rafmacalaba/datause-encoder-data huggingface dataset:
16

# 5.  Conclusions

This paper has used new data from 76 low- and middle-income developing countries to examine the demographic, economic and financial determinants of international remittances. Four main conclusions emerge.

First, the econometric results presented here suggest that the skill composition of migrants does matter in remittance determination: ceteris paribus, countries which export a larger share of high-skilled (educated) migrants receive less per capita remittances than countries which export a larger proportion of low-skilled migrants. Instrumental variable results suggest that a 10 percent increase in the share of high-skilled migrants from a labor-sending country will reduce the amount of per capita remittances received by a county by 11.2 to 19.7 percent, while a similar 10 percent increase in the share of low-skilled migrants will increase the level of remittances received by 9.1 to 19.8 percent. Although the country-level data presented here cannot provide a definitive explanation for these outcomes, one possible explanation is that high-skilled migrants remit less because they are more likely to bring family members and to remain in their newly adopted country,<sup>21</sup> and so they are less concerned with any eventual return to their home country. By contrast, low-skilled migrants tend to remit more because their migration is more temporary in nature and they are more concerned with returning home.

Second, the level of poverty in the labor-sending country does not have a positive impact on the amount of remittances received by a country. For low-skilled migrants, the level of poverty in a country – defined either in terms of the level or depth of poverty – has no statistically significant effect on the amount of remittances received. For highskilled migrants, holding all other factors constant, the level of poverty in a country
# **<u>Table A.20. The district fixed-effect RDD-DD regressions of individual-level outcomes using a ‘donut’ sample</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1479*|0.0597***|0.0331|0.1833|0.2307|0.1966|0.0656|1.5132*|
||(0.0780)|(0.0194)|(0.0796)|(0.5181)|(0.2178)|(0.2087)|(0.0689)|(0.7682)|
|(Poverty rate – 50) * Post-program|0.0063|-0.0044|-0.0040|0.0379|0.0039|0.0048|0.0112|-0.0234|
|period|(0.0118)|(0.0048)|(0.0079)|(0.0468)|(0.0487)|(0.0509)|(0.0104)|(0.0981)|
|Program * (Poverty rate – 50) * Post-|0.0022|0.0007|0.0052|-0.0387|-0.0160|-0.0035|-0.0365**|0.0025|
|program period|(0.0186)|(0.0058)|(0.0166)|(0.0937)|(0.0605)|(0.0586)|(0.0138)|(0.1728)|
|Age|0.0246***|0.0053***|0.0107***|0.1213***|-0.0056|-0.0048|0.1612***|0.3261***|
||(0.0017)|(0.0008)|(0.0009)|(0.0095)|(0.0041)|(0.0040)|(0.0134)|(0.0619)|
|Age squared|-0.0003***|-0.0001***|-0.0001***|-0.0016***|0.0003***|0.0002***|-0.0082***|-0.0160***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0006)|(0.0027)|
|Male|-0.0535***|-0.0108**|0.0845***|0.9882***|-0.1671***|-0.1556***|0.0382**|0.0873*|
||(0.0105)|(0.0051)|(0.0071)|(0.0740)|(0.0455)|(0.0442)|(0.0155)|(0.0440)|
|Ethnic minorities (yes=1, Kinh=0)|0.2283***|-0.1146***|-0.0401*|-0.1407|-0.3247**|-0.3183**|-0.1617***|0.8786***|
||(0.0278)|(0.0116)|(0.0223)|(0.2000)|(0.1396)|(0.1368)|(0.0318)|(0.1500)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1385***|0.0352**|-0.0681**|-0.6624***|0.8494***|0.7637***|0.2270***|-1.2148***|
||(0.0402)|(0.0137)|(0.0261)|(0.2409)|(0.0941)|(0.0934)|(0.0753)|(0.3581)|
|Observations|<br>62,432|<br>62,432|<br>62,432|<br>62,432|<br>43,227|<br>43,227|<br>23,967|<br>23,967|
|R-squared|0.198|0.073|0.173|0.165|0.095|0.090|0.202|0.298|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

70
relative price distortions to agriculture – that is of particular relevance in the context of estimating the growth effects in largely agrarian Sub-Saharan African countries. Second, we control in the panel regressions for country and year fixed effects, identifying the effects that distortions to trade have on economic growth from the within-country variation of the data. Third, we provide an instrumental variables estimate of the response of policy induced price distortions to economic growth, thereby informing the political economy debate on the extent to which trade and price policies are endogenous to economic growth.

The remainder of our paper is organized as follows. Section 2 describes the data. Section 3 discusses the estimation strategy. Sections 4 and 5 present the empirical results. Section 6 concludes.

# **2. Data**

**_Relative Agricultural Price Distortions._** The World Bank recently completed a major global empirical study that estimated annual policy induced price distortions to agricultural incentives since 1955.<sup>4</sup> The study estimates nominal rates of assistance (NRAs) to agricultural industries as well as nominal rates of assistance to producers of nonagricultural tradables. The NRA is defined as the percentage by which government policies directly raise the gross return to producers of a product above what it would be without the government’s intervention (or lowered it, if NRA<0). For agriculture this was carefully estimated by comparing domestic and border prices of like products (at similar points in the value chain) for each of the covered farm industries, drawing on national statistical sources supplemented where necessary by producer prices and unit values of exports and imports from FAO (2011). To obtain the weighted average NRA for the agricultural sector as a whole, the World Bank study’s contributors calculated the weighted average of product

> 4 The study is summarized in four regional volumes, including one on Africa that also describes the myriad policies adopted and their drivers (Anderson and Masters, 2009), and a global overview volume (Anderson 2009). The panel dataset of estimates of price distortions has been made freely available (Anderson and Valenzuela, 2008), and the methodology is documented in Anderson et al. (2008).

5
# GIHANA REAL COCOA PRICE Producer price deflated by rural CPI

## 1977 C/MT (Thousands)

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>O ,,, I' ,,1 I 1111 f I_ Ij 11 I I 'ji 'ii ' '11 I'<br>1940/41 1945/46 1950/51 1955/56 1980/61 1965/66 1970/71 1975/78 1980/81 1985/86<br><!-- End of picture text --> Crop Year

- 2 Figure Real producer price
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Efficient market gap, benchmarked against Central African countries**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source:_ Mayer and others (2009) using GSM coverage figures as of 2005. _Note:_ Existing access represents the percentage of the population currently covered by voice infrastructure as of the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets.

Coverage gap represents the percentage of the population for whom services are not viable without subsidy.

The presence of multiple operators in the mobile market (table 12) has contributed to the rapid expansion of connections and the impressive drop in prices. Between 2004 and 2009 the number of connections (prepaid and postpaid) increased eightfold, from 60,000 connections to more than 500,000. The price of an international call dropped 50 percent, from $1.20 in 2004 to $0.60 in 2009. The connection charge to a prepaid mobile dropped 76 percent, from $25.60 to $6.30.

**Table 12. Players in the ICT sector**

|**Market**|**Mobile**|**Internet**|**Fixed line**|
|---|---|---|---|
|**Operators**|Telecel Centrafrique (1995)<br>Nationlink Telecom (2004)<br>Moov (2005)<br>Orange Centrafrique (2007)|Telecel Centrafrique<br>Nationlink Telecom<br>Moov<br>Orange Centrafrique<br>SOCATEL (ADSL)|SOCATEL|

_Source:_ AICD and Agency for Telecommunications Regulation (http://www.art-rca.org). _Note:_ SOCATEL = Société Centrafricaine des Télécommunications; ADSL = asymmetric digital subscriber line; ICT = information and communications technology.

In 2008 the telecommunications sector contributed 5 percent of the overall GDP and was growing at a rate of about 40 percent per year. The ICT sector is the most significant fiscal contributor in the country and attracted more than $80 million FDI from mobile operators in 2008 and 2009 (World Bank 2009d).

# **Challenges**

A number of factors external to the ICT sector have hindered the CAR’s access to and use of modern electronic communications, resulting in relatively high Internet prices and low access (table 11, figure 15). Among other factors, the CAR’s poor electricity infrastructure and fiber-optic links are major barriers to the expansion of the ICT sector.

31
inant analysis shows that management practices, some business characteristics and skills of the entrepreneur can be used to separate both groups, but the distinction based on these observables is only partially successful, which suggests the existence of unobservables determining both the decision to set up a business, based on a perceived opportunity, and firm performance. Finally, our instrumental variables results confirm that opportunity entrepreneurs have higher performance measured as profits and management practices, even after controlling for the observable skills of the entrepreneur.

The policy relevance of these findings is twofold. First, as mentioned before, identifying the observable characteristics that define opportunity entrepreneurs could potentially improve the targeting of business support programs, by concentrating them on the entrepreneurs with the highest growth potential. This by no means implies disregarding low-performing necessity entrepreneurs. On the contrary, the latter might be better served by interventions that help ease their transition to salaried employment. Second, our focus on female entrepreneurs is also of great relevance given that their businesses seem to be at a particular disadvantage when trying to scale-up. For instance, in Latin America, micro-firms led by women have been found to have an even smaller size and lower productivity, compared to those led by men (Bruhn, 2009). Thus, the evidence provided in this paper can be readily applied to those in more need of targeted support.

# **2 Data and descriptive analysis**

Our data come from a baseline survey conducted in 2014 in eight urban areas in Mexico: Aguascalientes, Dolores Hidalgo, Irapuato, Le ́on, Mexico City, Quer ́etaro, San Juan del R ́ıo and Toluca. For the sampling design, a number of census tracks (called AGEBs by the Mexican Statistical Institute, INEGI) with relatively high concentration of commercial activity were chosen.<sup>2</sup> The questionnaire was applied only to women who are business owners or partners, i.e. who take managerial, financial and marketing decisions, and the sample included firm with no more than 5 employees or sales that are less than 4 million Mexican pesos per year (which is the definition of a

> 2These areas with higher concentration of commercial activity were identified relying on the 2009 Economic Census data.

3
**Figure 3.5. Base: Real household consumption per capita and headcount poverty**

<!-- Start of picture text --> Real household consumption per Headcount poverty (%)<br>capita (2015 USD) 50.0<br>800 45.0<br>700 40.0<br>600 35.0<br>30.0<br>500<br>25.0<br>400<br>20.0<br>300<br>15.0<br>200<br>10.0<br>100 5.0<br>0 0.0<br>2017 2030 2017 2030<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

# **Non‐base scenarios**

The non‐base scenarios are defined in Table 3.1. They demonstrate some of the issues that can be addressed with GEM‐Core when it is applied to a two‐sector macro database. As shown in Table 3.1, the first three scenarios test the effects of an expansion in government investment, accompanied by an adjustment in one of three alternative financing sources (direct taxes, net domestic financing, or net foreign financing) that clears the government budget. The two remaining scenarios address the effects of external shocks: increases in remittances (transfers to households) from abroad and the world export price.

**Table 3.1. Definitions of non‐base scenarios***

|**Name**|**Description**|
|---|---|
|**ginv‐tdir**|Government investment increase by 2 %‐age points of base<br>GDP in 2018‐2030;budget cleared via direct tax adjustment|
|**ginv‐dbor**|Same as ginv‐tdir except for that budget is cleared via domestic<br>borrowingadjustment|
|**ginv‐fbor**|Same as ginv‐tdir except for that is budget is cleared via foreign<br>borrowingadjustment|
|**pwe**|10.1% increase in the world price for exports during the period<br>2018‐2030|
|**remit**|Remittance increase by 2 %‐age points of base GDP in 2018‐<br>2030|

‐18‐
<!-- Start of picture text --> c =(0.18,0.21]<br>; e m(0.14,0.18]=(0.08,0.14]<br>. =[-0.03,0.08]<br><!-- End of picture text --> <!-- Start of picture text --> 2 = (0.104,0.146]<br>. == (0.082.0.104](0,036,0.082]<br>4 [0.007,0.036]<br><!-- End of picture text --> <!-- Start of picture text --> ‘<br>a(Q. ,0: ff<br>‘ (0.155.339 “<br>; © (0.036.0.155] =<br>_—_—,<br>; (<br><!-- End of picture text --> <!-- Start of picture text --> ff<br>“ = (0.33,0.57]<br>= =hs<br>: [-0.11,0.01]<br>(<br><!-- End of picture text -->
# **1. Introduction**

With an average per capita GDP of about $240 ($1,420 in purchasing power parity), Nepal is the poorest country of South Asia. About 42 percent of the Nepali population lived on income below the poverty line in 1995–96, 46 percent of the adult population remains illiterate (Central Bureau of Statistics 2003), and almost half the children five years and under are malnourished (Nepal Ministry of Health 2002). During the period of stabilization and liberalization in the mid-1980s and early 1990s, the Nepali economy grew at about 5 percent per year. The impact of these relatively high rates of economic growth on improvements in living standards was dampened by the country’s high population and urban-centered growth, limited access to basic services, poor governance, and increasing political instability. Economic growth slowed in the early 2000s due to a global economic slowdown, diminishing export markets, and the escalation of violence resulting in declaration of the state of emergency.

Despite these negative trends, the overall poverty rate in Nepal declined to about 30 percent by the end of 2003. Between 1995 and 2004, real per capita expenditures grew by more than 40 percent in real terms (The World Bank 2005). That growth in per capita expenditure was accompanied by increasing income inequality, as indicated by the Gini coefficient, which climbed from 0.34 in 1995 to 0.44 in 2004. The country’s balance of payments increased to 9 billion Nepalese rupees (NPR) and foreign currency reserves reached NPR 75 billion. The common explanation for these developments, both in the press and among local and international scholars, is the sharp increase in remittances from Nepali expatriates working abroad.

Work migration and remittances, along with the higher agricultural growth, are usually considered the key factors behind declining poverty in Nepal since 1996. Indeed, more than a million prime-age (mostly male) adults are currently working outside Nepal. Remittances from expatriates grew at 30 percent per year and from less than 3 percent of GDP in 1995 to about 15 percent by the end of 2003 (World Bank 2004), exceeding the combined share of tourism, foreign aid, and exports. According to official government statistics, about 1 billion dollars comes into the country as remittances, and inflows through private and unofficial channels could be even larger (Thieme 2003).

2
# **New Set of Indicators to Measure Population Remoteness and Dispersion: Estimates for 100 Countries, with Detailed Analysis of Pacific Island Countries**<sup>**1**</sup>

Imogen Halstead Robert Utz Boer Xia JEL Classification: R1, R12, R23 Keywords: Population dispersion, remoteness, Pacific Island Countries

> 1 This paper was prepared as part of the “Spatial Development Issues of Pacific Island Countries” project.

1
32

|Table 8<br>Summary of costs by|different tvDes of|food distribution programs|
|---|---|---|
|From Mateus (|l983)|From present paper|
|ProgrsmTVDO<br>$/'O0O Calories/<br>Beneficiarv|Non Food Costs<br>AsXTotal|$/'000 Calories/<br>Non Food Costs<br>Day/Year/Beneficiary<br>As X Total<br>In 1988 US S|
|1. Untargeted<br>food subsidies<br>500-600|(very<br>low)|-|
|2. Untargeted<br>food rations<br>50-120|20-30|75^<br>-|
|3. Targeted<br>food rations<br>-<br>(geographic/self<br>targeting)|20-30|36-172b<br>14-26b<br>164<br>117|
|4. Food coupons|||
|- income targeting<br>60-180|2-5|42c<br>12-37d|
|- health status<br>60-120|2-5||
|5. Feeding programs<br>- MCH and school feeding<br>80-200|10-30|74<br>25'|
|- mostvulnerable<br>group<br>80-150<br>targeting|10-30|134'<br>71'|
|6. Food for work<br>-|-|-<br>20-34b|

# Notes and Sources:

- a. Egypt: Table 2.

- b. Brazil, Mexico, Philippines: Table 2.

- c. Sri Lanka: Table 2.

- d. Colombia and Jamaica: Table 2.

- e. Median, 52 programs: Table 3. f. Median, 81 programs: Table 6.

- g. Tamil Nadu: Appendix Table 1: note non food costs fall to 56 per cent of health component is excluded: targeting is based on growth monitoring.

- h. Bangladesh: Mills (1983). He argues higher figure is from program with better data, and therefore more accurate.
# **1. Introduction**

Vietnam’s current labor market policies have evolved in the context of its long and ongoing transition from a mainly planned to a modern, market-led economy with a fast-growing middle class. This transition started in the mid-1980s with the _doi moi_ (“renovation”) reforms. Over the last 30 years, it has led to significant shifts of employment away from agriculture toward manufacturing and services; away from household enterprises toward registered and regulated businesses; and away from state owned enterprises (SOEs) toward private domestic and foreign-owned firms (cf. McCaig and Pavcnik, 2013).

So far, Vietnam has been remarkably successful in managing its transition. Observed through an employment lens, regional disparities persist but unemployment has been low, wage growth pronounced and – at least among private sector workers – wage inequality has been declining (cf. Pierre, 2012, Imbert, 2013, and Sakellariou and Fang, 2014). Moreover, there are few signs that Vietnam’s labor market policies have thus far constrained the pace of the country’s growth. In fact, results from World Bank investment climate surveys show that a smaller proportion of firms identifies labor market regulations as a constraint to doing business in Vietnam than in Cambodia, Malaysia, Thailand and many other countries in the East Asia and Pacific (EAP) region (cf. World Bank, 2014a).

At the same time, Vietnam’s transition from central planning to a market economy is far from complete. In many sectors SOEs remain dominant and workers with access to state jobs continue to enjoy rents. In 2008, wages were 12 to 14 percent higher in the public than in the private sector even for workers with comparable observable characteristics (cf. Phan and Coxhead, 2013). Moreover, Vietnam’s transition from an agrarian, rural and informal economy, to an urban, manufacturing and services-based and formal economy is not complete either (cf. World Bank, 2014b). According to an investment climate survey, in Vietnam more enterprises see the practices of competitors in the informal economy as the biggest obstacle for doing business than is the case for any other element of the business environment apart from access to finance (cf. World Bank, 2009). Moreover most working people in Vietnam remain engaged in agrarian, rural and informal work.

Table 1 breaks Vietnam’s workforce down by sector and type of employment. Almost half of the country’s workforce is still engaged in small-scale agriculture. Another third of the workforce is active in non-agricultural household enterprises. With regard to types of employment, Table 1 shows that a majority of working people actually work outside of the employer-employee, “dependent employment” model on which most labor market policies are based: 45 percent of workers are self-

- 2 -
conditional cash transfers: a test of stated opportunity costs relative to actual program payments, and practical suggestions for improved design. Studies focused on how much to compensate participants for enrolling in schemes that promote behaviors that may be costly to them are rare, usually because measuring these opportunity costs is non-trivial<sup>2</sup> . While many environmental mechanisms are structured to help set payments at the level of the opportunity cost – for example, through emissions trading – land conservation programs in developing countries tend to operate based on best guesses about prices, yields, and production costs from a few surveys.

In principle, one could use auctions to help set prices, as in the U.S. Conservation Reserve Program (Hellerstein 2017). This has also been done for tree-planting programs on a small scale in Tanzania (Jindal et al. 2013), Malawi (Jack 2013) and Indonesia (Jack et al. 2008), but not yet for avoided deforestation contracts. Furthermore, auctions are often politically infeasible and might disadvantage communities with less connectivity or knowledge of market mechanisms. A key contribution of our paper is therefore to present an alternate strategy to improve program costeffectiveness based on stated preference from a simple referendum question and an index of risk characteristics. Stated preference methods have been developed and extensively studied by environmental and resource economists for more than 30 years (Hanemann 1984, Cameron & James 1987) but rarely applied to problems of CCT targeting. Stated preference approaches have been previously examined in the PES literature (Southgate et al. 2009, Yu & Belcher 2011, Cacho et al. 2014), but used to estimate possible payment levels rather than to re-target CCTs. Our study is also unique in that we use a representative sample from a national level program, directly link our estimates to the current payment scheme, use both individual and community level responses to the WTA question, and simulate both changes in targeting as well as adjustments to payment levels. Our approach is also unique in that it uses household responses to inform our interpretation of the community aggregate response.

Finally, our work also contributes to the discussion surrounding the social costs of land conservation programs. Estimating the costs of environmental policies is essential to understanding the welfare gains from a particular intervention. Yet measuring such costs is difficult due to the

> 2In Mexico’s pioneering educational conditional cash transfer program (now called Prospera), payments were originally set at 40 percent of an approximation of what children of school age earn when they work, but this proxy does not take into account the myriad other reasons why children might not attend school, and the true opportunity costs vary widely depending on context (De Janvry & Sadoulet 2006).

5