rafmacalaba/datause-encoder-data huggingface dataset:
Korea, but is there a causal relationship? The answer hinges critically on whether we can evidence that the domestic of Korean MNCs declined more find employment relatively than that of non-MNCs after the policy changes. A disproportionate drop in domestic employment in MNCs after the labor market shock can be interpreted as task substitution between domestic workers and foreign factors of production within their internal network. Formally, we test whether Korean firms that already had foreign affiliates before 2017 reduced their domestic employment to relocate tasks abroad in response to the exogenous domestic labor market policies. The task reallocation would take place primarily through foreign affiliates in nearby low-wage countries if cost saving was the key motive. A subsequent assessment therefore is centered on whether MNCs with foreign affiliates in those countries experienced a steeper decline in domestic employment. Since our data set comprises a nontrivial number of Korean firms with affiliates in China and the ASEAN countries prior to the labor policy changes, the current economic, geographic, and policy circumstances all provide a unique setup for our empirical test.

# **3 Data**

Our primary data set is the Survey of Business Activities (SBA) from Statistics Korea, an annual that was carried out in 2006. The data set covers firm-level panel survey first firms that conduct business activities in Korea as of the survey reference date, employing at least 50 full-time employees and reporting a capital stock value of at least 300 million Korean won (about 267,869 USD using the 2010 exchange rate). The sample size in each year is approximately 13,000 firms in Korea, which represent 80% of the total output of Korea in the year 2008.

The data set provides typical firm-level variables such as employment, tangible assets, sales, purchases, value added, wage bill, exports, and imports. In addition, it allows us to identify each firm’s foreign affiliates and their locations (if any), along with equity shares.<sup>15</sup> For each firm, we can also identify the region, sector, and industry. The regions match 16 first-tier administrative divisions of Korea: one special city, six metropolitan cities, eight provinces, and one special autonomous province.<sup>16</sup> The sectors are identified by 18 one-digit divisions based on the Korean Standard Industrial Classification.<sup>17</sup> At a

> 15This data set is also used by Choi and Greaney (2022). They found that foreign acquisition increases the share of women in management positions by exploiting information on foreign ownership in the data set. In contrast, in this paper, we utilize the information on Korean firms’ foreign affiliates.

> 16Note that, in July 2012, Sejong Special Self-Governing City was created. We classify Sejong as Chungcheongnam Province (Sejong City’s original province before July 2012) throughout the paper.

> 17From 2007 to 2016, the KSIC is based on revision 9; while, from 2017 to 2019, the KSIC is based on

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# **TABLE OF CONTENTS**

|I. INTRODUCTION AND BACKGROUND.................................................................... 5|
|---|
|II. MEASURING MISALIGNMENT .............................................................................. 6|
|A. LITERATURE SURVEY.......................................................................................... 6|
|B. METHODOLGICAL OVERVIEW AND DATASET............................................. 6|
|C. PPP APPROACH....................................................................................................... 8|
|III. REAL EXCHANGE RATE BEHAVIOR IN THE TWO ZONES............................ 12|
|A. REAL EXCHANGE RATE BEHAVIOR IN THE UEMOA ZONE...................... 12|
|B. REAL EXCHANGE RATE BEHAVIOR IN THE CEMAC ZONE ...................... 12|
|C. SIMULATIONS....................................................................................................... 14|
|IV. SINGLE-EQUATION ANALYSIS OF THE REAL EFFECTIVE EXCHANGE|
|RATE................................................................................................................................ 15|
|A. THEORY ................................................................................................................. 15|
|B. ECONOMETRIC METHODOLOGY..................................................................... 16|
|C. ECONOMETRIC RESULTS................................................................................... 17|
|V. IMPLICATIONS AND FUTURE ............................................................................... 20|

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also exclude from the sample declarations that list petroleum products (HS chapter 27) or vehicles, which are subject to different clearance procedures.<sup>20</sup> Finally, we exclude from the sample import declarations that arrive to airports and to the minor seaports in Madagascar and focus on those arriving to the main port Toamasina as well as the next seven largest ports. The final estimating sample used in Section 4 to study the impact of valuation advice accounts for 79.3 percent of collected taxes and 68.5 percent of the total import value for non-oil import declarations cleared by seaports, on average across the period ranging from January 1, 2016 until mid-October 2018. For the analysis of the RCT presented in Section 5, the sample includes only high-risk declarations, that is, those subject to valuation advice and with a risk score of 8 or 9, as will be further discussed in Section 5. The RCT sample accounts for 53.6 percent of collected taxes and 45.3 percent of the total import value of non-oil import declarations cleared by seaports, on average across the 14 weeks of the RCT from October 15, 2018 to February 2019.

# **3.2. Measuring customs performance**

Our analysis uses the set of outcome and control variables described below.

# **Outcome variables**

Our outcomes of interest are measures of customs performance and inspector actions: a dummy indicating whether or not the inspector “upgraded” the declaration by moving it to the red channel; a dummy indicating whether or not the containerized goods in the declaration were scanned; log clearance time measured as the number of hours from the time the declaration was registered to the time of assessment;<sup>21</sup> a dummy for whether or not fraud was recorded;<sup>22</sup> the log change in

> 20 In Madagascar, used cars are subject to specific clearance procedures and are examined by the service provider GasyNet.

> 21 While we use the expression “clearance time” to designate our outcome variable, note that the variable is actually capturing what could be called ‘assessment time’ which is the only time that is under the control of the inspector. The complete clearance time (time from registration to clearance) is not only attributable to customs but depends also on other agents such as importers and banks if payments need to be made for imported goods to be cleared and if time is taken to pick up the goods from the port. The use of a measure of time that is entirely under control of the customs agent is an improvement over the other studies in the literature, namely Volpe Martincus et al. (2015) and Fernandes et al. (2019).

> 22 Since we do not have direct measures of fraud, we have to rely on fraud records which could reflect a combination of true fraud, extortion, and reporting error. Given the widespread prevalence of undervaluation, it seems a priori reasonable to assume reported fraud is a good proxy for actual fraud.

15
is a diagonal matrix, with household-specific entries given by σ ˆ ε 2, _ch_ . The sum of these two matrices is Σ<sup>ˆ</sup> , the estimated variance-covariance matrix for the original model given by equation (2). Once this matrix has been calculated, the original model is estimated by GLS.

In the second stage predicted log expenditures and subsequently local-level estimates of poverty and their accompanying standard errors can be generated via several routes. Elbers et al (2002) describe a method based on numerical gradient estimation. An alternative approach known as parametric bootstrapping (Pfeffermann and Tiller, 2005) has been found to yield closely similar results and proceeds as follows.<sup>6</sup> A series of simulations are conducted, where for each simulation _r_ a set of first stage parameters are drawn from their corresponding distributions estimated in the first stage. A set of beta and alpha coefficients, **_β_**<sup>~</sup> _r_ and **_α_**<sup>~</sup> _r_ , are drawn from the multivariate normal distributions described by the first stage point estimates and their associated variance-covariance ~ 2 _r_ matrices. Additionally, ( ση ) , a simulated value of the variance of the location error component is drawn.<sup>7</sup> Combining the alpha coefficients with census data, for each census household ( σ ~ ε 2, _ch_<sup>)</sup> _r_ , the household-specific variance of the household error component, is estimated. Then, for each household simulated disturbance terms, η<sup>~</sup> _cr_ and ε<sup>~</sup> _chr_ , are drawn from their corresponding distributions.<sup>8</sup> A value of expenditure for each ˆ _r_ ′ ~ _r_ household, _ych_ , is simulated based on both predicted log expenditure, **_x_** _ch_<sup>**_β_**</sup> , and the disturbance terms:

> 6 We will see below that while the methods yield very similar point estimates, the approach employed in ELL (2002) produces slightly wider (and possibly more plausible) confidence intervals.  In Appendix 1 we outline yet a third approach that yields confidence intervals that also more closely track those obtained with the method outlined in ELL (2002).

> 7 The ( σ ~ η 2 ) _r_ value is drawn from a gamma distribution defined so as to have mean σ ˆ η 2 and variance ˆ 2 _V_ σ . ( η)

> 8 Non-normality is allowed for in the distribution of both η _c_ and ε _ch_ .  For example, for each distribution, a Student's t-distribution can be chosen with degrees of freedom such that its kurtosis most closely matches ˆ

> that of the first stage residual components, η _c_ or _ech_ .  An alternative, semi-parametric, approach can also be adopted in which stardardized residuals are drawn from the first-stage survey residuals.

6
level of development and non-linearities in development (Table 2). Other South Asian countries including Pakistan and Sri Lanka also have significant positive coefficients. Bangladesh is the only country in South Asia whose modern service export is no different than the norm. Figure 7 compares the share of IT and IT-enabled service, the fastest growing modern impersonal service, in total service export against real GDP per capita for South Asia, East Asia and OECD countries. Bangladesh, Pakistan and Sri Lanka have a much higher share of IT and IT enabled services in their service export for their level of income while most East Asian countries including China are below the global norm. India is a huge outlier on this count.

<!-- Start of picture text -->
-<br>Figure 7: Share of IT enabled services exports and log per capita GDP 2006<br>India<br>SWZ<br>SLB LBN<br>IRL<br>BRA<br>NLD<br>CANUK<br>ROM ARG BELDEU SWE<br>NOR<br>BangladeshAZERUSCRICZE ESP ITA AUTFIN<br>CMRGUYSri LankaWSMMKD POLHUN CYP USA<br>PakistanPHLPRYChinaMARBWAMYSSVKLVAESTURY MLTSVN AUSFRA<br>DMA<br>UKRARMEGYBLRBGRPERBLZGRDMUSVENHRVCHL PRT NZL JPN<br>GTMKAZFJITUNPANLTU GRC<br>NICGEOBOLHNDCPVNAMJAMTURMEXLBY<br>0 20000 40000 60000<br>Real GDP per capita in 2006<br>80<br>60<br>40<br>20<br>0<br>IT and IT enabled services / Total services exports in 2006<br><!-- End of picture text -->

_Source:_ Balance of Payments statistical yearbook, IMF. 2008 _. Note_ : IT and IT-enabled services is measured by adding the Computer and information services plus miscellaneous business services category in the Balance of payments _._ In general, service exports from South Asia, which are mostly skill intensive, have more in common with OECD countries, than with other developing countries. The share of modern services export (such as IT and IT enabled service exports) in total service exports for South Asia resembles the export patterns of more developed economies like USA. Thus in terms of export sophistication South Asia is like advanced economies.

In order to firm up this idea we construct an index to measure the sophistication level of service exports of South Asian countries on the lines of sophistication level of goods 16
> FIGURE 3.4

<!-- Start of picture text --> 24<br>3.5<br>Reasons for saving reported by savers<br>Adults saving for specified purpose in the past year (%), 2014<br>East Asia & Pacific<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Middle East<br>For old age<br>South Asia For education<br>To start, operate, or expand a business<br>Sub-Saharan Africa<br>0 10 20 30 40<br>Note: Respondents could report saving for more than one purpose.<br>Source: Global Findex database.<br>4<br>THE GLOBAL FINDEX DATABASE 47<br><!-- End of picture text --> Semiformal saving, 2011 and 2014 Adults saving through an informal savings club or person outside the family in the past year (%) <!-- Start of picture text --> 2011 2014<br>East Asia & Pacific 6<br>Europe & Central Asia 7<br>Latin America & Caribbean 8<br>Middle East 12<br>South Asia 9<br>Sub-Saharan Africa 24<br>Source: Global Findex database.<br><!-- End of picture text -->

# What are the main reasons for saving?

> FIGURE 3.5

For what future expenses do people save? The 2014 Global Findex survey asked about three specific reasons for saving—for old age, for education expenses, and to start, operate, or expand a business.<sup>3</sup> Worldwide, almost 25 percent of adults reported having saved in the past year for old age, a similar share for education expenses, and 14 percent for a business (figure 3.5). But marked differences emerge across regions.

In high-income OECD economies and East South Asia Asia and the Pacific people were more likely Sub-Saharan Africa to report saving for old age than for either of the other two reasons. Around 40 percent 0 10 20 of adults in these two regions reported sav- _Note:_ Respondents could report saving for more than one purpose. _Source:_ Global Findex database. ing for old age, a far greater share than the roughly 10 percent who reported doing so in all other regions. In high-income OECD economies this may reflect in part the aging population. In East Asia and the Pacific an important factor may be the generally higher savings rates than in other developing regions. Beyond this, however, a growing awareness of increasing life expectancy and slowing birthrates in the region has prompted public sector action on increasing income security in older age through both contributory and noncontributory mechanisms.<sup>4</sup> More than 30 percent of adults in East Asia and the Pacific and some 20 percent in highincome OECD economies and Sub-Saharan Africa reported saving for education. And in Latin America and the Caribbean, the Middle East, and South Asia, while a smaller share of adults saved for education than in most other regions, more saved for this reason than for old age or for a business.

In East Asia and the Pacific and Sub-Saharan Africa about 20 percent of adults reported saving to start, operate, or expand a business, about twice the share in all other regions.
Philippines. Since the program’s inception, Filipino migration has increased dramatically, and Filipinos now migrate in large numbers to an extraordinarily diverse range of destination countries. The top ten destinations account for approximately 86 percent of all new overseas Filipino worker (OFW) hires (see Table 1). Countries such as Saudi Arabia, the U.A.E., and Kuwait, in the Middle East, and Japan; Hong Kong SAR, China; Taiwan, China; and Singapore in East Asia, are the most common destinations, but Italy, the U.K., Canada and the U.S. are also among the top fifteen destinations. By comparison, 98 percent of Mexican migrants are in the United States (World Bank, 2011). Migration from the Philippines is largely temporary and legal, and occurs through licensed private recruitment agencies. Overseas temporary contract work is the primary channel through which Filipinos migrate, and in order to be cleared to leave the Philippines, an OFW must have a job contract in hand. Between 1992 and 2000, 83 percent of Filipinos abroad were engaged in contract work,<sup>2</sup> with most of the rest being non-temporary workers migrating through family reunification policies or other permanent migration channels. This form of legal temporary work is likely to become more common in future years as countries like Bangladesh, Indonesia, Sri Lanka and India seek to follow the Philippine model, and destination countries consider how to balance demands for labor with public concerns about migrant settlement.

# **2.2 Large Potential Supply**

Data from the 2010 Gallup World Poll suggest that there are many individuals in the Philippines who would like to work abroad but who are not currently doing so. This poll asked a representative sample of 1000 adults in the Philippines the question “Ideally, if you had the opportunity, would you like to go to another country for temporary work, or not?” Overall, 51.1 percent of adults aged 15 and over said they would like to work abroad in temporary work (and 18.6 percent said they would like to migrate permanently abroad). Desire to migrate temporarily abroad is highest for individuals in the 15-34 age range, for individuals in urban areas, and for more educated individuals. The voting age population (18+) in the Philippines is approximately 52 million, so taking 51 percent of this gives approximately 26 million people who say they would like to migrate temporarily. This is ten times the magnitude of the 2.0 million who

> 2 Authors’ calculation from the Survey of Overseas Filipinos (SOF), an offshoot of the Labor Force Survey in the Philippines.

5
# Financing Municipal Water and Sanitation Services in Nairobi’s Informal Settlements<sup>∗</sup>

## Aidan Coville

Sebastian Galiani Paul Gertler Susumu Yoshida

### _JEL Codes_ : C93, D04, O18

### _Keywords:_ Water and Sanitation, Informal settlements, Enforcement, Financial sustainability.

> ∗ _Affiliations_ : Coville: DIME, World Bank, acoville@worldbank.org; Galiani: University of Maryland and NBER, galiani@umd.edu; Gertler: UC Berkeley and NBER, gertler@berkeley.edu; Yoshida: DIME, World Bank, syoshida@worldbank.org.

_Human Subjects and Study Registration_ : The study received local and international ethical clearance from Maseno University (MSU/DRPC/00070/14) and Innovations for Poverty Action International Review Boards (Protocol : 13December-004). The study was registered in the American Economic Association RCT registry (AEARCTR-0003556) _Acknowledgements_ : The authors are grateful to the World Bank’s Strategic Impact Evaluation Fund (SIEF), Development Impact Evaluation Department (DIME), J-PAL/MIT Urban Services Initiative and the International Growth Center for financial support. The authors have benefited from comments by Edward Glaeser, Marco Gonzales, Kelsey Jack, Bryce Millett Steinberg, Guadalupe Bedoya, Gustavo Saltiel, Catherine Signe Tovey, Josses Mugabi, George Joseph, Ruth Kennedy-Walker, Jeffrey Mosenkis, Laura Burke, Douglas MacKay, Chris Prottas, Mitsunori Motohashi, Martin Gambrill, Arianna Legovini, Keziah Muthembwa, Camille Nuamah and participants in the 2019 Cities and Development conference, the 2020 NBER Summer Institute Urban Economics Conference, and from a seminar at NYU in Abu Dubai. The study would not have been possible without the continued efforts and long-term collaboration with the Nairobi City Water and Sewerage Company staff including Nahashon Muguna, Jackson Munuve, Kagiri Gicheha, Jason Mwangi, Beldina Owade, Christine Machio, Paul Mwarania, Ephantus Mugo, Martin Nangole, Lucy Njambi, Daisy Nyaboke and Owen Wanjala. Likewise, we thank Christine Ochieng, Paul Mbanga, Wendy Ayres, Rajesh Advani, Jessica Lopez and Clifford Mwaura from the World Bank who helped ensure a strong link between the research and operational activities. The Kenya Innovations for Poverty Action (IPA) team provided professional field support throughout the program, with particular thanks to Frank Odhiambo, Bonnyface Mwangi, Geoffrey Onyambu, John Paul Buleti, Allison Stone and Alice Kirungu. The authors also benefited from excellent research assistance from Amy Dolinger, Marco Valenza and Duncan Webb. DIME Analytics provided technical support throughout the analysis with Luiza Andrade conducting code reproducibility checks. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors have no financial or material interests in the results in the paper.
Cáceres, L. and Saca, N., (2006). What do remittances do? Analyzing the private remittances transmission mechanism in El Salvador. International Monetary Fund Working Paper 06/250.

- Cox-Edwards, A. and Ureta, M., (2003). International migration, remittances, and schooling: Evidence from El Salvador. National Bureau of Economic Research, Working Paper 9766.

- Demirgüç-Kunt, A., López Córdova, E., Martínez Pería, M.S., and Woodruff, C., (2011). Remittances and banking sector breadth and depth: Evidence from Mexico. _Journal of Development Economics_ 95(2), 229-241.

- Dupas, P. and Robinson, J., (2009). Savings constraints and microenterprise development: Evidence from a field Experiment in Kenya. National Bureau of Economic Research Working Paper  14693.

- Dustmann, C. and Kirchkamp, O., (2002). The optimal migration duration and activity choice after remigration. _Journal of Development Economics_ 67(2), 351-72.

- Fuentes, J., (2001). Estructura competitiva del mercado bancario Salvadoreño en la década de los noventa. Reserve Central Bank of El Salvador, Economic and Financial Research Department.

- Giuliano, P. and Ruiz-Arranz, M., (2009). Remittances, financial development, and growth. _Journal of Development Economics_ 90, 144-152.

- Gupta, S., Pattillo, C., and Wagh, S., (2009). Effect of remittances on poverty and financial development in Sub-Saharan Africa. _World Development_ 37(1), 104-115.

- Hanson, G. and Woodruff C., (2003). Emigration and educational attainment in Mexico. Mimeo, University of California at San Diego.

- Hildebrandt, N. and McKenzie, D., (2005). The effects of migration on child health in Mexico. _Economia: Journal of the Latin American and Caribbean Economic Association_ 6, 257289.

- Kanaiaupuni, S. and Donato, K., (1999). Migradollars and mortality: The effects of migration on infant survival in Mexico. _Demography_ 36 (3),  339-353.

- Karlan, D. and Zinman, J., (2010). Expanding credit access: Using randomized supply decisions to estimate the impacts _.  Review of Financial Studies_ 23 (1), 433-464.

- Khandker, S., (2005). Microfinance and poverty: Evidence using panel data from Bangladesh. _World Bank Economic Review_ 19(2), 263-286.

- King, R. and Levine, R., (1993). Finance and growth: Schumpeter might be right. _Quarterly Journal of Economics_ 108, 717-737.

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