rafmacalaba/datause-encoder-data huggingface dataset:
12 pronounced increase in poverty. Table 4 now reveals that the male-female educational gap has been eliminated, with females older than ten being on average slightly more educated than males. Clearly, this must imply a large disparity in favour of girls in recent cohorts. While a cohort analysis of educational trends is beyond the scope of this paper", such a rapid reversal may in fact warrant a shift in public policy towards programmes aimed at keeping boys in school, without in any way discouraging the growth in female schooling. Finally, note the remarkable disparity in educational attainment across ethnic groups, with Asians substantially above average, and blacks and those of mixed race below it.

**Tabe 4 Fuc~tina1a~ LaorFore artciatin1tat#i,b gedr and race~**

||1976|**1981**|**1985**|**1996**|
|---|---|---|---|---|
|Average Years of Schooling (Males)||4.04|4.36|_5.20_|
|Average Years of Schooling (Females)|3.14|3.99|4.37|_5.43_|
|Average Years of Schooling (Blacks and MR)|-|-|-|4.20|
|Average Years of Schooling (Whites)||||6.16|
|Average Years of Schooling (Asians)||-|-|8.13|
|Labor Force Participation (Males)|73.36%|74.63%|76.04%|71.31%|
|Labor Force Participation (Females)|28.62%|32.87%|36.87%|42.00%|
|Labor Force Participation (Blacks and MR)||-|-|55.92%|
|Labor Force Participation (Whites)||||56.41%|
|Labor Force Participation(Asians)|_|||54.88%|

Notes: Average 'effective' years of schooling for persons ten years or older, in urban areas. Labor Force Participation in urban areas only.

As for labour force participation, the persistent and substantial increase in female participation from 29% to 42% over the two decades, was partly mitigated by a decline in male participation rates. Those trends notwithstanding, the male-female participation gap remains high, at around 30 percentage points. There is little evidence of differential labour force participation across ethnic groups.

" See Duryea and Szekely (1998) for such an educational cohort analysis of Brazil and other Latin American
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(Bouis et al., 1992). Moreover, while calorie intake is a direct function of calorie expenditure, as the principles of nutrition suggest, the intakes of various macro- and micro-nutrients are not (World Health Organization, 1985). Despite men’s (as opposed to women’s) engagement in energy-intensive activities, the requirements for many micronutrients are higher for women, particularly, for pregnant and lactating women, and children than men due to reproduction and growth requirements.

Moreover, PRH and previous studies on intrahousehold nutrient allocation have applied a unitary framework. A number of studies in recent decades fail to accept the fundamental assumption of the unitary model —- resource pooling —- in a range of outcomes, such as household expenditure, agricultural production, schooling, and health in developed and developing countries.

Applying a bargaining framework<sup>4</sup> , I demonstrate that (i) while there is lack of gender disparity in the calorie adequacy ratio, for a range of critical nutrients, the disparity is prominent within children, adolescents, and adult groups; (ii) pregnant and lactating women receive much less of these nutrients vis-a-vis their requirements; (iii) there is evidence of significant intrahousehold bargaining with a wife’s bargaining power, as opposed to her husband’s<sup>5</sup> , significantly and positively affecting the allocation of various nutrients for children and adolescents of both sexes and adult females; and (iv) these findings combined with the estimates of health technology imply that perhaps the nutrition-health-labor market linkage as a key explanation for gender disparity in PRH is overemphasized.

I thus attempt to contribute to the literature in four ways. First, I expand PRH analysis from calorie allocation to the allocation of a number of critical nutrients. While calorie has been the focal point in previous literature, it neither addresses the growing concern of micronutrient malnutrition, nor provides adequate understanding of gender-role in alleviating malnutrition. Although, some studies (Behrman, 1988; Behrman and Deolalikar, 1990) focused on nutrients other than calorie, they applied a unitary framework, which could be misleading if the household decision-making process is affected by the bargaining power of different members. My second contribution is the demonstration of intrahousehold bargaining (in addition to typical income and price effects) on nutrient allocations. Third, previous studies have demonstrated intrahousehold bargaining on various outcomes other than individual nutrient intakes. I add to this literature by focusing on nutrient intakes using an innovative panel dataset of rural Bangladesh. Fourth, with the exception of a few natural experiments, previous studies have used bargaining measures that

> zinc (IFRPI-BIDS-INFS, 1998).

> 4To the best of my knowledge, this is one of the first exercises that applies a bargaining framework to analyse intrahousehold nutrient allocation based on actual individual dietary intakes.

> 5I use household head or husband and his wife or spouse interchangeably.

3
|P106975|2010|2015|12/22/2014|9 Active|Congo,Republic of|
|---|---|---|---|---|---|
|P106975|2010|2015|6/22/2015|10 Active|Congo,Republic of|
|P106975|2010|2016|12/28/2015|11 Active|Congo,Republic of|
|P106975|2010|2016|6/17/2016|12 Active|Congo,Republic of|
|P106975|2010|2017|12/27/2016|13 Active|Congo,Republic of|
|P106975|2010|2017|6/14/2017|14 Active|Congo,Republic of|
|P106975|2010|2018|12/8/2017|15 Active|Congo,Republic of|
|P106975|2010|2018|5/30/2018|16 Active|Congo,Republic of|
|P106975|2010|2019|12/7/2018|17 Active|Congo,Republic of|
|P106975|2010|2019|6/14/2019|18 Active|Congo,Republic of|
|P106975|2010|2020|12/24/2019|19 Active|Congo,Republic of|
|P106975|2010|2020|6/26/2020|20 Active|Congo,Republic of|
|P106982|2011|2011||0 Closed|Congo,Democratic Re|
|P106982|2011|2012|7/5/2011|1 Closed|Congo,Democratic Re|
|P106982|2011|2012|3/11/2012|2 Closed|Congo,Democratic Re|
|P106982|2011|2013|10/29/2012|3 Closed|Congo,Democratic Re|
|P106982|2011|2013|3/24/2013|4 Closed|Congo,Democratic Re|
|P106982|2011|2014|3/10/2014|5 Closed|Congo,Democratic Re|
|P106982|2011|2015|1/10/2015|6 Closed|Congo,Democratic Re|
|P106982|2011|2015|3/31/2015|7 Closed|Congo,Democratic Re|
|P106982|2011|2015|5/15/2015|8 Closed|Congo,Democratic Re|
|P106982|2011|2016|7/30/2015<br>|9 Closed|Congo,Democratic Re|
|P106982|2011|2017|10/11/2016|10 Closed|Congo,Democratic Re|
|P106982|2011|2017|1/27/2017|11 Closed|Congo,Democratic Re|
|P106982|2011|2017|5/26/2017|12 Closed|Congo,Democratic Re|
|P106982|2011|2018|10/27/2017|13 Closed|Congo,Democratic Re|
|P106982|2011|2018|5/24/2018|14 Closed|Congo,Democratic Re|
|P106982|2011|2021|7/16/2020|15 Closed|Congo,Democratic Re|
|P106986|2006|2008||0 Closed|Croatia|
|P107037|2009|2009|3/31/2009|1 Closed|Yemen,Republic of|
|P107037|2009|2009|6/15/2009|2 Closed|Yemen,Republic of|
|P107037|2009|2010|8/5/2009|3 Closed|Yemen,Republic of|
|P107037|2009|2010|6/29/2010|4 Closed|Yemen,Republic of|
|P107037|2009|2011|1/11/2011|5 Closed|Yemen,Republic of|
|P107037|2009|2012|7/6/2011|6 Closed|Yemen,Republic of|
|P107037|2009|2012|9/27/2011|7 Closed|Yemen,Republic of|
|P107037|2009|2012|6/30/2012|8 Closed|Yemen,Republic of|
|P107037|2009|2013|1/1/2013|9 Closed|Yemen,Republic of|
|P107037|2009|2014|12/28/2013|10 Closed|Yemen,Republic of|
|P107037|2009|2014|5/3/2014|11 Closed|Yemen,Republic of|
|P107037|2009|2015|11/7/2014|12 Closed|Yemen,Republic of|
|P107037|2009|2015|5/15/2015|13 Closed|Yemen,Republic of|
|P107037|2009|2016|11/18/2015|14 Closed|Yemen,Republic of|
|P107037|2009|2016|6/15/2016|15 Closed|Yemen,Republic of|
|P107046|2007|2008||0 Closed|Central America|
|P107046|2007|2009||0 Closed|Central America|
|P107050|2010|2010|6/22/2010|1 Closed|Yemen,Republic of|
**Table 4: Data Transparency and Sovereign Bond Spreads (IV), Baseline with Total Public Debt**

|IV variables<br>Freedom of information, lag of ICRG, lag of interaction<br>between transparency and ICRG, lag of public debts and<br>lag of interaction between transparency and public<br>|||dependent va|riable: sprea|d||
|---|---|---|---|---|---|---|
|debts|||||||
||||1995|-2018|||
|Variables|[1]|[2]|[3]|[4]|[5]|[6]|
|GDP per capita (constant 2010 US$)|-0.525**|-0.413**|-0.518**|-0.450**|-0.421***|-0.653***|
||(0.211)<br>|(0.207)<br>|(0.215)<br>|(0.207)<br>|(0.163)<br>|(0.176)<br>|
|Growth (annual %)|-0.0340***<br>(0.00635)|-0.0331***<br>(0.00630)|-0.0358***<br>(0.00634)|-0.0351***<br>(0.00635)|-0.0346***<br>(0.00545)|-0.0431***<br>(0.00590)|
|CPI Inflation (average consumer prices)|0.0632**|0.0684**|0.0602**|0.0627**|0.0699***|0.0414|
||(0.0283)|(0.0280)|(0.0282)|(0.0285)|(0.0243)|(0.0281)|
|Primary Balance to GDP (%)|0.00477<br>(0.00752)|0.00675<br>(0.00743)|0.00519<br>(0.00749)|0.00424<br>(0.00756)|0.00976<br>(0.00771)|0.0120<br>(0.00773)|
|Current Account Balance to GDP (%)|0.00116<br>(0.00449)|0.00111<br>(0.00444)|0.000819<br>(0.00449)|0.00163<br>(0.00452)|-0.00182<br>(0.00446)|-0.000514<br>(0.00476)|
|VIX Index|0.510***<br>|0.537***<br>|0.506***<br>|0.503***<br>|0.633***<br>|0.551***<br>|
|US 10-year Treasury Bond Yield|(0.0558)<br>-0.371***<br>|(0.0554)<br>-0.339***<br>|(0.0555)<br>-0.380***<br>|(0.0559)<br>-0.380***<br>|(0.0599)<br>-0.224***<br>|(0.0657)<br>-0.207**<br>|
|Transparency Index (WB)|(0.0812)<br>2.745***<br>|(0.0810)|(0.0810)|(0.0812)|(0.0842)|(0.0936)|
|Methodology Assessment|(0.560)|2.417***|||||
|||(0.507)|||||
|Periodicity and Timeliness Assessment|||2.535***<br>||||
|Data Source Assessment|||(0.717)|2.224***<br>(0493)|||
|Transparency Dummy (IMF) Subscription||||.|4.375**<br>(1846)||
|Transparency Dummy (IMF) Standard compliance|||||.|12.22***<br>(4.107)|
|Transparency (WB) x ICRG|-0.604***<br>(0.113)||||||
|Methodology x ICRG||-0.612***<br>(0.120)|||||
|Periodicity and timeliness x ICRG|||-0.574***||||
|`<br>Dt ICRG|||(0.121)|0531***|||
|aa source x||||-.<br>(0.117)|||
|Transarenc (IMF) Subscrition x ICRG|||||-1375***||
|py p|||||.<br>(0.449)||
|Transparency (IMF) Standard compliance x ICRG||||||-3.135***<br>|
|Transparency (WB) x public debt|0.00225***<br>|||||(0.973)|
|Methodology x public debt|(0.000395)|0.00272***|||||
|||(0.000421)|||||
|Periodicity and timeliness x public debt|||0.00210***<br>0000374||||
|Data source x public debt|||(.)|0.00230***<br>(0000404)|||
|Transparency (IMF) Subscription x public debt||||.|0.0159***<br>(000139)||
|Transparency (IMF) Standard compliance x public debt|||||.|0.0125***|
|||||||(0.00202)|
|Constant|7.543***|7.940***|7.883***|7.883***|8.234***|10.19***|
||(1.910)|(1.813)|(2.006)|(1.868)|(1.487)|(1.600)|
|Observations|532|532|532|530|713|713|
|No. Countries|57|57|57|57|61|61|
|Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||||

26
One additional way that survey design may affect data quality is by increasing the non-response rate. In the ALTA study, the non-response rates at the individual level for Arms 1 and 2 are 13% and 12%, respectively, with the difference not statistically different from zero. We also assess whether the level of detail of the questionnaire may influence the willingness for participation in the self-respondent modules, Arms 1 and 2. To do so, we conduct a t-test on a set of dummy indicators capturing whether the individual responded to the questionnaire and reason for non-response, if applicable. Note that the first respondent that is interviewed within a given household is not aware of the length or structure of the questionnaire module. The second and/or third interviewed respondents may have a sense, however, if they are informed by the first respondent. As Table 5 suggests, neither the differences in the share of individuals not responding to Arms 1 and 2, nor the reason for not responding, are significant. The only notable exception is the share of individuals not responding as out of marz, but this does not appear to be linked with the length of the questionnaire itself.

_Table 5. Non-response rate and reason for non-response in self-respondent arms_

||Self-Respondent,<br>Parcel Level<br>(Arm 1)|Self-Respondent,<br>Aggregate Level<br>(Arm 2)||
|---|---|---|---|
|Variables|Mean|Mean|T-test|
|Non-response (1=yes)|0.13|0.12|0.43|
|Non-response as out of the marz (1=yes)|0.08|0.05|0.03|
|Non-response as ill/not well (1=yes)|0.02|0.02|0.83|
|Non-response as refused (1=yes)|0.00|0.01|0.17|
|Other reason for non-response (1=yes)|0.03|0.04|0.28|

Table 6 reports the non-response rates by gender to investigate whether there are differences in the non-response rate and the reasons for non-response based on gender. These findings indicate that amongst individuals that are randomly selected for interviewing, there is a higher rate of non-response for men relative to women in both Arms 1 and 2. In terms of magnitude, males appear 3.5 times less likely than women to respond to the self-respondent parcel-level questionnaire, and 1.5 times less likely to respond to the aggregate-level questionnaire. The migration of men out of the marz for periods of time, likely for employment, is a main driver of this increased non-response. Finally, we do not observe any statistical difference in the rate of outright refusal, which may indicate that the difference in the rate of non-response across men and women is driven by circumstantial and socio-economic reasons as opposed to behavioral differences.

17
For each household, the survey collected extensive information on socioeconomic status. For this exercise, we focus on survey-based measures of household wealth, education and ethnicity. We index wealth by counting total asset possession from a possible set of 29 assets.<sup>3</sup> We assign households to three wealth categories using breaks in the distribution at ownership at 2 and 4 assets. The survey reports the education level of the household head in 8 categories.<sup>4</sup> We classify education levels in 3 categories: primary, secondary, and post-secondary.

In the survey, households performed separate ranking exercises for perceived changes in general environmental conditions and livelihood-related conditions. In particular, surveyed household representatives were asked to rank perceived changes in 2 categories: (1) five general environmental conditions (less rain, more rain, more salinity, more frequent droughts, more heat); (2) ten environment-related threats to livelihood (tree loss, livestock loss, crop damage, fish loss, water pollution, soil fertility loss, water loss, pests and insects, animal diseases, forced outmigration of prime-age workers).

For understanding the variation in rank assignments, we first computed the numerical scores for each factor within each change group (5 for general environmental conditions, 10 for livelihood-related conditions) using the ranks for each household. For each change group, we then computed correlation coefficients for all unique household pairs in the sample. Finally, we estimated regression models to test the effect of four categorical variables on inter-household correlations: (1) common locality; (2) common status in the highest socioeconomic group; (3)

> 3 Assets queried were Own car, Taxi, Auto rickshaw, Truck/small truck, Bicycle, Motor bike, Gas stove, Mixer, Refrigerator/Freezer, Washing machine, Iron, Geyser, Radio/Cassette recorder, Color television, Black and white television, DVD Player, Land telephone, Mobile, Sewing machine, Power generator (Kerosene), Power generator (Solar), Electric fan, Air conditioner, Personal computer, Motor boat, Row boat or sail boat, Animal drawn cart, Jewelry, Other building excluding dwelling.

> 4 Education levels queried were Pre-Primary (below 1), Primary (1-4), Upper Primary (5-8),  Secondary (9-10), Higher Secondary (11-12), Undergraduate (B.A/B.Sc/B.Com), Post-graduate (M.A/M.Sc/M.Com), Post-Masters (Phd/Voc/Doctor/Eng).

5
We begin the analysis by calculating the current average net revenue per hectare and the average fraction of cropland from total land in each AEZ. These values simply reflect current cropland and crop net revenues earned by farms in each AEZ. Seo et al (2008) reveal that farms in some AEZs, such as high-elevation moist savannah and mid-elevation sub-humid forest, are more valuable than farms in other AEZs, such as lowland semi-arid, lowland sub-humid, and mid-elevation dry savannah. The climate conditions in these high valued AEZs are more temperate and conducive to rainfed cropland. We then use a multinomial logit model to determine the probability (a value between 0 and 1) of each AEZ in each district across all of Africa given its climate, soils and elevation. Using current climate, this econometric model predicts the current distributions of different AEZs. Combining this information with the average crop net revenue per hectare and fraction of cropland for each AEZ, the expected cropland and crop income in each district in Africa today is estimated.

We then use this econometric model to examine the likely implications of climate change. Climate change will change the probability of each AEZ in each district. The changing probabilities imply that the AEZs will shift across the geography. For example, with warming, land will shift from temperate AEZs to more tropical AEZs or with drying, to more arid AEZs. We assume that future farms in a particular AEZ will have the same performance as current farms in that AEZ. As more land leaves one AEZ and shifts to another, expected cropland and crop income will change. We use this model to examine the impact of two very different climate scenarios for 2100. We rely on data from the Parallel Climate Model (PCM) (Washington et al. 2000) and the Canadian Climate Centre (CCC) (Boer et al. 2000) to examine the effects of two different climate predictions for 2100. These predictions were chosen because they span the range of likely outcomes predicted by the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC 2007). The PCM model predicts mild temperature increases of about 2<sup>◦</sup> C and marginal gains in precipitation during summer and winter months. The CCC model predicts warming of over 5<sup>◦</sup> C with drier summers and wetter winters (see Table 4).

The next section of the paper reviews the methodology and data in detail. The third section describes the results. The final section of the paper summarizes the results and briefly discusses the policy implications.

# **2. Methodology**

The AEZ analysis presented in this paper is a comparative static analysis. It is intended to measure the long-run consequences of AEZs shifting over space from one equilibrium to another. As a long-run equilibrium analysis, it captures adaptation. It reflects the fact that farmers have adapted to the AEZ to which they are accustomed. This long-run approach also captures secondary effects such as changes in insects or other pests that may go along composite and land cover maps using as input observations from the 300m MERIS sensor on board the ENVISAT satellite mission.
Policy ReseaRch WoRking PaPeR 4447

# **Abstract**

This paper is motivated by the emphasis on secure property rights as a determinant of economic development in recent literature. The authors use village and household level information from about 800 villages throughout China to explore whether legal reform increased protection of land rights against unauthorized reallocation or expropriation with belowaverage compensation by the state. The analysis provides nation-wide evidence on a sensitive topic. The authors find positive impacts, equivalent to increasing land values by 30 percent, of reform even in the short term. Reform originated in villages where democratic election of leaders ensured a minimum level of accountability, pointing toward complementarity between good governance and legal reform. The paper explores the implications for situations where individuals and groups hold overlapping rights to land.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to assess the impact of land policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or jins@anr.msu.edu.

_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
Following the model estimation stage, we apply the cluster effect at the municipality level in the simulations, generating FGT(α) measures for each one of 853 municipalities in the state of Minas Gerais. Plotting on the horizontal axis the observed FGT(α) measured on the basis of the Census data and in the vertical axis the simulated FGT(α) measure, we can examine whether predictions are close to the main diagonal. Figures 12 to 14 show the correlation among observed and simulated FGT measures for two out of 41 poverty maps we have produced. The size of the bubbles represents the size of the municipality to illustrate some imprecision of simulations in small municipalities. The figures demonstrate that poverty estimates are randomly assigned around the main diagonal and the R<sup>2</sup> representing the correlation of between observed and estimated poverty rates ranges from 75% to 90% depending on the survey type and FGT(α) measure.

Figure 12 - FGT(0) measures at Municipality level, ELL simulations and True Census estimates PNAD pseudo-sample POF pseudo-sample <!-- Start of picture text --> 100% 100%<br>80% 80% R 2 = 0.88<br>R 2 = 0.88<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Census Census<br>Source: Authors’ Calculation.<br>'PNAD' 'POF'<br><!-- End of picture text --> 23
# **<u>Regulation and Noncompliance: Magnitudes and Patterns for India’s Factories Act</u>**<sup>**1**</sup> **Urmila Chatterjee Ravi Kanbur**

**JEL Classification** : K31, K42, J08, J21, J46, E26, D22

**Keywords** : Labor laws, compliance, informality, firm size, productivity, enforcement, India

**Sector Board** : Economic Policy

1 We thank the World Bank’s South Aisa Urban Development team for providing the primary data sets used in this paper. We thank the seminar participants at CDS-Trivandrum, the Reserve Bank of India and the World Bank, New Delhi, for their comments and feedback.

Author institutions and contact details: Chatterjee, World Bank, uchatterjee@worldbank.org; Kanbur, Cornell University, sk145@cornell.edu
# FIGURE 1: SHARE OF TOTAL SERVICES (INCLUDING MILITARY) IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 80%-<br>DNK USA<br>60%-<br>NOR<br>SDElDEU<br>PRT GR<br>wLi<br>.~~~~R GRC j-AFNDEU<br>~~~~~~~DEU<br>40% t YWG US6R US6R<br>YWG<br>2070%<br>8 9 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->