions to the literature. First, the paper analyzes the cumulative impact of the fiscal system as a whole on poverty and inequality in Turkey; in contrast to existing studies which have focused on specific fiscal interventions of Turkey’s system. In fact, the empirical literature on the distributional impact of fiscal interventions in Turkey is relatively scarce. On the tax side, Albayrak (2010) finds that indirect taxes increase income inequality using data from 2003. Albayrak (2011) compares the distributional impacts of tax policies implemented before and after the 2008 financial crisis, and finds that the decrease in Value Added Tax (VAT) and Special Consumption Tax (SCT) rates introduced after the crisis made indirect taxes more regressive. Cross-country studies show that the social security system in Turkey is the least redistributive among all OECD countries since the coverage of the population is lower (OECD, 2008). In addition, while the share of VAT is uniform across income groups in Turkey, excise taxes are regressive (OECD/Korea Institute of Public Finance, 2014).

On the spending side, although public spending on education, health and employment constitutes an important and increasing share of the national budget in Turkey, the articles assessing the redistributive effects of social spending are very few. Koç and Sarisoy (2010) show that social spending decreased poverty in Turkey between 2002 and 2008. Caner and Okten (2012) find that publicly financed higher education in Turkey is progressive since students from rich families select themselves into private universities while students from poor families enroll in public universities. OECD cross-country studies find that the distributional impact of overall cash transfers is very limited in Turkey, and that while the contributory pension system is progressive, its performance is below the OECD average (Causa and Hermansen 2019, Joumard et al, 2012).

The second contribution of the paper, building on the fact that the CEQ methodology has been applied in over 50 countries, is to benchmark Turkey’s performance with peer upper-middleincome economies, such as Argentina, Brazil, Chile, Croatia, Mexico, Poland, and the Russian Federation, and with the developed context of the United States.<sup>4</sup> Third and finally, the paper introduces some innovations to the methodology by incorporating tax avoidance in consumption taxes in a distributionally-sensitive form, and by constructing Fiscal Incidence Curves (FICs) to visualize the incidence of each fiscal intervention along the income distribution.<sup>5</sup>

> 4 A regularly updated list of countries can be found in www.commitmentoequity.org.

> 5 These curves are the analogous of the growth incidence curves that Ravallion and Chen (2003) introduced to assess the incidence of economic growth along the income distribution.

4
full program group. When examining effects by age, we see a (significant) increase in formal care for age 0-2 children by 60% and significant increases in ORT use in age 2-4 children in the full group. Overall, the full-program group receives better care for acute bouts of diarrhea than the other groups.

Although we see mixed effects on anthropometric measures of malnutrition, a striking finding is that the full program group experiences a large and significant reduction in the number of days with diarrhea symptoms (out of the past 7 days), equivalent to over 70% of the control group average. While the effects in the other two treatment groups are not significant, they are negative as well, and we cannot reject that the full-program effect equals the sum of the CHW-only and subsidy-only effects. There is also a large (although not significant) decline in the number of days the mother/caretaker is concerned about the child’s health.

Our results are overall encouraging for the combination of CHW and subsidy interventions. There is no evidence for moral hazard, and if anything, preventive behavior is slightly higher in the full-program group. Children in the full program group also get diarrhea significantly less often and receive better care when they do fall sick.

The findings in this paper complement the analysis of the 2013 data in Sautmann et al. [2020]. The 2013 paper does not analyze health outcomes or prevention, but instead quantifies the impact of subsidies and CHW visits on underuse and overuse of acute care, using a unique nine-week panel data set of complete illness spells, care sought, and cost. Health care demand is benchmarked against WHO-IMCI careseeking recommendations based on symptoms. Subsidies increased care seeking by 250%, with most of the increase in care classified as necessary by WHO guidelines. There were no CHW effects on average, but exploratory analysis indicated a complementarity between CHW and subsidies for the youngest children, similar to the effects on care for diarrhea in the 2014 follow-up reported here. Again, these findings are encouraging for layering CHW interventions with the expansion of UHC. In both surveys, we found the strongest effects of the CHW for the very youngest age groups, 6
rainfall data. These are considered to be more reliable than data from mandal stations, and include a longer and more complete history of past rainfall to construct a putative dataset of insurance payouts.

Our source data consist of policy terms for contracts indexed to 14 different IMD weather stations in Andhra Pradesh (one contract per station), as well as IMD historical rainfall data for each station. Rainfall data are measured at a daily frequency. Although the earliest rainfall data is from 1970, the starting point of the data varies by weather station, and there are also scattered individual months and years where data is missing. Across 14 stations, there are 1,089 individual contract phases for which at least some rainfall data is available. However, for 135 phases data is missing for at least one day during the contract period. We drop these from our analysis, leaving a sample of 954 phases for which we have complete daily rainfall to calculate payouts.

The amount of missing data varies significantly across weather stations. At one extreme there are 91 phases of complete rainfall data for the Anantapur weather station (equivalent to 30.3 monsoon years). At the other extreme, for the Adilabad and Nalgonda stations, only a small number of complete phases of rainfall data is available (8 and 18 phases respectively). At least 64 phases (21.3 monsoon years) of complete daily historical data is available for 11 of the 14 stations; our empirical findings are similar if we restrict analysis to these stations only.

Applying the insurance contract terms to historical rainfall data, we calculate the hypothetical payout on the contract for each station, phase and year. Data on estimated payouts and information on contract features are presented in table 1. Strikingly, the 6
# **5. Data**

Data were collected in the summer of 2013 from 1,300 randomly selected households in both the hill areas and plains (Terai) in Nepal. Of the households 650 were from 65 communities that currently have CFs and 650 were from 65 communities that currently do not have CFs. The location of the sites are shown in Figure 2. The sampling design for CFs were adopted from the data set of the CF impact study (MFSC, 2013). For each CF, the matching communities not having CFs were selected based on criteria such as the socioeconomic characteristics, forest types and accessibility.

# **_Household Characteristics_**

A summary of the household characteristics is provided in Table 2 for both CF and nonCF households. On average the CF and non-CF households were very similar. For CF households, 81.2% of the respondents were male, 38.9% of the households were categorized as “poor” and 52% of the households were categorized as “medium” with regards to social status. For non-CF households 86.3% of the respondents were male, 37.5% of the households were categorized as “poor” and 51.4% of the households were categorized as “medium” with regards to social status. Both groups were similar in educational achievements; for CF households 21.6% was illiterate, 33.8% had only a primary education, and 17.3% didn’t finish secondary school and 11.4% finished secondary school. For CF households 20.4% was illiterate, 37.1% had only a primary education, and 16.2% didn’t finish secondary school and 11.4% finished secondary school.

The CF and non-CF communities were also on average similar; the CF communities had a total of 3901 family members, with an average family size of 6.02 and an average age of 29.41.

19
- Mitchell, B.R., _International Historical Statistics: Americas 1750–1993_ , New York: Stockton Press, 1998.

- , _International Historical Statistics: Europe 1750–1993_ , New York: Stockton

- Press, 1998.

- Mundlak, Yair, _Agriculture and Economic growth. Theory and Measurement._ , Harvard University Press., 2001.

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- Neary, Peter and Sweder Van Wijnbergen, _Natural Resources and the Macroeconomy_ , Cambridge, Mass.: The MIT Press, 1986.

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- Rodriguez, Francisco and Jeffrey Sachs, “Why do resource abundant Economies Grow More Slowly?,” _Journal of Economic Growth_ , September 1999, _4_ (3), 277–303.

- Romer, Paul, “Increasing Returns and Long Run Growth,” _Journal of Political Economy_ , October 1986, _94_ (5), 1002–37.

- Sachs, Jeffrey and Andrew Warner, “Natural Resource Abundance and Economic Growth,” December 1995. NBER Working Paper No. 5398.

- and , “The Big Push, Natural resources Booms and Economic Growth.,”

- _Journal of Development Economics_ , 1999, _59_ (1), 47–76.

- and , “The Curse of Natural Resources,” _European Economic Review_ , May

- 2001, _45_ (4-6), 827–38.

- Solow, Robert, “Intergenerational Equity and Exhaustible Resources,” _Review of Economic Studies, Symposium_ , 1974, pp. 29–45.

- Stijns, Jean Philippe, “Natural Resource Abundance and Human Capital Accumulation,” June 2001. Mimeo, UC Berkeley.

30
More generally, our study contributes evidence on the effectiveness of public service delivery reform in a low-income setting where government capacity and accountability are considered to be poor.

Initiated in mid 2010, the program offers yearly cash bonuses linked to school performance to public primary school teachers. At program registration, teachers are offered some basic school management, teaching, and exam preparation tips. The program is however designed to incentivize incumbent teachers to raise school performance by increasing their effort rather than by directly increasing their skill.<sup>2</sup> The bonuses are offered to teachers on top of their standard salaries, and are set as a linear function of a composite score of school performance. The composite score is obtained from a weighted sum of three indicators:

- the gain in the school’s enrollment in first through fifth grade,

- the gain in the school’s mean score on Punjab’s standardized fifth-grade examination, and

- the participation rate of the school’s fifth-grade students in the examination.

The first two indicators are consistent with the government’s main education goals: to raise school participation and student academic achievement. Participation and achievement are both acutely low in Punjab. The exam participation rate is included as a deterrent against potential exampool selection by program schools. The data for the indicators come from the Punjab Examination Commission (PEC) fifth-grade exam, which is a yearly standardized exam, and the Annual School Census (ASC) survey, which is a yearly field survey of all public schools. Both data instruments are administered by the government.

Increasing the effort of head teachers may be an important way to raise school performance, and bonuses to head teachers may help elicit greater effort from them. Studies find

> In spite of these mixed results, a few general patterns can be drawn from the collective evidence. Programs tend to have targeted schools with low baseline levels of student academic achievement. Program impacts are usually modest if present, but are large in a few cases. Programs in low-income countries have a higher success rate than those in high-income countries. Discussions of program implementation, in particular the fidelity and quality of implementation, are limited, if any. There is little or no rigorous evidence on, where relevant, the conditions and pathways behind intended (and unintended) impacts or the factors behind the lack of intended impacts.

> 2 Growing credible evidence suggests the prevalence and severity of low teacher effort, of which two basic measures are whether the teacher is present in school and whether the teacher is on-task. Teacher absentee rates are found to be high in many low- and middle-income countries, and appear to result from many teachers being occasionally absent rather than a few teachers being frequently absent. When teachers are present in school, a large share of them are found to be off task (Glewwe et al 2010; Chaudhury, Hammer, Kremer, Muralidharan, and Rogers 2006).

3
# **II. The Poverty Decomposition Methodology**

# **Overview**

In order to understand drivers of poverty reduction in Nepal between 1995 and 2010, we employ the decomposition methodology developed by Inchauste et al. (2014) and Azevedo et al. (2013).<sup>27</sup> The standard decomposition methodology such as the Datt-Ravallion decomposition (1992) and the Ravallion-Huppi method (1991) rely on summary statistics to explain changes in poverty. The major advantage of the method proposed by Inchauste, et al. (2014) and Azevedo, et al. (2013) is that it utilizes all possible permutations of counterfactual income distributions to estimate the contributions of income components and individual and household characteristics to changes in poverty. Although it does not allow for causal interpretation of poverty changes, this method is a useful accounting exercise to describe empirical regularities that can shed light on certain factors that are quantitatively more important in describing distributional changes.

We apply this method to decompose changes in the poverty headcount rate in Nepal between 1995 and 2010. Due to a major revision to the official poverty lines in 2010, the national poverty rate of 25 percent in 2010 is not comparable to the earlier estimates in 1995 or 2003. To construct comparable poverty estimates over time, two alternative trends are presented in Figure A1. This exercise uses the “upper” trend in Figure A1, i.e., the trend based on the new poverty line and the consumption aggregate that uses the 30-day recall food consumption. The poverty headcount rate declined from 64 percent to 31 percent between 1995 and 2010 according to this trend.

# **Model**

Two alternative specifications of income are implemented to assess robustness of the results. In the first specification, per capita consumption is defined as follows: where ������ is per capita consumption expenditure of household �, �� is ratio of consumption � 28 � to income, �� is household size, �� number of adults in household �, �� number of workers in household �,<sup>29</sup> ��,�� is labor income of individual � in household �, ��,��� is non-labor income of individual � in household �.

The second specification divides total income into three components, agricultural income, nonagricultural income, capital income and remittances received as in equation (2) below:

> 27 We use **_adecomp_** , a user-written Stata command developed by Azevedo et al. (2012).

> 28 Anyone who is older than 15 years old is considered an adult in this exercise.

> 29 A worker is an adult household member who reported as working in the last 12 months either as wage workers or self-employed.

28
preferences and meeting ROOs.<sup>18</sup> Table 1a summarizes the trade environment in the US and EU.

# 2.2 The Data

We use customs data at the transaction level for the ...scal year 2004 as our main source. This has information on sales, quantity, product (at the HS 8 level), weight, destination, currency of transaction as well as a ...rm identi...er. Since the customs data has no information on whether preferences were invoked or not, we obtained a list of ...rms that export woven apparel to the EU and obtain preferences for their exports. For these ...rms, we obtained information on their exports of wovens to the EU, their quantity and price. However, the ...rm IDs in the two data sets are not the same. To match the ...rms, we did the following. In our customs data, we aggregated the sales per ...rm to the EU of woven apparel, the quantity exported to the EU, and its unit value over the ...scal year. We then matched the ...rms according to sales and quantity, which resulted in 799 ...rms.<sup>19</sup> Table 1b provides some summary statistics. Firms that do not meet ROOs are smaller than those that do, especially if they sell to both the US and EU. This is what one would expect, if more productive ...rms choose to meet ROOs and enter the US market, which is the tougher one to be in.

|Table 1b: Summary Statistics|||||||
|---|---|---|---|---|---|---|
|Markets ...rms export to|Only|EU|Both E|U and US|Only US|Total|
|Do ...rms meet ROOs in EU?|Yes|No|Yes|No|||
|Number of ...rms|373|134|107|48|137|799|
|Average sales in EU ($’000)|568|294|1,993|373||729|
|Average price in EU ($ per unit)|3.7|4.4|4.7|5.1||4.1|
|Average sales in US ($’000)|||922|597|776|800|
|Average price in US ($ per unit)|||4.7|4.4|4.2|4.4|

> 18We could not estimate documentation costs separately from other ...xed costs of exporting if all ...rms choose to meet ROOs as in non-wovens. This is the main reason why we focus on the woven sector here.

> 19There is little need to worry about mismatches that might have occurred in this procedure as we only target the share of ...rms that meet ROOs in our estimation procedure.

11
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Table D.7: _(... continued)_ Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference: Alternative Samples and Specifications - Analysis of Results from Table 4

||||Sector of|Work||||
|---|---|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|Ideal Number<br>of Children|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|(6)<br>|(7)<br>|
|||||||_Nat_|_onal_|
|||||||_(Separate)_|_(Combined)_|
||||F. U|sing 1984 Census Mat|ched Start Ages|||
|�<br>Years of Schoolingizy|0.071<br>|0.027<br>|0.088<br>|-0.061<br>|-0.345<br>|-1.116<br>|-1.198<br>|
||(0.051)|(0.014)|(0.026)|(0.036)|(0.518)|(0.933)|(0.472)|
||[0.160]|[0.057]|[0.001]|[0.089]|[0.505]|[0.232]|[0.011]|
|First Stage F-Statistic|6.22|5.79|5.79|5.79|6.30|1.99|6.74|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||G. Using 1994 Census|Start Ages|||
|�<br>Years of Schoolingizy|0.083|0.014|0.112|-0.028|-1.326|-1.333|-1.605|
||(0.056)|(0.029)|(0.048)|(0.045)|(2.144)|(0.675)|(0.794)|
||[0.139]|[0.613]|[0.020]|[0.531]|[0.536]|[0.048]|[0.043]|
|First Stage F-Statistic|1.23|1.36|1.36|1.36|1.49|3.45|18.70|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||H. Three-Part Distr|ict Trends|||
|�<br>Years of Schoolingizy|0.082|0.090|0.020|-0.008|-0.648|-0.728|-0.746|
||(0.060)|(0.024)|(0.046)|(0.033)|(0.446)|(0.618)|(0.426)|
||[0.171]|[0.000]|[0.670]|[0.806]|[0.146]|[0.239]|[0.080]|
|First Stage F-Statistic|17.90|16.29|16.29|16.29|17.91|5.89|18.70|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||I. Regional Tr|ends|||
|�<br>Years of Schoolingizy|0.133|0.078|0.083|-0.063|-1.163|-1.032|-0.946|
||(0.107)|(0.065)|(0.057)|(0.066)|(0.768)|(0.715)|(0.561)|
||[0.217]|[0.229]|[0.144]|[0.344]|[0.130]|[0.149]|[0.092]|
|First Stage F-Statistic|1.60|1.57|1.57|1.57|2.06|4.95|14.49|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||J. No Trend|s|||
|�<br>Years of Schoolingizy|0.048|0.042|0.015|-0.026|-0.711|-0.675|-0.950|
||(0.016)|(0.011)|(0.011)|(0.016)|(0.220)|(0.245)|(0.311)|
||[0.002]|[0.000]|[0.185]|[0.107]|[0.001]|[0.006]|[0.002]|
|First Stage F-Statistic|21.77|21.00|21.00|21.00|23.08|13.20|21.45|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|

A.58
samples and the inclusion of additional controls. However, with the smaller samples of developing countries, the estimated partial correlation coefficients seem to be larger than when estimated with the larger sample that includes high-income countries (with imputed informality rates). It remains an open question whether the OLS estimates of the partial correlation coefficients reflect endogeneity, whereby economies with high unemployment rates tend to have lower digital payments than economies with low unemployment, after controlling for informality.

# _D. IV Results_

To further explore the partial correlation coefficients between unemployment and proxies of the digital economy, Table 2 presents results from Two-Stage Least Squares (TSLS) estimations. We present three alternative specifications with our two proxies for the digital economy treated as endogenous variables. Panel A presents the second-stage unemployment equation results; Panel B presents the first-stage results. The specifications under Columns 1 and 2 are just identified; the number of IVs equals the number of endogenous variables. Column 3 presents the results from an over-identified specification.

Specifications 1 and 2 use the average fixed telephone lines per capita (in logs) during 1975-99 as an IV. Presumably this variable would be a good IV for internet access and possibly for digital payments, which require access to the internet. As mentioned in the data section above, the expectation is that countries that had high coverage of telephone infrastructure in the decades prior to the 21<sup>st</sup> century were more likely to have extensive coverage of the internet thereafter. In fact, it is well known that the physical infrastructure used to offer fixed-line telephony was used to place the fiber-optic cables that brought broadband internet (Geere, 2011). Indeed, APEC (2013) explains that modern undersea 19