rafmacalaba/data-use-mentions-extended huggingface dataset:
was chosen over OLS as a more flexible estimator; sensitivity tests with OLS produced similar results. Estimated coefficients from the regression model were then applied to the future values of each predictor variable to compute the future predicted physician density.

Alternative model specifications were explored, which included different ways in which input parameters could be calculated (e.g. percentage of the population aged 65+, out‐of‐pocket health spending as a percentage of total health expenditures). To select the appropriate model, the data were split into an initializing data set (data years 1995‐2004) and an attesting data set (data years 2005‐2013) with which to assess the precision of predicted values resulting from different specifications. The model in Equation 1 yielded predictions with the lowest mean errors.

We conducted two additional sensitivity analyses of the projections of physician demand resulting from the optimal demand model chosen to alternative input parameters. First, we assess the stability of the predictions to alternative estimated future values of GDP per capita. We use alternative estimated real GDP per capita (US$2010) from the Economic Research Service (ERS) International Macroeconomic Data Set published by the United States ‐ Department of Agriculture (USDA), available at <u>http://www.ers.usda.gov/data</u> 6
g., the Border Guard (4,000).

GDP at market prices is taken from Statistical Handbook 1995: States of the former USSR and relates to 1992. Wages and salaries are taken from IMF Government Finance Statistics and relate to 1992. Average Government wages are from Statistical Handbook 1995: States of the former USSR and relate to 1992. Data on wages in manufacturing (monthly basis) are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1992.

# **Moldova**

Unemployment rate is taken reflects only official unemployment for 1994. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and is for 1994. Central Government and Local Government employment are taken from the Salvatore Schiavo-Campo's BTOR of May 7, 1996 from the Public Sector Management Mission and refers to 1996. Education and health data is a staff estimate for 1996 based on data available in the Statistical Handbook 1996: States of the Former USSR and information available on Mr. Schiavo-Campo's May 6, 1996 BTOR. Data on military employment include conscripts (11,000), but exclude personnel in paramilitary units, i.e. the Internal Troops (2,500) and the Riot Police (900), both under the authority of the Ministry of Interior.

GDP per capita estimate is from Moldova Country economist Arud Bannerjee. GDP estimate is calculated on the basis of the data mentioned above. Average Central Government wage is taken from BTO Report of May 7, 1996 for Public Sector Management Mission and relate to 1996.

# **Russian Federation**

Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992.

Central Government, Non Central Government, Education and Health employment data are drawn from Towards a New Civil Service in Russia: Current Issues and Future Prospects (draft) of September 1995, and relate to 1992.
Overall, the application outcome and eligibility process was similar for both groups (p-value of joint test is 0.67).

Panel C of Appendix Table 3 shows the take-up rates by recruitment modality. Door-to-door promotions and financial events had similar take-up rates of just over 20% of interested potential clients while over half of potential clients who came to the branch ended up with a loan. In all three recruitment modalities take-up rates were similar for standard and flexible offers.

# _Selection Effects on Observables_

This lack of differences in take-up _rates_ between the offers of standard and flexible loans suggests we are unlikely to see differential composition of clients across the two groups (if one assumes that the addition of flexibility is a free-disposal feature, and hence does not lower take-up rates for any set of individuals). Table 1 compares loan characteristics (from the administrative data) and client and business characteristics (collected by credit officers at the time of the loan application) between borrowers that accepted flexible and standard loan offers. Column 5 reports the p-values of a test of equality of means in columns 1 and 3 and shows that only one difference out of 18 is statistically significant at the 5% level (client’s age). It also reports the p-value of an F-test of joint equality for loan characteristics (p-value= 0.81), client characteristics (p-value= 0.37), business characteristics (p-value=0.81) and all characteristics combined (p-value=0.79). We conclude there is no evidence of differential selection on observables using a wide range of observable characteristics.

# _Selection Effects on Unobservables_

We next examine selection on unobservable characteristics by focusing on the use of flexible passes described in Figure 1 and Appendix Table 6. About a third of flexible clients used a pass at any point during the loan (Appendix Table 6, column 1), compared with only 2% among standard loan clients (column 2).<sup>7</sup> This take-up rate compares to a 31
# **Appendix B: Data Sources and Assumptions**

# **Residential Sector**

There were approximately 1,192 thousand households in Georgia in 2014. Based on survey conducted by the Winrock International (Winrock International, 2014) the average size of dwelling is 100 sq.m. and 89% of households use modular heating options, such as electric heater, kerosene stoves that heat only a portion of the households<sup>10</sup> and on average 43% of dwelling living space is heated. Although about 60% of the total households have access to natural gas, only 38% use it for heating. Majority (57%) of the households still use firewood for space heating. Electricity is usually used as supplementary heating fuel for other types of fuels – only 6% use it as a main source. The general tendency is that households switch over to natural gas as their income rise in places where gas distribution infrastructure exists. We assumed that households not only switches to natural gas for space heating but also will increase the heated area as income rises. Since the Winrock survey is the first of its kind carried out in Georgia and there exist no earlier survey which would help to determine trends, expert assessment was used to make projections for future. It was assumed that annual rate at which heated area increases is 2% and the number of households switching to natural gas increases by 1% per year. The Ministry of Energy assumes that by 2035 75% of population will have access to gas.

Major appliances owned by households are TV, refrigerator and washing machines. Almost all households have TV, from which around 11% is new LED type TVs. 84% of household own refrigerator and among them 48% state that their refrigerator is energy efficient. 68% of households use washing machines, 43% of which is purchased after 2010. The average number of bulbs per household is 8, and they operate around 5.26 hours daily. 70% of
The correlation between electrification and welfare outcomes does not indicate the causal effect of electricity access because of potential non-random adoption of electricity at both household and village level. To address the potential endogeneity issue, we use the following model to estimate the causal effects of electrification: where _Wi_ denotes the outcome variables of interest, including women’s monthly employment hours, labor force participation, health outcomes, and media exposure at home; _Xi_ is a vector of household- and village-level characteristics, as in equation (6); _Ei_ is a dummy variable measuring access to electricity; _T_ is common yearly shocks; ηi represents unobserved household- and village-level determinants of outcome variables; _ε i_ is an idiosyncratic error term.

To control for unobserved variable ηi , we rely again on propensity score matching and exploit the correlation between initial characteristics and unobserved heterogeneity. Specifically, we estimate a two-stage propensity score-weighted model. In the first stage, a weight variable is created based on the probability of a household adopting electricity given household and village-level characteristics in 2005. In the second stage, the effect of electrification is estimated using _p_ -weighted OLS model using 2012 data.

Table 8 reports estimation results of equation (7). For comparison, we also report findings from a simple OLS estimation. Based on our preferred specification, the p-weighted model, electrification is associated with almost one hour increase in girls’ study time each week. In addition, with access to electricity, girls’ grade attainment on average increases by 0.48. Women’s labor force participation increased 3.8 percentage points, and women’s employment hours increased 36.3 percent because of electrification. Electrification also brought positive health benefits for women. The probability of having a fever, cough, or diarrhea for women falls when a household is connected to the grid. Also, the number of sick days of women falls by 12.5 percent because of grid connectivity. Electrification also increased women’s exposure to electronic media—radio and television. The probability
the likelihood of death for newborns, excess rainfall can also be protective in certain circumstances as a result of positive income shocks. Finally, Kudamatsu et al. (2011), using data from 28 African countries, show that infants who in utero experience worse malaria conditions than the site-specific seasonal means (based on a weather-prone-to-malaria index) face a higher risk of death. In addition, infants born in arid-climate regions of Africa who in utero experience droughts face a much higher risk of death than other babies. The reported results are quantitatively important. For example, a six-month malaria epidemic in a place with little average exposure to malaria can raise infant mortality by more than 3.5 points. Interestingly, they report that rainfall during the growing season before birth has a protective effect on child health. They interpret this finding as the result of a positive income shock due to higher agricultural production.

Rainfall and temperature also affect child morbidity. Hoddinott and Kinsey (2001) use panel data for rural Zimbabwe to examine growth rates of young children after a major drought. They find that children aged 12-24 months lose 1.5 to 2 centimeters of growth in the aftermath of a drought. Wang et al. (2009) find that extreme precipitation and temperatures have significant impacts on the incidence of diarrhea and weight-for-height malnutrition among children under the age of three. The reported impacts are of considerable magnitude. They estimate the health costs of more frequent events by 2020 to be in the order of 0.2 to 0.5 percent of Sub-Saharan GDP.

# **Do Weather Shocks Have Long-run Impacts on Child Health?**

Weather shocks in early life may also have long-term implications. For example, a shock during infancy may affect a child’s health so severely as to have an impact on their long-run health status. Alternatively, a weather shock which occurs in early childhood may also affect well-being during adulthood, not directly through health, but indirectly through other outcomes that are related to health status at any particular point in
z-scored). Treatment effects are estimated using ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The two indices of contributions to the clinic are composed of variables that measure support and contributions once from the perspective of key informants in the villages and once by health personnel. In the community survey, we ask about meetings between the clinic and community as well as labor contributions to the clinic. In the clinic survey, we ask about labor or financial contributions as well as disputes between the community and the clinic. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.17:** Community Development and Political Engagement (CDPE, z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**CDPE index**(SUR)||0.132<br>(0.049)<sup>_∗∗∗_</sup><br>[0.033]<sup>_∗∗_</sup>|0.116<br>(0.060)<sup>_∗_</sup><br>[0.114]|0.150<br>(0.056)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|508|
|(1) Projects with local council/chief|0.
identify the amount of misallocation derived from specific sources but provides an allencompassing measure.

HK find sizeable misallocation of resources, which decreases aggregate TFP. If the distortions that cause the misallocation were to be eliminated, TFP in the manufacturing sector in China would increase by 86%-115%; in India by 100%-128% and in the United States by 30%-43%. Interestingly, their analysis allows to take a step towards the direct method. While sources of misallocation are not identified, the extent of misallocation is correlated with various observables to shed more light on the underlying mechanisms. Through this analysis, HK show that highproductivity firms in all three countries are smaller than efficiency would dictate, while some lowproductivity firms (such as state-owned enterprises in China) are too large. The low estimation requirements coupled with the capacity to inform about potential sources have contributed to the popularity of the HK approach. It has been widely applied –see, for example, Busso, Madrigal, and Pagés (2013) for Latin American countries; Nguyen, Taskin, and Yilmaz (2016) for Turkey; Cirera, Fattal Jaef, and Maemir (2020) for African countries; and Chuah, Loayza, and Nguyen (2020) for Malaysia.

While being a valuable first step toward measuring resource misallocation, the HK approach depends on rather strong assumptions.<sup>4</sup> One important assumption is that firms’ inputs and revenue are free of measurement error. Since the HK approach uses revenue and inputs to estimate marginal productivity, this assumption could bias the estimated resource misallocation. Mismeasurement is likely present in most firm-level data sets, especially in developing countries, because of weak capacity at firms’ accounting systems and at national statistical agencies.

<mark>Bils, Klenow, and Ruane</mark> (2020) (henceforth BKR) propose a methodology that exploits the panel structure of the data to correct for potential measurement error. The authors build on the HK approach and allow for additive<
pan when it reaches a critical temperature of about _115_<sup>0</sup> C, and cooled into solid substances by continuous stirring. One principal difference between white sugar /khandsari and gur is that molasses does not appear as a by-product when gur is made. As noted previously, gur is a non-centrifugal sugar since it does not use centrifuges to separate the sugar crystals.

# **INDIA** AND **THE WORLD MARKET**

# **_World production and consumption_**

Brazil is the largest sugar cane producer in the world , but most of its cane is used exclusively to produce ethanol. Excluding this Brazilian cane from world production statistics (as does FAO- see Data S.3)- India is by far the largest producer, accounting for about 22 percent of world production. Since 1964 world cane production used for sugar has grown at an average rate of about 1.28 percent a year, and Indian cane production has grown at approximately the same rate. However average Indian per hectare yields have increased faster than world average yields, and in the early 1990s were 5 to 10 percent above world average yields, whereas they were 10 to 15 percent lower in the 1960s. Consequently the Indian share of total world sugarcane production did not change despite a decline from about one quarter to about one fifth of the total world area planted to cane used for sugar cane production.

5
our sample. For this analysis, we pool the two treatment groups (BAF and Own vouchers) because (a) women in both groups received exactly the same voucher, and therefore the same monetary incentives, for their own use and (b) we do not expect that a woman’s financial constraints are affected by her peers’ access to vouchers for themselves since the vouchers are not transferable.<sup>55</sup>

Table A.13 shows that the positive effects of voucher receipt on a woman’s likelihood of visiting the ADC or any clinic for FP services and on her modern contraceptive use were larger for women who were poorer at baseline. For instance, poorer women experienced a 13 p.p. larger increase in the likelihood of visiting the ADC and a 19 p.p. larger increase in modern method use relative to less poor women. These effects are substantial especially given that the poorer control women had almost never visited the ADC at baseline, suggesting that the ADC, which is a private clinic, might have been out of reach for financially constrained women. Overall, these results are consistent with prior literature in showing that vouchers can overcome constraints to women’s FP use.

> 54About 8 percent of our sample women at endline reported ever experiencing problems or negative experiences from discussing or accessing or using a method of FP and only 1 percent of the sample reported having a negative experience with their husbands or parents-in-law.

> 55This would not be the case if BAF women, for instance, were able to extract transfers from their peers in exchange for inviting them to visit the ADC with them. However, treated women did not report doing so in our endline survey.

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transfer for raising investments|

Source: Authors' modelling Design The first scenario, called “trade adjustment assistance”, involves fiscal support and policy advice to help countries cope with any transitional adjustment costs from liberalization. This scenario implies an income transfer from the donors to the recipient countries. The exogenous shocked variable in the AIDCGEM is the AfT transfer (AIDr) in region _r_ . The AfT transfer is not constrained, that is, it is not related to any project or public expenditure in the recipient countries. The aid for trade data comes from OECD.STAT data base. The amount of aid for

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# **III. Identification and Profile of Minimum Wage Workers** Data and methodology to identify minimum wage workers

**This research is based on administrative tax data from the Romanian Ministry of Finance** . The monthly microeconomic datasets encompass all employees who paid income tax based on their labor income during 2020-2021, comprising the full formal-sector universe. The income tax is deducted monthly from employee salaries by employers. The dataset comprises all salaried employees, excluding self-employed individuals. Our unit of observation is the taxpayer, but details about the employer (firm) are also available. The monthly count of taxpayers ranges from 6.9 million to 7.4 million. The dataset provides comprehensive information from the entire tax form, allowing observation of various characteristics of both employees and firms.

**There are several advantages of using tax administrative data instead of household survey data for labor market analysis.** The mandatory requirement to participate in administrative data programs is a notable advantage compared to voluntary survey participation. This legal obligation helps mitigate challenges related to non-response, as identified by UNECE (2011) and minimizes concerns about sample selection and nonrandom attrition. Unlike survey data, administrative records are less prone to selective underreporting, particularly among high-income individuals. Additionally, administrative records offer more detailed and frequent information than census data. While survey data may encounter problems like misreporting and misunderstanding of questions, leading to measurement errors, these issues can also impact administrative data.

**However, since the administrative tax data does not include individuals engaged in informal sector employment, this poses a significant challenge in understanding the entirety of the labor market landscape.** Not being able to identify informal sector workers from the administrative tax data creates a void in our understanding of a substantial and often economically significant workforce segment. The absence of their representation in the administrative tax data limits our ability to capture this sector's dynamics, challenges, and contributions. Information to identify informal sector workers is available in the EU SILC data; however, the small
|0.022|0.063|0.513|
|**KAIROUAN**|0.026|0.062|0.481|
|**KASSERINE**|0.021|0.074|0.583|
|**SIDI BOUZIDE**|0.031|0.069|0.513|
|**GABES**|0.021|0.077|0.499|
|**MEDNINE**|0.023|0.071|0.540|
|**TATAOUINE**|0.030|0.087|0.649|
|**GAFSA**|0.024|0.085|0.570|
|**TOZEUR**|0.028|0.084|0.600|
|**KEBILI**|0.037|0.079|0.428|

Source: our elaborations from INS (2020).

In order to better highlight the findings, in the Annex we have reported four graphs corresponding to the 1,800 clusters selected for the new EBCNV 2021 survey (see Section 4). In particular, Figure A1 reports confidence intervals for all 1,800 clusters, whereas Figures A2, A3 and A4 report confidence intervals for the bottom of the distribution, the distribution near the national mean consumption level, and the top of the distribution, respectively.

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Figure 6: Probability of an SOE Receiving a Subsidy, and Value of Subsidies Received, Relative to POEs by Ownership Type, 2011-2019 <!-- Start of picture text --> (a) Probability of an SOE Receiving a Subsidy (b) Log Value of Subsidy Received by SOEs<br>Relative to POEs Relative to POEs<br>7.00 *** 2.50 ***<br>***<br>6.00 *** 2.00 *** ***<br>5.00 ****** 1.50<br>4.00<br>1.00<br>3.00 *<br>0.50<br>2.00<br>1.00 0.00<br>0.00 -0.50<br>10-100% 10-24.9% 25-49.9% 50% or 10-100% 10-24.9% 25-49.9% 50% or<br>more more<br>Ownership % Ownership %<br>Probability of receiving a government subsidy Log subsidy amount (among recipients)<br>Probability of receiving a government subsidy (controlling Log subsidy amount (among recipients & controlling for<br>for productivity level) productivity level)<br>Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.<br>Note: The sample includes all firms. Estimates in graph (a) are odds ratios indicating for the probability of receiving a subsidy based on a logit<br>model with a dummy variable taking the value one if the firm reported a positive subsidy amount. The estimates in (b) are in logarithmic terms<br>with the dependent variable being the log of the subsidy amount the firm reported as received. SOE variables are dummy variables taking the<br>value one if national or subnational governments have ownership stake of 10-100% in the firm. The
day in 2017 $PPP, the share of the population living below the prosperity threshold, and the number of people living below the prosperity threshold in 2019 for various income groups, regions, and select countries. Income groups are classified according to World Bank FY21 definitions, which uses data from 2019 (see <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups).</u> Regions are classified according to definitions used in the Poverty and Inequality Platform (see <u>https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#regionsandcountries).</u>

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