rafmacalaba/datause-encoder-data huggingface dataset:
where _Y_<sup>�</sup> _jst_ is estimated aggregate TFP (Φ<sup>�</sup><sup>_AGG_</sup> _jst_<sup>),averageTFP(</sup> Φ<sup>�</sup><sup>_U_</sup> _jst_<sup>),orreallocation(</sup> _R_<sup>�</sup> _jst_ ) for industry _j_ and state _s_ at time _t_ , _τ j,t−_ 1 and _τ_<sup>_I_</sup> _j,t−_ 1<sup>arefinalgoodstariffsandinputtariffs,</sup><sup>_Delicj,t−_1</sup> is a dummy variable equal to one if any products in an industry are delicensed, zero otherwise, _FDI j,t−_ 1 is a dummy variable equal to one if any products in an industry are FDI-liberalized, zero otherwise; and _α js_ and _αt_ are state-industry and year dummy variables, respectively. Since our firm data are annual, and policy changes occurred throughout the year, we lag all policy variables by one year. We employ a fixed-effects estimator to estimate Equation 2, and cluster all standard errors at the state-industry level. We use the balanced panel of state-industries in order to avoid confounding within-group effects with the entry and exit of certain industries in particular states, and we weight all observations using the total number of firms in each state-industry group over all years. This ensures that industries (and states) with large firm populations will receive higher weight in the analysis, and will make the results more representative of the all-India level.<sup>16</sup> Table 6 presents baseline results for 1986 to 2004.<sup>17</sup> Column (1) suggests that the trade liberalization is strongly correlated with aggregate productivity increases. The coefficient on final goods tariffs (-0.055) indicates that a 10 percentage point reduction in final goods tariffs yields an 0.55% increase in aggregate productivity. The impact of input tariffs is an order of magnitude larger, with a 10 percentage point reduction in input tariffs yielding a 5.6% increase in aggregate productivity. Moreover, FDI liberalization increases aggregate productivity by 2.3%.

Columns (2) and (3) present results for the average and reallocation components of productivity, respectively. Column (2) indicates that 10 percentage point declines in final goods and input tariffs raise average productivity by 0.44% and 5.6%, respectively, though the coefficient on final goods tariffs is not statistically significant at the 10% level. FDI liberalization increases average

> 16The results are robust to not including these weights; see Appendix A.

> 17We exclude 1985 because we do not have lagged policy variables for this year.

21
## **_(v) Child Marriage_**

In Mali, it is customary for husbands to pay a bride price to the parents of the new wife. The bride price is a transfer of money and/or livestock from the groom and/or his parents to the bride’s family at the time of marriage (Giuliano and Nunn, 2018). This practice could have negative effects on girls and women, as it could encourage child marriage (Corno et al., 2020), and thus exacerbate gender inequalities. The bride price provides the bride’s family with a source of income that can be used to smooth family consumption or modernize the agricultural production system to improve crop yields. We test this mechanism using DHS (Mali) data of 1995 ́ 96, 2001, 2006 and 2012 ́ 13. We follow Corno et al. (2020), by constructing our marriage variable as a binary variable to 1 if the female had her at _t_ and 0 otherwise. We limit our equal first marriage age sample to women between 12 and 17 years of age.

# **3.4 Drought Data**

Following Harari and Ferrara (2018); Miao and Popp (2014) and Vicente-Serrano et al. (2010), we use the Standardized Precipitation-Evapotranspiration Index (SPEI), to construct a measure of droughts. Our SPEI data are from the SPEI Global Drought Monitor. The SPEI Global Drought Monitor provides information on drought on a global scale. The SPEI time scales provided range from 1 to 48 months and the SPEI calibration period for our data is from January 1950 to December 2019.

Our SPEI value is observed during the 2014 and 2017 rainy seasons in Mali (JuneOctober). We use GPS information from EAC-I data to match each EA to the SPEI grid cell and calculate rainfall shocks at the EA level. Drought occurred if and only the SPEI value is below -1, which captures moderate to extreme droughts (Vicente-Serrano et al., 2010). Interestingly, the SPEI index accounts for the joint effects of precipitation, potential evaporation, and temperature. This measure then allows to control for weather variables strongly correlated with rainfall. This index is, therefore, less susceptible to omitted (weather) variables threats.

In Mali, rains are brought seasonally by monsoon winds that feed the lower layers of 19
# Research Compoonents of world Bank Education Projects, since 1982.

|Area|Countrya<br>nc<br>lLunber|Aproved<br>I|Research Carponents<br>|<br> <br>erCompleted|Current Statns of Studdies, includingl<br>Documents/Data|
|---|---|---|---|---|
|ENEMA|<br>Pakistan CR 1670-PAK|1986|Second Vocational<br>Training.<br>Studies to focus<br>on: (i)<br>cost recovery and financing<br>of<br>vocational<br>education;<br>(Hi) women's training<br>andemptoVment<br>opportLnit i es;<br>(iii)<br>financial<br>compensation schemes and<br>training<br>in private<br>enterprise;<br>(iv)<br>rural<br>apprenticeship<br>programs;<br>(v) survey of industrial<br>sector needs<br>(para.<br>2.17 p. <sup>16 </sup>Annex 8)<br>(due Dec 1S88).|Studiesnotinitiated.|
|ENEMA|<br>Pakistan CR 1821-PAK|1987|Third Primary Education.<br>Study to review<br>non-monetary incentives<br>to rural<br>teachers (para<br>3.24p.24)<br>(due 1989);<br>(ii)<br>review possible<br>methods of reorganizing<br>teacher career system (para 3.25 p.24)(Dec 1988<br>(iii)<br>conduct surveys of Goverrwnent Colleges<br>for Elementary Teachers to establish<br>physical<br>repair<br>system;<br>(iv)<br>study on teacher performance related<br>to<br>teacher training<br>certification<br>(Amex 6p.7).|Studies not initiated.|
|ENEMA|Portugal<br>LN 2456-Po|1984|Maanpouer Training<br>andDevelopment. Self<br>evatuations<br>by:professional<br>engineering<br>development center;<br>National<br>Institute<br>of<br>Adninistratior;<br>project<br>Skill<br>Training<br>Centers;<br>and project<br>tourism centers.|ji)<br>There have been considerabte<br>delays in the<br>implementation<br>of this project.<br>Boththe professional<br>engineering<br>development center and the skill<br>training<br>coamponents have been cancelled<br>as part of the loan.<br>INAdelayed.<br>Tourism not yet ready for student<br>intake<br>(ii)<br>not initiated|

54
<!-- Start of picture text --> Rice Export Price, 2004-2008, Thai 1006<br>tooo + —- —--— —--— —----—Philippines~~ ~~fourth tenderEe>$1,100/ton (April 17,fe2008}——"|<br>aoo + — —-—- ——- —~~ ~~ —-~—-~-~~—-~~—-~—-~~~~~ ~~ ~~ ~~ ~~~ -~-~-f--<br>5 Philippines buying<br>B 600 |> $700/ton ——__<br>a<br>8 Vietnam tightens export restrictons——___,<br>490 ~~ Ef<br>200 a<br>India imposes export restrictons<br>Sources: USDA. Fao<br>"SERPS SRREBSREERERRSBEBRERSESE BEBE EES<br>SESS¢ 1 FSRESSESTFSRERST¢ = @ - &eE = @FS2 &&RERRTAGF& a SERRE> £& &<br><!-- End of picture text -->

Source: Brahmbhatt M and L Christaensen (2008) “Rusing Food Prices in East Asia: Challenges and Policy Options’
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1019 How Effective are Directed Credit<br>Policies in the United States?<br>A Literature Survey|Anita M. Schwarz|November 1992|M. Raggambi<br>37664|
|WPS1020 Another Look at Population and<br>and Global Warming|Nancy Birdsall|November 1992|S. Rothschild<br>37460|
|WPS1 021 Measuring Welfare Changes from<br>Commodity Price Stabilization in<br>Small Open Economies|Jonathan R. Coleman<br>Chris Jones|November 1992|D. Gustafson<br>33714|
|WPS1022 A New Approach to Evaluating Trade<br>Policy|James E. Anderson<br>J. Peter Neary|November 1992|M. T. Sanchez<br>33731|
|WPS1023 Tarfff Index Theory|James E. Anderson|November 1992|M. T. Sanchez<br>33731|
|WPS1024 An Exact Approach for Evaluating<br>the Benefits from Technological<br>Change|Will Martin<br>Julian M. Alston|November 1992|D. Gustafson<br>33714|
|WPS1025 Openness and Economic<br>Performance in Sub-Saharan Africa:<br>Evidence from Time-Series Cross-<br>Country Analysis|Kazi M. Matin|November 1992|D. Ballantyne<br>38004|
|WPS1026 Financial Liberalization and<br>Adjustment in Chile and New Zealand|Paul D. McNelis<br>Klaus Schmidt-Hebbel|November 1992|A. Maranon<br>31450|
|WPS1027 Lessons from Bank Privatization<br>in Mexico|Guillermo Barnes|November 1992|W. Pitayatonakarn<br>37664|
|WPS1028 Socioeconomic and Ethnic<br>Determinants of Grade Repetition<br>in Bolivia and Guatemala|Harry Anthony Patrinos<br>George Psacharopoulos|November 1992<br>|L. Longo<br>39244|
communities had access to improved sanitation.<sup>13</sup> Access to improved sanitation had increased only very slightly, by about 1 percentage point in both treatment and control communities by endline. Figure 3 presents the percentage of households with no sanitation, unimproved sanitation, and improved sanitation in the baseline and endline by treatment status. It similarly shows small improvements in sanitation in both treatment and control communities.

**Figure 3. Households’ sanitation status**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>No Sanitation<br>50%<br>Unimproved Sanitation<br>40% Improved Sanitation<br>30%<br>20%<br>10%<br>0%<br>Control-BL Control-EL Treatrment-BL Treatment-EL<br><!-- End of picture text --> Table 8a also shows that although more than 40 percent of the households in the sample had access to improved sanitation before the program, another 40 percent of households lacked access to any form of sanitation. These households, many of which are particularly poor, defecate in open spaces. The percentage of households with no access to sanitation of any sort declined by about 4 percentage points between the baseline and endline. Again the decline in treatment and control communities appears similar.

The endline data specifically asked households whether they had constructed a toilet in the past two years. This variable is reported in Table 8a and indicates that more toilets were built in treatment communities than in control communities. Approximately 16 percent of households

> 13 Improved sanitation is defined according to the WHO/UNICEF (2012) Joint Monitoring Programme for Water Supply and Sanitation (JMP; http://www.wssinfo.org). Improved sanitation facilities include: a) a flush toilet or latrine that flushes to a sewer, septic tank, or pit; b) a ventilated improved pit (VIP) latrine; c) a pit latrine with the pit well covered by a slab; or d) composting toilets. Shared and public toilets are considered “unimproved” regardless of their type.

15
is below average and 14% said such officers are not productive at all, with the variation depending mostly on the nature of the illness.

Other reported impacts across target organizations include low morale, inadequate program monitoring and evaluation, and reduced quality of services resulting from unqualified staff or employees occupying higher and/or professional positions that their skills warrant. For example, the required training of extension workers has been substantially reduced to allow for faster filling of vacant posts: this has resulted in many instances where new staff communicate low quality or plainly wrong messages to farmers, in particular in the case where generalists act in the place of Subject Matter Specialists.

At M.Water, managers reported that the loss of even a small number of highly skilled engineers places the entire water system at risk. Water systems require constant maintenance of wells, boreholes, dams and embankments, and after floods only highly skilled water engineers can bring these systems back to standard. Vacancies in very low-skill posts can also be very detrimental: even the collection of hydrological data was reportedly hampered by lack of staff. As a result, river flow measurements are now plagued with missing data and cannot be used for identifying climate change signals, impairing the ability to plan for climate adaptation.

MET is severely affected by the declining human resource capacity, making it extremely difficult to support the country with information about climate change. Staff attrition due to deaths, early retirement, and prolonged illnesses, together with other causes like vandalism and budgetary limitations, resulted in closure of about 700 rainfall observation stations because of the compromise in data quality and gaps in the climate data bank. Table 6 shows the decline in weather observing stations between 1988 and 2006. In view of this situation, MET no longer has the level of rainfall station density needed to capture the variability caused by Malawi’s irregular topography, thereby increasing the error margins on weather and climate information disseminated to the public. There have been complaints and loss of public confidence in weather and seasonal climate forecasts – products that would be much more reliable with a denser 25
Policy Research Working Paper 5204

# **Abstract**

The measurement of inequality of opportunity has hitherto not been attempted in a number of countries because of data limitations. This paper proposes two alternative approaches to circumventing the missing data problems in countries where a demographic and health survey and an ancillary household expenditure survey are available. One method relies only on the demographic and health survey, and constructs a wealth index as a measure of economic advantage. The alternative method imputes consumption from the ancillary survey into the demographic and health survey. In both cases, the between-type share of overall inequality is computed as a lower bound estimator of inequality of opportunity. Parametric and non-parametric estimates are calculated for both methods, and the parametric approach is shown to yield preferable lower-bound measures. In an application to the sample of ever-married women aged 30–49 in Turkey, inequality of opportunity accounts for at least 26 percent (31 percent) of overall inequality in imputed consumption (the wealth index).

This paper—a joint product of the Office of the Chief Economist for the Latin America and the Caribbean Region and the Poverty and Inequality Team in the Development Research Group—is part of a larger effort in the two departments to better measure and understand inequality of opportunity and its consequences for well-being. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fferreira@worldbank. org, gignoux@lea.ens.fr, and meltem.aran@economics.ox.ac.uk.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure IV: Bunching At First Income Tax Kink for Self-Employed

<!-- Start of picture text --> 2006 2007 2008<br>1500 2000 2500 3000 3500 1500 2000 2500 3000 3500 1500 2000 2500 3000 3500<br>Taxable Income (in '000 colones) Taxable Income (in '000 colones) Taxable Income (in '000 colones)<br>2009 2010 2011<br>1500 2000 2500 3000 3500 1500 2000 2500 3000 3500 1500 2000 2500 3000 3500<br>Taxable Income (in '000 colones) Taxable Income (in '000 colones) Taxable Income (in '000 colones)<br>2012 2013 2014<br>1500 2000 2500 3000 3500 1500 2000 2500 3000 3500 1500 2000 2500 3000 3500<br>Taxable Income (in '000 colones) Taxable Income (in '000 colones) Taxable Income (in '000 colones)<br>1500 2000 1500<br>1500<br>1000 1000<br>1000<br>Number of Taxpayers 500 Number of Taxpayers Number of Taxpayers 500<br>500<br>0 0 0<br>1500 2000 2000<br>1500<br>1000 1500<br>1000<br>Number of Taxpayers 500 Number of Taxpayers 1000 Number of Taxpayers<br>500<br>500<br>0 0<br>2000<br>2500 1200<br>1500 2000 1000<br>1500 800<br>1000<br>Number of Taxpayers Number of Taxpayers Number of Taxpayers<br>1000 600<br>500 500 400<br><!-- End of picture text --> Notes: The gures show the frequency distribution of taxable income of the self-employed (personas sicas con actividad lucrativa) around the rst kink in the income tax schedule, for the years 2006-2014. The data is aggregated in bins of  20,000. The black solid line marks the kink in year _t_ (as per the gure title), the black dashed line marks the kink in year _t −_ 1 .
In particular, we see that the cash grants have no signi...cant impact for any size female ...rm, while the in-kind grants only have an impact for the 40 percent or so of ...rms with higher initial pro...ts or higher initial capital stock. The impact of the in-kind grants is extremely large for these female ...rms –monthly pro...ts increase by 77 to 96 cedis per month for the female ...rms in high initial pro...ts quadruplets, compared to an insigni...cant 2 to 5 cedis per month for the low pro...ts female ...rms. This di¤erence is statistically signi...cant. In contrast, there is no such pattern for male-owned ...rms –the point estimates for the lower pro...ts ...rms are typically just as large as those for the higher pro...ts ...rms, and the di¤erence is not statistically signi...cant.

Taking these results together, it appears that cash grants are not increasing pro...ts for female-owned ...rms, and the in-kind grants only increase pro...ts for female-owned ...rms which were larger in size to begin with. The in-kind treatments also increased pro...ts for male-owned ...rms, and the e¤ect of the cash grants is inconclusive for males. There does not appear to be the same heterogeneity by initial ...rm size in terms of male responsiveness to the grants. This suggests that female-owned ...rms are better able to resist internal or external pressure when the treatment comes in the form of inventories or equipment, while the di¤erence in treatment is less relevant for male-owned businesses. We now scrutinize this interpretation more in detail.

# 6 Interpretation of the results

## 6.1 Where do the grants go?

Table 5 examines the extent to which the grants are being used to increase the capital stock of the ...rm, to make transfers to non-household members, and for household spending. In panel A we show the results of estimating equation (2) with di¤erent outcomes, while in panel B we show the results of estimating equation (3) for the female sample and the categorization of low and high initial pro...ts groups, since this is where we found large di¤erences in treatment e¤ects. For reasons of space we report the ...xed e¤ects estimates only (with the exception of transfers out which were not measured pre-treatment), but note when the OLS results show large di¤erences.

We begin by looking at the impact of the grants on the capital stock of the ...rms. Column (1) shows this for total capital stock. In order to reduce the in‡uence of large outliers, column (2) truncates capital stock at the 99.5th percentile, which is 6130 cedis. Both speci...cations suggest that capital stock is increasing by more for the in-kind treatments than for the cash treatments, both for men and women. However, the capital stock data is noisy and the standard errors 27
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39
distributional change in India has centred on consumption-based trends);<sup>6</sup> second, the 1993/94 survey collects data on rural areas in India only, and thus its panel households with the 2004/05 survey are restricted to rural India and are not nationally representative.

Furthermore, the sample size of these surveys is large in absolute terms, but is not sufficient to allow an analysis of mobility at a finer, state-level disaggregation. The IHDS data are also prey to the standard concerns leveled at panel data regarding attrition (which is estimated to be around 20 percent) and the impact of measurement error. In short, it appears that these survey data do not enjoy the same official standing and recognition as the NSS surveys, at least in terms of household consumption and poverty consumption analysis.

Setting aside these concerns, emerging findings from the IHDS/HDPI-based analysis point to considerable churn within the income distribution, with both upward and downward mobility occurring alongside rising average income levels (Azam 2019; Ranganathan et al. 2017). Azam (2019) suggests that rural income mobility does not show a clear trend increase over time when one compares the 1993/94–2004/05 interval against the 2004/05–2011/12 interval. He goes on to show, however, that the characteristics of households experiencing upward mobility are different from those prone to downward shifts in relative position. Notably, households belonging to the disadvantaged Hindu castes (Other Backward Castes, as well as Scheduled Castes and Scheduled Tribes) are less likely to experience upward mobility than forward Hindu castes. By the same token, forward Hindu castes are less likely to experience downward mobility (Azam 2019). Muslim households display roughly similar mobility patterns as disadvantaged Hindu castes, but in urban areas (covering the period 2004/05–2011/12 only) they stand out as most likely to experience downward mobility. Azam (2019) goes on to show that mobility patterns across these population groups are attenuated, but do not disappear

> 6 The IHDS also collects consumption data. But compared with the NSS surveys, the IHDS has less than half the sample size and collects a much-reduced version of household consumption data (i.e. 47 consumption items in the latter, versus more than 400 items in the former).

9