rafmacalaba/data-use-mentions-extended huggingface dataset:
commune size is smaller than for nightlights, suggesting that the Landsat measure is rather independent of the factors that drive commune size. In case a researcher is not comfortable using commune size as a predictor, for example in out-of-sample prediction in a different country, the results suggest that Landsat is the more powerful correlate of economic activity.

# **3.2.3 Combining Nightlights and Landsat**

The focus is now on exploring combinations of nighttime light data and Landsat imagery as predictors for the economic outcome variables. Due to their different observation period during the day, the two satellites might pick up very different correlates of economic activity. Combining them as independent sources of variation could increase the predictive power. Initial analysis shows that at the commune level, nighttime lights are positively correlated with NDBI ( _ρ_ = 0.17) but negatively with NDVI ( _ρ_ = -0.05). This would be a natural conclusion if nighttime lights are indicative of urban settlements that are picked up by the NDBI measure whereas rural areas with high share of vegetation emit less light.

Table 2, Panel C summarizes the regression results for the combinations of Landsat indices and nighttime light. Independent of the Landsat indices, the log sum of light measure remains a strong predictor of economic activity with a p-value of less than 0.001. Compared to the base regressions of the two measures in Panel A and B, the coefficients on all three predictors are very similar and nested in each other’s confidence band. This suggests that nightlights and Landsat pick up very different correlates of economic activity. Naturally, model fit measures increase when incorporating both satellite datasets. Percentage increases in R- squared are 23% for enterprises, 37% for employment and 6.7% for expenditure. Combining the two data sources thus improves prediction accuracy for economic differences between communes.

15
exists across municipalities.<sup>5</sup>

# **2.2.1 Validation of the Proxy for Income at the Municipality Level**

To validate the use of NTL data as a proxy for income in El Salvador I first show NTL density is a strong predictor of income at the municipality level. For the unbalanced panel of municipalities for which income data is available, Table 2 shows that, indeed, NTL density does a great job predicting per capita household income, average household income, and per capita household labor income.<sup>6</sup> Even after controlling for municipality and year fixed effects, NTL density and the different measures of income are strongly and positively associated. All these associations are statistically significant under the standard levels of confidence. The implied elasticity from the different specifications in Table 2 ranges between 0.2 and 0.32. Moreover, the results in Table 2 show that using the average of NTL for two consecutive years does a better job than just using

> 5Li et al. (2020) recommends removing non-lit areas with low values of digital numbers. I removed from the analysis three municipalities with an average NTL density below 1.5 for the period of analysis. These excluded municipalities are Cacaopera and Joateca in the department of Moraz ́an, and Nuevo Ed ́en de San Juan in the department of San Miguel.

> 6All income measures come from SEDLAC’s harmonized versions of Encuesta de Hogares de Prop ́ositos M ́ultiples (EHPM). All waves from 2000 to 2018 were available through the World Bank’s DATALIBWEB.

11
land rights. We find a clear difference between rights to transfer land, which increase the propensity to undertake visible investments and thus can be expected to affect land values in case of a sale, and more traditional measures of tenure security, which in addition increase the tendency to manage soil fertility in a sustainable manner. Similarly, households’ awareness of their land rights in a number of dimensions has a significant and large impact on outcome variables that is in many respects equal to the impact of the land rights variables considered in the earlier literature, suggesting that failure to account for this variable may result in biased estimates.

Although a large number of African countries have recently embarked on revisions of their land legislation that aim to increase the level of tenure security enjoyed by customary land owners and women, we know of no study aiming to assess the economic impact, both actual and potential, of such steps. We show that, under reasonable assumptions, use of an objective measure of households’ knowledge of these provisions can be used to approximate the “value” of the legal changes. Results point towards a large

> 7 The 2002 Census of Population and Housing puts the number of rural households at 4.4 million but does not report the share of those who operate agricultural holdings. As we did not have access to disaggregated Census data, we use the 1999 Uganda National Household Survey which indicates that 77% of rural households own or occupy agricultural land. Absence of data for land, including agricultural land that is rented out, owned by urban dwellers, constrains our ability to make inferences beyond rural land that is either owned or occupied, thus providing a conservative estimate of the potential benefits from the land Act.

> 8 As half of our sample comprises of areas where at least some dissemination activities have been undertaken, the level of knowledge about the Land Act in the overall population is likely to be significantly below the figures that were obtained in this sample.

20
intervention by education, income and ethnic affiliation. Column 1 shows that participants with education above the median (completed bachelor’s and master) that were exposed to T3 experienced an increase of 0.55 standard deviation on their “use of public spaces index”. On the other hand, as shown in Column 2, the same intervention resulted in an increase of only 0.23 standard deviation for those with belowmedian education levels (completed primary and secondary). This difference is statistically different at the 5 percent confidence level.

Table 2.A. Subgroup Analysis: Willingness to Use Public Spaces (by education, income and ethnicity)

|VARIABLES<br>Index Public<br>Space (z-|(1)<br>Education<br>above<br>median|(2)<br>Education<br>below<br>median|(3)<br>F-test<br>(p-<br>value)|(4)<br>Income<br>above<br>median|(5)<br>Income<br>below<br>median|(6)<br>F-test<br>(p-<br>value)|(7)<br>Urdu<br>Speaker<br>s|(8)<br>Non-<br>Urdu<br>Speake|(9)<br>F-test<br>(p-<br>value)|
|---|---|---|---|---|---|---|---|---|---|
|score)|||(1)-(2)|||(4)-(5)||rs|(7)-(8)|
|[T1] Urban|0.41***|0.27*
|15.8|
|Domestic water consumption|liter/capita/day|72.4|**109.4**|Na|
|Urban water assets in need of rehabilitation|%|35.5|**42.0**|25.0|
|Revenue collection|% sales|96.0|**50 (est.)**|99.2|
|Distribution losses|% production|33.0|**34.6**|23.1|
|Cost-recovery|% total costs|56.0|**67.7**|80.6|
|Total hidden costs as % of revenue|%|130.0|**153.7**|84.9|
|U.S. cents per m<sup>3</sup>|**Malawi**|Scarce water|resources|Other developing<br>regions|
|Residential tariff|**35.7**||60.26|30–600|
|Nonresidential tariff|**54.0**||120.74|..|

_Source:_ Banerjee and others 2009; Morella and others 2009. Derived from AICD water and sanitation utilities database, http://www.infrastructureafrica.org/aicd/tools/data.

In sanitation, the main policy challenge is how to upgrade traditional latrines to improved standards. While more than 80 percent of Malawi’s population has access to a traditional latrine, little more than 1 percent has access to an improved latrine. While this imbalance is typical across Africa, it is particularly marked in the case of Malawi. This suggests that the main sanitation challenge for the country is how to make it easier for households to improve their latrines. Given that a widespread subsidy policy is likely unaffordable, the right place to begin would be on the supply side, investigating the barriers that prevent the
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Transport

Owing to its strategic location neighboring Nigeria and providing access to the landlocked countries of West Africa (Burkina Faso, Niger), Benin is a natural hub for the region, with the port of Cotonou as the main entrance. Cotonou is also the starting point of the Benin-Niger Railway (OCBN), which extends 578 kilometers into the interior and ends in Parakou (Benin). From there, goods must be trucked another 380 kilometers to the Niger River, increasing transport costs and transportation time. Firms must wait up to a month to get rail service, and two to four months for the return of containers from Niger. It appears that OCBN would lose market share and would not be competitive if additional costs were not transferred to surface transport carriers in the form of official fees (World Bank 2005a).

There are two paved North–South corridors between Cotonou and Niamey (Niger), and Cotonou, Ouagadougou (Burkina Faso), and Bamako (Mali). Those axes are complemented by five East–West corridors, of which two are paved, connecting Togo, Benin, and Nigeria.

Despite its poor and limited infrastructure, in particular in the port sector, Benin’s capacity to efficiently move goods and connect manufacturers and consumers with international markets is among the best in West Africa, trailing only Senegal, according to a recent survey of trade logistics. Feedback on the logistics ―friendliness‖ of the countries in which operators on the ground (global freight forwarders and express carriers) is packaged in the Logistics Performance Index (LPI).<sup>1</sup> Benin’s LPI, at 2.79, is above the regional average of 2.46 (figure 5). The components that received the lowest scores

**Figure 5. Benin’s Logistics Performance Index is one of the highest in West Africa**

<!-- Start of picture text --> 3<br>2.5
- Soares, R. R. and J. Naritomi (2010). Understanding high crime rates in Latin America: The role of social and policy factors. In _The Economics of Crime: Lessons for and from Latin America_ , pp. 19–55. University of Chicago Press.

- Sousa, C., T. I. Herrenkohl, C. A. Moylan, E. A. Tajima, J. B. Klika, R. C. Herrenkohl, and M. J. Russo (2011). Longitudinal study on the effects of child abuse and children?s exposure to domestic violence, parent-child attachments, and antisocial behavior in adolescence. _Journal of interpersonal violence 26_ (1), 111–136.

- Taheri, S. A. and B. C. Welsh (2016). After-school programs for delinquency prevention: A systematic review and meta-analysis. _Youth violence and juvenile justice 14_ (3), 272–290.

- UNICEF (2017). A familiar face: Violence in the lives of children and adolescents.

- Verma, G. and U. S. Tiwary (2014). Multimodal Fusion Framework: A Multiresolution Approach for Emotion Classification and Recognition from Physiological Signals. _Neuroimage 102_ , 162–172.

- Webb, V., L. Nuno, and C. Katz (2016). Influence of risk and protective factors on school aged youth involvement with gangs, guns, and delinquency: Findings from the El Salvador Youth Survey. Technical report, Center for Violence Prevention and Community Safety. Arizona State University.

- WHO (2011). World Health Organization Statistics.

- WHO (2016). _World Health Statistics 2016: Monitoring Health for the SDGs Sustainable Development Goals_ . World Health Organization.

40
pull the consistency limit toward the result that induces the optimal conditional distribution implied by the weighted kernel across all penalties and weights, as judged by the out-of-sample criterion.<sup>1</sup> We use the framework together with out-of-sample selection of fixed effects, to model remotely sensed and reported environmental data for 95 countries that include 85% of the world’s population, 83% of global carbon output and 72% of all forest cover. We find that the production gradually favors conserving the earth’s finite resources as GDP increases, but our results are not supportive of a single Environmental Kuznets Curve. Rather, countries follows individual curves with heterogeneity in the location, shape, and height of tipping points, depending on the composition of the economy. Focusing the policy dialog on a single average curve may thus result in harmful economic outcomes, as the traditional EKC may provide a poor estimate of local conditions. Across different possible paths of development, we do observe that average development is associated with an inverted _U_ -shape in deforestation, pollution and carbon intensities of production units. Per capita carbon emissions, follow a _J_ -curve. We use the models to extrapolate environmental output forward to 2030 under alternative growth scenarios, allowing each country to progress further on it’s specific development path. These results highlight the daunting implications of continued development under the current standards of practice and remind us once more that immediate and sustained global efforts are required on multiple fronts to mitigate forest loss, improve air quality, and shift the global economy to a 2<sup>_◦_</sup> pathway.

The remainder of this paper is as follows. We discuss estimation methods in Section 2. Additional discussion on the role of the penalty in establishing identifiable uniqueness, and the interpretation of the estimation results are provided in our appendix. Section 3 details the data used for our empirical analysis in section 4. Finally in section 5, we use our empirical descriptions to explore the implications of continuation of growth
000505||0.000468|0.000235|
||(0.000215)**||(0.000218)*|(0.000308)|(0.000240)**||(0.000243)*|(0.000344)|
|Share of hhlds with computer||-0.0901|-0.133|-0.166||0.116|0.146|0.169|
|||(0.0914)|(0.0831)|(0.0978)*||(0.0803)|(0.0748)*|(0.0835)**|
|IDN exports per worker *||||-0.000646||||0.00116|
|Share of hhlds with computer||||(0.00104)||||(0.00113)|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.962|0.962|0.962|0.962|0.831|0.831|0.832|0.831|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2004 U.S. O*Net database, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of
# without long-lasting welfare consequences or the need to resort to migration.

This conceptual framework offers valuable insights and constitutes a practical tool for analyzing climate-induced mobility. First, it enables the assessment of the inherently non-linear relationship between climate shocks and mobility. Second, it allows for a specific investigation of the key mediating factors influencing this relationship, which can be attributed to household-specific endowments and contextual elements, as in the NELM framework. Overall, it helps in identifying the heterogeneous effects of climate shocks, where outcomes include not only dichotomic choices but also encompass mobility, immobility, and poverty traps. Lastly, it emphasizes the importance of estimating critical thresholds that determine households' migration responses to climate stress based on their assets, adaptive capacity, and liquidity constraints.

It is important to note that most conventional resilience and vulnerability indicators and traditional econometric tools are not adequately equipped to capture the intrinsically non-linear dynamics of the heterogeneous (im)mobility responses induced by climate shocks depicted in Figure 1 (Montalbano and Romano, 2022). Therefore, novel empirical approaches based on fully flexible and nonparametric methods are needed to estimate the conditional probability of farmers' mobility under climate risk (as explored by Cissé and Barrett, 2018). Our empirical analysis aims to explore these issues using household panel data and methodologies capable of detecting the type of conditional treatment effect heterogeneity that, according to our framework, characterizes the climate-mobility nexus in rural agricultural contexts.

In conclusion, based on the above discussion, we formulate the key hypotheses subject to subsequent empirical testing, presented in Table 1. The table also reports the expected empirical results that one would anticipate _ex ante_ based on the relationships described in the conceptual framework.

10
working-age population with college by their corresponding employment rate, separately for men and women. Similarly, to obtain the share of unskilled workers, we multiply the share of working-age population with less than college in each country by their corresponding employment rate, separately for men and women. We then follow Pennings (2022) and assume that in most countries there are 100 women and 100 men to simply aggregate employment rates for men and women within each level of skill. In Qatar and United Arab Emirates, which have large populations of migrant workers, we adjust for the relative size of the male and female populations.

**Results with the current skill distribution.** We consider first an increase in employment rates to close gender gaps for _both_ skilled and unskilled women, using existing education rates. Recall that from Figure 7, most countries in MENA have larger gender employment gaps for unskilled women, and so closing gender employment gaps will lead to large increases employment among the unskilled, reducing average skill rates.

The left panel of Table 1 presents the impact of this change on the skill distribution _s_ (summarized by the share of skilled workers), and aggregate labor supply ( _L_ ), capital per worker ( _k_ ) and output per worker ( _y_ ). Consider the case of Egypt. Closing the gender gap in Egypt would decrease the share of skilled workers in 22% while increasing the number of workers in 56% (columns 1 and 2). If the stock of physical capital remains fixed, output per person would increase by 42% (column 7). If the stock of capital fully adjusts to the shock, the total effect on standards of living would be only 5pp higher (from 42 to 47%).

**Table 1:** Aggregate effects from closing gender employment gaps relative to the baseline (baseline scenario = 1).

|Country|_s_|_L_|_k_|_y_|_wU_|_wS_|Output<br>per<br>person.<br>Short
*|||
|||(0.02)|||
|Distance to health clinic||<br>-0.15|||
|||(0.001)|||
|Distance to extension services||<br>-0.09**|||
|||(0.0005)|||
|Observed probability %|9.57|<br>9.32|5.99|3.73|
|Predicted probability %|7.49|6.97|4.25|2.92|
|Observations|5256|5247|5224||

Note: Marginal effects in percentage points, they are the change in the probability for an infinitesimal change in each independent, continuous variable and, the discrete change in the probability for dummy variables. These changes are calculated at the mean predicted probability. For example, increasing men’s education from none to primary (keeping all other variables at their actual value) will increase the probability that they become non-farm self-employed from the mean of 7.79 percent to 9.43 percent. An infinitesimal increase in agricultural land holding will increase the probability that men become non-farm self-employed from the mean of 7.79 percent to 8.79 percent. The latter change is not exactly equal to the change in predicted probability corresponding to a one-unit increase in agricultural land holding, but it is very close to it.

Robust standard errors in parentheses, corrected for clustering; ** significant at 5% level; * significant at 10% level. Dependents ratio: Ratio of dependents to total number of household members.

23
education erasing nearly half the baseline marriage penalty. At the same time, educated husbands exert a quantitatively similar effect. Because of positive assortative mating, spousal education levels are highly correlated, necessitating the inclusion of both interactions in a single regression. In this model, the wife’s education remains significant while the coefficient on the husband’s education falls to zero.

Interpreting these results through the lens of our hypotheses, we argue that a woman’s education affects _both_ household gender norms and her outside employment options. In contrast, her husband’s education affects household norms, but does not directly affect her employment prospects. This suggests that outside options at least in part play a role in determining the marriage penalty. Finally, we directly test the role of gender attitudes by interacting the marriage indicator with DHS measures of household gender attitudes. We find strong evidence that women in households with more liberal gender norms experience smaller marriage penalties. The effects of education and social norms appear to be independent, suggesting that both opportunity costs and social norms play a role in driving the marriage penalty.

We contribute to the large and growing literature on gender inequality. The specialization of women in the home and men in the marketplace relates to women’s real or perceived comparative advantage in the home (Becker, 1973). Under specialization, women invest in home specific human capital, while men invest in market specific human capital, raising wages. Empirically, married men have substantially higher wages than unmarried men, even after controlling for human capital and job characteristics (Hersch and Stratton, 2000), suggesting that specialization through marriage makes men more productive. Our evidence on men is consistent with this literature. Nevertheless, despite substantial attention to gender inequality in recent years, there is limited causal evidence about the impact of marriage on women’s labor market outcomes. This paper begins to fill that gap.

Despite rising women’s human capital and narrowing gender gaps in education across the world, female labor force participation remains low
Source: Authors’ calculation using HIES 2016. Note: C, P, B = coffee, pepper, betel.

The crop mix of female farmers contains a greater share of export-oriented crops than that of male farmers, which increases their overall productivity. Figure 2 shows the composition of the crop mix by the farmer’s gender, as measured by the average share of area devoted to each crop. Among male farmers, about 45% of land was accounted for by paddy, which is more than twice as much as the share among female farmers (20%). In addition, the crop mix of female farmers tends to skew toward export-oriented crops: for example, an average of 31% of land area was dedicated to tea cultivation among female farmers, whereas the share was only 14% for male farmers.

Figure 2. Crop mix by gender of farmers <!-- Start of picture text --> banana/fruits horticulture Male horticulture Others Female<br>3% 0% Others banana/fruits2% 5%<br>4%<br>2%<br>coffee,<br>pepper,<br>betel coffee, paddy<br>6% pepper, 20% chilies<br>betel<br>coconut 1%<br>12% paddy 7%<br>rubber2% 45% coconut16% vegetables9% onions0%<br>other<br>tea<br>cereals<br>14%<br>tea 4%<br>rubber<br>31% yams<br>2% 1%<br>yams<br>chilies<br>1%<br>other vegetables onions 1%<br>cereals 8% 1%<br>3%<br><!-- End of picture text --> Source: Authors’ estimations using HIES 2016.

In contrast to the previous literature, initial analysis suggests that female farmers are more productive than male farmers,