rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10082

# **Abstract**

Over the past two decades, the causal relationship between climate change and migration has gained increasing prominence on the international political agenda. Despite recent advances in both conceptual frameworks and applied techniques, the empirical evidence does not provide clear-cut conclusions, mainly due to the intrinsic complexity of the phenomena of interest, the irreducible heterogeneity of the transmission mechanisms, some common misconceptions, and, in particular, the paucity of adequate data. This data-oriented review first summarizes the findings of the most recent empirical literature and identifies the main insights as well as the most important mediating channels and contextual factors. Then, it discusses open issues and assesses the main data gaps that currently prevent more robust quantifications. Finally, the paper highlights opportunities for exploring these research questions, exploiting the potential of the existing multi-topic and multi-purpose household survey data sets, such as those produced by the World Bank’s Living Standards Measurement Study. The paper focuses on the Living Standards Measurement Study– Integrated Surveys on Agriculture program to discuss potential improvements for integrating standard household surveys with additional modules and data sources.

This paper is a product of the Development Data Group, Development Economics. 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 apaolantonio@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/
.0pp), which is substantively similar to the unconditional difference in latrine ownership in the IHDS data. This unconditional difference is in large part attenuated in columns 3, 4, and 5 when controlling for household location characteristics and/or fixed effects for PSU. Comparing Hindus and Muslims within the same location, with similar access to sanitation infrastructure, Hindu households are 5.7 percentage points less likely to report owning a – latrine (95% confidence interval: 5.2pp less likely 6.3pp less likely). This estimate is

> 17Note that surveyor-observed fecal material near homes reflects that household’s sanitation practices and potentially those of nearby homes.

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Table 2 shows the descriptive statistics of our sample at each point in time. The combined household survey comprises of 9,228 observations from five VUP cohorts. For the analysis in this paper we focus on households in cohort 3 so that we can exploit baseline properties. Our sample thus amounts to 1,947 individual observations. Within this we have complete information from all three survey rounds for 615 out of a total of 618 households surveyed at baseline. The rate of attrition is less than 1 percent. Hence, systematic attrition is not a concern for our analysis. In later survey rounds households have been added to increase the sample size but we are not considering these households here since we do not have baseline information on them.

The descriptive statistics show that compared to other contexts the share of female headed households in the study location and in our sample is quite large with 40 percent of all reported heads of household being female.<sup>12</sup> The average age of household heads in our population was 48 years old at baseline. Heads of direct support households are older – 58 years at baseline – whereas heads of public works households are younger – 41 years at baseline.

Finally, the descriptive statistics also show that the sample size of the direct support and public works beneficiaries is very small, i.e. just over 12 percent of the sample population.<sup>13</sup> Overall the education level in the sample population is low with over half of the household heads never attending school. For direct support households it is even more than three-quarters of the heads of households. Disability in our study refers to physical disabilities or chronic inhibition such as continuous or debilitating pain or mental illness. The proportion of heads of households with a disability is also quite high in our context reaching almost 50 percent. Again, the shares are higher for direct support households, reflecting the eligibility criteria applied by the program. Within the education levels and reported disability status we see some changes over
cross-sectional surveys of households (in the same Enumeration Areas, or EAs) that gathered comprehensive data on household characteristics and living conditions. The surveys, with a focus on agriculture, provided detailed and precise plot-level information. For each edition of the survey, households were visited twice: the same households interviewed on the first visit were re-interviewed on the second visit, and the visits were planned to match the timing of the post-planting (between August and October) and post-harvest (November and February) periods of the 2017/18 and 2018/19 agricultural rainy seasons. Semi-resident enumerators conducted these visits, with an additional visit between the two for crop-cut harvesting. In both survey waves, crop cuts were carried out for all crops grown during the main agricultural season. A third of the plots per crop were randomly selected for crop cutting from a list of all plots, which was stratified by the crops cultivated by the sampled households within an enumeration area.

## **3.1 Summary statistics and sample counts**

In this analysis, we concentrate on six crops that appear in 80% of all observations and for which non-standard unit conversion factors are used: millet, sorghum, rice, maize, cowpea, and groundnut. Table 2 presents the sample details for these crops across each survey wave.

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<mark>Sample Survey Theory</mark>_ <mark>. Berlin: Springer Science+Business Media.</mark>

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**Table 3: Dwell time in Sub-Saharan Africa**

|Durban|Douala|Lome|Tema|Mombasa|Dar-es-<br>Salaam|**Average**<br>**(Durban**|
|---|---|---|---|---|---|---|
|South<br>Africa|Cameroon|Togo|Ghana|Kenya|Tanzania|**excluded)**|
|4|19|18|20|9|19|**17**|

_Source: Refas et al (2011) for Douala, surveys for the other ports._ When cargo dwell time is broken down between operational, transactional and discretionary storage<sup>10</sup> , it appears that Durban favorably compares with Mombasa and even more so with Dar-esSalaam<sup>11</sup> . The most significant difference concerns clearance procedures, where up to 6 days can be gained, as well as for discretionary storage. The last factor seems to play a major role and is therefore discussed in detail below.

> 10 Operational dwell time is mainly the time to unload vessels and store in yards. It mainly depends on the efficiency of the port and the availability of equipment combined with the level of occupancy of the storage facilities. Transactional dwell time mainly concerns the transaction time between the importers/port services and customs. Discretionary storage is the residual after having taken into account operational and transactional dwell times.

> 11 In an ideal situation, the minimum time for the transactions is 1 day for clearing the cargo depending on whether a physical inspection is required. Another 1-2 days for the transfer from the vessel to the yard and a similar amount for the transfer from the container yard to the ODCY. Finally it would require half a day/ a day to pay the port and shipping fees and exit the port, which means that mean dwell time can not really
property rights” we mean the ability to exclude noncooperative fishers from the cooperative’s established fishing grounds.

We test these implications using daily logbook data from three cooperatives in the Gulf of California region, in northwest Mexico. One cooperative, operating on the Pacific coast, retains an exclusive concession for some species and is able to exclude outside fishermen for all other species (Cota-Nieto 2010; McCay et al. 2014). The other two cooperatives are located close to La Paz, the state capital of Baja California Sur (B.C.S.), and compete with other cooperatives and noncooperative fishermen for fish (Basurto et al. 2013; Sievanen 2014). Analysis using the fishing team-level logbook data reveals that cooperative members respond to cooperatives’ chosen prices as posited by the model. Exploiting the fact that one cooperative has stronger property rights than 3
individuals often resort to other methods of reducing vulnerability, some of which can be quite costly. For instance, people may choose less profitable but less risky investments (Morduch 1995) or sell productive assets in bad times (Rosenzweig and Wolpin 1993).

In this paper, we explore whether transactional sex provides inter-personal insurance in addition to a higher income level, and whether such insurance affects the decision of women to enter (or continue) in transactional sex. In particular, we are interested in the insurance that sex workers receive from “regular” clients. Regular clients differ from “casual” clients in that they tend to have longer-term relationships with a particular woman. Over time, women who supply transactional sex typically develop relationships with one or more such regulars, and these relationships can extend beyond just sex. For instance, some regulars are thought of as akin to boyfriends or lovers, and it is not unheard of for women to marry regular clients.

We examine whether regular clients become part of a woman’s risk-coping network and provide insurance through transfers when negative shocks occur. To do this, we utilize a unique panel dataset constructed from 192 daily sex worker “diaries.” The diaries were essentially preprinted questionnaires that were filled by the women on a daily basis. In the diaries, women recorded each transaction that they had with a client, including all of the sexual activities performed, the price paid, whether a condom was used, and whether the client was a regular or a casual. They also recorded any shocks that they experienced, their income, expenditures, and, most important for this paper, the transfers they had given to or received from other individuals.

To measure this, we asked women to record all the transfers they received from other people, whether from regular clients or from anybody else (usually friends or relatives). For transfers from regulars, women separately recorded whether the transfer was in cash, for rent, for other household expenses such as school fees, or whether the
the town or city level, with the exception of Rwanda, where prices are collected at the (more aggregated) district level.

The analysis in section 4 uses clean samples of price data, obtained by applying a cleaning algorithm to the raw data. Moreover, as in Atkin and Donaldson (2015), we convert all prices to constant U.S. dollars by deflating them using inflation rates at origin locations and bilateral nominal exchange rates between local currencies and the U.S. dollar. The cleaning and deflating procedures are described in Appendix A.

## **2.2 Products’ origin locations**

The estimation strategy proposed by Atkin and Donaldson (2015) depends on precisely identifying locations where a product is produced or imported. To do this, we search over the Internet to determine which manufacturers produce each brand and where their factories are located. For imported goods, we identify the main ports of entry in each 4
Salaam- Lubumbashi Lubumbashi<br>Shares of costs of importing freight<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam-Lusaka Dar es Salaam-Lusaka Durban-Lubumbashi Durban-Lubumbashi<br>alternative modes<br>Costs (US$ per tonne) of importing freight via<br><!-- End of picture text -->

- b. Composition of cost (% of total cost)

_Source:_ Based on data collected from World Bank 2009; AICD ports and railway databases; Nathan Associates 2010; Teravaninthorn and Raballand 2009; and World Bank 2010.

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change much, the point estimates shift somewhat. Note, however, that a different interpretation of the data is that the (symmetric) order bias is simply lower in the MPL that compares one and two electricity vouchers vs. the MPLs that compare electricity and cash (see columns (1)-(3) of Table A.2). Note also that this approach to estimating bias is only possible with data from at least 3 MPL, and so in many cases may not be a strategy available to the researcher.

**(Re-)Introducing bias through standard procedures** In Section 2.3 and Table 1, we described some common approaches to addressing MSB, NSB and the interval nature of MPL data. Recall that all approaches discussed above distill the information from a subject’s MPL into an interval or even just a single point; some additionally drop inconsistent information or otherwise restrict the data. Here, we replicate some of these approaches to demonstrate how this influences estimated WTP.

We make our points mostly using interval regression estimates. Interval regression is a generalization of a tobit specification and uses one upper and lower bound for the WTP of each subject to estimate average WTP for an electricity voucher. Therefore the researcher has to assign an interval for WTP to each subject based on the observed MPL choices. This is often done by using the first and last observed “switch”. Subjects with MSB are thus assigned a wider interval to accommodate all of their choices, and switches between the interval endpoints are effectively not used.<sup>25</sup> Subjects with NSB can be assigned one-sided (open ended) intervals. They may alternatively be assigned a maximum or minimum of WTP based on the researcher’s assessment which values of WTP are plausible. For example, in the South Africa data, a WTP below -100 would indicate that a subject would rather pay cash than receive the R100 electricity voucher.

Table 3 columns (1) to (4) implement these
On the revenue side, we analyze the two main components of state government income—taxes and federal transfers—representing 80 percent of their total revenue.

The third data set is the quarterly GDP of Mexico’s 32 states, which started being collected in 2014by INEGI. In addition to the total GDP, we also use information on the GDP contributed by each state’s primary sector—mining and agriculture—secondary sector—manufacturing and construction—and tertiary sector, mainly services.

The fourth data set consists of state-level panel data at the biennial frequency on a series of poverty indicators on even years from 2010 through 2018. Data are collected and compiled by the National Evaluation Council (CONEVAL). CONEVAL created a methodology to evaluate poverty in a state according to the following indicators: income per capita, average educational attainment, access to health services, access to social security, quality and housing spaces, access to nutritious and quality food, and access to paved roads.

The fifth data set, which we refer to as the loan-level data, consists of credit registry data containing bank commercial loans in Mexico to private firms and government-backed entities from July 2014 to June 2018. Its coverage includes the universe of bank loans issued to governmentbacked entities and all bank loans issued to a nationally representative 10 percent random sample of private firms. Loans to government-backed entities include federal, state, and municipal governments, as well as firms owned by the government (federal, state, or municipal). The data are obtained from regulatory reports monthly submitted by every commercial bank to the bank regulator (CNBV). Reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing loans made to all firms. All business loans, regardless of their size, are reported. For each loan, we use information on the issuing bank, the outstanding amount, the interest rate, loan guarantees, and the type of financing (i.e., whether the loan is intended to finance working capital or investment)
most African trade is transoceanic. Trade among the contiguous eight members of the West African Economic and Monetary Union (WAEMU) represented less than 3 percent of their total trade for each year in the 1990s (Coulibaly and Fontagn ́e, 2006).<sup>4</sup> If anything, one would expect more trade among these countries than other sets of neighbors, because they share a common currency and thus lack one important trade friction. Other cities in the periphery have relationships with their country’s core that are potentially critical to their success. And countries spend to improve those links or simply to reverse decay. Almost $7 billion is invested per year in roads in Sub-Saharan Africa, with a substantial portion funded by donors (World Bank, 2010). Worldwide, transport accounts for 15 to 20 percent of World Bank lending, with almost threequarters of that amount going to roads (World Bank, 2007a).

This paper relates primarily to four bodies of work. The first is on the effect of transport costs on the size and growth of cities and regions. This work has been done primarily using cross-country data (e.g. Lim ̃ao and Venables, 2001) or the construction of very large national transport networks in the United States (Baum-Snow, 2007; Chandra and Thompson, 2000; Atack, Bateman, Haines and Margo, 2010; Duranton and Turner, 2011), India (Donaldson, 2010), and China (Banerjee, Duflo and Qian, 2009). However, no comparable work has been done in Sub-Saharan Africa, which has worse roads, lower urbanization, lower income, and much less industry, and consists of many countries, as opposed to one unitary state.<sup>5</sup> Similarly ambitious transport infrastructure projects have not been carried out in post-independence Sub-Saharan Africa. This paper instead relies on the plausibly exogenous annual changes in transport costs induced by world oil price fluctuations, which allow me to determine the short run impact of
The remaining columns of Table 8 then examine the extent to which these same variables are correlated with other clear differences of developing courses from top-20 courses. Column 2 considers the share of the course devoted to empirical topics. We see that these same variables have much less explanatory power for this outcome, with the only significant variable being having the instructor not listed for the course (which is associated with less focus on empirical topics). Column 3 looks at the adoption of the Banerjee and Duflo (2011) book. This is more prevalent in countries with higher overall tertiary education attainment, and less prevalent in more statedominated economies. Column 4 looks at use of the Todaro and Smith (2014) book which is not used in top-20 U.S. courses. We see that it is less likely to be used in richer countries. Finally column 5 considers whether students are required to do homework that uses statistical software. This is again more prevalent in countries with higher levels of overall tertiary attainment, but conditional on this, less prevalent in richer countries.

The survey data allow us to examine one additional aspect of why development economics is taught as it is. One might hypothesize whether language is a barrier to teaching more modern materials to students, if recent research and resources are only in English. However, we find that 60 percent of the courses are taught in English, and 88 percent of the instructors surveyed say that availability of materials in the right language is not a constraint. Of course if these instructors were to seek out more empirically-oriented material they may then run across this constraint, but given the prevalence of courses in top universities in developing countries which are taught in English, we do not believe language is likely to be the principal barrier.

# **6. Conclusions and Suggestions for What Instructors Should Be Changing**

The field of development economics has changed dramatically over the past 20 years. This is reflected in changes in the topics that command most research attention, and particularly in the rapid growth
| POLICY RESEARCH WORKING PAPER 2069

# Summary findings

Gonzalez and Maloney derive a methodology for analyzing logit models in a rotating panel context. They then apply the technique to test two theories of why and when salaried workers enter the informal self-employed sector.

In the traditional view, workers fired from formal jobs queue in the informal sector to reenter the formal sector. Gonzalez and Maloney argue that for many, selfemployment is a desirable goal, but that credit constraints often dictate that they work in the formal sector until enough start-up capital is accumulated.

They model the decision to move as a stopped Markov process in which, in each period, the worker compares accumulated savings with the target level for switching sectors dictated by the forecasted stream of discounted utility arising from employing labor and cal ital in each sector.

They test and find support for the model using the new logit methodology and rotating panel data from Mexico.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Latin America and Caribbean - Region is part of a larger effort in the region to understand the functioning of developing country labor markets. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/ home.html. The authors may be contacted at wmaloney@worldbank.org or paroca@socompa.cecun.uncn.cl. February 1999 (19 pages).

_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