rafmacalaba/datause-encoder-data huggingface dataset:
To construct the IPW and IV weights, we need conditional probabilities for stratification, for example, the randomiza on rules provide the stratum pecific treatment probabilities. Otherwise, we need to esti ate and predict grist and Imbens (2002) recommend sorting t e data into cells and using the proportion of treated observations within each cell. This me hod is nonparametric and efficient. For continuous covariates, Hirano, Imbens and Ridder (2003) suggest a flexible logistic specification, including polynomials of covariates and interaction terms. The authors give conditions under which the resulting estimator is nonparametric. The appendix of Imbens (2015) presents an references. Alternative semiparametric or nonparametric strategies are available. Because Both the IV and the IPW estimators are sensitive to observations in the tails of Unfortunately, t is difficult to predict this conditional probability with much accuracy n ar its boundaries. Crump et al. (2009) suggest trimming the sample and ignoring observations is close to zero or one when estimating quantiles. If the rate of trimming decreases with ample size, the resulting estimator remains consistent. The authors suggest using only obse vations with predicted probability between 0.1 and 0.9 as a rule of thumb. \=2| ( )4 |=2c 4 ( eC )E(e ). ca_ be estimated from the treatme roup residuals and ea E(e ) ca e fro the control group residuals. The e, this is a system of ind penden qu nsand tions for the first moment of the treatmente ect istributio E(A )=E[( I= ( )e@ EC ).

These moments can be estimated recursively u t e sample counterparts to each term in the above equation. Specifi ~~—~~ Vv (e) ~~=—~~ (YY 8B)
# **Descriptive Statistics**

# **_Consumption and Investment Choices by Reintegration Amount_**

We start by examining the relationship between the reintegration amounts and the consumption bundle that the population of returning refugees spent the assistance on. Households in the treatment group were more likely to use the reintegration assistance to purchase long-term assets, and households in the control group were more likely to purchase consumption goods (table 2). Almost half of returnees in the control group spent more than half of their reintegration assistance on food, compared with only 17 percent in the treatment group. Households in the treatment group were more likely to purchase land (21 percent) than households in the control group (7 percent). Returnees in the treatment group were also more likely to use it to pay for transportation and rent.

**Table 2 Items on which returnees spent their assistance (percent of total)**

|Item|$350per returnee|$150per returnee|
|---|---|---|
|Land|21.0|6.2|
|Rent|20.1|7.8|
|Transportation|20.0|7.4|
|Food|16.9|46.2|
|Shelter|9.9|7.1|
|Health|6.7|5.4|
|Debt|5.3|5.2|
|None|0.2|14.6|

The 2020 follow-up survey asked returnees to rank the usefulness of UNHCR reintegration cash assistance for multiple spending items related to reintegration. The largest difference between the $350 and $150 per returnee recipients was their assessment of the usefulness of the assistance for purchasing land and building a house (table 3).

**Table 3 Returnees’ assessment of usefulness of UNHCR reintegration assistance for different purposes**

||_Recipients of $350 per returnee_<br>|_Recipients of $150 per retur_<br>|_nee_<br>|
|---|---|---|---|
|_Possible use of assistance_|_allowance_|_allowance_|_Difference_|
|Purchase land|2.17|1.53|0.63|
|Build house|2.09|1.74|0.35|
|Return to province of origin|3.08|2.80|0.28|
|Pay rent|3.04|2.84|0.20|
|Pay expenses associated with an<br>illness|1.73|1.62|0.11|
|Pay loan|1.78|1.67|0.11|
|Open business|1.44|1.35|0.08|
|Purchase a house|1.29|1.24|0.05|
|Pay for a child’s schooling|1.48|1.44|0.03|
|Acquire legal documentation<br>(_tazkira_)|1.55|1.63|–0.08|
|Ret. to desired province|2.24|2.59|–0.35|

_Note_ : Ratings are from 0 to 10.

10
**<u>Table 4: Land use status for agricultural estates</u>**

||**Total area**<br> **(1,000 ha)**|**Share of land under**<br>**crops (%)**|**Share of estates with at least**<br>**70% of area under crops (%)**|**No. of**<br>**obs.**|
|---|---|---|---|---|
|Total|683.83|42.07|18.09|24,823|
|**Region**|||||
|North|101.04|34.97|11.34|3,758|
|Center|455.38|44.51|20.59|18,526|
|South|127.41|38.99|9.85|2,539|
|**Lease duration/validity**|||||
|Expired/indet. lease|569.86|42.36|18.24|23,034|
|Valid lease|113.97|40.65|16.21|1,789|
|Valid lease > 10 years|102.20|40.28|15.37|1,171|
|Has deed|400.49|41.22|17.02|12,259|
|Has SD plan|382.79|41.89|17.58|12,637|
|**Time of transfer**|||||
|Before independence|0.43|45.88|8.33|12|
|1964-1985|76.52|40.02|17.44|1,193|
|After 1985|502.53|43.96|18.23|19,814|
|**Size**|||||
|<10 ha|9.04|51.10|23.56|1,957|
|10-20 ha|205.42|48.32|21.77|15,224|
|20-50 ha|169.06|40.57|10.83|5,828|
|50-100 ha|77.82|34.96|4.78|1,151|
|100-500|108.68|35.50|4.24|590|
|>= 500 ha|113.82|43.43|6.85|73|

_Source:_ Own computation National Geographical Estates Database overlaid with SPOT imagery. _Note:_ Crop use is defined as maize and other crops. Figures are reported only for estates for which satellite imagery is available.

19
disparity problem, for which the further accumulation of farm household income data based on micro household surveys is badly needed. Unless comparisons between farm and non-farm households are made in terms of aggregates of farm and non-farm incomes across regions and over time, the effective allocation of public resources to cope with the disparity problem by region can not appropriately be designed. The sectoral value added data from social accounts used for broad comparisons in Tables 1 of this paper are grossly insufficient for the purpose of such concrete policy design. Social engineering to cope with the disparity problem, which is by nature very complex and sensitive, must be based on careful field–level research for different socio-economic and ecological environments, such as those presented in the set of papers compiled in the November 2006 special issue on “the role of nonfarm income in poverty reduction: evidence from Asia and East Asia” of Agricultural Economics 35:393-478.

20
<!-- Start of picture text --> Figure 8<br>Forecasted RE energy share in t+30 according to the year of publication Forecasted share Forecasted share =<br>0.13 x Publication Year - 245.8<br>Robust Tukey smoother<br>R 2 = 0.12<br>Linear regression line<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>RE share (%x100)<br><!-- End of picture text --> 15
**Table B1: Urban/Rural Ratio in Annual Disposable Income Per Capita, 2000 and 2008**

|**_Province_**|**2000**|**2008**|**Direction of**<br>**variation**|
|---|---|---|---|
|**Anhui**|2.74|3.09||
|**Beijing**|2.25|2.32||
|**Chongqing**|3.32|3.48||
|**Fujian**|2.30|2.90||
|**Gansu**|3.44|4.03||
|**Guangdong**|2.67|3.08||
|**Guangxi**|3.13|3.83||
|**Guizhou**|3.73|4.20||
|**Hainan**|2.46|2.87||
|**Hebei**|2.28|2.80||
|**Heilongjiang**|2.29|2.39||
|**Henan**|2.40|2.97||
|**Hubei**|2.44|2.82||
|**Hunan**|2.83|3.06||
|**Inner Mongolia**|2.52|3.10||
|**Jiangsu**|1.89|2.54||
|**Jiangxi**|2.39|2.74||
|**Jilin**|2.38|2.60||
|**Liaoning**|2.27|2.58||
|**Ningxia**|2.85|3.51||
|**Qinghai**|3.47|3.80||
|**Shaanxi**|3.55|4.10||
|**Shandong**|2.44|2.89||
|**Shanghai**|2.09|2.33||
|**Shanxi**|2.48|2.89||
|**Sichuan**|3.10|3.07||
|**Tianjin**|2.25|2.46||
|**Tibet**|5.58|3.93||
|**Xinjiang**|3.49|3.26||
|**Yunnan**|4.28|4.27||
|**Zhejiang**|2.18|2.45||

_Source: DevInfo_ 38
the context of common-property pastures shared by _ejido_ members in Mexico. They show how the traditional governance structures of these collective farms combine with variable costs to affect environmental and sustainability outcomes.

Although, conceptually, it is possible to explain the wide variation in the environmental effects of trade under fairly simple household incentives, the case studies suggest a stronger dynamic link between access and poverty. Descriptively, the households that access natural resources in the case studies are poor in all forms of private assets, including human capital. Earlier studies suggest that, when this is so, common property serves as a substitute for private property in the livelihood strategies of the poor. Components of the strategies include activities in which households extract incomes from common property and often a reliance on common property as a substitute for insurance or precautionary wealth.<sup>8</sup> Studies also suggest that households that access common resource pools are also self-selecting and that households without better alternative opportunities are attracted to open-access areas.<sup>9</sup> Dynamically, the type of poverty associated with common pool resource use can lead to downward spiraling traps across generations, as poor households fail to invest in their children who continue to the use and degrade common pool resources.<sup>10</sup> Policies that directly target the incentive structure of poor households as a mechanism to better manage natural resources are uncommon but some experimental programs are in place. Of particular interest are payment-for-environmental-services (PES) programs that pay smallholders to engage in conservation practices.<sup>11</sup> For example, Zhang, Tu and Mol (2008) analyze efforts in China to convert steeply sloped cropland to forests or grasslands by changing household incentives, a policy that is also discussed in the case study from China.

> 8 See Delacote (2009) for an overview. See also Bene et al. (2009), which discusses how fishing addresses cash needs among poor Congolese.

> 9 For example, see Cartier’s (2009) description of the livelihoods of migrant artisanal ruby and sapphire miners in Madagascar.

> 10 See Ikefuji and Horii (2007) discussion of the dynamic relationships between education, incomes and environmental degradation.

> 11 As background to their survey of 25 PES schemes in 15 countries, Mayrand and Paquin (2004) report finding more than 300 payment programs in 2004. For a discussion of design issues and practical outcomes see, Engel, Pagiola and Wunder (2008), Wunder (2008) and Alix-Garcia, de Janvry and Sadoulet (2008).

6
WPS4119 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# **_Child labor across the developing world: Patterns and correlations_**<sup>�</sup>

# **_Jean Fares and Dhushyanth Raju,_**

## **_The World Bank_**

_World Development Report_

### **Abstract.**

The aim of this study is two-fold. First, based on summary data at the country-level for an unusually large set of developing countries originally obtained from household sample surveys conducted between 1993 and 2003, we construct a detailed profile of child economic activity and child labor, attempting, wherever the data permit, to identify similarities and differences across regions and between genders. Second, we link our country-level data on child economic activity and child labor to country-level indicators of the state of economic and social development in the same time period in order to (1) ascertain if cross-country correlations previously identified in the literature are found in our data and (2) illumine other possible correlations that may exist. As part of this exercise, we examine one important relationship that has thus far not been directly investigated in the literature, namely, the cross-country correlation between child labor, agriculture and poverty.

## World Bank Policy Research Working Paper 4119, February 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> � We are very thankful to Michael Justesen for research assistance, and for UCW project for making the data available to us.

1
# **Can Minimum Wages Close the Gender Wage Gap?**

# **Evidence from Indonesia**

_Mary Hallward-Driemeier_<sup>_1_</sup> _Bob Rijkers_<sup>_1*_</sup> _Andrew Waxman_<sup>_1,2_</sup>

**Key words:** Wage Gaps, Minimum Wages, Indonesia, Manufacturing, Gender

## **JEL Codes:** J71, J31, D22

> _1_ The World Bank, _2_ Cornell University. _*_ Corresponding author: brijkers@worldbank.org.

We would like to thank Luis Serven, David Newhouse, the 2015 World Development Report Team and two anonymous referees for excellent comments. We also thank David Newhouse for generously sharing his minimum wage data and incessant support of this project. Research for this paper has been supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID) through the Strategic Research Partnership on Economic Development.
<!-- Start of picture text --> (1) (2) (3) (4)<br>IDP 0.126** 0.134** 0.132** 0.133**<br>(0.031) (0.016) (0.017) (0.017)<br>Female -0.026 -0.024 -0,022 -0.023<br>(0.364) (0.397) (0.434) (0.434)<br>IDP x Female 0.009 0.008 0.006 0.008<br>(0.842) (0.856) (0.885) (0.863)<br>Household size 0.009 0.005 0.012<br>(0.169) (0.498) (0.106)<br>No. of adults - No. of children -0,029***<br>(0.001)<br>No. of working females - No. of working males 0.050***<br>(0.007)<br>Age Yes Yes Yes Yes<br>Head gender Yes Yes Yes Yes<br>Ward FE Yes Yes Yes Yes<br>N 1417 1417 1417 1417<br>R? 0.160 0.162 0.177 0.172<br><!-- End of picture text -->

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|IDP|0.126**|0.134**|0.132**|0.133**|
||(0.031)|(0.016)|(0.017)|(0.017)|
|Female|-0.026|-0.024|-0,022|-0.023|
||(0.364)|(0.397)|(0.434)|(0.434)|
|IDP x Female|0.009|0.008|0.006|0.008|
||(0.842)|(0.856)|(0.885)|(0.863)|
|Household size||0.009|0.005|0.012|
|||(0.169)|(0.498)|(0.106)|
|No. of adults - No. of children|||-0,029***<br>(0.001)||
|No. ofworking females - No. ofworking males||||0.050***<br>(0.007)|
|Age|Yes|Yes|Yes|Yes|
|Head gender|Yes|Yes|Yes|Yes|
|Ward FE|Yes|Yes|Yes|Yes|
|N|1417|1417|1417|1417|
|R?|0.160|0.162|0.177|0.172|
where ∆indicates deviation of observations from household mean. However, eliminating household fixed effects also eliminate time-invariant observable household characteristics, in my case which include spouses’ bargaining measures. Therefore, to assess intrahousehold bargaining, I can now only test the restrictions in equation 13. Moreover, the data is collected over four rounds within a year, so there will be very limited variation of household size, demographic composition, and per capita landholding (as landholding data is only from first round) across rounds. So the effects of these variables could be imprecisely estimated. The HFE estimates will be also based on a restricted sample of households that have at least one member of each of the age-sex group under consideration. Also, the noise to signal ratio is likely to increase due to differencing.

Finally, as I analyze intrahousehold bargaining for a number of nutrients, the likelihood that no gender differences are found along any margin is very low. So the results might be biased towards finding discrimination. Moreover, individuals’ consumption of one nutrient may affect the consumption of others as typically they consume a food-bundle in which different food items contain different nutrients in different proportions. To address these issues, adequacy ratios for all nutrients under level specification 7 and HFE specification 14 are estimated simultaneously in a seemingly unrelated regression (SUR) framework (henceforth, referred to as _SURLS_ and _SURHFE_ , respectively).

# **5 Data and Descriptives**

I use an innovative household survey data from the International Food Policy Research Institute (IFPRI). The data come from four rounds of surveys at four month intervals during 1996-97 (Round 1: June-September, 1996; Round 2: October-December, 1996; Round 3: February-May, 1997; and Round 4: June-September, 1997) in 47 villages from three sites in four districts of Bangladesh<sup>11</sup> . The survey objective was to evaluate the impact of commercial vegetable production in Saturia (site 1), polyculture fish production in household-owned ponds in Mymensingh (site 2), and polyculture fish production in group-managed ponds in Jessore (site 3) on household income, nutrition, and time allocation. In each site, villages were categorized into program villages (A villages) where the technology was already introduced and comparable control villages (B villages) where the technology was yet to be introduced. From each of these categories, surveyed A and B villages were randomly selected. A household census was conducted in all the randomly selected A and B villages, from which households of two categories (adopters and

> 11Bangladesh is divided into six divisions. A division is then divided into districts. A district is composed of several thanas. Thanas are divided into unions. A union is composed of several villages.

14
Table 3A: Robustness check (1), alternative measure of services development (services value added per worker)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Df|0.050<br>(0.076)||||||
|**Df*SIIf**|<br>**3.667****|||**3.992****|||
||**(1.690)**|||**(1.948)**|||
|Db||0.106<br>(0.068)|||||
|**Db*SIIb**||**1.237****|||**1.247****||
|||**(0.574)**|||**(0.621)**||
|Dfb|||0.132*<br>(0.079)||||
|**Dfb*SIIfb**|||<br>**1.125****<br>**(0.457)**|||**1.160****<br>**(0.504)**|
|log(GDP/capita)|-0.221<br>(0.168)|-0.320**<br>(0.163)|-0.428**<br>(0.170)||||
|TFP|<br>0.069***|<br>0.062***|<br>0.063***|0.063**|0.062**|0.062**|
||(0.022)|(0.021)|(0.021)|(0.029)|(0.029)|(0.029)|
|log(emp)|<br>0.937***|<br>0.934***|<br>0.939***|<br>0.953***|<br>0.950***|<br>0.955***|
||(0.093)|(0.092)|(0.093)|(0.100)|(0.099)|(0.100)|
|SKratio|0.586|0.512|0.514|0.788|0.708|0.689|
||(0.629)|(0.630)|(0.629)|(0.823)|(0.822)|(0.823)|
|K/L|<br>0.069***|<br>0.068***|<br>0.069***|<br>0.070***|<br>0.069***|<br>0.070***|
||(0.026)|(0.025)|(0.025)|(0.026)|(0.025)|(0.026)|
|rwage|<br>-0.002|<br>-0.000|<br>-0.000|<br>-0.001|<br>-0.001|<br>-0.001|
||(0.007)|(0.007)|(0.007)|(0.008)|(0.008)|(0.008)|
|GVC Participation|<br>2.428***|<br>2.433***|<br>2.425***|<br>2.530***|<br>2.512***|<br>2.506***|
||(0.445)|(0.443)|(0.442)|(0.495)|(0.490)|(0.490)|
|Country FEs|Yes|Yes|Yes||||
|Sector FEs|Yes|Yes|Yes||||
|Year FEs|Yes|Yes|Yes||||
|Country*Year FEs||||Yes|Yes|Yes|
|<br>Sector*Year FEs||||Yes|Yes|Yes|
|Observations|5,674|5,674|5,674|5,674|5,674|5,674|
|R-squared|0.573|0.574|0.576|0.591|0.591|0.592|

Notes: The dependent variable is manufacturing export RCA. Df (Db) refers to the share of financial (business) services value added per worker. Dfb is the services value added per worker for both financial and business services sectors combined. SIIf (SIIb) is the ratio of the U.S. embodied domestic financial (business) services to U.S.’ manufacturing value added, averaged over 1995-2007. SIIfb is the combined measure for both financial and business services. All WIOD manufacturing sectors 3-16 are covered (not grouped together). Robust standard errors in parentheses, clustered by country*sector. *** p<0.01, ** p<0.05, * p<0.1.

36
# **3. Econometric Evidence**

While they provide useful information, such anecdotes can only offer suggestive insights. In this paper, we use newly-available data for a more systematic assessment of crosscountry growth in Internet intensity, or Internet subscriptions per telephone mainline. We focus particularly on the impacts of income, govemment policy and urbanization. Widespread concern about the digital divide reflects the view of Duncombe (2000) and others that human resource constraints may significantly reduce Internet use in lowincome countries. If they are correct, then income per capita should be a major determinant of Internet intensity. A second potentially-important factor is government competition policy, which may affect both the supply of Internet services and the intensity of their use by local firms. Our proxy for this variable is the World Bank's rating of competition policy in its Country Policy and Institutional Assessment database. According to the database glossary, this rating "assesses whether the state inhibits a competitive private sector, either through direct regulation or by reserving significant economic activities for state-controlled entities. It does not assess the degree of state ownership p se, but rather the degree to which it may restrict market competition." The index varies from 1 (most inhibition of a competitive private sector) to 6 (least inhibition). As Table 1 shows, it does not automatically consign poor countries to poor ratings. In our econometric exercise, we test the proposition that Internet intensity grows more rapidly in countries with high competition policy scores.

**Table 1: World Bank Index: Govermnent Inhibition of Competition in the Private Sector (1999)**

||**Degree of In**<br>|**hibition of Private-Sec**<br>|**tor Com etition**<br>|
|---|---|---|---|
|**Region**|**Low (4-5)**|**Medium (3)**|**High (1-2)**|
|**Africa**|**Uganda**<br>**Botswana**<br>**Malawi**|**Cameroon**<br>**Ghana**<br>**Nigeria**|**Somalia**<br>**Liberia**<br>**Congo (Rep.)**|
|**Latin America**|**Guatemala**<br>**Brazil**<br>**Peru**|**Bolivia**<br>**Costa Rica**<br>**Venezuela**|**Ecuador**<br>**Paraguay**|
|**Asia**|**Malaysia**<br>**Philippines**<br>_**Bangladesh**|**China**<br>**Indonesia**<br>**India**|**Laos**<br>**Vietnam**|

We also include urban population in the model, to test the hypothesis that network economies cause Internet intensity to grow more quickly in urbanized societies. Although it would be useful to estimate price and quality elasticities for telephone services, we do not have sufficient data to include these variables.

We test the impact of income, policy and urbanization using a Gompertz technology diffusion model first introduced by Chow (1983). In this model, a technology's adoption rate (n) is directly proportional to the log difference between current use and long-run equilibrium use (the latter determined by a set of exogenous variables Xi).

3