rafmacalaba/data-use-mentions-extended huggingface dataset:
urbanization process. This is consistent with the idea that inequality in the region of origin has an inverse U-shaped relationship with migration.

From an econometric point of view, the study of changes in inequality due to migration and remittances can be tricky. An important issue is whether the labor-market participation of the rural families receiving remittances is considered to be endogenous or not (i.e. whether money transfers are a potential substitute for home earnings or not). If this is the case, what should be investigated is how the observed income distribution at the place of origin compares to a counterfactual scenario without migration and remittances. Barham and Boucher (1998) study this issue using cross-section data from a survey of households in a Nicaraguan town with a long history of migration. To build the counterfactual scenario, the authors impute the earnings migrants would have had if they had remained in town, and what other family members’ decisions would be as regards labor participation. They then use the results to impute participation decisions and earnings for migrants and non-migrants in migrant households. While simply comparing income inequality measured by a Gini index with and without remittances would indicate that remittances from internal migration reduce inequality, the comparison to the counterfactual scenario shows that, in fact, internal migration increases inequality -- a result qualitatively similar to that of international migration.

# **Consequences for production at the place of origin**

Another issue is how migration can affect production at the place of origin. In the case of rural surplus labor as postulated in the Lewis model, migration should have no effect on production other than through remittances spent on productive investments or because of increased demand on rural goods. If labor is not in surplus, labor transfers have to be accounted for since the two effects play in opposite directions.

Lucas (1987) uses time series data over a long period to study inter-regional migration in Southern Africa (international migration to South Africa’s mines from neighboring countries). The econometric model incorporates both the determinants of international 40
||the<br>shorter<br>questionnaire|to<br>shorter<br>questionnaire||inequality (Figure 1 and Figure<br>2) (Beegle et al. 2012)|
|---|---|---|---|---|
|**Cross-Survey**<br>**(X-Survey)**<br>**imputations**|Non-response is an<br>issue but not as<br>much<br>as<br>in<br>full<br>consumption due to<br>the<br>shorter<br>questionnaire|Very small issue as no<br>consumption section|Very small issue as<br>questions are simple|Reliance<br>on<br>old<br>data<br>introducing bias in structural<br>model<br>leading<br>to<br>biased<br>poverty estimates (Figure 3)|

Using the full consumption data from KIHBS 2015/16 as benchmark, in this section, first, we investigate the empirical trade-off between the number of core items and the number of optional modules for the rapid approach with respect to the performance of poverty indicators. Second, we compare the rapid approach with a traditional reduced consumption aggregate, without any adjustment of the poverty line. Third, we again use a reduced consumption aggregate, but adjust the poverty line based on previously observed consumption from 2005/6. Fourth, we compare the application of the rapid approach in the pilot in Kenya with a cross-survey imputation approach also using the 2005/6 data as baseline.

# **Trade-off with Number of Core Items and Optional Modules**

As discussed in the methodology section, the rapid approach creates a trade-off between the number of core items and the number of optional modules, as a larger number of core items (smaller number of optional modules) will improve the
form of banking regulation. Technology can help in other ways. In Uganda, over the past two years, Hewlett-Packard and others active in the microfinance industry have been working to increase the scale of microfinance. The team has developed, tested and is implementing a remote transaction system using handheld devices, which capture transaction data and use a GSM network to transmit this back to a head office server and, in turn, management information system. Hand-held tools are being used more generally by several micro-finance institutions to provide on the spot loan applications and approvals (see Microsave.org for other examples).

In Mexico, there have been innovative ways of trade finance using reverse factoring. The program developed by Nafin, a government development bank, allows many small suppliers to use their receivables from large credit-worthy buyers, including foreign MNCs, to receive working capital financing, effectively transferring their creditworthiness to allow small firms to access more and cheaper financing. What makes Nafin special is that it operates an internet-based platform, providing on-line services, reducing costs, increasing transparency and improving security. In the short-run, there is a subsidization of overhead costs, but by lowering costs for SME working capital, it expects to generate more business and become sustainable (see further Klapper, 2004).

Paulson and McAndrews (1998) provide a case study of how Standard Bank of South Africa tried a new ways of addressing an un-banked population. Already a decade ago, in 1993, Standard Bank set up E-Bank. It was a simple savings product offering card-only access, but supported by dedicated staff speaking a mix of relevant local languages and operating out of dedicated outlets to help overcome problems of illiteracy and concerns about security in a high crime environment. It had high start-up costs, but provided financial services to a low-income segment. Since then E-bank has been absorbed in the bank’s more general provision of financial services to low-income households.

These are examples of some specific market approaches and government interventions that can further enhance access. Many other examples like these exist
and so lack of impact on other outcomes may reflect measurement problems, or that the information alone was not enough to change intentions.

Finally, column 9 examines our action-oriented measure of interest in migration. 11.4 percent of the control group took the action of sending an SMS message to find out more about wages for Gambians in Europe. We find that the Senegal treatment significantly lowered this by 4 percentage points, suggesting less interest in considering Europe as a migration destination. Taken together, we view these short-term results as suggesting that the information part of the interventions was able to generate new knowledge, and, for the Senegal migration alternative at least, change intentions to migrate.

# **5.2 Impacts on migration knowledge and intentions over the longer-term**

We begin our analysis of the follow-up survey data by asking whether the interventions had lasting impacts on knowledge about migration to Europe and Senegal, and migration intentions over the next five years.

The first three columns of Table 3 examine impacts on knowledge. We find no significant impact on knowledge about backway migration (column 1), nor in the likelihood that the individual is over-optimistic about the wages they can earn abroad or the likelihood of asylum (column 2). Column 3 shows the Senegal treatment resulted in a lasting improvement in knowledge about migration to Senegal. The effect size of 0.14 questions out of five is similar in magnitude to the impact measured immediately after showing the informational video, and shows that any knowledge depreciation from the video has been offset by experiential learning from more lasting migration to Senegal.

Given the issues we experienced with asking probabilistic likelihoods of migrations at baseline, our registered report specified that we would switch to using a 5 category Likert scale to measure migration intentions at endline. We then look at impacts on saying they will “surely” move in the next five years (5 on the Likert scale), or that they will “likely or surely” move (4 or 5 on the Likert scale). Columns 4
value in the 98<sup>th</sup> percentile. Similarly, weights in the 1<sup>st</sup> and 2<sup>nd</sup> percentile would be replaced with the lowest value in the 3<sup>rd</sup> percentile.

After trimming W3, we have the adjusted weights W4.

# _8) Post-stratification_

To reduce the overall standard errors and weight the population totals up to the known population figures, a post-stratification correction (Wps) is applied. This correction also reduces overall standard errors (see Little et al, 1997). The level of disaggregation for the post-stratification correction should be at the lowest reliable level available from an auxiliary data source, regardless of the level of representativeness for which the survey was designed. For example, if reliable census projections are available for the subregional level, these should be used even if the sample was designed only to be representative at the regional level. Note that close attention should be paid to the reliability of the outside data source underlying the post-stratification calculations. With proper design and implementation, the survey population estimates should be close to the actual population estimates. Post-stratification should therefore be seen more as a fine-tuning adjustment rather than a major realignment. If the weights are adjusted using poor quality auxiliary information, there is the possibility of reducing precision or introducing bias into the estimates.

In order to calculate the post-stratification adjustment for the LSMS-ISA surveys, census projections of household populations at the regional–urban/rural level are generally used. These projections are provided by the national statistics bureaus. The adjustment is done at the household level since households are the units of analysis which are sampled and for which the weights are calculated. If the census projections give the population totals in terms of individuals, the average household size is calculated from the dataset for each category in the disaggregated data and the totals calculated in terms of households. Within each category the weighted total number of observations is calculated and the census projection is
This paper investigates and compares the evidence of the drivers of job loss from two countries. It provides an evidence base for quantifying further risk of job loss and econometric results to inform the construction of a risk index. Our methodology holds the promise to be applied in a broader set of countries as well. Our comparative findings suggest some degree of generalizability of risk factors – and in particular the importance of closures, customer infection risk (especially when cases and transmission are high), supply chain disruptions, and other sector-specific shocks to consumer demand. Despite Jordan and Georgia’s having different macroeconomic contexts and labor market structures, we find that they shared similar main drivers of job loss at the firm level, and the magnitudes of impact of those drivers was very similar for each.

Feasible approaches for broadening the application of our methodology and findings are likely to vary according to countries’ data availability. Where sufficient firm-level and labor force data exist, analysts can follow the same steps to derive and utilize country-specific coefficients, considering all possible risk factors observed at the firm or occupation level. Alternatively, analysts can assign a job loss risk value based on a rescaled index using our coefficient estimates. Although it is never perfectly safe to assume external validity, in the absence of other country-specific evidence or data, where our coefficient estimates are similar for the two countries, it may be justified to use them for other middle-income countries. Where firm or other data on initial shocks to sales (or its drivers, such as closures) exist, but data on other variables do not, the estimated effect of sales shocks can be linked to jobs in the latest labor force survey.

We propose improvements to future firm surveys that would facilitate the ex-ante assessment and empirical understanding of jobs impacts, not only of future crises, but also of key labor market dynamics in non-crisis times. The rapid evolution of COVID-19 has highlighted the need for more detailed firm-level data (from formal and informal firms, ideally, with more than one employee) on their
acteristics for Accra, Dar es Salaam, and Antananarivo can be find in Erman et al. (2020), Erman et al. (2019), and WB (2017) respectively.

TABLE 2: Databases descriptions

|**City**|**Database**|**Area**|**Date**|**Flood/s**|**HH selection method**|
|---|---|---|---|---|---|
|Accra|Disaster-Poverty<br>household survey|9 neighborhoods<br>considered slum areas<br>in the Odaw basin area|May-Jun/2017|2015 food|4 strata: combination of high/low<br>risk of food and high/low level of poverty.<br>Informal settlements.<br>Not representative at the citylevel.|
|Antananarivo|Urban Living<br>Standards|Greater Antananarivo|Oct-Dec/2017|2015 food<br>“Chedza food”|2 strata: risky and no risky areas, where<br>risky are EAs affected by Chedza.<br>Representative at the citylevel.|
|Dar es Salaam|Disaster-Poverty<br>household survey|Dar es Salam|Nov/2017,<br>Sep/2018|Most recent food,<br>2018 food|3 levels of food risk (no risk, low risk and<br>high risk). Representative at the citylevel.|
|Addis Ababa|Disaster-Poverty<br>household survey|Addis Ababa|May-Jun/2017|Most recent food|Representative at the city level.|

# **3.2 Accessibility to jobs**

In an efficient housing market, property prices rise with increased access to jobs and amenities (Ahlfeldt, 2013), and in choosing where to live people make a trade-off between low housing value and proximity to jobs. To understand rents, we
1 if financial liberalization had occurred in the previous 5 years and 0 otherwise.<sup>6</sup> To ease interpretations, we report the effects of one-unit changes in the dependent variables on the probability of a crisis (expressed in percentage points) evaluated at the mean value of the data. We also report the associated z-statistics that test the null hypothesis of no effect.

The first column in table 3 presents results of a probit model where the dependent variable takes the value of 1 during years with a financial crises and 0 otherwise (excluding 5 years after the crises starts or while the crises last). The macroeconomic variables have the expected signs and are significant except for the growth rate of terms of trade and the claims of the private sector as a share of GDP. In this specification, the probability of a crisis increases both in the 5 years following liberalization and in liberalized financial systems.

These results, however, are very sensitive to the sample and the definition of financial liberalization. In the second column we report the results excluding Turkey<sup>7</sup> . The results now are very different. The probability of a crisis is higher only during the 5 years following liberalization and there is no evidence that the probability of a crisis is higher in liberalized financial system.

The evidence is even stronger once we use date of financial liberalization based on Williamson and Mahar (1998) and Drees and Pazarbasioglu (1998) surveys (see table 1). Column 3 reports the results (including Turkey) using the adjusted dates of financial liberalization. The coefficients of the financial liberalization dummy variable is insignificant (and negative) while the coefficients of the dummy variable for the 5 years period following a financial liberalization variable is significant and positive. Thus, there is supporting evidence

> 6 The results are simialir when we use other definition such as 3 years period instead of 5 years.

> 7 One reason to exclude Turkey is that Turkey had additional banking crises during 1980-95, but these crises
Policy Research Working Paper 6200

# **Abstract**

In many African countries, the timing of important life events—such as school-leaving, first marriage, and entry into the labor market—is thought to be strongly tied to migration. This paper investigates the relationship between major life events, household characteristics, and migration among adolescents and young adults in contemporary Malawi. The specific research questions are twofold. First, what are the socio-economic and demographic determinants of migration? Second, how do school attendance, first marriage, and employmentseeking relate to migration patterns? The study uses panel data collected from a survey designed specifically to explore socioeconomic and demographic aspects of youth transitions to adulthood and which tracked respondents as they moved to new dwellings. Among the sample, they find that moves are not uncommon, and the predominant reasons for moves are non-economic. Although historically ethnic traditions in this area have held that girls and women usually did not move upon marrying, the data show that women were more likely to move between survey rounds than boys and men, and that marriage was the main reason for doing so. Closer ties to the head of the household are associated with less movement for both women and men.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@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
assigned to the tradable category if those goods are typically imported or exported. For example, some tradable spending items include apparel and consumer electronics.

We summarize both MCC categorizations in Table 1. As there are thousands of individual MCCs, we report summary statistics using standard groupings of MCCs that are commonly used by financial institutions and in the academic literature. Column (1) reports the average spending for each MCC group in our sample. Column (2) indicates the fraction of spending within that MCC group that is classified as durable (the remainder is classified as nondurable by construction). Column (3) indicates the fraction of spending within the MCC group that is classified as tradable (with the remainder classified as nontradable). For example, the third row corresponds to the MCC group “automotive expenditures”, in which 100% of codes are classified as durables and 0% as non-durables, and 64% of the spending in this MCC group is classified as tradable versus 36% as nontradable (primarily codes corresponding to services). The last row of the table summarizes codes that we group as “uncategorized.” These miscellaneous MCCs do not contain enough information to categorize them as durable, as opposed to nondurable, or tradable, as opposed to nontradable, expenditures.

11. We do not obtain data on debit card transactions because that they account for a negligible fraction of spending according to pre-intervention summary data (debit cards are used primarily for cash withdrawals).

16
why the utilization of health facilities is low despite the high prevalence of HIV infections, citing provider problems such as a lack of medicines and supplies, absent and disrespectful doctors, and long waiting times at facilities as main reasons for dissatisfaction.

7. **Identifying gaps in data and knowledge** . While issues of social inclusion are acknowledged as being important in most macro level analyses such as the SCD, gaps in data and knowledge can prevent an empirically grounded narrative; in turn keeping them outside the purview of prioritization. Not only it is essential to do a thorough review of the extant literature and to undertake creative, inter-disciplinary analysis of data, but it is also important to highlight areas where data gaps prevent relevant issues from being addressed. Examples are data on disability, knowledge base on sexual orientation, and gender disaggregated data across a number of outcomes. There is now increasing empirical evidence on processes of social exclusion – such as on stigma, stereotyping and overt or covert discrimination. Often, small tweaks to survey questions can yield significant results in terms of data and knowledge and if highlighted in the SCD, the proposed changes are likely to get greater policy traction in the country.

8. **Going beyond the “who” and “how” questions to asking “why** ”. As pointed out earlier, the “why question” is an essential step towards designing the right policies and programs for social inclusion. It helps in figuring out what to do by identifying 26
there was a gradual trend-conversion in the monthly earnings of the affected fishes; by the end of 2016, the average monthly income of these individuals seemed to have recovered to the lower-bound level of their pre- _Formosa_ earnings. Also consistent with the descriptive statistics from Table A1, we observe a rather flat earning profile for the control group (i.e. the unaffected fishers) across the entire analysis period.

# **3.1.2 Satellite data on boat detection**

To measure the impact of _Formosa_ on fishing activity, we use the Visible Infrared Imaging Radiometer Suite (“VIIRS”), which documents high-resolution night-time light luminosity at the globe’s surface. VIIRS is administered by the National Oceanic and Atmospheric Administration (NOAA). Specifically, we utilize a special Boat Detection Module of VIIRS (“VBD”), which detects the ocean’s night-time light source emitted from fishing boats.<sup>11</sup> VBD is jointly sponsored by the U.S. Agency for International Development, NOAA, and the World Bank. The project collects and processes remote-sensing images from the Suomi National Polar-orbiting Partnership (Sunomi-NPP) satellite. Joint Polar Satellite System (JPSS) is the new generation polar-orbiting operational environmental satellite system in the U.S. The VIIRS itself is the primary imager on Sunomi-NPP.<sup>12</sup> The use of night-time light brightness as a measure of economic activity has become increasingly popular in economic research. Especially in developing countries where subnational accounting data are often missing or unreliable, luminosity at night has been shown to provide an unbiased proxy for growth outcomes. It is noted that the majority of existing studies adopting night-light measure have relied on the Meteorological Satellite ProgramOperational Line Scan package (DMSP-OLS). DMSP-OLS final output provides composite annual light density measures at a coarse footprint of 5km _×_ 5km pixel resolution (see Donaldson and Storeygard (2016) for a comparative analysis on this literature). Compared to the imaging sensors suite in DMSP-OLS