rafmacalaba/datause-encoder-data huggingface dataset:
quality. This evidence is quite limited; no studies examine a broad range of soil quality measures or the gender of the owners.

What factors underlie women’s poorer access to land? To begin this discussion, figure 5 shows how individuals in Latin America acquire the land that they own.

<!-- Start of picture text --> Figure 5<br>Form of land acquisition by gender (%)<br>100<br>90<br>Inheritance<br>80<br>70 Market<br>60 Community<br>50<br>State<br>40<br>Other<br>30<br>20<br>10<br>0<br>n = 4,345 34,593 271 411 497 1,593 512 2,547 125 656 310 1,512 247<br>Brazil Chile Ecuador Mexico Nicaragua Peru<br>Source: Deere and Leon 2003, World Development.<br><<<br>Women Men Women Men Women Men Women Men Women Men Women Men Couples<br><!-- End of picture text --> It is evident, first, from the numbers above each country’s name that women are less likely to own land. Second, for the land that women own, inheritance is the major source of land acquisition for both men and women (with the exception of men in Brazil). However, inheritance is much more important for women than for men. Men in almost all countries are more likely than women to receive land from the community, state, and market (with the exception of Ecuador). Thus, although men are more likely to inherit land in absolute terms, in relative terms, inheritance is a more important source of land acquisition for women.

10
destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1.

Theories of work migration predict that individuals move to increase their utility or welfare. The 1995/96 NLSS asked respondents a number of questions regarding their subjective satisfaction level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. We apply the same methodology to these data — i.e., we estimate a regression of the same form as (4) and apply formula (5) to construct an expected subjective satisfaction index. If migrants correctly anticipate the subjective satisfaction they will enjoy from moving to di ff erent destinations, these subjective satisfaction measures may o ff er a better way of controlling for expected welfare di ff erences across destinations.

To control for migration costs, we construct variables proxying for geographical and social distance. For geographical distance between districts, we use the arc distance between the district of origin and each possible district of destination, computed from the longitude and latitude of each districts’ administrative center. We expect the cost and risk of migration to increase with physical distance.

17
The impact of emigration on source country wages: Evidence from the Republic of Moldova<sup>1</sup>

# Lawrence Bouton<sup>a*</sup> , Saumik Paul<sup>b</sup> , and Erwin R. Tiongson<sup>c2</sup>

_abEurope and Central Asia Unit, World Bank, 1818 H Street N.W., Washington DC 20433 United States c World Bank, Asian Institute of Management, and IZA, 123 Paseo de Roxas, Makati City, 1260 Manila, Philippines._ _JEL Classification_ : F22, J23 _Keywords_ : Emigration, wages

* Corresponding author.  Tel.: +1-202-473-9158

> 1 Background paper prepared for the 2011 World Bank Moldova Country Economic Memorandum. 2 Authors’ names are listed in alphabetical order. We received valuable comments and suggestions from Abdurrahman Aydemir, George Borjas, Robert E.B. Lucas, and Madeline Zavodny. We also received useful guidance from Toman Omar Mahmoud at various stages of this research. All the remaining errors are ours. The opinions do not necessarily reflect the official views of the World Bank or its Executive Directors.
Table 5: Entry Decisions and Market Structure Dependent Variable: Indicator for Opening a New Program <u>(Narrow</u> Entrant Definition)

||Private|HEIs|Public|HEIs|
|---|---|---|---|---|
||OLS|2SLS|OLS|2SLS|
||(1)|(2)|(3)|(4)|
|N. of SENA SCPs (lagged)|-0.0004|0.006|0.0018**|0.010|
||(0.0002)|(0.003)|(0.0006)|(0.006)|
|N. of Private SCPs (lagged)|-0.0036***|-0.112***|0.0029*|-0.056|
||(0.0006)|(0.011)|(0.0011)|(0.035)|
|N. of Public SCPs (lagged)|0.0027**|-0.074|-0.0221***|-0.095**|
||(0.0010)|(0.045)|(0.0038)|(0.037)|
|HEI share in Dept.(lagged)|0.1138***|-0.541***|0.0411|-0.158|
||(0.0223)|(0.139)|(0.0247)|(0.109)|
|Field share in HEI (lagged)|0.1610***|0.086***|0.1401***|0.093*|
||(0.0099)|(0.012)|(0.0210)|(0.042)|
|Constant|0.0055|1.811***|0.1413***|1.303***|
||(0.0090)|(0.408)|(0.0277)|(0.336)|
|N. of Observations|49,848|45,581|14,688|13,405|
|Mean of Dep. Variable|0.01|89|0.0|240|
|Weak identifcation tests:|||||
|Cragg-Donald Wald F stat||25.723||5.492|
|Kleibergen-Paap rk Wald F stat||140.835||3.280|
|Anderson-Rubin Wald test||100.61||21.95|
|Overidentifcation test:|||||
|Hansen J statistic||18.404||4.495|

Note: Each column presents coefficients from a regression where the dependent variable is an indicator for whether institution _j_ opens a new program in department _d_ and field _k_ in year _t_ , for the 2004-2019 period. The independent variables are the market number of SCPs offered by private institutions, public institutions, and SENA, plus controls for proxies of the cost of opening a program: HEI’s enrollment share of SCP students in the department (total over all fields) and the field’s enrollment share within the HEI. Columns (1) and (3) show OLS estimates, and columns (2) and (4) show the respective 2SLS estimates. The endogenous variables are the number of SCPs offered by private HEIs, public HEIs, and SENA (first three independent variables). Instruments are the number of programs offered by each provider type in 2004 interacted with lagged GDPby-field and SENA’s budget for the department and year. All the variables indicating number of programs are measured in tens. All explanatory variables are included with a one-year lag. All regressions include department, year, and field-category fixed effects. *,**, and *** denotes significance at the 10, 5 and 1% level. Standard errors are clustered at the institution-year level.

25
# **1. Introduction**

Research by Imbs and Wacziarg (2003) showed that economic development is associated with increasing diversification across industries rather than specialization in lower- and middle-income countries, using both international employment and production data. More recent research established that export diversification across products also appears to increase with the level of development up to a certain point (Klinger and Lederman 2004; Cadot, Carrere and Strauss-Kahn 2010). This article studies one aspect of economic diversification, namely the introduction of new export products during the process of development. Indeed, the probability of introducing a new export product also appears to fall with the level of development, following a similar pattern as export diversification during the early stages of development (Klinger and Lederman 2004).

In light of this descriptive evidence, export diversification might be related to the self-discovery market failure hypothesis of Hausmann and Rodrik (2003a), which suggests that discovering that a product can be profitably produced and exported by a particular country creates knowledge spillovers. Firms that imitate the first mover derive benefits from such discoveries, learning about production costs and foreign demand, yet they pay nothing for it. The prospect of imitation therefore might raise the social returns relative to the private returns of export discoveries. While imitation is clearly desirable from a social viewpoint, the market-failure hypothesis implies that the first mover cannot necessarily appropriate all the value created by their investments in discovery when entry by imitators either reduce the price of the new export or raise its unit costs (when prices are determined in global markets but some inputs are non tradable), hence they might 2
can they be used for making household living standard comparisons.

# **5.2 Gender and time use**

As emphasized in the policy research report of the World Bank (2001), gender differences in the allocation of resources can be observed on many dimensions: health, education, access to productive resources and the allocation of time to work and leisure. The report suggests that differences on these “life” dimensions can be seen as indicators of poverty and inequality in developing economies. An underlying assumption is that, in general, it makes sense to assume that individual preferences for these “quality of life” variables do not differ significantly by gender. If preferences are identical then individuals facing the same resource constraints would chose to be equally healthy and equally educated, and they would choose to work the same total number of hours, either at home or in the market place.<sup>48</sup> Gender differences with respect to these endogenous variables would then reflect unequal “outside” opportunities and/or unequal shares of household full income. While there is an extensive body of research that investigates health and human capital outcomes as measures of poverty and inequality, it is only relatively recently that attention has focused on gender differences in work-leisure allocations as an indicator of inequality.

Evidence on time use presented in the World Bank (2001) Report shows that in most countries women work longer than men, when the “invisible” work done inside the home is included.<sup>49</sup> There is also a high degree of specialization. The report notes that gender differences in education and work begin at an early age, and become more pronounced as the children get older: “In most of the world differences in household expenditure on girls’ and boys’ education tend to increase when children move from primary to secondary school. When girls

> 48 This would seem to be a perfectly reasonable assumption, and has the advantage of avoiding the gender bias associated with assuming that observed differences, particularly in regard to labor supply, reflect differences in preferences by gender.  As acknowledged by Heckman (1993), there may be no such “innate” differences.

> 49 The vast majority of studies for Africa examined by Brown and Haddad (1995) also suggest that women work more hours on average than men.

46
Table 5. Shares of smartphone diary entries lasting for less than 15 minutes in each aggregate activity category, by gender

||Women|Men|
|---|---|---|
|Agriculture|0.17|0.16|
|Nonfarm employment|0.16*|0.14*|
|Unpaid work:|||
|Obtaining services/resources for HH|0.39***|0.44***|
|Cooking, cleaning in house|0.12***|0.20***|
|Care of children, elderly|0.26|0.29|
|Transport|0.41|0.42|
|School/studying|0.11|0.11|
|Leisure|0.24***|0.26***|
|Community activities|0.15|0.15|
|Personal care|0.30***|0.40***|
|Eating/drinking|0.38***|0.42***|

<u>Notes:</u> (1) Based on 138,609 activities recorded across Days 3-10 of the survey period (data from deployment on Day 2 were dropped).

- (2)  T-tests of equality of means conducted across men and women. ***p<0.01, **p<0.05, *p<0.10.  Sleep was excluded from the table.

Table 6 presents individual-level, ordinary least squares (OLS) regressions of treatment effects of the recall arm on daily time allocation (unconditional minutes),<sup>16</sup> for the pooled matched sample across days 5, 8, and 10: i β β i 1 2 i i (1) ii ii ii ii yy = αα+ β ββ+ γγXX + δδ AA + δδ DD + εε In Equation (1), i represents average daily time in each of the activity categories in Table 3, for individual _i_ in household _j_ and enumeration area _k_ ; _Recall_ is the dichotomous variable that is equal to 1 if the individual ii yy was part of the recall treatment; _X_ is a vector of individual- and household-level characteristics, as included in Appendix Table A2; _A_ and _D_ represents the fixed effects for enumeration areas and days of reporting, respectively; and is the error term. The regressions were run separately for the total, men, and women samples, along with an additional disaggregation by urban/rural, in view of geographic differences in εε employment and transport.

The results in Table 6 reveal similar patterns observed earlier; participation is lower in the recall sample for economic activities in agriculture, unpaid work (especially care work), studying, community activities – but higher in the recall arm for leisure, personal care, sleep, and eating/drinking, as well as some unpaid domestic activities for women, focused mainly on cooking and cleaning. The reported time was lower in the recall arm concerning (a) agricultural activities of both men and women in urban areas and (b) nonfarm employment of urban women and rural men. The negative recall treatment effects on participation in unpaid domestic and care work stemmed mainly from rural areas, with some significant negative effects for men in urban areas on care work and obtaining services/resources for the household. Despite this variation in participation, however, treatment effects on time spent in different activities are generally higher in the recall arm, except for time in transport and a subset of unpaid domestic activities for men

> 16 Descriptive statistics on conditional minutes are in Appendix Table A5.

16
|**Table 1: Sample summary statistics**|**Bench**|**markyear**|||**Total**|
|---|---|---|---|---|---|
|**1988**<br>|**1993**|**1998**|**2003**|**2008**||
|**Number of surveys**<br>75|115|121|133|121|565|
|**Years between survey year and benchmar**|**k year (%, by**|**benchmar**|**k year)**|||
|-2<br>12.0|9.6|9.1|7.5|7.4|8.9|
|-1<br>26.7|18.3|14.9|18.8|11.6|17.4|
|0<br>29.3|34.8|41.3|30.1|65.3|40.9|
|1<br>18.7|20.9|18.2|21.1|11.6|18.1|
|2<br>13.3|16.5|16.5|22.6|4.1|14.9|
|Within +/- 1 of benchmark<br>74.7|73.9|74.4|69.9|88.4||
|**Income vs. Consumption surveys (%, by b**|**enchmark ye**|**ar)**||||
|**Consumption**<br>33.3|46.1|48.8|57.1|55.4|49.6|
|**Income**<br>66.7|53.9|51.2|42.9|44.6|50.4|
|**GDP (% of regional GDP represented in the**|**database)**|||||
|**World**<br>90.6|97.0|96.5|95.9|93.0|94.6|
|**Mature economies**<br>95.7|99.9|99.8|98.4|96.9|98.1|
|**China**<br>100.0|100.0|100.0|100.0|100.0|100.0|
|**India**<br>100.0|100.0|100.0|100.0|100.0|100.0|
|**Other Asia**<br>90.5|96.1|98.0|97.3|97.1|95.8|
|**M. East & N. Africa**<br>52.1|55.4|45.4|48.5|22.3|44.7|
|**Sub-Saharan Africa**<br>21.9|82.9|78.3|81.8|77.4|68.5|
|**L. America & Caribbean**<br>94.8|98.3|99.0|98.9|98.4|97.9|
|**Russia, C. Asia, SE Europe**<br>50.7|94.2|94.3|100.0|90.8|86.0|
|**Population (% of regional population repr**|**esented in th**|**e database)**||||
|**World**<br>81.1|92.3|91.9|93.6|90.6|89.9|
|**Mature economies**<br>95.0|99.9|99.6|96.7|97.0|97.6|
|**China**<br>100.0|100.0|100.0|100.0|100.0|100.0|
|**India**<br>100.0|100.0|100.0|100.0|100.0|100.0|
|**Other Asia**<br>74.6|85.4|88.7|88.7|88.7|85.2|
|**M. East & N. Africa**<br>60.4|69.5|63.6|68.4|47.8|61.9|
|**Sub-Saharan Africa**<br>28.5|72.9|68.0|80.2|74.1|64.7|
|**L. America & Caribbean**<br>88.2|92.9|94.9|96.4|94.4|93.4|
|**Russia, C. Asia, SE Europe**<br>28.4|87.4|87.4|99.4|84.4|77.4|
|_Notes: Observations are weighted by popul_<br>_otherwise unweighted. The last column is th_<br>_values_|_ation size in t_<br>_e (unweighte_|_he computat_<br>_d) average_|_ion of glob_<br>_over the 5_|_al coverage,_<br>_benchmark y_|<br>_ear_|

lower than the average Gini over the income surveys (48.44%). This is more than the Gini adjustment proposed by Li et al (1998) of 6.6 Gini points.

33 For example, the income data could be scaled down by savings in the national accounts (i.e. the gap between national consumption and national income) (Deaton, 2001). We did not adopt such an approach because of (1) the issues with national accounts data discussed above and (2) evidence that the savings rate is not invariant with income (Dynan et al., 2004). Chen and Ravallion incorporate such an adjustment in early estimates of global poverty but abandon it in later work (Chen and Ravallion, 2004).

18
Policy Research Working Paper 6931

# **Abstract**

Using data from an experimental supportive intervention to India's malaria control program, this paper studies the impact of leveraging local non-state capacity to promote mosquito net usage and recommended fever care-seeking patterns. The supportive activities were conducted simultaneously by three nongovernmental organizations in two endemic districts in the state of Orissa. The study finds that program impact varied significantly by location. Examining three potential sources of this variation (differential population characteristics, differential health worker characteristics, and differential implementer characteristics), the analysis provides evidence that both population and nongovernmental organization characteristics significantly affected the success of the program. The paper discusses these findings as they relate to the external validity of development policy evaluations and, specifically, for the ability of the health system to benefit from limited non-state capacity in under-resourced areas.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ekandpal@worldbank.org.

_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
# _4.2 The Amount of Trade Affected_

Tables 4a and 4b show separately for LDCs and non-LDCs the amount of trade in 2002 that entered the U.S. market with GSP or AGOA preferences as well as trade in zero duty products and exports on which the full MFN duty is paid. The table shows that _in 2002 LDCs without clothing benefits did not export any products under AGOA_ . This reflects that AGOA led to very little additional market opening for these countries relative to the preferences already available under the GSP. It is worth noting that these countries are currently not exporting to the U.S. products that are excluded from preferences.

|**Table 4a: Value of LDCs Exports to the U.S. under AGOA in**|**2002 ($mill)**<br>LDCs without apparel<br>benefits|LDCs with apparel benefits|
|---|---|---|
|Total Value of Exports to US|280|683|
|Value for Exports RequestingPreferences|146|454|
|_GSP_|_146_|_17_|
|_Total AGOA_|_0_|_437_|
|_AGOA apparel_|_0_|_406_|
|Exports for which MFN rate is zero|122|198|
|Exports ofproducts excluded from Preferences|1|0|
|Exports Eligible for Preferences but Preferences not Requested|11|30|
|Countries|Benin, Central African Rep.,<br>Chad, Dem. Congo, Djibouti,<br>Gambia, Guinea, Guinea-<br>Bissau, Mali, Mauritania,<br>Niger, Rwanda, Sao<br>Tome&Prin., SierraLeone|Cape Verde, Ethiopia,<br>Lesotho, Madagascar,<br>Malawi, Mozambique,<br>Uganda, Tanzania, Zambia|

## Data source: USITC dataweb

Note: The classification of countries in the table is that pertaining in 2002, to allow for a match with the available trade data. It should be noted that subsequently the Central African Republic has been removed whilst Angola has been added to the list of AGOA eligible countries. Benin (2004), Mali, Niger, and Rwanda (2003) have also become eligible for apparel benefits since 2002.

In terms of development the key sectors here are the agricultural products subject to tariff rate quotas, textiles and clothing. Preferences on clothing products are available under AGOA. Thus, key issues for these countries are whether to take the necessary steps to request eligibility for clothing preferences and to consider how to improve the domestic investment climate to allow firms to better take advantage of existing opportunities created by AGOA and new opportunities for diversification that would arise with preferences for clothing. For these countries AGOA will be of value if in the future it provides for the export of a greater range of products.

13
in the private formal sector and transfers from the government, TRSOC:<sup>14</sup> Total pension outlays are given by the product of an average benefit, BENEF , which is fully indexed (with a one-period lag) on the price index for the urban formal sector, PF : The number of pensioners at the current period, NUMPEN, consists of last period’s “stock” (adjusted for a fixed mortality rate), plus the flow of skilled and unskilled workers retiring in each period, NEWPEN: NUMPEN = (1 − δN )NUMPEN−1 + NEWPEN, where δN is the proportion of pensioners who die in each period. The number of new pensioners is defined as NEWPEN = δ<sup>U</sup> NP<sup>(U</sup> P,<sup>d</sup> −1<sup>+ UG,−1) + δ</sup> NP<sup>S(S</sup> P,<sup>d</sup> −1<sup>+ S</sup> G,<sup>T</sup> −1<sup>).</sup> This equation indicates that at the beginning of each period a fixed fraction δ<sup>U</sup> NP<sup>(respectively δS</sup> NP<sup>) of employed unskilled (respectively skilled) work-</sup> ers retire from the formal sector labor force.

Thus, total pension outlays are given by If we assume that the pension fund cannot borrow directly from the public, and that its accounts must be balanced, government transfers are determined from (26), given (28): Alternatively, if government transfers are considered fixed, the budget constraint can be used to determine the pension benefit, BENEF , after dropping (27):

> 14Note that, in our model, only employers pay payroll taxes that serve to finance the pension system. In practice, workers also make significant contributions. However, we abstract from these contributions, given that the focus of our simulation experiments is on changes in payroll taxes paid by firms.

18
# Figure 2: The Formation of the Traditional Economic Structure in China

Exogenous Develooment StrateAy Endogenous Economic Structure and Economic Endowments

## EConomfc Performance

Heavy-IndustryDistorted Macro-Policy Environment Planned Allocation Structurat Imbalance Oriented Development System Strategy Low Interest-Rate Policy . Overvalued Exchange-Rate Policy Low Input-Price Policy Capital-Scarce Low Wage-Rate Policy Micro-Management System Low Technical Efficiency Agrarian Low Living-Necessity Price PoLicy ._ L -ctes Economy Puppet-like State Enterprises CoLleitive Farms depress wage rates. As a result, although real GNP per capita tripled between 1952 and 1978, the real wage rate was kept almost constant, increasing only 10.3 percent, during the same period (China State Statistical Yearbook 1987, p. 151).