rafmacalaba/datause-encoder-data huggingface dataset:
3 Trujillo, 2005, for a survey). Estimates of cost functions and returns to scale are very scarce; we found only one study that calculated returns to scale from a DEA study.<sup>2</sup> The current paper contributes to fill this gap by deriving measures of density and scale economies from the estimation of a Translog cost function using panel data from IBNET on water and sewerage utilities in four countries that differ substantially in economic development: Brazil, Colombia, Moldova, and Vietnam.<sup>3</sup> Because of high collinearity between the volume of water supplied and the volume of wastewater treated for utilities providing both water and sewerage services, we estimate a single product Translog cost function for all four countries. In this case economies of scale are a sufficient condition for natural monopoly (Joskow, 2005). We find evidence of economies of scale in Colombia, Moldova and Vietnam, implying the existence of natural monopoly. In Brazil, we cannot reject the null hypothesis of constant returns to scale, which is inconclusive in terms of natural monopoly characteristics. We also find evidence of economies of customer density in Moldova and Vietnam: additional water users could be connected to the piped water network at decreasing average cost. We discuss the policy implications of these results in significant detail.

This study provides new insights about the cost structure of water utilities in lower- and middle-income countries. Understanding the size of economies of scale in the water supply and sewerage industry helps to ensure that firms and policymakers can make informed decisions. If there are economies of scale and growing demand, firms may find it profitable to add more capacity than they expect to use in the immediate future. Furthermore, if there are economies of scale over the domain of market demand, large firms could produce at lower average costs than smaller ones; thus a competitive equilibrium would not be sustainable and a valid policy argument can be made for the establishment of a large firm (or monopoly) in order to gain the benefits of these economies (Kim, 1987).

We define the concepts of cost function, economies of density and scale, and natural monopoly in section 2. The specification of the cost function that is estimated for each of the

> 2 The one study we found was conducted by Seroa da Motta and Moreira (2006) who compute returns to scale from a DEA approach using data from Brazil. Findings from this study will be discussed later.

> 3 The International Benchmarking Network (IBNET) is developed by the World Bank with the objective to improve the service delivery of water supply and sewerage utilities through the provision of international comparative benchmark performance information. For more information, see www.ib-net.org
totals or weighted-averages for use in our empirical analysis. Importantly, these are measures ofthe relative health of SMALL banks - higher market shares for SMALL banks implies lower market shares for other banks, and higher weighted-average efficiency ranks for SMALL banks means lower efficiency ranks for other banks. For the developing nations, we also include STATE SHARE, STATE COST EFF RANK, STATE PROFIT EFF RANK to measure the relative health of state-owned banks, and FOREIGN SHARE, FOREIGN COST EFF RANK, FOREIGN PROFIT EFF RANK to measure the relative health of foreign-owned institutions.

The control variables for the market, regulatory, and legal environments in which the banks compete, which are shown in other research to be important. We include MKTCAPGDP, the market value of all publicly held debt and equity securities relative to GDP, as an indicator of the development of public capital markets, which is compiled primarily from the IMF International Financial Statistics.1<sup>2</sup> We include ECON FREEDOM, the Heritage Foundation/World Street Journal Index of Economic Freedom, an index that reflects the extent of government intervention in the economy and monetary policy, banking and financial regulations, relative openness of trade, and related factors. This variable ranges from 1 (most freedom) to 5 (least freedom). We include COMMON LAW, a dummy for whether the justice system is English Common Law, taken from La Porta, Lopezde-Silanes, Shleifer, and Vishny (1997). Common law is shown to offer greater creditor rights and be associated with more developed financial systems and improved ownership structures (e.g., La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1997, 1998). We also specify BANK CR3, the three-bank concentration ratio computed from BankScope, to account for differences in market power and access to credit across nations.'<sup>3</sup> The structureconduct-performance hypothesis predicts higher prices and restriction of credit from greater concentration. However, high concentration may encourage banks to invest in lending relationships because the borrowing frms are less likely to find alternative future sources of credit (e.g., Petersen and Rajan 1995). The empirical research

> 12 In some cases, missing information was filled in using the Annual Reports of stock exchanges in the individual nations. 13 The measurement of BANK CR3 may be significantly affected by some of omissions from the BankScope database discussed below.

12
**Table E.19:** Balance: Minimum Distance to Specialized Ebola Facilities

||Control Mean|Pooled|CM|NFA|N|
|---|---|---|---|---|---|
|NERC||||||
|ETU|33.986|0.588|-1.302|2.056|160|
||(17.422)|(3.795)|(4.626)|(4.319)||
|EHC|20.715|-2.034|-0.315|-3.369|160|
||(11.479)|(2.115)|(2.563)|(2.394)||
|CCC|49.794|-5.353|-3.257|-6.98|160|
||(31.244)|(4.09)|(4.984)|(4.654)||
|UNMEER||||||
|ETU|33.57|0.82|-1.156|2.354|160|
||(17.495)|(3.777)|(4.602)|(4.297)||
|EHC|20.586|-2.361|-1.105|-3.337|160|
||(10.445)|(2.011)|(2.446)|(2.284)||
|CCC|54.163|-7.277|-5.159|-8.921|160|
||(43.64)|(3.819)*|(4.651)|(4.343)**||

_Notes_ : Differences estimated using OLS including matching-triplet fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01 A51
usually not well determined, as evidenced from the large literature on the East Asia crisis and the potential role of a credit crunch there (Agenor et al., 2000; Ding et al., 1998). An alternative approach to addressing the question of good banking and macro OLCL;LJL At **_LW_ LU** A **MALLF** OWL&&_ 1LOLL **LJ.LA%.JAIO** **_V._** r **_tJ AL1_** ; **_%JM LAL_** t **_LA_** _V_ A _-_ C **_fl_** performance of economies by reference to tnese instruments. **Tne** advantage of tuis approach is that it can provide guidance as to the type of banking system that government officials could expect to maximize stabilizing influences. Two distinct types of instrument on which some data are available are (i) the nature of bank regulation and (ii) presence of, or openness to, foreign bank ownership.' Already there is a literature on the cross-country contribution of financial depth to macroeconomic stability (Easterly, Islam, Stiglitz, 2000; Beck, Lundberg, Majnoni, 001) **T.It c-ncliicet** that deeper **financialv** sytems - at leat **unp to a** certain noint - do seem **Lo UV** able to insulatLe **ecUUVIUIe** against e **L** La O o VL shIock. **TJh L-tLLion** posed in this paper can be seen as addressimg **tte** same question out along adiferent almensions. of banking sector quality. While the presence of reputable foreign banks is usually held to contribute to the institutional strength of the banking system (cf. Levine, 1996), heavy reliance on foreign banks could be destabilizing if they introduce or transmit foreign shocks to a greater extent than they absorb shocks of domestic origin.

Schematically we can see the banking system as a filter through which foreign an.d dorlmestic shocks feed L'1o0ug to the dom.estic econo.my. **_Tae_** filter _cAn_ **Aov,en** or amplify the shocks, through various credit market channels including credit growtn,

**The** mainr **qnurne** nfmnat nfthic data is **the Wnrld** Rank'c qurvev **nf reoultinan** (Rarth-Canrin-Levine 2001a)

3
# **2. Background, Sample, and the Financial Literacy Intervention**

# **2.1. Background and Context**

Formal, government-administered placement of labor migrants from Indonesia began in 1969 and in the 1970s a regulated private sector for migration recruitment and placement was authorized and developed. Hugo (2009) estimates that Indonesia is the second largest source country for labor migration in Asia after the Philippines, with an estimated 2.7 million Indonesians working abroad with official permission, and many more irregular migrants. The majority (78% in 2007) of migrant workers from Indonesia are female, primarily working as domestic workers, nannies and aged care workers in Hong Kong SAR, China; Malaysia; Singapore; and the Middle East (Hugo, 2007; IOM, 2010). Official remittances received from migrants have grown from US$5.4 billion in 2005 to US$7.3 billion in 2010.

Indonesian overseas migrant workers, known locally as _Tenaga Kerja Indonesia (TKIs)_ , apply for jobs with privately owned recruitment agencies, which are registered and licensed by the government, and locally known as PPTKIS.<sup>6</sup> These agencies not only recruit migrants for jobs abroad, but are responsible for preparing workers for these jobs abroad, and arranging their travel. It is mandatory under the Migrant Worker Placement and Protection Law for all such workers to undertake job and language training (Ignacio and Mejia, 2009). Typically individuals recruited to work abroad come and attend training sessions of several months with the recruitment agency where they learn occupation-specific skills for working abroad (e.g. domestic workers learn about the use of modern household appliances and overseas standards for room cleaning), as well as receive a general pre-departure briefing covering issues such as safety and cultural differences abroad. The recruitment agencies are also responsible for enrolling the workers in a mandatory insurance program (which migrants pay Rp 400,000 for as part of their placement fee), which covers migrants in the event of accidents, illness, unpaid wages or premature contract termination by the employer, or death while abroad.

The migrants and their families typically come from rural areas and have relatively low levels of education, and limited use and knowledge of formal financial services. A diagnostic study found respondents reporting that very little of the money received as remittances is saved or invested (World Bank, 2010a, 2010b). To address such knowledge constraints, the World

> 6 PPTKIS = Perusahaan Penempatan Tenaga Kerja Indonesia Swasta (Privately-owned Indonesian Manpower Placement Company).

5
**Table 6 Estimation Results with Pooled OLS Method**

||Regulations|Finance|Entry|Time to<br>Export|Infrastructure|Property|
|---|---|---|---|---|---|---|
|RealGDP(PPP)|0.989|1.006|1.064|0.976|0.978|0.994|
||(0.031)***|(0.045)***|(0.034)***|(0.032)***|(0.030)***|(0.034)***|
|Log(Area)|-0.082|-0.081|-0.129|-0.060|-0.079|-0.091|
||(0.035)**|(0.043)*|(0.043)***|(0.037)|(0.040)**|(0.041)**|
|Remoteness|-0.384|-0.470|-0.422|-0.474|-0.504|-0.533|
||(0.155)**|(0.188)**|(0.182)**|(0.157)***|(0.163)***|(0.175)***|
|Export Growtht,t-1|0.094|0.094|0.077|0.101|0.134|0.121|
||(0.046)**|(0.060)|(0.049)|(0.047)**|(0.048)***|(0.047)**|
|log(TTRI)|-0.265|-0.498|-0.458|-0.432|-0.397|-0.411|
||(0.094)***|(0.111)***|(0.109)***|(0.085)***|(0.084)***|(0.096)***|
|log(MATTRI)|-0.185|-0.805|0.202|0.269|-0.526|-0.536|
||(0.046)***|(0.318)**|(0.131)|(0.257)|(0.170)***|(0.167)***|
|log(MATTRI)*Indicator|0.124|0.173|-0.037|-0.141|0.299|0.058|
||(0.055)**|(0.089)*|(0.011)***|(0.077)*|(0.117)**|(0.025)**|
|Indicator|0.281|-0.028|0.004|-0.345|0.599|0.114|
||(0.076)***|(0.139)|(0.021)|(0.134)**|(0.195)***|(0.039)***|
|Constant|3.493|4.357|2.915|5.761|4.321|4.457|
||(1.612)**|(1.959)**|(1.843)|(1.671)***|(1.591)***|(1.833)**|
|Observations|205|175|204|203|193|189|
|R-squared|0.953|0.933|0.944|0.952|0.954|0.954|

* Pooled OLS results. Robust standard errors clustered by country are in parentheses. All regressions control for year fixed effects.*** p<0.01, ** p<0.05, * p<0.1.

**Table 7 Impacts of Improvements in Investment Climate Indicators on Exports (in percentage points)**

|Indicator|Regulations|Finance|Entry|Time to<br>Export|Infrastructure|Property|
|---|---|---|---|---|---|---|
|Total Impact|10.1|9.9|12.4|7.9|10|9.6|

Note: Improvements in IC indicators mean increase for regulations, finance, infrastructure, and property indicators where it means decrease in entry time and time to export.

17
As a first cut at international comparison, Figure 1 ranks various Latin American and OECD countries by the minimum wage standardized by the mean wage (SMW). Latin America spans the range with Uruguay, Bolivia, Brazil, Argentina, Chile and Mexico with the lowest values and Venezuela, El Salvador, Paraguay and Honduras with among the highest. 4 While informative, standardizing by the first moment is not sufficient to tell us whether the minimum wage is binding for two reasons. First the number of workers affected will depend on the higher moments of the distribution as well: more disperse endowments of human capital (variance) or a particularly large fraction of poorly endowed workers (skewness) would lead to more workers being affected by a given SMW. Second, if the minimum wage is not enforced, very high SMWs are irrelevant.

As a second cut, Table 1 offers several additional measures that attempt to provide a more rounded view. The first column presents the SMW and the second standardizes by the wage at the _50&_ quantile (median). The median is a better measure of the central tendency since it is less sensitive to extreme values in the upper tail and to compression of the lower tail by the minimum wage. Using it to standardize reverses the SMW rankings of Argentina and Brazil. This effect is even more dramatic if we standardize by the wage at the 1Oh quantile of the distribution, arguably the range that is of more concern than the center. 5 Brazil suddenly appears among the countries with the

> _3_ Source: For countries discussed in detail here, IBRD staff estimates for most recent years available. All others IDB (1999) for the early 1990s.

> 4 The analysis uses the Permanent Household Survey (EPH) from Argentina, the Continuous Household Survey (ECH) from Bolivia, the National Survey from the Sample of Households (PNAD) from Brazil, the (CASEN) from Chile, the Multi-purpose Permanent Survey (EPHM) for Honduras, the National Urban Employment Survey (ENEU) from Mexico. We restrict the sample to those 16-65, working between 3050 hours a week for informal salaried workers (those working for firms of five employees or below) and formal salaried workers (six workers and above).

> 5 10% of the sample earns below this wage and 90% above.

6
Our study is original in several dimensions, namely the use of variation in enforcement to understand the effect of labor regulation, the assembly of a new administrative dataset with information on labor inspections in each city in Brazil, and the explicit integration of the formal and informal sectors (and linkages between the two sectors) in an empirical analysis of the effects of labor regulation. However, the paper also builds on and contributes to a long literature.

The theoretical framework on which we draw upon follows Harberger (1962), Harris and Todaro (1970), Fields (1975, 2005), MacDonald and Solow (1985), Bulow and Summers (1986), Acemoglu (2001), Maloney (2004), and Albrecht, Navarro and Vroman (2006).<sup>5</sup> Although labor regulation is strict in Brazil, there is surprisingly large wage and employment flexibility (e.g., Barros and Mendonca, 1996, Barros, Cruz and Mendonca, 1997). The reason for this may be low enforcement. Therefore, when interpreting our findings we think of a model with minimal rigidities, except for frictions in the job search process in the formal sector and a minimum wage. More recent contributions to the literature on informality include work by Schneider and Enste (2000), Friedman, Johnson, Kaufmann, and Zoido-Lobaton (2000), Amaral and Quintin (2005), Galiani and Weischelbaum (2007), Boeri and Garibaldi (2006), Loayza, Oviedo and Serven (2005), de Paula and Scheinkman (2006), Bosch, Goni and Maloney (2007), and World Bank (2007). Especially related to us are studies of inequality in economies with dual labor markets, such as Fields (1979, 2005), or Bourguignon (1990).

Modern surveys of the role of labor market institutions include Layard and Nickell (1999), or Kugler (2007), among many others. The increasing availability of micro data lead to the emergence of several studies examining the effect of labor market regulations in developing countries, such as Kugler (1999, 2001, 2004), Kugler and Kugler (2003), Eslava, Haltiwanger, Kugler and Kugler (2006), Ahsan and Pages (2007), Petrin and Sivadasan (2006), or the studies in Heckman and Pages (2004). Two papers are especially close to ours. Besley and Burgess (2004) explore within country (district level) and across time variation in labor reforms in India to study the effect of labor regulations on productivity, investment, employment and poverty. We explore a very different source of institutional variation, and use labor market data disaggregated at the city level. Marrufo (2003) examines the consequences of the reform of social security in

> 5 Several papers try to empirically distinguish different models of the labor market (segmented and non-segmented). See e.g., Dickens and Lang (1985), Heckman and Hotz (1986), Maloney (1999), Filho, Mendes and Almeida (2004), Navarro-Lozano and Schrimpf (2004), Bosch and Maloney (2006), Almeida and Bourguignon (2006).

4
between each country’s GDP with the World’s GDP. The results are robust to such a specification.<sup>20</sup> Finally, additional analysis presented in appendix also show that our findings are virtually identical when separating inputs and capital trade index proximity.

**Table. 3.** Separating by Income Groups

||c|orr GDP<sup>_HP_</sup>|||corr ∆GDP||c|orr GDP<sup>_BK_</sup>||
|---|---|---|---|---|---|---|---|---|---|
|Include X inc.|High|Middle|Low|High|Middle|Low|High|Middle|Low|
|ln<br>�<br>_TPI_+_K_<br>�|**0.019**<sup>_∗∗∗_</sup>|-0.001|0.018|**0.023**<sup>_∗∗∗_</sup>|**0.011**_∗_|0.010|**0.019**<sup>_∗∗∗_</sup>|0.002|0.015|
||(0.006)|(0.006)|(0.011)|(0.006)|(0.006)|(0.011)|(0.006)|(0.006)|(0.011)|
|ln<br>�<br>_TPf inal_<br>�|**0.012**<sup>_∗∗_</sup>|0.007|-0.007|0.002|0.006|-0.002|0.008|0.005|-0.012|
||(0.006)|(0.006)|(0.010)|(0.006)|(0.005)|(0.010)|(0.006)|(0.006)|(0.010)|
|_thirdIM_|0.075|-0.054|0.091|0.098|0.040|**0.525**<sup>_∗∗∗_</sup>|0.096|-**0.129**<sup>_∗_</sup>|**0.288**<sup>_∗∗_</sup>|
||(0.072)|(0.078)|(0.132)|(0.072)|(0.076)|(0.135)|(0.074)|(0.078)|(0.159)|
|_thirdex_|**0.221**<sup>_∗∗∗_</sup>|**0.224**<sup>_∗∗∗_</sup>|**0.262**<sup>_∗∗_</sup>|**0.190**<sup>_∗∗∗_</sup>|**0.180**<sup>_∗∗∗_</sup>|**0.206**<sup>_∗_</sup>|**0.168**<sup>_∗∗∗_</sup>|**0.193**<sup>_∗∗∗_</sup>|0.159|
||(0.059)|(0.064)|(0.108)|(0.059)|(0.062)|(0.110)|(0.060)|(0.064)|(0.109)|
|CP FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|TW FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|11,597|11,216|2,814|11,597|11,216|2,814|11,597|11,216|2,814|
|R<sup>2</sup>|0.214|0.227|0.057|0.189|0.199|0.051|0.215|0.245|0.064|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. In parenthesis: std. deviation.

**Table. 4.** Separating by Income Groups: without third index

||c|orr GDP<sup>_HP_</sup>||c|orr GDP<sup>_B_</sup>|<sup>_K_</sup>|c|orr ∆GDP||
|---|---|---|---|---|---|---|---|---|---|
|Include X inc.|High|Middle|Low|High|Middle|Low|High|Middle|Low|
|ln<br>�<br>_TPI_+_K_<br>�|**0.023**<sup>_∗∗∗_</sup><br>(0.006)|0.002<br>(0.006)|**0.021**<sup>_∗_</sup><br>(0.011)|**0.023**<sup>_∗∗∗_</sup><br>(0.006)|0.003<br>(0.006)|**0.019**<sup>_∗_</sup><br>(0.011)|**0.028**<sup>_∗∗∗_</sup><br>(0.006)|**0.013**<sup>_∗∗_</sup><br>(0.006)|0.018<br>(0.011)|
|ln<br>�<br>_TPf inal_<br>�|**0.015**<sup>_∗∗∗_</sup><br>(0.006)|0.009<br>(0.006)|_−_0.006<br>(0.010)|**0.011**<sup>_∗_</sup><br>(0.006)|0.006<br>(0.006)|_−_0.009<br>(0.010)|0.004<br>(0.006)|0.008<br>(0.005)|0.002<br>(0.010)|
|Country-Pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time Window FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_<br>|11,597|11,216|2,814|11,597|11,216|2,814|11,597|11,216|2,814|
|R<sup>2</sup>|0.212|0.225|0.052|0.213|0.244|0.058|0.188|0.197|0.035|
|_Notes:_|_∗_p_<_0.1; _∗_|_∗_p_<_0.05; _∗∗_|_∗_p_<_0.01.|In parenth|esis: std.|deviation.||||

20Surprisingly, the relationship between trade in intermediate inputs proximity and correlation with world GDP is significant and negative for the whole sample.

15
supplemented by adequate capital and liquidity regulation as well as host-country macroprudential supervision to curb externally funded credit booms.

More recent research work has included an effort to disentangle more clearly the factors underlying the relationship between foreign banks and credit in times of crisis. Thus, the recent literature has tried to distinguish between varying parent bank characteristics, home and host country profiles and balance sheet structures. In this regard, Claessens and Van Horen (2012) use a database on bank ownership for 137 countries over 1995-2009 to review foreign bank behavior and impact and conclude that in the recent financial crisis foreign banks reduced credit more compared to domestic banks. However, this was not a uniform finding. They also observed that foreign banks that generated a relative large part of their funding from deposits continued to lend relatively more in 2009, showing the importance of funding structures.

Kapan and Minoiu (2013) extend this line of investigation by drawing attention to the balance sheet strength of foreign banks in determining the transmission of financial sector shocks. Using data from the syndicated loan market, they review the variations in banks reliance on wholesale funding and their structural liquidity position just prior to the crisis (2007Q2) to estimate the impact of exposures to market freezes during 2007-08 on the supply of credit. They conclude that banks with strong balance sheets were better able to maintain lending during the crisis. Specifically, banks that were more dependent on market funding, had lower structural liquidity, and were less capitalized reduced the supply of credit more than other banks. They conclude that their results provide support for the recent regulatory proposals under Basel III.

Choi, Gutierrez, and Martinez-Peria (2013) reach similar conclusions based on an extensive bank-level panel dataset that allows them to explore the role of subsidiary and parent financial characteristics, as well as the impact of parent origin and geographic reach. Overall, the analysis finds robust evidence that foreign banks curtailed the growth of credit relative to other banks, independently of the host region. However, this was less so for foreign banks in countries that signed the Vienna Initiative and for those with higher deposit funding ratios and better capitalized parents. In other words, the funding structure of foreign bank affiliates and the 7
# **Figure 4. Differences in Headcount Poverty Rate for FHHs vs. non-FHHs by Number of Children under 14 (percentage points)**

<!-- Start of picture text --> Panel A: Self-reported FHH Panel B: Share of females>0.5<br>Egypt Egypt<br>Iraq Iraq<br>Jordan Jordan<br>Mauritania Mauritania<br>Palestine Palestine<br>Tunisia Tunisia<br>-10 0 10 20 30 40 -10 0 10 20 30<br>Panel C: Potential FHH Panel D: Educated females<br>Egypt Egypt<br>Iraq Iraq<br>Jordan Jordan<br>Mauritania Mauritania<br>Palestine Palestine<br>Tunisia Tunisia<br>-20 0 20 40 -20 0 20 40<br>0 1 2 3 4 or more<br><!-- End of picture text -->

**Note:** Authors’ calculation based on pooled cross section. Headcount poverty rates are estimated using per capita household expenditures. The number of children are shown for 0, 1, 2, 3, and 4 or more children. The headcount poverty rates are shown on the x-axis, with positive numbers indicating more poverty for FHHs. The error bars are the 95% CIs.

38