rafmacalaba/data-use-mentions-extended huggingface dataset:
# B.  Where do the unbanked get financial services?

Individuals who do not use banks still need to obtain financial services. If they have any financial savings, they need a means to safeguard their savings. Even if they receive income in the form of a check, they need a means to convert the check into cash. Finally, they many need to borrow periodically. This subsection draws on the OCC survey data to examine where and how the unbanked obtain these financial services.

Table 3 presents data on the forms in which the unbanked keep financial savings and contrasts this with the banked. As noted in Table 2 above, 86 percent of the unbanked said that they had no formal sector financial savings. The OCC survey also asked about the use of informal means of financial savings, such as holding cash, money orders, uncashed checks, jewelry that can be sold, etc. As shown in Table 3, 21 percent of the unbanked respondents said that they use such means and 16 percent of the banked did. Allowing for both formal and informal means of financial savings, 69 percent of the unbanked had no financial savings and 22 percent of the banked had none.

**Table 3 Use of informal savings methods**

||**Percentage**<br>**among the**<br>**banked**|**Percentage**<br>**among the**<br>**unbanked**|
|---|---|---|
|Kept savings in money orders, uncashed<br>checks, cash, jewelry, loans to others, by<br>participatingina savings circle, etc|16.3|20.5|
|Had neither informal financial savings<br>nor formalsector financialsavings|21.9|68.6|

Table 4 examines how people receive their incomes and convert that income into cash. Among banked individuals, 38 percent received an electronic deposit to their account
conducted by Beck, Demirguc-Kunt and Martinez Peria (2008), macroeconomic factors and competition in the SME segment were also identified as the major obstacles, not the legal and contractual environment. The greater concern expressed by MENA banks about the quality of financial infrastructure is not surprising, considering that the region fares very poorly in this area. As shown in Table 6, the region has the lowest legal rights index among all the regions. Moreover, while the credit information index has improved in recent years, but the coverage of credit reporting systems is still very limited.<sup>5</sup> In order to obtain more detailed information on the weaknesses of collateral regimes in MENA – an area where the region fares very poorly – the survey included questions on the problems faced by banks on the different elements of the secured lending chain – especially the registration, enforcement, and final sale of the seized collateral. Moreover, the questions were broken down by fixed and movable collateral. The answers are reported in Figures 8a-8b and 8c-8d, for GCC and non-GCC countries and for state and private banks.

As shown in Figures 8a-8d, there are significant problems in the registration, enforcement, and selling of collateral, especially movable collateral. While a relative low share of banks reports serious problems with the registration of fixed collateral, a high share of banks reports that registries of movables remain very deficient. Enforcement of collateral is an even bigger problem, especially for movables, but also for fixed collateral in the case of non-GCC banks. Finally, an even larger share of banks reports problems in selling the seized collateral. Again,

> 5 Maddedu (2010) provides a detailed analysis of the quality of credit information systems in MENA, while Alvarez de la Campa (2010) and Uttamchandani (2010) examine the effectiveness of collateral and insolvency regimes.
to study the effects of “deep” integration through preferential trade agreements combined with infrastructure investment. More generally, our work is also related to the findings of Goldberg & Reed (2022) on the role of demand constraints in national development and, in particular, on how these constraints can be alleviated through international market integration.

The remainder of the paper is organized as follows. Section 1 describes the data used in the analysis. Section 2 presents the ex-post quantification of the trade effects of trade and transport reforms in Africa. Section 3 quantifies the impact of planned infrastructure investments on transport time. Section 4 presents a general equilibrium counterfactual analysis. Concluding remarks follow.

# **1 Data overview**

Analyzing trade and infrastructure in a general equilibrium structural gravity framework requires specific data. The focus of this section is the development and validation of an original dataset on transport times of African exporters. Built using Geographic Information System tools and network analysis, it provides a detailed, finescale perspective on transport times. Given the critical role that transport times play in shaping trade patterns, the production of such data is a contribution in its own right. We also discuss the sources of trade and production data and how we combined them to get an accurate picture of domestic and international trade flows.

## **1.1 Transportation Time**

Transportation times become prohibitive not only when roads are in poor condition, but also when there are delays at borders or operations at ports are inefficient. To get a complete picture of transportation costs for African exporters, we take all these aspects into account and construct a new database of transportation times at the city level. We first map the current infrastructure network to obtain our baseline transport time matrix. We then use the list of investments from the Infrastructure Development Program for Africa (PIDA) in its long-term vision (2012-2040) to create our counterfactual.

We start by mapping the current transportation network to calculate transportation times between large cities in African countries and the rest of the world.
We recoded this question to a dummy indicator of “interested in migration”, which takes a value of 1 if “very”, “quite”, or “somewhat interested”, and 0 otherwise. In our data, 23 percent of individuals (18 percent of women and 31 percent of men)<sup>7</sup> declare that they are interested in migrating. This represents a larger share of individuals than the estimated migration rates from Indonesia. The World Bank estimated the stock of international migrants from Indonesia at 9 million in 2016, which represents 7 percent of the total labor force (World Bank, 2017).<sup>8</sup> Figure 2 shows the share of individuals interested in migration by gender and age. Women are generally less interested in migration than men and there seems to be a decreasing trend of interest in migration with age for both groups.

**Figure 2. Interest in migration by gender and age**

_Source_ : Authors’ calculations from DESMIGRATIF (2018)

# 3.2. Methods

This study aims to leverage rich survey data with household characteristics, roster of household members’ employment participation, and individual preferences and perceptions of migration to identify and rank the determinants of interest in migration for women and men. However, standard econometric methods are not best suited for this task because it runs the risk of overfitting with the

> 7 This difference is statistically significant at the 1% level.

> 8 The official estimated number of _documented_ emigrants from Indonesia in 2018 was 283,640 which represents approximately 0.2 percent of the workforce (BP2MI, 2019). The official number relies on proper registrations, which could severely underestimate the actual number of migrants if many migrants go through _undocumented_ routes (e.g. gain employment overseas while entering the destination country with a tourist visa, which is legally allowed in some countries).

10
gc.Leral secondary schools or those from vocational schools employed in nor. course related occupations. Since these results seem to be at odds with the predominantly held view (of economists) that vocational schooling is a socially inefficient form of education, particularly in relation to traditional academic schools, it seems appropriate to probe further this apparent discrepancy.

In the standard text on the economics of education, Mark Blaug discusses the usual distinction drawn between academic and vocational education, in this way: This distinction, which is actually grounded in the nature of the two curricula, is allowed to carry the implication that some education prepares students for the 'world of work' and some does not. All too frequently, however, those who have taken courses of study generally called 'academic' ...... reap substantial financial returns from their education, thus producing the paradoxical conclusion that academic education has a greater 'vocational' value than vocational education. The traditional distinction was developed by educators but the labor market has its own way of appraising qualifications. (Blaug 1970, p 247).

And, indeed, a large number of cost-benefit analyses, based on labor market earnings follow-up studies of vocational and academic secondary school completers, have shown that the higher costs of vocational school education (as measured in these studies) are not offset usually by any positive or sizeable earnings differential accruing to vocational school completers. The main thrust of all this research is to reveal general secondary schools as the superior societal investment, thus giving some underpinning to the conventional wisdom, referred to above.

- 47 -
period (> 1995) and 0 in the pre-treatment period (≤1995). Consequently, captures aggregate factors that would cause changes in manufacturing exports after 1995 even in the absence of a policy change. is an indicator variable coded 1 if the observation is in the treatment group (automotive sector) and in the second time period (>95), 0 otherwise. is thus the D-in-D estimator, capturing the response of total manufacturing exports to the change in export tax incentives related to the MIDP in South Africa.

37. The vector contains other control variables that vary over time and that typically affect a country‟s export supply. The choice of these variables was based on results of studies on the determinants of South Africa‟s export performance that highlight the importance of supply rather than demand factors (for instance Edwards and Alves, 2006). The following variables are identified as main factors enhancing the profitability of export supply in South Africa (also in line with Tsikata, 1999; and Golub and Ceglowski, 2001): relative prices, inflation, industry concentration, infrastructure, variable production costs, tariff rates and skilled labor.

38. Based on this research and data availability, we have considered the following control factors in our study: (i) as a proxy of relative prices, we use the manufacturing terms of trade index (having also considered the general Terms of Trade), which proves to be

> 18 Due to missing observations for the sectoral variables in the latter years, we had to limit the study to 2004.

14
imputation solutions to fill the gap for this measure. We group these situations into two broad categories. In the first category, where the data is ”partially missing”, the household survey collects plot level information and agriculture practices, and crop-cuts on a subsample usually selected randomly. A within-survey imputation framework is applied in this case. In the second category, the household survey has plot level information on agriculture practices and self-reported crop production and no harvest data based on crop-cutting. In this case an across survey imputation framework is applied. We provide more details for each of these cases in the reminder of this section.

# **2.1 Partially missing: Within-survey imputation**

The situation of partially missing crop-cut data is a common scenario in most farm surveys.<sup>2</sup> In such cases, crop yield measurements are obtained in two ways: through relatively inexpensive, but less accurate, self-reported harvest data, and through more fieldwork-intensive, expensive, but more objective measures acquired via crop-cutting on a subsample. This survey design is adopted for cost-efficiency and has the advantage of providing multiple measures of crop yields for a small proportion of plots in a single survey.

In the case of LSMS-ISA surveys, several country programs<sup>3</sup> have integrated crop-cutting into their survey operations. The proportion of the sample with crop-cuts (CC) varies across programs. Generally, a list of plot-crop combinations is made at the Enumeration Area (EA) level. If the number of these combinations exceeds a minimum threshold, a random sample of plots is selected for crop-cut implementation.<sup>4</sup> This survey design is conducive to within-survey (within agricultural season) imputation. A key reason for this is that plot-level information and agricultural practices are collected for all plots in the survey, providing a common basis for the covariates used in statistical modeling. In this context, an important question arises: What proportion of crop-cuts is necessary to obtain valid crop yield statistics? This
quality relative to domestic capital equipment goods produced in India since most of the imports of capital goods come from developed economies.

Our sample contains information for around 3,744 firms in organized industrial activities from manufacturing sector for the period 1990-1997. Since we lagged control variables, our estimating sample starts in 1990 and the total number of observations firm-year pairs is 14,425. In order to keep a constant sample throughout the paper and to establish the stability of the point estimates, we keep firms that report information on all the firm and industry level control variables. Although our panel of firms is unbalanced, there is no statistical difference in the average firm characteristics between the initial year and the final year of our sample.

# _Input-tariff data_

To identify the impact of input-trade liberalization on firms’ foreign technology choice, we use input tariffs at the 3-digit-NIC industry level. Tariffs data is provided by WITS (World Bank) and corresponds to India’s effectively applied most favorite nation (MFN) import tariffs with respect to the Rest of The World at the industry level ISIC (rev 2).<sup>25</sup> In order to identify the effect of input tariff changes on firms’ decision to import capital goods, we construct different tariffs measures for capital goods and for variable intermediate goods. In this sense, we depart from previous studies on input-trade liberalization to consider both variable inputs and capital goods in the construction of input tariffs.

This methodology allows us to disentangle the indirect effects of tariffs on intermediate goods on firms’ decision to import capital goods from the direct effects of tariffs on capital goods. For each 3- digit industry, _s_ , we generate a capital goods tariff as the weighted average of tariffs on the capital goods used in the production of final goods of that 3-digit industry, where the weights reflect the share of capital goods of the final goods industry on total expenditures in capital goods using India’s input-output matrix

> 25We use correspondence tables to convert tariffs into ISIC rev 3.1
paper. Section 6 describes the concept and measurement of mobile savings. Section 7 presents the core analysis. Section 8 discusses the future of mobile savings and concludes.

# **2 The Reach of Mobile Phones in Kenya**

This section reviews recent data on the extent to which mobile phone and mobile money usage has penetrated Kenyan society. The information presented here comes from the Kenya Afrobarometer survey, which was conducted in November and December 2011. The Afrobarometer sample is representative of the population of Kenyan adults (age 18 and older).

The Afrobarometer data show that mobile phone access has reached extremely high levels in Kenya (Table 1). The survey asked each respondent if he or she every uses a mobile phone and, if so, who owns the phone that he or she uses most often. Eighty percent of Kenyan adults own their own individual phones, an additional 10 percent use a phone owned by someone else in their households, and 3 percent use a phone owned by someone outside their households. Slightly lower numbers of women and rural residents own their own individual phones—78 percent of women and 77 percent of rural residents— and women and rural residents are more likely to use a phone owned by another member of their household. Overall phone usage rates differ very little between men and women and between urban and rural households. On average Kenyan households own 2.4 mobile phones, and 93 percent own at least one phone (Figure 1).

The survey separately asked how often respondents normally use a phone to 1) make or receive a call, 2) send or receive a text message, and 3) send or receive money or pay a bill. A detailed tabulation of the basic findings from these questions is shown in Figure 2. As with the mobile access figures, the usage rates for particular mobile functions are strikingly high. At least once a day, 81 percent of Kenyan adults make or receive a call, 61 percent send or receive a text message, and 22 percent
Policy Research Working Paper 9740

# **Abstract**

In the presence of credit constraints, temporary migration abroad provides an effective strategy for workers to accumulate savings to finance self-employment when they return home. This paper provides direct evidence of this link and its effects on workers’ employment trajectories by using a new, large-scale survey of temporary migrants from Bangladesh. It constructs and estimates a dynamic model that establishes connections between asset accumulation and credit constraints, and, thus, between workers’ migration and self-employment decisions. Interlinked impacts also emerge from simulations of three key policy interventions that target migration costs or domestic credit constraints for entrepreneurship. Lowering migration costs increases emigration, reduces the age at which workers depart, and reduces the duration of their time abroad, which together lead to higher savings and domestic self-employment. Reducing the interest rate for entrepreneurial loans reduces migration and savings levels, undercutting the positive effects on business creation at home. Correcting workers’ inflated perceptions about overseas earnings potential reduces emigration rates and durations, triggering a decrease of both repatriated savings and self-employment in Bangladesh. The findings, which have implications for migrant-sending countries, highlight the need for policies to take into account the linkages between migration and self-employment decisions.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research 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 lbossavie@worldbank.org, josephsimon.goerlach@unibocconi.it, cozden@worldbank.org, and hwang21@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
trade is equal to the 25 percent of the Official Development Assistance (ODA). Table 3 reports the AfT distribution applied in each scenario.

**Table 3. Aid for trade distribution**

|_Donor_|_Income transfer_<br>_(US $ million change_<br>_w.r.t. baseline scenario)_|
|---|---|
|United States|-3998.94|
|Canada|-3617.13|
|Western Europe|-4240.08|
|Japan|-4300.37|
|Australia,New Zealand & Oceania|-3938.65|
|_Recipient_||
|Eastern Europe|742.63|
|Former Soviet Union|342.94|
|Middle East|3271.26|
|Central America|1606.96|
|South America|1635.11|
|South Asia|2959.25|
|Southeast Asia|1684.83|
|Mainland China|482.96|
|North Africa|492.5|
|Sub-Saharan Africa|6209.75|
|Rest of the World|667|

Source: Modeling results based on OECD.STAT and GTAP data base In the other three scenarios, the amount of AfT transfer is unchanged with respect to the first scenario, but the AfT is now constrained for the recipient countries. In fact, the second scenario, called “institutional reforms”, is designed to reduce transaction costs and introduce quality assurance such that the demand for exports expands. This is simulated through an AfT transfer by donors countries which reduces their income. This income transfer is used to reduce the export tax revenues for the recipient countries. The third scenarios, called “technical assistance and capacity building”, aims to improve the productivity of factors, through supplying training and awareness of production process. This is simulated by an AfT transfer by donors countries which

**16**
<!-- Start of picture text --> Central Government General Government Public Sector Private Sector<br>1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010<br>year year year year<br>EAP ECA EAP ECA EAP ECA EAP ECA<br>LAC MENA LAC MENA LAC MENA LAC MENA<br>NA SA NA SA NA SA NA SA<br>SSA SSA SSA SSA<br>35 35 35 100<br>30 30 30<br>25 25 25<br>80<br>20 20 20<br>15 15 15<br>60<br>10 10 10<br>5 5 5<br>0 0 0 40<br><!-- End of picture text --> Source: calculations based on ILO Stats

# **v. Determinants of public employment**

This section explores the potential factors that explain these differences across regions and over time, and has two parts. The first one focuses on Rodrik’s (2000) hypothesis of public employment as a tool to mitigate the country exposure to undiversifiable external risk. The second one expands the list of potential determinants of public employment.

We initially replicate Rodrik’s baseline econometric specification, but with more recent data and additional countries. The summary statistics and cross correlations of the main variables (Tables 2 and 3) are presented to facilitate comparisons with the original work. The central variable in Rodrik’s model is exposure to external risk, which is calculated as the product of the volume of trade with the unanticipated component of variability in the external terms of trade, which he argues, it is the theoretically appropriate measure of external risk, as it yields the unpredictable variation in the streams of incomes associated with foreign trade. Hence, let x, m, and y stand for the volumes of exports, imports, and GDP, respectively, and T represent the terms of trade. The measure of