rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Migrants working outside their country of birth returned more than US$281 billion to family members in their countries of origin in 2007 (World Bank, 2009). Recent research suggests these remittances have important implications for the economies of remittance-recipient countries. Numerous studies analyze their impact on poverty, inequality, growth, education, infant mortality, and entrepreneurship.<sup>1</sup> However, surprisingly little attention has been paid to the question of whether remittances affect the financial sector in recipient economies. This issue matters because financial systems perform a number of key economic functions and their development has been shown to foster growth and reduce poverty.<sup>2</sup> Burgess and Pande (2005) show that the expansion of the banking sector in particular can have a very large impact on poverty levels and growth. Furthermore, the link between remittances and the banking sector is important because some argue that intermediating remittances through the banking sector can magnify the developmental impact of remittance flows (See Hinojosa-Ojeda, 2003; Terry and Wilson, 2005, and World Bank, 2006).

This paper analyzes the impact of remittances on the breadth (or outreach) and depth of the banking sector in Mexico. There are several reasons why remittances might affect banking sector breadth and depth. First, the fixed costs of sending remittances make the flows lumpy, providing households with excess cash for some period of time. This might potentially increase their demands for banking services (and, hence, foster banking outreach and depth), since banks offer households a safe place to store this temporary excess cash. Second, interbank and wire transfers that might be collected in bank branches are an important means of receiving remittances. Banks charge processing fees for these transactions, which can be a significant source of income for commercial banks in remittance-receiving countries. The potential to collect these fees might induce banks to expand their outreach and locate close to remittance recipients. Third, a substantial portion of remittances flow to households that are likely to be

> 1  For a review of the literature on remittances see López Córdova and Olmedo (2006). For the specific case of Mexico, Amuedo-Dorantes, Sainz and Pozo (2007) consider the impact on healthcare expenditures; Esquivel and Huerta-Pineda (2007) look at the impact on poverty; Hanson (2007) looks at labor force participation; Hanson and Woodruff (2003) analyze the impact on schooling; Hildebrandt and McKenzie (2005) and Kanaiupuni and Donato (1999) consider infant mortality; López Córdova (2005) analyzes the impact of remittances on schooling, infant mortality and poverty; Woodruff and Zenteno (2007) and Woodruff (2007) look at entrepreneurship.

> 2 See King and Levine (1993), Beck, Levine and Loayza (2000a,b), and Beck, Demirgüç-Kunt, and Levine (2007).

2
8 The duration of credit from diffierent sources reported in the 1985 survey is presented in Table 1.6. Both institutional and noninstitutional credit sources lend primail to short-term bonowers The table also shows that only institutional sources do significant medium- and long-term lending.

Table 1.6: Distnrbution of Loans by Term (oercentage)

|.Category|Short term|Medium term|Long term|
|---|---|---|---|
|By number of borrowers||||
|Institutional|78|10|11|
|Noninstitutional|96|4||
|By loan amount||||
|<br>Institutional|32|16|52|
|Noninstitutional|90|9||

Source: Rural Credit Survey, 1985.

Richer Rural Households Have Better Access to Cheaper Institutional Credit Whereas Poorer Households DPend Mainly on Expensive Noninstitutional Sources The IFPRI survey substantiates that credit is important to all rural households. Fifty-seven percent of the surveyed households use credit, which finances 17 percent of household expenditure on farm inputs, farm capital, and consumption. Not surprisingly, there is an inverse cofrelation between household wealth and credit use (Table 1.7). This table shows the shares of various types of expenditure financed through borrowing by the five asset quintiles. Thirty-three percent of the expenditures of poor households are financed through credits compared with 11 percent for richer households. This pattern is observed regardless of whether credit is used for the purchase of inputs or capital assets or to finance consumption. Poor households rely mainly on informal, more expensive, credit sources (Table 1.8). Only 1 percent of their total credit is from formal sources, compared with 98 percent from friends and relatives and other informal sources. Richer households, by contrast, obtain most of their credit (58 percent) from formal sources.

Rich farmers' superior access to institutional sources is also corroborated by the 1985 rural credit survey. It shows that within the crop sector, the major part of credit is preempted by large farmers. About 60 percent of small and subsistence farmers do not borow at all. Of those who borrow, 90 percent have recourse only to the infonnal market Because of the enormous expansion in availability since 1973, there has been some improvement in the smaUlholders' access to institutional credit The evidence is distorted, however, due to the abuses associated with interest-free loans from 1979 to 1988. These loans were generally preempted by large farmers, who either used the names of smallholders to obtain loans or understated the size of their own holdings.
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higher income levels or amenities. Spain’s proximity to more attractive labor markets in other European countries, which are possibly less accessible via direct migration, is likely to enter into the decision processes and utility calculations of Moroccan migrants.

The objective of this paper is to explore dynamic migration decisions and transit paths using analytical modeling and empirical analysis. We first construct a sequential multi-period model of migration by drawing on recent papers on dynamic decision making processes in labor markets (such as Kennan and Walker, 2011 and Artuc, Chaudhuri and McLaren, 2010). Agents are identified by their current country of residence in addition to their birth countries. In each period, they can move to a third country or remain in their current location. Their mobility decisions depend on location-specific utility flows as well as bilateral moving costs between each pair of countries and a stochastic individual component for each choice. The decision to move or to stay put is reconsidered in every period. The dynamic structure of the decision process results in a continuation value for each potential destination. The main insight of the model is that the continuation value includes an option value for each location in addition to an instantaneous utility. This option value of a destination is based on the access it creates to other higher value destinations.

Proper accounting of dynamic decision processes, such as reversing migration decisions or moving to third countries in later periods, leads to better identification of spillovers between alternative destinations. Otherwise, we would end up with biased estimates when we try to identify the determinants of migration patterns between pairs of birth and destination countries or to assess the effectiveness of migration policies. Bertoli, Brucker and Fernandez-Huertas Moraga’s (2014) analysis, in the context of the European crisis, shows how biases can occur in estimating the determinants of migration when sequential decision processes and resulting spillovers are ignored.

Incorporating the option value of a location and viewing migration processes from this dynamic lens can generate many additional insights. In standard static models, decisions to migrate to 3
on transportation costs from the center of each district to Kampala. These costs are reported in the community questionnaire of the Uganda National Household Survey as the monetary cost of reaching Kampala by car/truck.<sup>14</sup> This strategy requires a careful control of the regression so as to make sure that out-of-district transportation costs do not have a direct effect on the left-hand side variable (poverty). In our regression model, there are two sets of such controls. A major control is the export price index. Under imperfect competition among intermediaries, out-of-district transportation costs are borne both by intermediaries (directly) and by farmers (via lower prices). In districts where out-of-district transportation costs are lower, the profits of intermediation are higher, more intermediaries enter, and farmers enjoy higher prices and are less likely to be poor. For the instrument to satisfy the exclusion restriction, we need to control for the fraction of the out-of-district transportation costs that are borne by farmers. We achieve this by including the export price index, which summarizes farmer prices and includes the pass-through of out-of-district transportation costs. After including the price index, there is no direct effect of out-of-district transportation costs on poverty via cost pass-through.

The other set of controls comprises all district characteristics that could be affected by out-of-district transport costs and that could have an effect on poverty. Districts with lower out-of-district transport costs to Kampala could be less poor due to improved infrastructure, higher local development, and better institutional quality. This effect is also already controlled for in regression (10) with the extensive set of districts characteristics included in **z** .<sup>15</sup> After including producer prices and district characteristics in the model, all the channels through which out-of-district transport costs could affect poverty are accounted for.

As a robustness check, we also include an additional district control, the density of food markets, defined analogously to export market density. These markets are less sophisticated than agriculture produce markets (coffee stalls, intermediaries and plantations) and are thus

> 14In this section, we estimate the model using one instrumental variable for our endogenous regressor. In section 5, we provide a sensitivity analysis by estimating the model with an additional instrument.

> 15These include access variables (to credit, roads, equipment, inputs, and extension services), agricultural constraints (land quality, land availability), social infrastructure (education, health), security and conflicts.

16
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|1980|11.58|20.2|21.9|18.3|32.4|-2.6|12.4|
|---|---|---|---|---|---|---|---|
|1981|3.00|2.7|_5.4_|1.5|-4.9|-1.0|8.9|
|1982|27.22|23.0|18.2|21.1|21.4|43.8|3.9|
|1983|12.89|15.9|9.4|20.0|22.5|4.7|3.8|
|1984|-|--|9.9|8.8|6.3|16.4|4.0|
|1985|--|--|20.0|23.5|32.2|30.9|3.8|
|1986|--|--|14.1|12.8|18.5|19.8|1.1|

Notes: 'Public pension plan data from SEI Financial Services reported by Berkowitz & Logue (1986), T. AIII-3.

2 Large U.S. private plan data from SEI Financial Services reported by McCarthy & Tumer (1992) T. 12.1, p. 253.

'Large U.S. private plan data from 5500 Repo-ts reported by McCarthy and Tumer (1992) T. 12.1, p. 253.

'Large Canadian private pension plan data from SEI Financial Services reported by Pesando and Hyatt (1992) T. 1, p. 21.

'McCarthy and Tumer (1992), Table 12.1, p. 253.

Olivia S. Mitchell - Public Pension Govemance and Performance
This finding of generally modest impacts on the average farm household stands in sharp contrast to the strong opposition from agricultural lobbies in the rich countries. This opposition can be better understood when we use our household survey data for the US to show that the degree of earnings diversification diminishes for the wealthiest farms in the highly protected commodities, and this provides them with strong incentives to prevent the very substantial drop in household welfare that can be expected under trade reform. Consequently, we can expect that some compensation mechanism is necessary to solve the political impasse currently plaguing the Doha talks. We explore one such mechanism by which payments are aimed at neutralizing the loss in average on-farm income for each commodity group. This program would introduce around $25 billion of new agricultural subsidies into global agriculture from the three countries where farm income declines (Japan, EU, and US) and would likely make the Doha scenario much more palatable to the farm lobbies.

In the poorest countries, we find that, with one minor exception, rich country agriculture reforms benefit low-income farm households. Regardless of the poverty line considered, the poverty headcount in this part of the developing world falls. However, the impact on non-farm population groups is mixed. In those countries where agriculture makes up a large share of the unskilled labor force, rich country reforms tend to increase the demand for labor sufficiently to benefit unskilled workers throughout the economy. However, self-employed households in the non-agricultural economy, as well as those dependent on transfer payments, systematically lose. Therefore the national poverty outcome inevitably depends on the relative weights of these different groups in the national poverty picture. Since a large share of the poor reside in agriculture, national 39
dimensions.

9The CCTS records all merchandise transactions passing through Chinese customs and contains basic firm information (e.g., name, address, ownership), product code (8-digit), and destination country. We collapse the data to 6-digit products for consistency with the international trade data from BACI.

> 10In the subsection titled “Robustness checks and alternative indicators”, we verify that our main results are robust to the exclusion of trading firms, identified following Ahn et al.’s (2011) approach.

> 11The sample of firms used in the analysis is a highly selective subset of the data because, given the firm and product fixed effects, firms can only be included in the final regression if they had positive exports in both 2000 and 2006 for at least 2 products. We computed that roughly 17% of exporting firms in 2006 also exported in 2000. Two-thirds of these firms were pluri-product exporters in both years. We verified that the distribution of export value and number of exported products are nevertheless similar when considering the full or the restricted sample. The main difference is that pluri-product firms and surviving firms export slightly more, on average.

> 12Aggregate indicators (at the locality and product level), such as density or RCA, are computed using all of the information from customs.

> 13Roughly 27% of the city-hs6 RCA dummies turn out to be dependent (are converted from 1 into 0 by excluding the export information of a firm in our final sample). The revised density variable is strongly correlated to our benchmark density indicator: the correlation coefficient is 0.87.

> 14 We thank the editor for this suggestion.

> 15Table 10 in the appendix verifies that our estimates are unaffected by the selectivity of our dataset, referred to in endnote 11. The results are rather similar when keeping all firm-product pairs that are active in 2006, regardless of whether they report positive export flows in 2000.

> 16The weights for each product correspond to its share in the firm’s residual exports.

> 17We use the list established by Wang and Wei (2010). It includes the four types of policy zones established by the Chinese government: special economic zones (SEZs), Economic and Technological Development Areas (ETDAs), Hi-Technology Industry Development Areas (HTIDA), and Export Processing Zones (EPZs).

> 18The average density in a given location is computed as the weighted average of the product density in

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Policy Research Working Paper 9644

# **Abstract**

The recognition of prior learning provides opportunities for workers to have their skills assessed and certified. In many countries, recognition of prior learning is expected to broaden individuals’ empowerment and economic opportunities. Using a randomized control trial method, this impact evaluation study aims to assess whether and to what extent assessment and certification of prior learning effectively improve economic and other job outcomes among assessment participants in Bangladesh. Five hundred applicants were randomly assigned to the treatment group and 500 applicants to the control group. The baseline survey took place in June 2018 and the end-line survey in January 2019. The findings indicate that assessment prior learning positively impacts workers’ employment outcomes and quality of employment, including the chance of getting employed, wage levels, formality of employment, and workers’ confidence in their skills and jobs. The findings also suggest that women may benefit more than men from certificates of recognition of prior learning in Bangladesh. The transition analysis further supports the finding that recognition of prior learning facilitates the transition of unemployed or not-working workers into employment. Recognition of prior learning also increases the chances of finding work through formal job search channels and at formal and larger private companies. These findings align with the assumption and expectation behind the recognition of prior learning programs in the context of economic development. The participants in assessments and certification of prior learning had overwhelmingly positive opinions about them. The study has some limitations and suggestions for future research.

This paper is a product of the Education 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 snakata@worldbank.org, usharma@worldbank.org, trahman3@worldbank.org, and mrahman2@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 governments they represent._ Produced by the Research Support Team