rafmacalaba/data-use-mentions-extended huggingface dataset:
lens of (a) the level of exposure to negative shocks, (b) the extent of impacts of shocks on household welfare, and (c) the type of coping mechanisms available to the household—including access to social protection programs. It is also useful to examine these caveats along the lines of different types of shocks—covariate and idiosyncratic shocks—as well as factors which affect the extent of exposure to shocks such as location and occupation or livelihood.

This paper uses this framework to examine the effect of shocks on the welfare of Sudanese households. By estimating the impact of various shocks on measures of household welfare as well as the strategies used to cope with shocks, a profile of households most vulnerable to shocks can be built through their characteristics—occupation/livelihood, gender of head, poverty status, and location. We consider both self-reported idiosyncratic shocks such as death/sickness of household members, loss of livestock, crops, or assets, and so on and covariate shocks such as deviation of rainfall from historical average to indicate flood/drought, conflict, and so on. In addition to identifying the characteristics of households most vulnerable to shocks, the paper also aims to highlight the shocks with most impact on household welfare while accounting for the heterogeneity of the impact across households based on wealth, livelihood, location, and access to safety nets/social protection programs.

# 2.1. Shocks in Sudan

This section discusses the exposure and vulnerability of Sudanese households to shocks beginning with the 2009 NBHS followed by the 2014/15 National Household Budget and Poverty Survey (NHBPS) , which gives a more recent picture. It describes the main types of shocks reported by households overall as well as the differences in the prevalence of shocks across rural/urban areas, states, household livelihoods, and quintiles of household per capita consumption. To complement this description of exposure to shocks across households, this paper profiles households most likely to be vulnerable to shocks based on statistical differences in the characteristics of households affected
Subgroup analyses show, somewhat surprisingly, that training impacts did not differ much by age, marital status, gender, educational attainment or home ownership. In Shenyang, training appears to have had a stronger negative impact on men and those with lower education. In both Shenyang and Wuhan, individuals who contributed personally to the cost of training had higher reemployment rates. It is expected that individuals who expect to benefit the most from an intervention would be willing to contribute more to its cost.

To understand the reason for the observed discrepancy in impact estimates across the two program sites, it is useful to consider the details of the program‟s operation. It appears that the Reemployment program may not have been properly implemented in Shenyang, as many workers seemed not to have received the layoff stipends that they were supposed to have received under the program. In Wuhan, the program appears to have been better implemented. Quality of training remains an issue in both sites, though, because it may have been difficult for workers to learn new skills in large, overcrowded classrooms.

# **3.2  Sectoral Policies: Commodity Commercialization in Nepal**

Paolisso, Hallman, Haddad and Regmi (2001) study the impact of a training program in Nepal that was designed to commercialize fruits and vegetables, improve farm output diversification and improve agricultural productivity. The program, called the Vegetable and Fruit Cash Crop Program (VFC), provided training on how to grow and process vegetables and fruits (e.g. into jams). Paolisso et al. (2001) base their analysis on data collected in 1991-1993 fieldwork on 264 households. The surveys collected detailed information on activity patterns in the household and their study examines how participation in the program affected time use patterns of both male and female program participants.<sup>12</sup> The VFC program was successful in the sense that household participation in the VFC program resulted in increased male and female time spent growing vegetables and fruits. The estimated average increase for women ranged
(0.012)|
|Divorced dummy|0.043***|0.013|0.036|0.012|0.006|-0.009|-0.009|-0.006|0.005|0.043*|0.058***|0.084***|0.000|0.084***|0.051***|
||(0.016)|(0.026)|(0.032)|(0.028)|(0.021)|(0.020)|(0.019)|(0.020)|(0.021)|(0.025)|(0.021)|(0.019)|(0.011)|(0.022)|(0.018)|
|Observations|73,503|29,285|28,009|27,419|49,094|46,201|47,898|47,467|47,021|35,789|46,517|60,097|57,135|44,345|48,502|

_Source:_ Calculations based on the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys, and the Sri Lankan 2006 Household Income and Expenditure Survey. _Note:_ Marginal effects of the labor force participation are evaluated at the mean of the independent variables. Standard errors are in parentheses. Never married is an omitted category for marital status. Standard errors are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks respectively.
- 7 -

13. Unfortunately, however, these national-level transport and socioeconomic indicators cannot present actual transport connectivity on the ground. By construction, these indicators barely change over time even if some roads are substantially improved. For instance, road density does not change unless a new road is constructed. In reality, many road projects aim at rehabilitating or maintaining existing roads, not developing new ones. In addition, the indicators aggregated at the national level ignore potential spatial heterogeneity in a country. Transport connectivity often differs significantly across subnational areas.

14. To capture historical changes in transport connectivity more precisely, spatial techniques are used to construct detailed georeferenced data based on stop-by-stop freight shipping data. The CMPP collected by the ADB database takes a time/cost-distance (TCD) approach (UNESCAP).<sup>4</sup> The data is focused on regional long-haul shipments originated from and/or destined for the CAREC member countries over the period of 2010-20. It comprises detailed micro shipping cost and time, which are tracked movements of a truck or cargo from door to door along transport corridors, including borders and other inspection points.

15. The original database contains over 187,000 observations, covering both road and rail shipments. Excluding data with missing information (e.g., origin or destination), the paper uses 93,786 road shipment data, which could be georeferenced. A total of 14,342 shipping carriers transported these cargos among 565 locations.<sup>5</sup> Note that as shown in **Figure 5** , each observation is recorded by a shipping carrier at every major stop point (normally, city or border point). Thus, even on the same road segment, we observe different transport costs and times. For instance, some carriers may have driven faster than others. The figure shows average speeds recorded by two Kazakh shipping carriers in 2013. Origins, destinations and stopped points can be different across carriers. Even if they are different (
Policy Research Working Paper 9433

# **Abstract**

This paper studies the dynamic sorting of workers prior to labor market entry that leads to skill differences across cities of different sizes, as well as its consequences on the estimation of agglomeration effects. Using rich administrative data for young, college-educated workers in Colombia, the paper shows that the most talented and best trained sort to big cities primarily because they attend college there and remain for work. The availability of colleges in an individual’s high school city, parental resources, and high school city size are the most important determinants of the decision to move for college. The relatively less able remain in medium and small cities or move there for work after attending college in big cities. Pre–labor market sorting thus concentrates population and skill in big cities. As a result of this sorting, agglomeration effects are stronger for college than work city size, even after controlling for mediating factors such as individual ability or college selectivity.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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://www.worldbank.org/prwp. The authors may be contacted at mferreyra@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
**Follow-up surveys** Our next three rounds of follow-up surveys preserved and expanded the baseline sample to add 496 farmer group members across the remaining 32 villages in our experimental sample, and an additional 246 farmer group members across the original 78 villages. Regressions that leverage baseline information such as social network relationships will not include these additional 32 villages and 742 group members; in Appendix Tables A19 and A20 we show that including or dropping these villages does not meaningfully affect our main results beyond the precision of our estimates.

In our first follow-up survey, just at the end of the demo year, we surveyed a much smaller sample of households and oversampled households in treatment arms with more demonstrators. We use the demo year household survey in two tables and one figure. In Table 5, we include demo year data for additional power when estimating the impacts of the new seeds on yields; these results are robust to excluding demo year data. In Appendix Table A1, we use this survey to construct measures of demonstration year profits for demonstrators. In Figure 4a, we show the dynamics of adoption of improved seeds across our four years of data.

In our second follow-up survey, one year post-demonstration, we additionally asked households about their knowledge of each of the new varieties of pulses, including characteristics of the new varieties. We construct an indicator that the household reported one of the new varieties for a given crop is a high-yielding variety; building on our conceptual framework, we interpret this as a binary proxy for households’ cropspecific beliefs about the returns to the new seeds, and we incorporate this measure of beliefs into analysis of mechanisms in Appendix C.

**Attrition** Our main analysis focuses on impacts of demonstration one and two years post-demonstration. We attempted to revisit our full baseline sample in each of these waves; we reached between 81% and 89% of households across treatment arms during these two survey waves, and find no evidence of differential attrition across treatment arms (Appendix Tables A7 and A8).

*
ownership variables as regressors. Results are generally robust to a large number of sensitivity analyses and robustness checks.

The next section summarizes the technique. Section 3 discusses the data and the approach to ensure comparability of the tests across the three countries. The main results are presented in Section 4, while Section 5 discusses a range of additional robustness checks.

# **2. Methodology**

This section largely relies on Lanjouw et al. (2011). We assume two rounds of repeated crosssectional surveys. Calling round _t_ household log per capita consumption or income (where _t =1, 2_ ) of household _i_ and _z_ the poverty line, we are interested in estimating the fraction of poor households in the first round of the survey who escaped poverty ( ) or remain poor ( ) in the second round of the survey, and the fraction of non-poor households in the first round of the survey who became poor ( ) or remained non-poor ( ) in the second round of the survey. This task cannot be performed directly by using repeated cross-sectional surveys, since all households are interviewed only once, either in the first or the second round of the survey.

However, we can straightforwardly estimate the relationship between income and time invariant characteristics in each round:

# (1)

where _xit_ is a vector of time-invariant characteristics (or characteristics that can be easily recalled from one round to the other one) of household _i_ in round _t_ of the survey and  _it_ is an error term. Using observations from the second round, we can predict consumption in the first round ( ) by means of the same observed vector of time-invariant or retrospective characteristics ( ) and the first round OLS estimates of parameters , where the superscript refers to observations of 5
On paper, agricultural land is allocated through local administrative processes in Senegal. Since 1964, most of the land (between 95 and 98%) has been part of what is called the national domain. Land use rights are attributed by local land committees on the basis of needs and capacity to farm. This land cannot be sold and in theory cannot be bequeathed either (Caveriviére, 1986).

However, the reality on the ground is clearly rather different. Bequests are common, including land inheritance. The survey data we use indicate that 31% of men, but only 17% of women, report that they inherited land. (If one confines the sample to those whose father has died, the proportions are 43% for men and 28% for women.) Looking at all forms of inheritance (including housing, durables, money and productive assets) 54% of men in the sample inherited something, while 38% of women did. In practice, heirs are given priority to obtain the use rights over any other potential users. Hence, inheritance of paternal lineage land is an important means of access to ownership.

Until the constitution of 2001, women were not allowed to own land in Senegal. Furthermore, until May 2010, they were not allowed to be part of the land committees in charge of the attribution of use rights. Hence, women very rarely received land through this allocation mechanism. On the intergenerational transmission side, several inheritance laws coexist in Senegal that give very different treatment to women. Each individual can choose before his death which law he wants to abide by. The French inspired system of inheritance dictates that wealth be shared equally among children, whatever their gender. By contrast, the Islamic inheritance law (which is the most common choice in practice) limits the inheritance of daughters to half of that of sons.<sup>12</sup> In addition, entrenched tradition favors sons for inheriting land. Daughters are supposedly compensated by their brothers with money or other forms
</sup> During the last two decades, the percentage of Ninis in the region has gradually declined (although the absolute number has increased), although there has been substantial heterogeneity in trends within this group (De Hoyos et al., 2014; Cardenas et al., 2015). The percentage of Ninis in the region declined from 23.4 percent to 19.6 percent (mainly driven by the decline in female Ninis), while the number increased from 16.4 million to 18.2 million between 1992 and 2010. Results for the region are heterogeneous across countries and by gender. Nini levels and trends have varied greatly at the country level, with some countries such as Peru and Venezuela showing sharp declines in the proportion of Ninis. Moreover, Ninis

> 1 In this paper, “neither in work nor in school” (Ninis) are those who are not working in the labor market nor part of the formal education system at the time of the survey.

> 2 Latin Americans identify crime, violence and unemployment as the main concerns for the region (Lagos and Dammert, 2012), which records the highest homicide rates in the world with an average rate of 24.7 homicides per 100,000 people (Chioda, 2013).

> 3 The analysis in De Hoyos, Popova, and Rogers (2014) draws on data from 238 household surveys compiled and harmonized in the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), augmented by 24 new country-year surveys added for their study.

> 4 This contrasts with an average of 12.9 percent (roughly 7.5 million individuals) of Ninis found among the same demographic in the EU27 in 2011 (Eurofund, 2012).

2
In this expression, _S_ is the set of economic activity sectors reported by DANE, and _GDPst_ is sector _s_ ’s national GDP in year _t_ . _GSdks_ is the share of SCP graduates who work in sector _s_ in department _d_ and have graduated from a field- _k_ program relative to all SCP graduates employed in sector _s_ across the 13 departments: These shares sum to one by sector when adding over all fields and departments in our sample. They are time-invariant and are calculated by pooling all years available in OLE (2007 through 2013).<sup>12</sup> Our GDP-by-field variable captures the notion that exposure to the aggregate, national shocks experienced by an economic sector varies across SCP graduates depending on their field of study, work location, and work sector (Bartik, 1991; Goldsmith-Pinkham et al., 2020). The aggregate shock to sector _s_ is captured by _GDPst_ . Although this sector-specific shock is common to all departments and fields, the degree of exposure for field- _k_ graduates who work in department _d_ is determined by the time-invariant employment share, _GSdks_ . In the simplest case, if all SCP graduates employed in sector _s_ in the country had graduated from the same field and worked in the same department, then that department and field would absorb the full shock to the sector.

**Field relative employment.** Similar to the construction of employment shares for GDP by field, we use OLE data from 2007 to 2013 to obtain the total number of SCP graduates employed in a given department, independently of the department where they studied. Among them, for a given year we calculate the proportion of graduates with a field- _k_ degree as follows:

The GDP-by-field and relative employment variables seek to capture local labor market

> 12If we had used time-invariant shares from a single year rather than pooling all available years, this would have yielded shares equal to zero or one for some small departments. Among the years with OLE availability, 2013 is the one where these issues are least
---|---|---|---|---|
|Round 2|0.054*|-0.053|-0.058*|0.087**|0.343***|
||(0.030)|(0.077)|(0.030)|(0.043)|(0.057)|
|Observations|2,355|516|516|516|516|
|R-Squared|0.004|0.003|0.017|0.031|0.141|

||Education<br>Activity|_Panel C_<br>Listened to<br>Education<br>Programs on<br>Radio|_: Nigeria_<br>Used Mobile<br>LearningApps|Watched<br>Educational TV<br>Programs|Meeting with<br>Lesson Teacher|
|---|---|---|---|---|---|
|Round 2|-0.012|0.267***|0.123***|0.141***|0.168***|
||(0.033)|(0.038)|(0.028)|(0.032)|(0.034)|
|Round 3|-0.113***|0.291***|0.182***|0.242***|0.304***|
||(0.034)|(0.038)|(0.032)|(0.035)|(0.036)|
|Observations|4,045|2,515|2,515|2,515|2,515|
|R-Squared|0.010|0.076|
Policy Research Working Paper 10581

# **Abstract**

This paper presents a coping strategy that small-scale cross-border traders adopted in response to the shock of the COVID-19 pandemic and examines its impact. Using a cross-sectional data set of 1,159 traders from the borderlands of the Democratic Republic of Congo and Rwanda, the paper assesses the impact of adopting the Cluster Trading Approach on trade outcomes, household income and poverty reduction. When applying a local average treatment effect approach, the findings reveal that adoption of the CTA causes at least 21 and 31 percentage point increases in traders’ turnover and profit respectively. The household income analysis and poverty decomposition highlight an increase in income, even within the depth and severity of poverty. Thus, although access to capital is important for small-scale cross-border traders joining a cluster according to the literature, the results show that the level of capital is less important for income increase once there. As the results are robust to competing explanations and heterogeneity of the sample, the paper concludes that the Cluster Trading Approach is a poverty-reducing strategy and discusses the challenges for its sustainability.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ckunaka@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
ranked according to their positions in the expenditure distribution of all rural and urban households and individuals by their positions in the expenditure distribution of all individuals. As a welfare measure, we use annual household expenditure consumption, which is the sum of the annual value of food consumption and expenditures on nonfood items,<sup>11</sup> education, meals out, and utilities.<sup>12</sup> Property taxes assessed on the value of land and buildings could affect the property investment decisions of owners (Bird and Slack 2005).<sup>13</sup> However, the land tax in Ethiopia is area-based, not value-based. The Ethiopian policy contexts—a tax on land area, fixed supply of land, no land market, and immobile landholders—are consistent with the assumptions that predict that the tax burden falls entirely on landholders, who cannot relocate because of the risk of losing their land rights. Further, in low-income countries, property taxes are not expected to affect property owner decisions to move to a lower tax jurisdiction because they do not receive adequate local public services (Bird and Slack 2005; Brockmeyer et al. 2021; Kalkuhl et al. 2018). They are also

> 9 The Instructional Guide on the Abbreviated Women’s Empowerment in Agriculture Index (A-WEAI) (Malapit et al., 2015) uses this classification.

> 10 Tax incidence studies typically group households into five quintiles rather than four quartiles. Because grouping femaleheaded landowning households into consumption quintiles results in a small sample size, we classify them into quartiles.

11 Nonfood items include personal care products, clothing, tobacco, transport, household fuel, costs for domestic and household services, spending on housing, and contributions to informal social security and community development.

12 Expenditures are considered to be a better welfare measure than income for measuring living standards because farmers’ incomes fluctuate between seasons and over years, and there are practical difficulties in measuring rural incomes (Deaton and Zaidi 2002; Martinez-Vasquez 2004)