rafmacalaba/data-use-mentions-extended huggingface dataset:
# Conclusion

Extreme remoteness and widely varying access to key services are facts of life in Nepal and acknowledged strong determinants of development outcomes. Yet despite this recognition, in practice policy makers and researchers in Nepal rarely quantify these factors and rely on anecdotal, household‐centric or heavily simplified representations of remoteness. As noted in the literature review, existing models are unsatisfactorily abstracted from the travel time‐ based measures commonly employed by Nepal’s government and citizens themselves, while reported travel times from survey data are difficult to generalize because of sampling constraints and highly idiosyncratic besides. This has limited the breadth and rigor of quantitative research based on remoteness and accessibility in Nepal, despite its uniquely strong role in shaping Nepal’s society, governance and economy. Other methods of quantifying accessibility exist, some promising and powerful, but none offered the same mix of analytical power, national coverage, scalability and communicative simplicity of a cost time model, particularly given available data.

The diversity of model inputs and challenge of collecting sufficient data frustrated previous attempts at geospatial modeling of travel times in Nepal. Addressing these challenges required a combination of technical research, consultations with interested partners, persistence in data gathering and tedious data cleaning better suited to our long‐term, country office‐based program than a typical short‐term research project. We were greatly assisted by World Bank staff on the ground and collaboration with the GeoNode open geospatial data sharing project, for which World Bank staff had amassed a considerable amount of geospatial data for Nepal. Despite the overall success of the effort, it must be noted that we could only incompletely collate several important data sets, most notably for education and markets, and therefore exempted them from this analysis.

A mixture of analysis imperatives and policy opportunities guided our research. As this research grew out of the HRVS survey work, we initially intended solely to assess remoteness as it interacted with HRVS survey data.

However, Nepal’s recent transition to a federal administrative structure and the creation of 7 new provinces and 753
in the same labor market to reduce matching costs and tap into suitable labor force in the market. In this case, the growth of a central excise tax-incentivized sectors in Uttarakhand can induce other sectors requiring similar types of labor to locate within the state.

Using these two indices, we investigate whether employment outcomes vary due to input-output linkages and/or labor pooling between industries in Uttarakhand relative to Uttar Pradesh at the border, following: m × T T T × T T T × T T 1 × T T T × I 2 × T T T × I 1 × T T T × Lmm 2 × T T Tmm × L mm D ;Tmm T TmmTT , T T mm m yy = αα+ ββ<sup>MM</sup> + γγ<sup>MM</sup> TT + θθ + θθ NNTT TT (5). NNTT mm mm mm mm mm mm mm mm mm + ηη +ηη + LL[D DDT TT TT ] + λλ + εε I represents the sum of the input-output linkage indices between sector _j_ and all other sectors that were treated. TT I represents the sum of the input-output linkage indices between sector _j_ and all other mm sectors that were not treated. NNTT L represents the sum of the labor pooling indices between sector _j_ and mm all other sectors that were treated, and TT L represents that between sector _j_ and all other sectors that mm were not treated. Everything else has the same meaning as in Equ.(4). NNTT mm As in (4), direct effects are controlled for in this specification. The coefficient on T T T × I measures the indirect impact of the New Industrial Policy through sectors’ buyer-seller relationship with TT mm mm treated sectors. The coefficient on T T T × I measures the indirect impact through sectors’ buyerseller relationship with nontreated sectors. The indirect impact due to sectors having similar labor NNTT mm mm 14
et. al’s estimates suggest that trade openness had no effects whatsoever on inequality, instead they suggest that skill biased technological progress (SBTC) rather than trade _per se_ has been the mechanism through which the reforms have been operating. Sánchez-Páramo and Schady (2003) reached a similar conclusion using repeated cross-section data for Argentina, Brazil, Chile, Colombia, and Mexico. In particular they suggest that even though the direct effect of trade liberalization on earnings inequality may be small, trade is an important source of technology transmission. However IADB (2004) argue that even when many economic sectors in LA countries have acquired new technologies after the period of trade and financial liberalization, changes in technology are far from being the unique source behind the trend in earnings differentials. In particular, trends in gender earnings gaps and the evolution of earnings for high-tech workers (such as engineers and computer science graduates) are difficult to reconcile with predictions from any version of the SBTC hypothesis. Moreover, some care must be taken in interpreting the results on the rising average returns to higher education and their relationship to relative demand shifts. While these demand shifts are likely to play a key role in rising earnings inequality, the observed upward trends can also reflect partially the upgrading of the unobservable skills of the labor force.

**The unequalizing role of unmeasured workers characteristics** Most of the disparity in wages cannot be generally explained by observed characteristics. Increasing evidence based on quantile regressions indicates the existence of unobserved wage heterogeneity among individuals with the same education, sector of work (formal/informal), gender, age and experience. Factors such as the quality of education, family background, individual ability, and labor market connections are distributed unevenly across workers. These unobserved (by the analyst) characteristics may grant an advantage in access to high paying jobs, affecting both human capital accumulation decisions and its returns in the labor market. In particular, numerous studies for LAC and developed countries point that returns to schooling increase along the earnings distribution so
the right answer may depend on the context.<sup>40</sup> Informative questions with one correct answer were difficult to find, precisely because the difficulty to make the right decisions in farming often comes from the difficulty to adapt to each context rather than applying a “one size fits all” solution. Obtaining better measures of technical skills may require the development of new techniques that assess whether the numerous micro-decisions taken by a farmer fit his environment.

# _5._ **CONCLUSIONS**

Cognitive, noncognitive and technical skills are thought to play a key role for many economic decisions and outcomes in developing countries and are increasingly incorporated in empirical analyses. Little is known, however, about the validity or reliability of commonly used skill measures in surveys conducted in developing countries. This study is the first to investigate the reliability, validity, and the predictive power of a large range of skill measures on a poor rural adult population in a developing country setting. We do so using data from a survey experiment, specifically designed for this purpose, and a variety of statistical tools and methodologies. The results show the cognitive skills measures are reliable and internally consistent, while technical skills are difficult to capture and very noisy. The evidence further suggests that measurement error in noncognitive skills is non-classical, as correlation between questions are driven in part by the answering patterns of the respondents and the phrasing of the questions.

These results first of all suggest that collecting comparable information on cognitive skills in large household surveys in field conditions is feasible. Further validation of such measures in other contexts will be important to establish whether these conclusions hold in different settings – including possibly when cognitive measures are less correlated to educational achievement – and the extent to which such measures allow to compare cognitive outcomes across countries, or across regions/groups within a country.

The study further shows how specifically accounting for measurement error through factor analysis and item response theory can help increase the validity, reliability and predictive power of the technical skill measures. It also
The second contribution is methodological: we are not aware of other studies applying causal machine learning to open the black box of climate-induced migration. Specifically, we propose a systematic, rather than _ad hoc_ , approach to shed light on the irreducible heterogeneity of climateinduced mobility outcomes. Our approach improves upon traditional _ad hoc_ subsample analysis (or use of interaction terms) typically employed in the specialized literature to uncover underlying heterogeneity because it limits researchers' discretion when selecting the relevant dimensions and thresholds of heterogeneity. Given such heterogeneity, traditional econometric tools for the estimation of average treatment effects are not fit for purpose, at least not without strong _a priori_ assumptions by the analyst. The tree-based machine learning techniques we employ (Athey & Imbens, 2016; Wager & Athey, 2018) are, instead, agnostic regarding functional forms, easily capture nonlinearities and interactions among key household characteristics, and identify the main heterogeneity drivers and thresholds in a data-driven manner. This enables us to estimate policy relevant group-average treatment effects and show how average impacts can be misleading, since they conceal substantial heterogeneity, with opposite effects for different groups. <mark>Importantly, given the multidimensionality of the migration phenomenon, this approach presupposes that the researcher has access to longitudinal multi-topic and multi-purpose household survey data, which collect information about the most important mediation channels and transmission mechanisms, including agricultural variables.</mark> The last contribution is conceptual: we frame and explore the climate-migration nexus through the lens of the poverty traps approach. Specifically, we provide an extension of the asset-based and geographic poverty traps literature (Carter & Barrett, 2006; Barret & Carter, 2013; Jalan & Ravallion, 2002) to the immobility framework, coupled with a perspective of the migration phenomenon as a household-level risk management strategy inspired by the NELM (Stark & Bloom, 1985). Such a framework serves as the domain knowledge basis for our empirical approach aimed at detecting potential regime shifts due to the existence of "climate immobility thresholds", below which households fall into immobility traps.
0.197)|
|Tuesday|-0.133|-0.166|0.165|0.020|
||(0.179)|(0.215)|(0.110)|(0.143)|
|Wednesday|0.076|0.086|0.318**|0.225|
||(0.301)|(0.228)|(0.144)|(0.264)|
|Thursday|0.024|0.105|0.276|0.307|
||(0.244)|(0.245)|(0.176)|(0.193)|
|Friday|-0.141|-0.130|0.025|-0.077|
||(0.199)|(0.225)|(0.143)|(0.202)|
|Saturday|0.162|-0.087|0.360***|0.172|
||(0.151)|(0.168)|(0.102)|(0.154)|
|Observations|6,240|6,328|6,547|6,315|
|R-squared|0.486|0.459|0.236|0.479|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC).
- Matsa D.A., A. R. Miller. 2013. “A female style in corporate leadership? Evidence from quotas.” _American Economic Journal: Applied Economics_ 5(3), 136-169.

- <mark>Miao, M., D.T. Tang, L.C. Xu. 2019. “Property rights, political connections, and corporate investment.”</mark> _<mark>World Bank Policy Research Working Paper No. 8973.</mark>_

- Ngai, R., B. Petrongolo. 2017. “Gender gaps and the rise of the service economy.” _American Economic Journal: Macroeconomics_ 9(4), 1-44.

- Niederle, M. and L. Vesterlund. 2011. “Gender and Competition.” _Annual Review of Economics_ 3, 601-630.

- Parker, Simon C. 2009. _The Economics of Entrepreneurship_ . Cambridge: Cambridge University Press.

- Pitt, M.M., M. Rosenzweig, M.N. Hassan. 2012. “Human capital investment and the gender division of labor in a brawn-based economy.” _American Economic Review_ 102(7), 3531-3560.

- OECD. 2008. “The social impact of foreign direct investment.” _OECD Policy Brief,_ _<u>https://www.oecd.org/els/emp/The-Social-Impact-of-foreign-direct-investment.pdf.</u>_

- OECD. 2018. OECD International Direct Investment Statistics 2018 _._

- OECD _._ 2019. _FDI Quality Indicators: Investment for Inclusive and Sustainable Growth_ . DAF/INV/WD(2019)15.

- Rau, H.A.. 2014. “The disposition effect and loss aversion: Do gender differences matter?” _Economics Letters_ 123, 33-36.

- Reyes, Jose Daniel, Mark Roberts, L. Colin Xu. 2017. “The Heterogeneous Growth Effects of the Business Environment: Evidence from a Global Sample of Cities.” _World Bank Policy Research
Argentina.<sup>22</sup> Although the average and median elasticity of high-income countries is higher than one, the lower range is less than one, overlapping with the upper range of the developing countries. The reasons are many: the boundary is ad hoc, and the income range for high income is wide; at the lower range are new entrants, such as former communist countries like Latvia and Poland, and island and Latin American countries like Barbados, Bermuda, and Chile. If we restrict the group to early OECD countries, the lower value is greater than 1.10 for both elasticities.

Table 3: Descriptive Statistics for developing and high-income countries

||<br>**Arming**|<br>**ton (σ)**|<br>**CET**|<br>**(Ω)**|
|---|---|---|---|---|
||**Developing**<br>**countries**|**High-income**<br>**countries**|**Developing**<br>**countries**|**High-income**<br>**countries**|
|**Average**|0.71|1.42|0.59|1.46|
|**Median**|0.74|1.39|0.59|1.33|
|**Standard deviation**|0.16|0.42|0.19|0.50|
|**Range**|0.32-0.99|0.78-2.49|0.22-0.96|0.74-2.87|

Source: Authors' calculations.

Our results are consistent with recent findings of lower elasticities for macro or aggregate elasticities than those of micro elasticity studies of specific sectors or commodities.
.<sup>3</sup> This implies that all other things being equal, crises would have larger distributional impacts in economies with greater pre-existing inequality of opportunities – in access to basic services, and to jobs, markets, and capital – that are also drivers of high income inequality. There is some evidence for this from the Gallup World Poll data: the socio-economic gap in job losses (difference in job-loss rate between those with college education and those without) due to COVID-19 tends to be significantly lower for countries with lower income inequality before the pandemic (Rothwell, 2021).

To what extent a shock widens pre-existing inequalities depends on who is exposed to the shock, to what extent, and for how long.<sup>4</sup> In the case of COVID-19, exposure to the economic risks has been disproportionately high among certain groups, relative to others. The distribution of impacts also depends on the adequacy (or lack thereof) of policies that support those who are affected, and how they might have adjusted their behavior to cope with the shock. Certain factors could alleviate the dis-equalizing effects in the short-run. In low-income countries, income inequality may be dampened by the initial impacts being lower on the poorest (who are often in rural areas and in agriculture in developing countries, which tend to be less affected by restrictions on economic activity) than among the less poor (who are employed in urban services and manufacturing). In middle-income countries, the moderating effect might come from social assistance provided by the government to temporarily replace incomes of poorer households. While the net short-term impact on income inequality is uncertain and varies widely across countries, simulations by the International Monetary Fund (IMF) estimated an increase in inequality across EMDEs by an average of 2.6 percentage points as a result of the crisis (IMF, 2020).

The short-term dis-equalizing effects could in turn lead to a more unequal recovery process, with worse implications for inequality of opportunities over time. As disadvantaged groups suffer
- A plot and crop-level dataset in which units are uniquely identified by the crop name variable, plot unit ID, survey wave ID and season ID.

- A plot-level dataset in which units are uniquely identified by plot ID and season ID.

- A household-level dataset in which units are uniquely identified by household ID, survey wave ID and season ID.

- An individual-level dataset, in which units are uniquely identified by individual ID, survey wave ID and season ID.

<mark>All datasets include a household ID. Individual IDs as well as plot IDs are nested within household IDs (see Usage Notes). All datasets are provided as Stata data files, and code is publicly available as Stata do-files on GitHub</mark> (Bentze, 2024).

# <mark>4) Technical Validation</mark>

The LSMS-ISA surveys have undergone rigorous technical validation and methodological innovation throughout their lifetime. For example, different approaches to land area measurement and labor time use have been tested in the context rigorous field experiments, and their insights drawn upon to ensure state-of-the-art data collection methods ( see, for example, Carletto et al., 2013).

Moreover, processes prior to publishing the raw LSMS-ISA data, substantial efforts were carried out by the World Bank LSMS-ISA team to clean and correct errors stemming from data collection, editing and entry. Errors were often corrected at various stages of the fieldwork. In PAPI and CAFE interviews, Stata and CSPro software were used to identify potential errors such as out of range values and inconsistent skip patterns. When deemed necessary, paper questionnaires were reviewed, and data was revised. In some cases, data entry was performed multiple times to better identify data entry errors. The use of CAPI software such as _Survey Solutions_ allowed for error and inconsistency flags in the field, and for real-time data availability which enabled high-frequency quality checks.

The harmonized variables created for and included in the HP were also validated. This involved inspecting key summary statistics of each variable: mean, median,
Married women in Jordan, even with no children, are far less likely to work for pay, indicating that social norms, including those related to fertility expectations, play a role in the household resource allocation process. Women with less housework and fewer care responsibilities are not more likely to be in paid employment than those who do more hours of work at home; however, the presence of other women in the household with paid employment increases the chance of women working themselves. Conversely, having other women in the household who do not work for pay (and therefore could potentially help out with care responsibilities) does not increase the likelihood of women working. These patterns indicate it is likely that social norms and expectations regarding roles that constrain women from accessing labor market opportunities rather than time allocations to unpaid social reproduction activities.

For Jordanian women, being married generates the most extra unpaid work.<sup>40</sup> Young children increase unpaid work modestly; school-aged children do not add to the burden. The presence of another adult woman in the house reduces women’s unpaid work, but another adult man increases women’s unpaid work. Unsurprisingly, there is high demand in Jordan among working women and women seeking employment for childcare; the hourly rate these women indicate they are willing to pay is similar to that being currently charged in the market. Only 18 percent of Jordanian women who are employed use formal childcare services but a further 69 percent would use those services if they were available, as would nearly half of unemployed Jordanian women. The increased avail- ability of childcare could also help shift childcare expectations of married Jordanian women, potentially reducing barriers to labor force entry.<sup>41</sup>

# d. Additional Analysis

Figure A1 of the appendix reveals that having dependents – especially younger dependents – creates financial burdens that have not substantially decreased (in cash terms) via fiscal policy. Time-use data (Figure 12) shows that dependents create significant care responsibilities that are met by females whether
facilities that were already implementing the system, and because P4P was managed at the district level, the GoR required that all facilities within those new districts be in the first phase (treatment) of the rollout. This led the evaluation team to switch the assignment of treatment and comparison for eight districts from four blocks, as well as add one block to the sample. In the end, the study’s nine blocks include 16 districts in total, of which 9 belonged to the treatment group and 7 to the comparison group. Facilities that had P4P prior to the study were excluded from the evaluation (Vermeersch et al., 2010). Only facilities offering HIV/AIDS services were included in the HIV/AIDS P4P model. The 24 facilities that offered HIV services in these districts were enrolled in the study, with 10 facilities in the treatment and 14 facilities in the comparison group.

Facilities in the treatment group started receiving the HIV/AIDS related P4P in January 2007, while the facilities in the comparison group continued with traditional input-based financing until July 2008. Exposure to the HIV P4P model varied between 12 to 15 months in treatment facilities.

Since a primary objective of the evaluation was to isolate the P4P incentive effect separately from the effect of an increase in financial resources, it was necessary to hold the level of resources constant across treatment and comparison facilities. To accomplish this, comparison facilities’ traditional input-based budgets were increased by the average amount of P4P payments to treatment facilities on a quarterly basis during the experiment. Therefore, the differences in outcomes between the two groups at follow-up must be attributed to the difference in incentive structures and not to a difference in available financial resources.

# _3.2 Data_

We conducted a baseline survey of the facilities from August until November 2006 and a follow-up survey from April until July 2008.<sup>5</sup> We also designed a household level survey that

> 5 The study team submitted the research protocol to the Rwanda National Ethics Committee, which approved the