rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The recent years have witnessed a dramatic increase in migrant remittances to developing countries. Officially recorded remittances, measured as the sum of workers’ remittances, compensation of employees, and migrant transfers, are estimated to have increased from US$58 billion in 1995 to US$167 billion in 2005. This growth rate has outpaced that of private capital flows and official development assistance (ODA) over the last decade, making remittances the second largest source of external funding for developing countries after foreign direct investment (FDI) (World Bank, 2005).

There are several factors that are said to explain for the recent increase in remittance flows. They include better data collection, growth in the number of migrants and their income, lower costs and wider networks in the industry that supports remittances, and government policies that improve banking access and the technology of money transfers, which promote transfers through official channels (World Bank, 2005). Whatever the reasons behind this surge, the growing importance of remittances as a source of foreign exchange has certainly attracted an increasing attention from policy-makers and academics as a potential means for economic development.

It is evidently important for developing countries to understand the determinants of remittances so that appropriate policies can be formulated to enhance access to remittances. The existing empirical research has, however, been rather limited and tended to be at the microeconomic level based on household surveys. Although there have been a number of studies that examine the response of remittance flows to macroeconomic factors (e.g. El-Sakka and McNabb, 1999; Aggarwal _et al_ ., 2005; Freund and Spatafora, 2005), the work in this area is less established. The main aim of this paper is therefore to improve our current understanding of how remittance flows are determined at the macroeconomic level. It also attempts to address some of the econometric issues that are inherent in this type of analysis, which are often overlooked in the existing literature.
Table 5.4 MSCI country indices

|Index|Value (with lags)|
|---|---|
|**Russia**|0.457***<br>(0.0827)|
|**Colombia**|0.413***<br>(0.0750)|
|**Brazil**|0.301***<br>(0.0988)|
|**Mexico**|0.221***<br>(0.0712)|
|**Qatar**|0.144**<br>(0.0654)|
|**Kuwait**|0.103***<br>(0.0380)|
|**Kazakhstan**|0.156<br>(0.107)|
|**United Arab Emirates**|0.141<br>(0.106)|
|**Nigeria**|0.0605<br>(0.0620)|
|**Oman**|0.0423<br>(0.0458)|
|**Saudi Arabia**|0.0280<br>(0.0782)|

# **VI. Discussion on potential channels**

<mark>At least three potential channels could explain the negative impact of lower oil prices on financial markets and the economy. The first one is the demand channel. Lower oil prices imply that many energy firms might have to scale down production. Since the sector buys many goods and services from other sectors (for example, electricity generation relies on a range of inputs such as construction and IT services), a decline in the sector reduces demand from the rest of the economy (usually referred to as the ‘indirect effect’). In addition, laid-off workers from the</mark> 26
Muravyev, A. (2010). Evolution of Employment Protection Legislation in the USSR, CIS and Baltic States, 1985-2009, _IZA Discussion Paper_ No. 5365.

Nikolovova, P., F. Pertold and M. Vozar (2010). Informal Economy in the Czech and Slovak Republics Microeconometric Analysis of Labor Force Transitions. Background paper for ― _In_ _from the Shadow: Integrating Europe’s Informal Labor_ ‖ World Bank, Washington DC, mimeo. OECD (2009). _Is Informal Normal? Towards More and Better Jobs_ . Paris: OECD.

Oviedo, A.M., M.R. Thomas and K. Karakurum-Ozdemir (2009). Economic Informality: Causes, Costs and Policies - A Literature Survey. _World Bank Working Paper_ No. 167.

Perry, G., W. Maloney, O. Arias, P. Fajnzylber, A. Mason and J. Saavedra-Chanduvi (2007).

_Informality: Exit and Exclusion_ . Washington D.C.: The World Bank Rabe-Hesketh, S., and A. Skrondal (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd ed. College Station, TX: Stata Press.

Riedmann, A. and G. Fischer (2008). _Undeclared work in the European Union_ . Special Eurobarometer 284: Report.

- Say, F. (2011), Does employment protection help immigrants? Evidence from European labour markets. _Labour Economics_ (forthcoming, doi:10.1016/j.labeco.2011.01.006 )

- Schneider, F. (2005). Shadow Economies around the World: What Do We Really Know. _European Journal of Political Economy_ 21, 598–642.

Schneider, F., A. Buehn and C.E. Montenegro (2010). Shadow Economies All over the World: New

- Estimates for 162 Countries from 1999 to 2007 (Revised Version). _World Bank Policy Research Working Paper_ No. 5356.

Schneider, F. and D. Enste (2000). Shadow Economies: Size, Causes, and Consequences, _Journal of Economic Literature,_ 38(1), 77-114.

StataCorp (2005). Stata Statistical Software: Release 9. College Station, TX: StataCorp LP.

Statistics Estonia (2010). Employees by Indicator, Place of residence, Sex, Type of employment relation and Year. Online statistical database: Social life/Labour market /Employees: ML262.

Stock, J.H. and J.H. Wright (2000). GMM with Weak Identification. _Econometrica_ 68(5): 1055-1096.

- Torgler, B. (2007). _Tax Compliance and Tax Morale: A Theoretical and Empirical Analysis_ , Cheltenham, UK: Edward Elgar

Torgler, B. (2010). Tax Morale and Compliance: Review of Evidence and Case Studies for Europe. Background paper for _In from the Shadow: Integrating Europe’s Informal Labor_ , a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries (Task number P112988).

- Torgler, B. and F. Schneider (2009). The Impact of Tax Morale and Institutional Quality on the Shadow Economy, _Journal of Economic Psychology_ . 30: 228-245.

Torgler, B., M. Schaffner, and A. Macintyre (2010). Tax Compliance, Tax Morale, adn Governance Quality, in: Alm, J., J. Martinez-Vazquez, and B. Torgler (eds.) (2010). _Developing Alternative Frameworks for Explaining Tax Compliance_ . London: Routledge: 56-73.

37
We use the special module on child labor available in the PNAD of 2001 to check whether the affected cohort is studying more hours per day than the comparison cohort and whether there is any evidence they are still in the labor force. Table A.3 in the appendix shows that the ‘treated’ cohort is 1.5 p.p. less likely to be looking for a job in the last 30 days than the comparison group. This means that, with the ban, the affected cohort left the labor force.

These results indicate that the law change made some boys who would combine work with schooling leave the labor force to dedicate full time to study. We perform the same estimates adding controls and the results are identical.<sup>26</sup> Finally, we investigate if the ban affected the occupations of banned boys. We perform the same exercise of equation (1) using the sample of working boys in 1999. Table A.4 presents the results. Among the comparison group, 36 percent work in the service sector, 25 percent in manufacturing, 15 percent in agribusiness, and 4 percent in administrative tasks. Although we do not find any statistically significant change in these compositions, the results suggests that there are some changes from service and agribusiness to manufacturing and administrative tasks. However, these exercises have low power due to the relatively small number of boys working for pay.

# **6.1. The Plausibility of the Identification Assumption**

The identification strategy used in this paper assumes that turning age 14 by the time the law changed (December 1998) was “random”. Thus, observed and unobserved characteristics are unexpected to be systematically correlated to both turning 14 around December 1998 and the outcomes of interest.

The official statistics for children at ages 14 and 15 in 1998 shows that 21.6 percent were in the workforce with about half (46 percent) in the formal sector. In 1999, the proportion of 14 and 15-year-olds in the labor force dropped slightly to 20.5 percent, but the proportion in the formal sector almost halved (9.6 percent vs. 5.3 percent). Most of the fall in participation in the formal sector were among children aged 14 (1.76 percent in 1999 compared to 5.8 percent in 1998). These numbers suggest that those who turned 14 before the ban were less likely to be affected by the law change. This is consistent with the monotonicity assumption made in the fuzzy RD design.

> 26 The results are available upon request.

18
Kpodar, K. (2007). _Manuel d’initiation a Stata_ . Centre d’Etudes et de Recherche sur le Développement International (CERDI), Clermont-Ferrand.

Mala, A., Ulabasoglu, M., “Foreign Direct Investment and Stages of Diversification,” Deakin University, Australia. Draft Paper Presented at The World Bank on Januray 29<sup>th</sup> , 2013.

Mohammed, A., Moya, R., Sookram, S., “Productive Development Policies in Trinidad and Tobago. A Critical Review.” Inter-American Development Bank, January 2010.

Political Risk Services, International Country Risk Guide, 2012.

UNESCO, UNESCO Institute of Statistics (UIS), 2012.

World Economic Forum, Global Competitiveness Report 2011/12, 2011.

World Economic Outlook, International Monetary Fund, 2011.

World Development Indicators, World Bank, 2011.

World Integrated Trade Database, 2012.

Woolridge. “Introductory Econometrics: A Modern Approach”. South Western, 2010.

22
(2016, p. 101) suggested, “better data collection, with an intent to collect richer measures of informality along multiple dimensions, may be important for further work”.

Finally, the narrow focus on informal enterprises overlooks broader links with the formal sector despite evidence suggesting these linkages exist. For instance, a small survey of enterprises in Indonesia by the ILO (2015) found that formal enterprises outsource parts of their production process to home-based, informal workers. However, the extent and features of these subcontracting arrangements remain poorly understood. At the same time, formal enterprises may also directly employ workers on an informal (e.g., unpaid) basis in addition to hiring formal workers (Marquez-Ramos, 2020).<sup>9</sup> Why formal enterprises engage in this arrangement is still unclear, highlighting the need for more research.

# **Informal employment in Indonesia**

Indonesia’s thriving informal economy has been a key source of livelihoods for a sizable share of the population. Recent estimates based on the 2019 SAKERNAS suggest that informal employment accounts for 75 percent of total employment in the country (Wihardja & Cunningham, 2021).<sup>10</sup> Even in relative terms, Indonesia’s informality rate emerges as one of the highest in Southeast Asia, exceeding the likes of Thailand, Vietnam, and even Myanmar (ILO, 2018).

Informal workers constitute a highly diverse group, ranging from owners of household enterprises, casual workers, unpaid family workers, and even employees. Of these, nearly half (48 percent) are owners of household enterprises; 26 percent are casual workers or wage employees without contracts working in those enterprises; and 15 percent are unpaid family workers (Wihardja & Cunningham, 2021). All in all, these three groups account for nearly 90 percent of informal employment.<sup>11</sup>

> 9 Both the IMK Survey and the Manufacturing Survey collect data on the number of paid and unpaid workers in an enterprise. The latter – i.e., unpaid workers – are likely informal workers (Marquez-Ramos, 2020). 10 BPS estimates of informal employment are much lower, ranging from roughly 48 to 56 percent of total employment, due to differences in definitions (Wihardja & Cunningham, 2021).

> 11 The remaining 10 percent are comprised of casual workers, wage employees, and unpaid family workers who are not in household enterprises (Wihardja & Cunningham, 2021).

15
# Table 4: Abnormal stock returns and the PEPP in the United Kingdom

The dependent variable is the abnormal return observed on March 19, 2020. Investment grade (Non-investment grade) is a dummy variable indicating a long term, domestic or foreign, issuer credit rating of at least (lower than) BBB-/Baa3 issued by either Moody’s, S&P or Fitch. Leverage is liabilities divided by assets at the end of 2019. Log of assets is the natural logarithm of assets at the end of 2019. ROA is net income before extraordinary items divided by assets at the end of 2019. Book-to-market is the book value of equity divided by the market value of equity at the end of 2019. Regressions 1 and 2 (3 and 4) include firms that are incorporated (headquartered) in the United Kingdom. Heteroskedasticity robust standard errors are in parentheses. *, ** and *** denote significance at 10%, 5%, and 1%, respectively.

||Incorporate|d in the UK|Headquarter|ed in the UK|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Investment grade|0.0288**|0.0328**|0.0203|0.0255*|
||(0.0139)|(0.0149)|(0.0138)|(0.0148)|
|Leverage|0.0206|0.0171|0.0321|0.0268|
||(0.0231)|(0.0237)|(0.0226)|(0.0229)|
|Log assets|-0.00185|-0.00252|-0.00107|-0.00191|
||(0.00306)|(0.00319)|(0.00295)|(0.00310)|
|ROA|-0.0377|-0.0345|-0.0385|-0.0347|
||(0.0308)|(0.0313)|(0.0310)|(0.0314)|
|Book-to-market|0.000842|0.000799|0.00377|0.00336|
||(0.00679)|(0.00690)|(0.00614)|(0.00622)|
|Non-investment grade||0.0153||0.0203|
|||(0.0193)||(0.0186)|
|Constant|0.0163|0.0301|-0.00423|0.0137|
||(0.0574)|(0.0606)|(0.0556)|(0.0590)|
|Observations|363|363|390|390|
|Adjusted R-squared|0.010|0.009|0.010|0.011|

40
O.A.M.D.G.

at various times during the stay abroad, are consistent with utility-maximizing behavior. Saving and return timing depend on the wage and price relations between host and home country and the dependent marginal utilities. In a descriptive paper, the same author (2000) analyzes simultaneous decision-making on human capital investment and migration, concluding that investments undertaken into human capital are conditioned on intentions at that point in the migration history and not on final realizations of returns. In a very recent study, Ibañez and Vélez (2004) examine displacement in Colombia and its relation to various factors, among others asset ownership. They find that asset ownership as well as the general improvement of welfare at the origin site tends to deter migration.

The literature still leaves significant space to breach. First, among the above there is a virtual absence of papers analyzing within-country variation. Most empirical studies are cross-country and therefore susceptible to omitted variable bias and endogeneity. Second, while recent papers recognize both violence and asset ownership as determinants of migration, there has been no empirical study into the joint nature of savings and migration decisions. This is an important gap, which the present paper attempts to fill.

Our analysis is novel in various ways. First, to our knowledge it is the first empirical test of simultaneous migration and saving, and it is the first test of these decisions in a violent context. Second, it exploits within-country, rather than cross-sectional variation of migration, savings and violence data. And last but not least it makes use of a unique set of household and municipal data from Colombia.

We have structured our paper as follows. The next section (2) explains how violence, migration and savings can be linked through a simple model of simultaneous decisionmaking. Section 3 describes the data and some key statistics for the relevant variables,
**Table 1: Basic Determinants of the Municipal Budget Execution Rate**

||Dependent|Variable: Actual Exp|enditure / Allocated|Budget (%)|
|---|---|---|---|---|
|_Explanatory variables:_|Total|Current|Capital|Capital Defined|
|Allocated Budget|-12.5127***|-5.9494***|-12.0434***|-13.0507***|
|(LCU, in logs)|(-9.71)|(-5.95)|(-11.21)|(-14.22)|
|Budget from Natural Resources|0.0340|-0.0714***|0.1363***|0.3185***|
|(% of actual expenditure)|(1.03)|(-6.93)|(4.62)|(9.99)|
|Population|7.4230***|4.4466***|4.6245***|7.0719***|
|(in logs)|(10.38)|(5.75)|(7.37)|(11.59)|
|Education|1.4839***|0.9050***|1.3852***|1.1866**|
|(average years of schooling,<br>population over 15 years old)|(3.26)|(2.86)|(2.84)|(2.26)|
|Staff / Population|4.3735***|2.8201***|2.0742**|3.2371***|
|(ratio of white-collar staff in municipality to<br>population, %)|(4.20)|(3.20)|(2.36)|(3.16)|
|Poverty Rate|0.0311|0.0334***|0.0829***|0.0936***|
|(% of total population)|(1.50)|(2.74)|(3.64)|(4.18)|
|Urban Population|-0.0778***|-0.0411***|-0.0483**|-0.0573***|
|(% of total population)|(-3.24)|(-2.88)|(-2.50)|(-2.65)|
|Percentage of Winning Votes|0.1963***|0.0540**|0.2060***|0.1965***|
|(% of votes obtained by winning Mayor)|(4.79)|(2.11)|(4.43)|(4.12)|
|Population Dispersion|-10.4850***|-5.6197***|-2.5238|-3.4284|
|(1 − normalized Herfindahl index of<br>population in villages)|(-3.26)|(-3.67)|(-1.08)|(-1.49)|
|Incumbent Mayor|-1.1441|-0.5936|-0.9901|-1.4643|
|(dummy variable = 1 if incumbent,<br>and 0 otherwise)|(-1.53)|(-1.09)|(-0.93)|(-1.43)|
|Incumbent Mayor * Year Effect 2007|5.3229***<br>(6.38)|2.2826***<br>(4.06)|7.1728***<br>(6.60)|8.3761***<br>(7.64)|
|Year Effect 2007|-6.5215***|-0.0071|-10.3776***|-7.7757***|
|(dummy variable = 1 if year is 2007,<br>and 0 otherwise)|(-10.57)|(-0.02)|(-12.28)|(-8.38)|
|Year Effect 2009|0.1366|-4.2505***|1.4315**|-4.3182***|
|(dummy variable = 1 if year is 2009,<br>and 0 otherwise)|(0.24)|(-10.49)|(2.18)|(-6.00)|
|Constant|200.1311***<br>(17.56)|134.3912***<br>(17.67)|194.8805***<br>(16.27)|177.9881***<br>(18.54)|
|R-squared|0.289|0.204|0.269|0.230|
|No. of observation / No. of municipalities|4858 / 1688|4858 / 1688|4858 / 1688|4858 / 1688|

Numbers in parentheses are the corresponding robust t-statistics. Standard errors are clustered by province.

*, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

12
# **References**

- Abay, K. A., Abate, G. T., Barrett, C. B., and Bernard, T. (2019). Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity relationship in agriculture. _Journal of Development Economics_ , 139:171 – 184.

- Athey, S. (2018). _The Impact of Machine Learning on Economics_ , pages 507–547. University of Chicago Press.

- Azzari, G., Jain, S., Jeffries, G., Kilic, T., and Murray, S. (2021). Understanding the requirements for surveys to support satellite-based crop type mapping: Evidence from sub-saharan africa. _Remote Sensing_ , 13(23).

- Bardasi, E., Beegle, K., Dillon, A., and Serneels, P. (2011). Do labor statistics depend on how and to whom the questions are asked? _World Bank Economic Review_ , 25(3):418–447.

- Carletto, C., Jolliffe, D., and Banerjee, R. (2015). From tragedy to renaissance: Improving agricultural data for better policies. _The Journal of Development Studies_ , 51(2):133–148.

- Desiere, S. and Jolliffe, D. (2018). Land productivity and plot size: Is measurement error driving the inverse relationship? _Journal of Development Economics_ , 130:84 – 98.

- Fermont, A. and Benson, T. (2011). Estimating yield of food crops grown by smallholder farmers: A review in the uganda context. _International Food Policy Research Institute discussion paper_ , 01097.

- Gourlay, S., Kilic, T., and Lobell, D. B. (2019). A new spin on an old debate: Errors in farmerreported production and their implications for inverse scale - productivity relationship in uganda. _Journal of Development Economics_ , 141:102376.

- Little, R. and Rubin, D. (1987). _Statistical Analysis With Missing Data_ . Wiley Series in Probability and Statistics. Wiley.

- Michler, J. D., Josephson, A., Kilic, T., and Murray, S. (2022). Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data. _Journal of Development Economics_ , 158:102927.

- Ngufor, C., Van Houten, H., Caffo, B. S., Shah, N. D., and McCoy, R. G. (2019). Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin a1c. _Journal of Biomedical Informatics_ , 89:56–67.

- Quartagno, M., Carpenter, J. R., and Goldstein, H. (2019). Multiple Imputation with Survey Weights: A Multilevel Approach. _Journal of Survey Statistics and Methodology_ , 8(5):965– 989.

- Rubin, D. B. (1987). _Multiple Imputation for Nonresponse in Surveys_ . Wiley.

- Storm, H., Baylis, K., and Heckelei, T. (2019). Machine learning in agricultural and applied economics. _European Review of Agricultural Economics_ , 47(3):849–892.

17
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 48 by the differences in country cost factors. A linear specification with initial dike height and no intercept was considered, as suggested by the results of Lenk et al. (2016) and some robustness checks. Country characteristics were also included: GDP per capita, logtransformed country averages of population densities and asset densities in low-lying areas, corruption and democracy variables, and a population-asset density interaction.

**Table A2:** Unit cost results (OLS, no intercept).

||Estimate|Std. Error|t value|Pr(>|t|)|
|---|---|---|---|---|
|Initial dike height|7.79E-01|1.74E-01|4.48|0.00 (***)|
|GDP per capita|4.64E-05|1.72E-05|2.69|0.01 (***)|
|Log Population density|6.12E-01|1.06E-01|5.78|0.00 (***)|
|Log Asset density|-3.17E-01|2.70E-01|-1.17|0.24|
|Corruption|-5.94E-01|3.36E-01|-1.77|0.08 (*)|
|Democracy|2.28E-01|6.44E-02|3.54|0.00 (***)|
|Log Pop. dens.*Log Asset<br>dens.|-6.71E-02|4.41E-02|-1.52|0.13|

Table A2 reports the results; the adjusted-R<sup>2</sup> is 0.77 and the F-test is 92.57 on 7 and 186 degrees of freedom, P-value is 0.00. Higher initial dike heights, higher GDP per capita and higher population densities are associated with higher unit costs. The results suggest that more expensive local labor costs and more expensive land inputs may increase unit costs of sea dikes. Asset density, in contrast, is insignificant. Democracy and corruption are associated with higher unit costs. The latter is only significant at α=0.1. The corruption variable has been scaled inversely, i.e. a higher score means less corruption, which explains the negative sign.