rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6809

# **Abstract**

This paper uses data from the General Household Survey Panel 2010/11 to analyze differences in agricultural productivity across male and female plot managers in Nigeria. The analysis utilizes the Oaxaca-Blinder decomposition method, which allows for decomposing the unconditional gender gap into (i) the portion caused by observable differences in the factors of production (endowment effect) and (ii) the unexplained portion caused by differences in returns to the same observed factors of production (structural effect). The analysis is conducted separately for the North and South regions, excluding the west of the country. The findings show that in the North, women produce 28 percent less than men after controlling for observed factors of production, while there are no significant gender differences in the South. In the decomposition results, the structural effect in the North is larger than the endowment at the mean. Although women in the North have access to less productive resources than men, the results indicate that even if given the same level of inputs, significant differences still emerge. However for the South, the decomposition results show that the endowment effect is more important than the structural effect. Access to resources explains most of the gender gap in the South and if women are given the same level of inputs as men, the gap will be minimal. The difference in the results for the North and South suggests that policy should vary by region.

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 author may be contacted at goseni@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
**Table A.3.9: Coverage and number of countries included in each simulation**

||Simula<br>(LAYS<br>|tion 1<br>based)<br>|Simulatio<br>(PISA and PISA- <br>|ns 2 and 3<br>D based, all years)<br>|
|---|---|---|---|---|
|Aggregate|N countries|Coverage|N countries|Coverage|
|EAP|24|99%|15|94%|
|ECA|46|93%|45|91%|
|LAC|20|91%|16|95%|
|MNA|18|94%|10|39%|
|NAC|2|100%|2|100%|
|SAR|6|100%|1|76%|
|SSF|41|97%|3|3%|
|Grand Total|157|97%|92|75%|
|HIC|50|99%|47|98%|
|UMC|42|97%|32|91%|
|LMC|40|99%|13|68%|
|LIC|25|92%|0|0%|
|Grand Total|157|97%|92|75%|
|Part I|44|96%|41|94%|
|IBRD|56|98%|44|92%|
|IDA/Blend|57|96%|7|4%|
|Grand Total|157|97%|92|75%|

Source: Authors’ calculation. Coverage of simulation 1 in terms of the population ages 4-17. Coverage of simulations 2 and 3 in terms of share of the enrollment in lower secondary.

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**Figure 1** : Distribution of inpatient beds* by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Kenya Sierra Leone<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Inpatient beds Inpatient beds<br>Hospitals Non-hospitals Hospitals Non-hospitals<br>Tanzania<br>0 20 40 60 80 100<br>Inpatient beds<br>Hospitals Non-hospitals<br>.15<br>.04<br>.03 .1<br>Density .02 Density<br>.05<br>.01<br>0 0<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text -->

* For clearer visualization, the x axis has been truncated at 100 beds for these figures, but all three distributions of hospital beds have a long right tail

While simple bed counts are one measure of bandwidth, inpatient bed occupancy rates are a de facto metric of surge capacity. Specifically, the inpatient bed occupancy rate indicates whether facility beds are full under normal circumstances, which can begin to shed light on the extent to which the facility can accommodate more inpatients during a pandemic. Combining information from the SDI on the number of inpatient beds in the facility with information on the number of inpatient bed-days spent in the facility over the past three months (Appendix Table 2) yields such an estimate of the occupancy rate. Overall, occupancy rates of inpatient beds in facilities are low: at the national level, estimates from Sierra Leone suggest a 30% occupancy rate of inpatient beds, 20% for Tanzania, and 22% for Kenya. Figure 2 converts these estimates into average numbers of occupied and unoccupied beds in a given facility on a given day. While the sample of hospitals from Tanzania includes only 26 facilities, there may be an average of 70 empty of the nearly 100 inpatient beds. Encouragingly, these numbers of “unoccupied inpatient beds” are high across the board, particularly among hospitals. This suggests that facilities have bandwidth to 7
equilibria and allows for the effect of factor accumulation on a given sector’s output to vary with a country’s endowments. Schott[33] uses value-added, capital stock, and employment data from UNIDO for up to 45 developed and developing countries across 28 manufacturing industries in 1990. The findings reject the single-cone framework in favor of a two-cone model with laborabundant countries producing relatively little of the most capital-intensive goods.

Romalis[31] examines how factor proportions determine the structure of commodity trade by integrating a multicountry version of the HeckscherOhlin model with a continuum of goods with Krugman[22]’s model of monopolistic competition and transport costs. His model assumes that there are no factor intensity reversals and that factor shares are fixed within industries and across countries. Two predictions emerge from this framework. First, countries capture larger shares of world production and trade of commodities that more intensively use their abundant factors. Second, countries that rapidly accumulate a factor see their production and export structures systematically shift towards industries that intensively use that factor.

In the second strand of the trade literature, Harrigan[17] provides the first empirical test of the factor proportions theory in a framework that accounts for international technology differences. The author uses manufacturing output shares and factor endowments data for up to 10 developed countries across 7 industries with data from 1970-1988. The most reliable inferences across sectors that can be obtained from this study are roughly consistent with Leamer[24] and Harrigan[16]. Capital and medium-educated workers are associated with larger GDP output shares in most of the seven industries (Food, Apparel, Paper, Chemicals, Glass, Metals and Machinery); while non-residential construction and high-educated workers are related to lower output shares.

Harrigan[17] improves substantially upon previous empirical frameworks, but his OECD data have little cross-country variation as high-income countries have similar factor endowments and sectoral output shares. To overcome this drawback, Harrigan and Zakrajsek[18] work with a larger sample, which includes data for up to 28 OECD and non-OECD countries and 12 industries from 1970-1992. Their evidence arguably supports the neoclassical theory.

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# **6.3 Fast Wage Growth after the Floods**

Section 6.2 showed that wages in Kerala increased relative to wages in unaffected bordering districts in Karnataka and Tamil Nadu in the post-flood months. In line with the results on aggregate economic activity, we hypothesize that a reconstruction boom increased wages. Kerala is heavily dependent on migrant labor from the rest of the country, particularly for menial jobs across sectors.<sup>26</sup> Hence, higher aggregate demand due to the reconstruction efforts may have increased wages.

Unfortunately, to the best of our knowledge, no data are available to test this hypothesis directly. Thus, we need to rely on indirect evidence. If our hypothesis is correct, we expect a tightening of labor market conditions in the post-flood period, that low-income earners benefited more, and that wages increased most where government relief efforts were strongest. In the following, we test these three implications one by one.<sup>27</sup>

# _Tightening Labor Market Conditions_

We use information on the number of households that actually provided employment under the MGNREGA scheme and the number of households that demanded employment by registering for the scheme. MGNREGA only covers rural employment under the government’s employment guarantee scheme, but it still provides information on labor market conditions. The need to repair rural infrastructure after the disaster is likely to have increased MGNREGA employment. There is usually a positive difference between those registered and those actually working. If this difference widens, it suggests that there is an increasing excess supply of labor. If the difference decreases, it suggests a tightening of rural labor markets. We compute the difference for each month for each of the districts and run the same difference-in-difference specification as before with districts in Kerala as the treatment 26A study sponsored by the State Planning Board of the Government of Kerala estimates that migrant workers from other states in the country constituted 26.3 percent of the total workforce in Kerala in 2017-18. The largest migrant receiving sector was the construction sector. See Parida and Raman (2021) for more details.

> 27We also analyzed whether agricultural yields in Kerala differed from those in unaffected areas after the disaster, but do not find any evidence that they did.

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grammatical gender explains cross-country variation in female labor force participation, but does not explain most of the observed cross-country variation in women’s educational attainment.

# **4.4 Gender Attitudes**

Our main measure of gender norms is a Gender Attitudes Index that we construct by taking the first principal component of the eight World Values Survey (WVS) questions related to gender. In Figure 3, we plot the cross-country relationship between each of these questions and the proportion of a country whose native language is a gender language. The prevalence of gender languages predicts responses to seven of the eight WVS questions. For example, WVS respondents from countries where gender languages are dominant are more likely to agree with the statement “When a mother works for pay, the children suffer” or “When jobs are scarce, men should have more right to a job than women”. The country-level prevalence of gender languages is also associated with an increased likelihood of believing that men make better business executives and political leaders than women, that being a housewife is as fullfilling as paid work, that university education is more important for boys, and that wage inequality within the household is likely to cause conflict.

In Table 4, we confirm the association between the prevalence of gender languages and our summary index of gender attitudes in a regression framework. After controlling for continent fixed effects and country-level geography, the coefficient estimate suggests that grammatical gender is associated with a decline in support for gender equality that is equivalent to approximately one standard deviation in our index of gender attitudes. To put this in context, the estimates indicate that grammatical gender alone could explain the gap in gender attitudes between Ukraine (at the 55<sup>_th_</sup> percentile) and Trinidad and Tobago (at the 80<sup>_th_</sup> percentile). Thus, the estimated association between grammatical gender and non-grammatical gender attitudes is both statistically and culturally significant.

If grammatical gender shapes gender attitudes, we would expect it to impact the beliefs of both men and women. In Table 5, we show that — as expected — we observe a negative 22
Regardless of potential endogeneity associated with the criteria selection, the simple probit model is performed; the results are shown in Table 12. The main results are broadly unchanged, but the coefficient of the dummy variable for multidimensional auctions is considerably greater that the baseline estimates in Table 11. Because of endogeneity, the simple probit coefficient is considered upward biased. Nonetheless, the basic implication still holds that applying the multidimensional mode would increase a risk of renegotiation and anti-corruption measures could mitigate such a risk.

<u>Table 12. Simple Probit Estimation of Renegotiation Equation</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Road length|0.0025 <sup>***</sup>|0.0018 <sup>**</sup>|0.0024 <sup>***</sup>|0.0024 <sup>***</sup>|
||(0.0009)|(0.0008)|(0.0009)|(0.0009)|
|Track length|-0.0009 <sup>***</sup>|-0.0002 <sup>*</sup>|-0.0010 <sup>***</sup>|-0.0010 <sup>***</sup>|
||(0.0003)|(0.0001)|(0.0003)|(0.0004)|
|Contract duration|-0.0548 <sup>*</sup>|-0.0201|-0.0507 <sup>*</sup>|-0.0491 <sup>*</sup>|
||(0.0293)|(0.0199)|(0.0267)|(0.0261)|
|Govt effectiveness|-1.1320|-0.3597|-0.4268||
||(2.0238)|(1.1329)|(0.6141)||
|Regulatory quality|1.0815|-1.6088||-0.4186|
||(2.8335)|(1.5981)||(0.8489)|
|Control of corruption|-4.6711 <sup>***</sup>||-4.7495 <sup>***</sup>|-4.7673 <sup>***</sup>|
||(1.1297)||(1.2218)|(1.2704)|
|_D MULTI_|6.0272 <sup>***</sup>|1.0555 <sup>***</sup>|6.2162 <sup>***</sup>|6.1290 <sup>***</sup>|
||(1.6694)|(0.3928)|(1.7825)|(1.9440)|
|Constant|-0.6219|1.4124|-0.0678|0.1531|
||(1.6651)|(1.1071)|(0.7049)|(0.8784)|
|Obs.|124|124|124|124|
|Wald test statistics|33.94|27.19|29.96|28.66|
|Pseudo R-squared|0.4220|0.1706|0.4208|0.4194|

Note: The dependent variable is the dummy variable for the incidence of renegotiation. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

One remaining empirical concern may be that the unemployment proxy could not capture the socio-economic and political conditions related to the auction design decision in infrastructure concession contracts. One might think that heterogeneity among countries would be underestimated in our model and that unobservable country-specific characteristics could overwhelm the effect of unemployment. Table 13 shows the estimated first stage selection model with different socio-economic proxies, which are country-specific in our data. The unemployment rate is only the statistically significant determinant; the Gini coefficient and poverty headcount ratio (US$1 per day) have been found insignificant.

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competition (Borjas 2014). On the other hand, cultural similarity and shared history between the two groups (such as by being part of the same country in the early 19<sup>th</sup> century) could lead to greater solidarity—and less restrictive preferences—in light of the serious humanitarian situation experienced by Venezuela.

Using a pre-registered conjoint survey experiment fielded online during March 2021 (N=2,508 adult Colombian nationals), we first address how Colombians perceive different packages of policy options towards Venezuelan migrants, and which dimensions they are more willing to support in the aggregate. Conjoint designs, as they have been generally used across political and economic settings, involve asking respondents to evaluate a profile or object that contains randomly varied features (Bansak et al. 2021). In our experiment, we presented respondents with sets of hypothetical policy packages (the ‘treatments’) comprising six dimensions available to policymakers and which are relevant to public debate in both Colombia and in many host countries globally: (1) labour market access, (2) location restrictions, (3) public service access, (4) family reunification, (5) numerical limits, and (6) length of residency.

This design reflects how people may encounter policy proposals as several options bundled together by legislators or parties, while also reduces social desirability bias by allowing respondents to justify their choices to themselves along any of the given dimensions. It is also resource efficient because it enables researchers to simultaneously test many more dimensions than would typically be possible using conventional designs that only vary a few elements in each treatment without resorting to unfeasibly large sample sizes.

Next, we look for heterogeneous effects involving three factors: the role of economic and humanitarian priorities, perceptions on the Venezuelan crisis, and personal contact with Venezuelans. The political economy literature on preference formation indicates that economic priorities more strongly relate to policies that are more likely to be perceived as protecting the labour market from foreign worker competition. Meanwhile, humanitarian priorities relate to policies that are more likely to be perceived as protecting the wellbeing of migrants (Bansak et al. 2016; Giray-Aksoy and Ginn 2021; Hager and Veit 2019). In forced migration situations, the humanitarian context and past conflict experiences may foster higher levels of empathy and altruism (Adida et al. 2018; Hartman and Morse 2017).

Finally, we explore whether heterogeneity also exists with respect to social contact. Increased _meaningful_ contact with migrants can foster more positive attitudes and increase understanding of the migrant community and their challenges and therefore further support for policies seen as humanitarian (Betts et al. 2021; Ghosn et al. 2019; McLaren 2015). By 3
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**Table 3: Household level characteristics by semi-formal credit market participation status**

||**E**|**ntire samp**|**le**||**Gojjam onl**|**y**||**Wollo only**||
|---|---|---|---|---|---|---|---|---|---|
||<br>**Total**|<br>**Cons.**|<br>**Uncons.**|**Total**|<br>**Cons.**|<br>**Uncons.**|**Total**|<br>**Cons.**|<br>**Uncons.**|
|**Credit market participation**||||||||||
|Received loan?|0.27|0.15|0.52|0.32|0.20|0.58|0.23|0.09|0.47|
|Amount of credit received (B)|244.57|105.28|524.74|200.18|117.17|386.94|283.45|94.13|629.81|
|<br>Loan from semi-formal sector?|0.22|0.08|0.50|0.26|0.12|0.57|0.19|0.06|0.44|
|If yes, amount received (B)|220.41|76.82|509.23|166.77|77.98|366.55|267.40|75.73|618.04|
|Loan from informal sector?|0.07|0.07|0.06|0.09|0.10|0.07|0.04|0.04|0.05|
|If yes, amount received (B)|24.16|28.46|15.50|33.41|39.19|20.39|16.06|18.40|11.77|
|**Endowments**||||||||||
|No. of male adults|1.77|1.72|1.87|1.76|1.70|1.89|1.78|1.74|1.86|
|No. of female adults|1.63|1.61|1.67|1.59|1.57|1.66|1.66|1.65|1.68|
|No. of oxen owned|1.49|1.38|1.70|1.81|1.63|2.21|1.20|1.15|1.31|
|Part. in off-farm activities?|0.50|0.48|0.53|0.36|0.38|0.33|0.62|0.58|0.68|
|Non-farm income (B)|699.54|607.93|883.81|399.46|406.46|383.69|962.39|796.88|1265.17|
|Head’s age (years)|50.92|51.75|49.23|49.84|50.31|48.78|51.86|53.10|49.57|
|Head’s education (years)|1.01|0.90|1.23|0.75|0.79|0.65|1.24|1.01|1.67|
|<br>Head is literate|0.35|0.32|0.41|0.36|0.33|0.41|0.34|0.30|0.41|
|No. of members below age 15|2.27|2.20|2.41|2.46|2.42|2.56|2.11|2.00|2.30|
|**Land ownership and plot chara**|**cteristics**|||||||||
|Own land in hectares|1.33|1.33|1.35|1.73|1.72|1.75|0.99|0.95|1.04|
|Total operated area (ha)|1.48|1.42|1.59|1.91|1.83|2.10|1.09|1.03|1.21|
|Crop output per hectare (B)|3017.99|2943.54|3167.41|2690.37|2480.37|3162.88|3304.94|3377.92|3171.41|
|Proportion good soil quality|0.56|0.56|0.57|0.43|0.43|0.43|0.67|0.67|0.68|
|Proportion medium soil quality|0.32|0.32|0.32|0.36|0.35|0.38|0.28|0.28|0.27|
|<br>Proportion flat|0.72|0.72|0.72|0.69|0.70|0.68|0.75|0.75|0.74|
|Proportion gently sloped|0.23|0.22|0.23|0.25|0.24|0.26|0.21|0.21|0.21|
|Proportion irrigated|0.07|0.07|0.07|0.04|0.04|0.03|0.11|0.11|0.11|
|<br>Years possessed|19.25|19.45|18.86|18.74|18.87|18.43|19.71|19.99|19.18|
|<br>**Identifying instruments**||||||||||
|<br>Value of household assets (B)|8104.51|7142.56|10039.36|4740.12|4406.16|5491.54|11051.33|9708.87|13507.27|
|<br>Value of livestock (excl oxen)|3622.00|3256.67|4356.81|4326.31|3777.50|5561.12|3005.10|2768.20|3438.48|
|No. of adult children|1.34|1.37|1.27|1.40|1.39|1.42|1.28|1.34|1.16|
|A member held /party position|0.24|0.20|0.32|0.23|0.20|0.32|0.24|0.20|0.31|
|<br>Member of service coop|0.20|0.17|0.27|0.20|0.18|0.24|0.21|0.17|0.29|
|Member of microfinance inst.|0.14|0.10|0.24|0.13|0.09|0.22|0.16|0.10|0.25|
|Number of observations|1587|1060|527|741|513|288|846|547|299|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07.

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The estimate for `proximity of previous event in neighborhood, 1st order' is anomalous, on the other hand. The estimate is negative, indicating that the risk of conflict events decrease for a time in the immediate neighborhood of an event. The reason why the estimate turns out this way is not clear, but is most likely due to collinearity.

Both population variables are also associated with the road network variable, since population totals figures were interpolated assuming that population distribution and densities are correlated with accessibility. This introduces another potential for collinearity. The estimate for the road variable pull in the same direction, however: Civil war events are most frequent in the most accessible squares containing primary roads _and_ high population concentrations.

# **5 Conclusion**

This paper represents the first attempt to analyze the ACLED data (Raleigh & Hegre, 2005), which codes the dates and exact locations of individual events within a set of internal armed conflicts. In the paper, we have also developed a statistical tool to handle both spatial and temporal dependence, and to allow analyzing the dynamics internal to civil wars. Since we analyze both initial event in a given conflict and the diffusion in time and space given by the subsequent events, our analysis bridges the gap between studies of the onset and the duration of civil war. The unit of analysis in the paper is a 8.6x8.6km square of territory, for which we have coded data on conflict events, population, quality of road network, and location relative to the borders and capitals of countries.

The analysis has illuminated some aspects of the relationship between country size and the risk of internal conflict that cannot be analyzed in national-level studies: Conflict events tend to have frequencies in proportion to the size of the population in a given location, as indicated by a `per capita propensity' hypothesis. However, we also found evidence supporting the hypothesis that conflicts happen predominantly where populations cluster locally. In the sample of Central African conflicts we have studied, conflict events happen more frequently in locations far from the capital of a country and close to the border.

We also find the importance of distance to be considerably less than that of population mass, except when combined with population concentration. Conflict events 32