rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3704

# **Remittances: Transaction Costs, Determinants, and Informal Flows***

Caroline Freund, World Bank Nikola Spatafora, International Monetary Fund Abstract: Recorded workers’ remittances to developing countries have grown rapidly, to more than $100 billion in 2004, bringing increasing attention to these flows as a potential tool for development. But even these statistics are likely to significantly understate true remittances, as a large share is believed to flow through informal channels. Estimates of the importance of the informal sector vary widely, ranging from 35 percent to 250 percent of recorded flows. The primary motivation of the study is to develop the first empirical methodology to estimate informal flows. We use insights from the literature on shadow economies and empirically estimate informal remittances for more than 100 countries, using historical data on workers’ remittances from the Balance of Payments (BOP), as well as data on migration, transaction costs, and various country characteristics. Our results imply that informal remittances amount to about 35–75 percent of official remittances to developing countries. There is significant regional variation: informal remittances to Sub-Saharan Africa and Eastern Europe and Central Asia are relatively high, while those to East Asia and the Pacific are relatively low. These estimates are supplemented with detailed household survey data on remittance receipts in a number of countries.

Our results also shed light on the determinants of recorded remittances and the associated fees in the formal sector. We find that the stock of migrants in OECD countries is the primary determinant of remittances. In addition, money transfer fees and the presence of dual exchange rates reduce the share of remittances reported in national accounts. In turn, transaction costs are systematically related to concentration in the banking sector, lack of financial depth, and exchange rate volatility. There is also evidence that remittances are misrecorded in the BOP as “Errors and Omissions”.

World Bank Policy Research Working Paper 3704, September 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues
about 57 percent in 2012. The number of people with improved water access throughout the country on the other hand has also increased from 21.5 million in 2007 to 22.4 million in 2012. While progress has been made, the MDG targets were not met. Meeting the targets will be difficult especially when the country has failed to meet its 2010 target (65 percent of water coverage for its rural population).

Part of the underwhelming success of WSDP’s first phase can be attributed to the pressures of rapid population growth as discussed earlier. However, there were several other challenges. Although the WSDP was successful in changing the institutional arrangements in the sector, systemic challenges in terms of capacity, operational costs, data accountability and coordination put the program’s implementation and sustainability at risk. At the village level for example, the village committees or the COWSOs have limited technical and managerial capacity while at the central government level, the ministry has struggled to provide timely support, guidelines and budget to its implementation partners (LGAs). Additionally, the high operating costs of certain types of water pumps that were selected by the participating communities resulted in approximately half of the population paying more than 5 percent of their household income for water access<sup>7</sup> . The process of data collection and management through MOWI’s ambitious project to map all the water points in the country was not fully institutionalized. Hence it is difficult to hold the district water engineers accountable for dealing with non-functionality. Lastly, while coordination between MOWI and other participating parties such as PORALG and LGAs have steadily improved, local level planners and decision makers rarely receive feedback on implementation issues, thus adversely affecting policy reforms and resource allocations at the national level. WaterAid (2009) provided several recommendations that could potentially improve the country’s rural water access: instituting, monitoring and regulation of COWSOs, improvement of the WPM data<sup>8</sup> ; promoting more community participation; providing autonomy in managing
# **Table B1: Descriptive Statistics**

Summary Statistics by Degree of Political Dependence as Measured by the Castle Suharto Dependency Index <u>(1=not dependent on Suharto, ..., 5= highly dependent on Suharto)</u>

|Suharto Dependency Index<br>|1<br>_(fir_<br>|2<br>_A. Sam_<br>_m surviving u_<br>|3<br>_ple_<br>_p until 2019_<br>|4<br>_)_<br>|5<br>|All firms<br>|
|---|---|---|---|---|---|---|
|Observations|4|13|13|12|9|51|
|% of firms|8%|25%|25%|24%|18%|100%|
||_B._<br>_(include_|_Original Fis_<br>_s firms that e_|_man sample_<br>_xited since 1_|<br>_997)_|||
|Observations|5|34|10|16|14|79|
|% of firms|6%|43%|13%|20%|18%|100%|
|Assets|2,145.76|2,228.57|2,206.20|1,634.08|1,765.51|2,033.19|
|Debt|707.18|791.32|813.25|397.83|712.57|717.37|
|Returnonassets|0.23|0.24|0.16|0.22|0.15|0.21|

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INSEC data files contain the number of conflict-related deaths per month per district of Nepal between February 1996 and December 2006, as well as the total number of school destructions and abductions by Maoists at the district level, which are used to construct most measures of exposure to conflict used in this paper.

I also use two indicators of Maoist control over the district by 2003, based on classifications reported by Hattlebak (2007). I consider in turn two alternative definitions of Maoist control. I first categorize as under Maoist control any district that is categorized as such by both Maoists and the government (Definition 1). I then apply what Hattlebak (2007) considers the more reliable classification of the two, namely the government classification (Definition 2). The definition of all the conflict exposure variables used in this paper can be found in Table 1.

> 1 NLSS Data was provided by the Nepalese Central Bureau of Statistics (CBS).

12
recognized and increasingly studied (Hoffmann, 2020), gaining more prominence, and possibly also spurring more controversy, in the development research agenda.

Climate migration is not a new phenomenon. Human populations have historically used migration as a strategy to adapt and respond to changes in the climate (IOM, 2009). However, as these changes become more severe, widespread, and frequent, more and more people are expected to move as a consequence of both slow onset climate events and sudden climate shocks (Clement et al., 2021). As a result, the field of climate migration modeling has been evolving fast in recent years, allowing researchers to start quantifying this new phenomenon. The latest projections indicate that more than 210 million people will be forced to migrate within their own countries by 2050 due to climate change (Clement et al., 2021) with the poorest countries bearing the greatest burden (World Bank, 2021). While these figures have become accepted in the media (Lustgarten, 2020) and among specialized international institutions, it is also recognized that they come with caveats and do not often have empirical support in the literature (Letta et al., 2023). This is because, at present, climate migration models are based on fairly general assumptions about migration behavior due to a lack of reliable global datasets on climate-related human movements (McLeman, 2013).<sup>4</sup> Moreover, too little is known about the impact of climate change on international migration. Empirical efforts have focused mostly on modeling internal migration and evidence from the literature seems to support the view that most of the movements induced by changes in climate are currently occurring within national borders (rural-rural or rural-urban). While it is reasonable to assume that in the short-run climate-related migration is mainly a domestic issue, the latent potential for large-scale, international movements is often referenced in public discourse (Lustgarten, 2020) although poorly studied and substantiated by empirical evidence.<sup>5</sup> In particular
individuals and their households are engaged in. There is a longstanding literature on how proxies for individual socioeconomic status (or bargaining power, in specific studies), including earnings, age, education, and marital status, affect individuals’ labor allocation.<sup>3</sup> Thus far, however, nationally representative studies examining the association between individual-level time use measures and ownership of physical and financial assets — and not only of a specific type, but a diverse set — have been limited. This has in turn hampered a broader understanding of how time use is linked with wealth.

The available studies have focused on the impacts associated with transfer of specific assets and property rights in specific sub-populations. Examples include the randomized trials to show how expanded property rights (see Field, 2007, in a study of an urban land titling program in Peru) and creation of financial accounts (Field et al., 2021, in a study of a cluster of villages in central India) can boost women’s labor force participation. Shocks to assets that women bring into marriage has been examined by Brown (2009), in a study of four counties from China, to understand the effects on women-focused expenditures, intrahousehold allocation of housework, as well as women’s leisure time. Wang (2014) also examines the effects of transferring property rights to men and women on time use, consumption and children’s health stemming from a 1994 housing reform in China, and documents that property rights transfer to men, as opposed to women, increases women’s time devoted to housework.

Beyond the gaps in our understanding of individual wealth and time allocation, a related gap in the literature is the lack of systematic assessments of the estimated relationships between time use measures and the covariates that are commonly used in empirical research as proxies for individual socioeconomic status (or bargaining power, in the context of intra-household analyses). These knowledge gaps are in part due to the weaknesses in the availability and quality of individual-disaggregated survey data
the poverty line or other absolute lines such as 2 times the poverty line. Households are considered vulnerable if they are non-poor but have more than 10 percent likelihood of falling into poverty. We also define middle class households as those with a relatively low likelihood of falling into poverty (less than 10%). Using these definitions, we find the size of the vulnerable and middle class population to be 45 percent and 22 percent respectively. An analysis of trends suggests that the growth in the size of the vulnerable as well as the middle class population has been increasing steadily over the years in Nepal.

Perceptions data as well as results from qualitative surveys generally suggest that Nepali households have experienced robust upward economic mobility along the economic ladder in recent years, but this mobility stands on fragile grounds. Based on data from Gallup and definitions of welfare that take into account realized mobility together with expected mobility, a significantly larger fraction of Nepalis is found to be either suffering or struggling as opposed to thriving. This implies that while Nepal has had a tremendous success in lifting people out of poverty, a majority of Nepalis is either directly at risk of falling back into poverty (more than 10% probability) or even if not directly vulnerable in this sense, they constitute what some have called the “struggling middle class”.

From the point of view of policy, the conclusions in this paper point to three key areas of priority. First, the unevenness in the mobility experience across people of different caste/ethnic groups and urban/rural location together with the high incidence of chronic poverty reinforces the call for policies that help equalize opportunities across space and across social groups. The point that not all Nepalis have benefitted in the same manner comes out quite strong. Second, the high degree of vulnerability and transitions around the poverty line with roughly one Nepali falling back into poverty for every two that escape suggests the need to strengthen social protection systems, particularly those that insure households against a variety of income shocks. A particular area of
34

# **Notes** to Chapter 3

1. This point **was** made in a **telephone** conversation with an analyst at the I.L.O. The I.L.O. has worked with many of these systems to establish their actuarial soundness.

2. Source: World Development Indicators - 1987.

3. Uruguay is now undertaking a substantial reform of its social security system, with some assistance from the World Bank.

4. [Substitute information from I.L.O. to avoid citing unavailable source and to use 1983 as a comon year.]
papers in this literature include Nickell (1996), Blundell et al. (1999), Griffith et al. (2004), Aghion et al. (1999), which show that competition affects innovation. Other papers, such as Lederman (2009), argue that the investment climate matters as a determinant of R&D effort. Another group of papers, including Aghion et al. (1997) and Aghion and Howitt (1992), shows that credit constraints affect the cyclicality of R&D investments, and the degree of financial development affects the introduction of new products, both for incumbent and new firms. Thus, vector **Z** _czt_ includes variables such as a Herfindhal index of sales, the percentage of firms that have difficulties to access financing, the percentage of firms that report difficulties in obtaining business licenses, the percentage of firms that worry about anti-competitive practices, the percentage of firms with foreign ownership, the percentage of firms with managers as the largest shareholders, the percentage of exporter firms, intellectual property right protection of trade partners, GDP per capita of trade partners, international backward linkages, and international forward linkages. The models are thus heavily parameterized, because the cross-sectional data do not allow us to control for firm fixed effects, and thus it is safe to err on the side of caution.

Finally, an important feature of this empirical strategy is that innovation in one dimension can affect the probability of innovation in the other; a firm’s introduction of a new product variety can affect its propensity to upgrade existing products, and vice versa.

# **5 Data**

The data on patents were taken from United States Patent and Trademark Office (USPTO) yearly statistics and cover the period 1963 to 2004. The R&D expenditure data were taken from Lederman and Saenz (2005) and updated with UNESCO data from its web site. The human capital related variables were taken from the World Bank’s World Development Indicators. We compute the stock of
market to enter, which in turn determine their business outcomes. Such constraints can also affect returns to their investments, thus directly impacting outcomes. Interventions are organized into four categories of constraints: 1) Human capital, encompassing gender gaps in access to skills and networks; 2) Factors limiting access to finance, 3) Factors inhibiting technology adoption and market expansion; and 4) Contextual factors, such as legal and regulatory barriers, social norms, access to care, and gender-based violence.

The contribution of this paper is to provide an updated narrative review summarizing how various interventions can alleviate constraints and enhance outcomes for women-led businesses. It focuses on a substantial body of high-quality randomized controlled trials (RCTs) primarily conducted with micro and small enterprises in low- or middle-income countries. Where RCTs are unavailable, the review acknowledges this and explores findings from studies using alternative identification strategies and operational insights to identify promising policy solutions.

This paper draws on previous literature reviewing interventions to support women-led businesses. Jayachandran (2021) explores differences in patterns for male and female-run microenterprises. Campos et al. 2019 focus on evidence in Africa, and World Bank (2021) on Southeast Asia. Love et al. (2023) identify

> 2 According to the Global Entrepreneurship Monitor data, the share of women-owned businesses with five or fewer employees was 91 percent in 2020, while that of men-owned businesses was 80 percent (Elam et al. 2021).

2
LSMS data also show similar small increases in the proportion of children who were wasted. It is noteworthy that in Ethiopia there is little difference in the prevalence of stunting between boys and girls. For wasting the prevalence is

> 16 While some studies use mother’s education, we use mother’s literacy since the majority of our sample comes from rural areas and most of the women surveyed have no education. Cintron, Seff, and Baird (Cintron et al., 2016) use the same approach.

> 17 Since there is a possibility of correlation between household variables, we use the Pearson Correlation Coefficient to select variables that highly influence child undernutrition. The Pearson Correlation Coefficient is a number between -1 and 1 that indicates the extent to which two variables are linearly related. For this analysis, we choose 0.6 as the rule of thumb. If two variables have a correlation coefficient of greater than 0.6, we select one of the variables and drop the other. The variable selected is the one that highly influences child undernutrition. In this instance, we choose improved sanitation.

> 18 For a detailed analysis on how each household variable is associated with child nutritional outcomes, see Cintron, Seff, and Baird (2016).

> 19 Contrary to Cintron et al (2016) who apply a fixed-effects model including only independent variables for which 10% or more of the sample experienced a change between periods 1 and 2, we do not exclude any of our control variables from the analysis.

9
Figure 3c: China’s imports of metals and minerals, 1996–2013 <!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>Source:Authors' construction using data from Chinese National Bureau of Statistics<br>in constant 2000 levels<br>Coal (10000 metric tonnes)<br><!-- End of picture text --> 28
Levine, R. (1999). "Law, Finance and Economic Growth," _Journal of Financial Intermediation,_ January.

Rajan, R. G. and L. Zingales. (1998). "Financial Dependence and Growth," _American Economic Review,_ 88, 3, June, 559-86.

Ramey, G. and V. Ramey. (1995). "Cross-Country Evidence on the Link Between Volatility and Growth," _American Economic Review,_ 85 (5), 1138-51.

Rousseau, P. L., and P. Wachtel. (1998). "Financial Intermediation and Economic Performance: Evidence from Five Industrial Countries," _Journal of Money, Credit, and Banking._ Street, J. O., R. J. Carroll, and D. Ruppert. (1988). "A Note on Computing Robust Regression Estimates Via Iteratively Reweighted Least Squares," _The American Statistician,_ 42, 152-54.

Summers, R. and A. Heston, 1991, "The Penn World Table: An Expanded Set of International Comparisons, 1950-1988," _Quarterly Journal of Economics,_ 106, 327-443.

22
situation in December 1994. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Central Government, education and health employment are taken from IMF Report No. SM 95/206 of August 18, 1995 and relate to 1994. Number of local Government employees is taken from Local Government in the CEE and relates to 1 January, 1994. Of the 43,931 local civil servants, 18,640 are working in the mayors' offices. Data on military employment exclude personnel in paramilitary units, i.e., Border Guards (600), and Internal Security Forces (250).

GDP estimate is taken from World Tables 1995 and is for 1992. Data on Average wages, and on wages in manufacturing are taken from IMF Report No. SM/951206 and are for 1992. Data on paid employment in nonagricultural activities is taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1994.

# Slovenia

Labor force and unemployment rate are drawn from Economist Intelligence Unit 1995-96 (p. 42) and reflect the situation in December 1994. Note that employed labor force includes self-employed. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Number of unemployed are drawn from Economist Intelligence Unit 1995-96 (pp. 28-32) and 1992.

Data on Central Government, Education and Health employment is taken from IMF Report No. SM/94/189 and relates to 1994. Central Government employment corresponds in reality to 'Government Sector' and probably includes local government employment. Data on military employment include conscripts (5,500), but do not include personnel in paramilitary units, i.e. the Police (4,500).

# Turkey

Data on Central, Local, Education and Health employment are taken from OECD PUMA: Public Management, OECD Country Profiles, 1992 and relate to 1991. Data on military employment include conscripts (415
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