rafmacalaba/datause-encoder-data huggingface dataset:
marize the characteristics of a generic low income country but allowing simultaneously for country-xed eects, country-specic exogenous variables, non-normal residuals, and exible dynamics using a panel vector autoregression with exogenous variables.

The paper proceeds as follows. Section 2 discusses the basic framework for a single country. Section 3 describes how to implement the methodology using a panel of time series - cross section data. This section also provides a detailed analysis of a ctitious country to illustrate the issues raised above. Section 4 considers Senegal as a case study and briey compares the proposed methodology with the approach currently used by the IMF and the World Bank. Section 5 provides robustness checks and Section 6 concludes.

# 2 The basic framework

This section describes the basic framework and identies key variables that aect the dynamics of external debt. To simplify the exposition, this section focuses on a single country. Details of the implementation and how to cope with the data problems of low income countries are discussed in the next section.

The methodology is an extension of the approach proposed by Garcia and Rigobon (2005), originally designed to study the sustainability of public debt, to the study of the sustainability of external debt. The idea is to write a debt accumulation equation, measuring and estimating a stochastic process for the variables that determine the evolution of debt, and performing Monte Carlo simulations to compute a number of statistics on projected debt trajectories. One contribution of this paper is to replace the Monte Carlo simulation step with a bootstrap procedure based on re-sampling from the set of estimated residuals. This modication makes the methodology robust to the tail risks inherent to probability distributions with fat tails and skewness.

By denition, the international investment position of a country at time _t_ , _IIPt_ , equals the position at time _t −_ 1 , _IIPt−_ 1 , plus the current account balance, the amount of debt relief, aid ows, grants, and valuation eects consisting in the change in the value of assets 5
greenfield FDI projects).<sup>71</sup> As explained in section 3, decomposition of the effects of financial development is achieved by controlling for the size of the source and recipient manufacturing sectors. Once these variables are included, we expect the coefficients on the SFD and DFD interaction terms to capture the FDI-specific effects only, with evidence for general effects coming from a comparison of these coefficients with those from a regression ignoring the scale of manufacturing activity. Tests for the statistical significance of the difference in estimates can be found at the bottom of Table 4.

# **5.2.1 Volume of bilateral greenfield FDI**

We first investigate the different effects of financial development on the volume of bilateral FDI. In column (1) of Table 4, for reference, we reproduce the estimates reported in column (5) of Table 3. In column (2), we show that our results hold when we limit our sample to those sector-country-pair observations for which we have data on 2002 sector-specific value added.<sup>72</sup> Column (3) controls for sector-specific activity in both source and destination countries.

On the source side, we observe that the magnitude of the coefficients on the SFD-ED and SFD-TANG interaction terms slightly decreases in column (3) relative to those in column (2). SFD appears to have a positive impact on the relative volume of bilateral FDI in financially vulnerable sectors partly because it favors relatively more domestic activity in those sectors. However, this general effect represents only about 8-11% of the overall access to external finance effect based on a comparison of the estimates of column (2) and column (3) and the difference in these estimates is not statistically significant. A qualitatively similar result is found by Manova (2013) in the context of international trade: only 20-25% of the total effect of higher financial development on exports is driven by financial development-induced changes in sector-specific output.<sup>73</sup> On the destination side, we find stronger statistical evidence of direct and indirect effects of financial development on FDI. Controlling for the value added of the destination sector results in a fall in the magnitude of

> 71At the extensive margin, we find similar results when using M&A data from the _Zephyr_ database in unreported regressions.

> 72The loss in sample size results in less precisely estimated coefficients, especially for the coefficients on the financial developmentTANG interaction terms.

> 73On theoretical grounds, Manova (2013) tends to prefer the number of establishments as a measure of sector-specific activity. The advantage of using value added is that it reflects both the number and size of active producers.

34
we used the occurrence of crop and rainfall shocks as instruments for child labor. In instrumental variables specifications, we find a negative and significant effect of child labor on school years, the probability of completing primary school, and marginal labor productivity 10 to 13 years later. Moreover, child labor is significantly positively associated with the probability of being married, which, in the context of this sample, is equivalent to an increased probability of earlier marriage.

The education results are mainly driven by the sample of boys. Working in childhood among boys is also found to increase the probability of being involved in farming 10 to 13 years later. For girls we find a robustly positive effect on the probability of marriage. In conjunction with our finding that the extra child labor induced by crop shocks is primarily chores, this suggests the possibility that child labor pushes girls away from agriculture and toward household activities and marriage. In future work we intend to use data on bride prices to examine whether child labor in fact increases girls’ value on the marriage market.

22
Explicitly, it solves where _Wd_ is a weighting matrix, which may be a function of the data. For now, the weighting matrix is going to be the identity matrix. As a result, the estimates are consistent, but not ecient.<sup>35</sup> The implementation of the estimation is as follows: for a given vector of parameters Θ , I simulate the model and as a rst step to nd the vector Θ<sup>ˆ</sup> that minimizes the objective function I use an annealing algorithm. This is a global optimization routine that jumps randomly around the parameter space while at the same time decreasing the frequency of landing in non-optimal ares in each iteration. After reaching a certain number of iterations where the objective function seems to be reaching a global maximum, I use a local search method to obtain the calibrated parameters.<sup>36</sup> In the next subsection, I describe the selected moments and the data used for the calibration.

# 5.2 Estimation and Moments

First, I explain the methodology and results to estimate the elasticity of substitution of the intermediate inputs _ε_ and then describe a set of relevant moments chosen to calibrate the remaining parameters. Even though all parameters aect the value of all moments, I also discuss the eects of each parameter on each moment individually.

## 5.2.1 Estimation of the Elasticity of Substitution between Intermediates

To calibrate the elasticity of substitution _ε_ between the two sectors, I follow a similar approach to Acemoglu and Guerrieri (2008). Using equation (6) and taking logs we obtain an equation that allows for estimation I exploit time variation in relative value added at current prices for the low and high nancially dependent sector, and variations in the ratio of real value added<sup>_<u>y</u>_</sup> _y_<sup><u>1</u></sup> 2<sup>_<u>t</u>_</sup> _t_<sup>toestimatetheelasticityof</sup> substitution between intermediates _ε_ .

The data used to estimate equation (19) comes from EUKLEMS. I use data for the U.S. from 1970 to 2005. First, I separate the 2-digit SIC industries provided in EUKLEMS in low and high external dependence following the measure of Rajan and Zingales (1998) for manufacturing and services, excluding the nancial sector. To construct sectoral value added at constant prices, I divide each industry current price value added by the corresponding price deator and I sum across sectors to construct the low and high nancially dependent sectors.<sup>37</sup> Using those two inputs, I

> 35If the model is overidentied, the weighting of each moment is extremely relevant as in the standard GMM. In particular, we need to put more weight on better identied moments. This would be implemented by using the inverse of the variance-covariate matrix of the data moments.

> 36To be more specic, I use the matlab function fminsearch that comes with the optimization toolbox.

> 37Refer to the Online Appendix C for all the steps in the procedure.

26
The pace of economic growth that is behind the divergent monetary policies in the US and the Eurozone has also triggered an adjustment of the exchange rate, with the USD hitting historical heights not seen since 2004. Since the end of 2013, when the exchange rate was 1.38, the EUR fell by more than 20% to close 2016 at 1.06.<sup>8</sup> To the extent that significantly lower interest rates in Europe are associated with the expectation of a weaker EUR, issuers are finding EUR-denominated obligations more attractive. As discussed later, the impact of a change in the exchange rate on the funding cost for an issuer could far outweigh the interest rate differential. It is therefore vital that the issuer has a clear understanding of the risks involved in the currency choice.

**Figure 5: Exchange rate EUR-USD and annual changes 2000-2017**

<!-- Start of picture text --> 30% 1.80<br>1.60<br>20%<br>1.40<br>10% 1.20<br>1.00<br>0%<br>0.80<br>‐10% 0.60<br>0.40<br>‐20%<br>0.20<br>‐30% 0.00<br>1‐Mar‐00 1‐Aug‐00 1‐Jan‐01 1‐Jun‐01 1‐Nov‐01 1‐Apr‐02 1‐Sep‐02 1‐Feb‐03 1‐Jul‐03 1‐Dec‐03 1‐May‐04 1‐Oct‐04 1‐Mar‐05 1‐Aug‐05 1‐Jan‐06 1‐Jun‐06 1‐Nov‐06 1‐Apr‐07 1‐Sep‐07 1‐Feb‐08 1‐Jul‐08 1‐Dec‐08 1‐May‐09 1‐Oct‐09 1‐Mar‐10 1‐Aug‐10 1‐Jan‐11 1‐Jun‐11 1‐Nov‐11 1‐Apr‐12 1‐Sep‐12 1‐Feb‐13 1‐Jul‐13 1‐Dec‐13 1‐May‐14 1‐Oct‐14 1‐Mar‐15 1‐Aug‐15 1‐Jan‐16 1‐Jun‐16 1‐Nov‐16<br><!-- End of picture text --> Source: Bloomberg, author’s calculations In addition, the compression in the CCS basis might have contributed to this process, making EUR funding increasingly attractive. As illustrated in Figure 6, the EUR-USD cross currency basis became negative after the global financial crisis and has experienced notable volatility since then, particularly when compared to the other element of the swap, the 3 vs. 6-months basis. The sharp compression in CCS basis in 2013 (becoming even positive in 2014) coincides with the step up in EUR issuance volumes.

> 8 Although the EUR has strengthened in the first semester of 2017.

7
<!-- Start of picture text --> R e al G D P a nd p u b lic-s e ct or e co n om ic in fr as tr u c tu ra l i n ve st m e n t<br>(g ro ss) an d fi xe d c ap ita l s toc k (in d ic es, a ll m e asu r ed p e r ca p ita )<br>26 0<br>22 0<br>18 0<br>14 0<br>10 0<br>6 0<br>19 60 1 97 0 1 98 0 199 0 20 00<br>I nf rastr u ctu ra l in vest m en t In fr astr u ct ur al f ixe d c ap ital st ock G DP<br>South A f rica : ind ice s of in fr astr uctur e<br>2 00<br>1 50<br>1 00<br>50<br>0<br>1870 1 880 18 90 19 00 19 10 192 0 1930 1940 1 950 19 60 197 0 198 0 199 0 2000<br>Railw ay lin es G ood s sto ck (r ail) Paved road s<br>Ele ct ricit y Ph on e line s, in c l. m obile<br>Index (1960 = 100) (all values per capita)<br>Index (1990 = 100)<br><!-- End of picture text --> Figure 4: Historical Evolution of Infrastructure in South Africa. Source: Fedderke, Perkins and Luiz (2005).

# **Organization of the Paper**

The paper has three related objectives. First, following previous studies of infrastructure investment needs, we use a panel of 52 countries to estimate robust functions of “demand” for electricity production and telephone lines (both fixed and cellular). We innovate by employing dynamic, heterogeneous panel estimation techniques that have not previously been employed in this context. Second, using these estimates, we forecast future infrastructure demand in both electricity generation and telecommunications for two alternative growth scenarios (“current” and “accelerated” scenarios). This serves to help assess and build on the current South African public policy effort to create conditions for accelerated and shared growth, inter alia, by eliminating infrastructure 6
# Table 4: The value of market access from individual Belt and Road projects

|||Project-l<br>|evel summa<br>Percent<br>|ry statistics<br>ile<br>|<br>||||Number<br>of<br>|
|---|---|---|---|---|---|---|---|---|---|
||Units|Min|10|25|50|75|90|Max|projects|
|A) Projects built in isolation||||||||||
|Annual Project Value (ΔV)|$ millions|0.0|0.0|0.0|0.0|8.3|32.9|750.0|68|
|Share ofΔV in proj. country (besides<br>China), ifΔV>0|percent|0.0|0.0|0.0|5.0|50.0|58.3|66.0|29|
|Share ofΔV in China, ifΔV>0|percent|0.0|9.7|13.0|38.0|38.0|42.6|55.0|29|
|B) Projects built in complement||||||||||
|Annual Project Value (ΔV)|$ millions|0.0|0.0|0.0|4.0|15.3|42.2|744.0|68|
|Share ofΔV in proj. country (besides<br>China), ifΔV>0|percent|0.0|0.0|0.0|0.5|21.3|43.2|57.0|43|
|Share ofΔV in China, ifΔV>0|percent|0.0|0.0|0.0|1.0|9.3|21.3|47.0|43|
|Ratio of_Δ_V iso./_Δ_V comp.||0.0|0.0|0.0|0.0|0.9|4.2|191.6|68|

Note: Table shows the distributions of individual BRI project values when built under two scenarios: (A) in isolation, wherein network on which the project is built does not include any other Belt and Road projects, and (B) in complement, wherein all other projects are assumed to be built before the project is built. Additional rows also show the share of project value either outside the project country or in China.

44
in the Republic of Korea.

Korea offers a unique setting for the purpose of this study.<sup>2</sup> First, an unprecedented regime change following the country’s impeachment of President Park Geun-hye in 2017 led to a dramatic shift toward more rigid labor market policies, including consecutive double-digit yearly minimum wage hikes and a reduction in the maximum allowed number of working hours per week by 16 hours (i.e., from 68 to 52 hours a week). The series of events around 2017 and in labor market were unan- subsequent changes regulations in the of the adverse labor cost shock as an ticipated eyes firms, yielding consequent effective source of exogenous variation for our empirical study. What is more, firm-level survey data from the Korea national statistical office provides a comprehensive set of operational information, including locations and industries of each firm’s foreign affiliates (if any), which enables us to identify MNCs and their responses to the labor cost shock through foreign internal networks.

Using the data set for the period 2013~2019, we investigate the differential impacts of the stricter labor market regulations across firms. We begin by conducting an event study analysis to track employment trends separately for Korean firms with foreign affiliates (i.e., MNCs) and for firms without foreign affiliates non-MNCs, finding that only the former group of firms experienced a decline in employment after 2017. To control for any potential time-varying confounding effects, we then estimate difference-in-differences as well as dynamic treatment effects by including industry-region-year fixed effects. In addition, to alleviate concerns about sample selection bias due to observable differences between MNCs and non-MNCs, we employ a propensity score matching (PSM) method. Overall, our empirical results suggest that, as a result of stricter labor market regulations, domestic employment declined by 3% in firms with foreign affiliates.<sup>3</sup> The negative employment effects for MNCs are consistent with task substitution between domestic workers and foreign factors of production through MNCs’ internal networks. In addition to the decline in employment, MNCs reduced production tasks; the reductions in were in with employment particularly pronounced firms foreign affiliates in emerging Asian countries and in firms with foreign affiliates performing similar tasks. Our findings are inconsistent with possible alternative explanations such as the replacement of domestic labor with domestic capital (or the replacement of one set of workers with differently skilled workers). Furthermore, the estimated results do not appear to be driven by foreign demand shocks. Collectively, all the evidence supports the interpre-

> 2Throughout the paper, “Korea” is used to refer to the Republic of Korea.

> 3We further confirm that the estimated effects are not driven by pre-period differences in employment trends, while they are robust to controlling for any time-varying firm attributes or to alternative strategies of categorization.

2
<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>Cassette Sales Wheat Aid (D̅≥.3)<br>Wheat Aid (D̅<.3)<br>80<br>1500<br>60<br>1000<br>40<br>(Million USD)<br>500<br>20 Wheat Aid (1,000 MT)<br>Value of Global Music Cassette Sales 0<br>0<br><!-- End of picture text --> FIGURE A7: CASSETTE SALES AND FOOD AID SHIPMENTS _Notes:_ Cassette sales data are in millions of USD and taken from IFPI (2009). Wheat Aid is the quantity of wheat shipped as food aid from the NQ replication file. �<sup>�</sup> is the proportion of years in which a country received any wheat food aid shipments from the US in the NQ dataset. The median value of �<sup>�</sup> is .3, so that the conflict trend is split by countries that are above and below the median propensity to receive aid.

56
Policy Research Working Paper 10566

# **Abstract**

Conclusive evidence on the relationship between corruption and migration has remained scant in the literature to date. Using data from 2008 to 2018 on bilateral migration flows across European Union and European Free Trade Association countries and four measures of corruption, this paper shows that corruption acts as both a push factor and a pull factor for migration patterns. Based on a gravity model, a one-unit increase in the corruption level in the origin country is associated with a 11 percent increase in out-migration. The same one-unit increase in the destination country is associated with a 10 percent decline in in-migration.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at andrea.bernini@economics.ox.ac.uk; lbossavie@worldbank.org; dgarrotesanchez@worldbank.org; mmakovec@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
carry hidden and large social costs. These two sets of work – Kahan et al. (2012, 2013) and Sunstein (2007) – both substantiate the role of beliefs and norms of behavior in shaping citizens’ demands for public policies. They differ in their view of the extent to which citizens’ resistance to technical evidence is because of cognitive constraints. Kahan et al. (2012, 2013) highlight the importance of cultural norms instead of cognitive constraints arguing that citizens engage in motivated reasoning to conform an assessment of available facts to the beliefs of the social groups to which they belong.

Going back to the Tanzania survey, here is another question which reveals widespread citizen support for policies that would provide the private benefit of government jobs: “How strongly do you agree/disagree with the statement ‘Governments should provide employment to people’?” Table 3 reports the percentage of respondents who agreed/disagreed with this statement, showing that the overwhelming majority (95 percent) agreed that governments should provide employment.

**Table 3: Citizen responses to “How strongly do you agree/disagree with the statement ‘Governments should provide employment to people’?**

||**Frequency**|**Percent of total**<br>**(3000**<br>**responses)**|
|---|---|---|
|Don’t know|14|0.47|
|Strongly Disagree|15|0.50|
|Disagree|51|1.70|
|Neither agree nor disagree|57|1.90|
|Agree|1214|40.47|
|Strongly Agree|1649|54.97|

Citizens in poor countries may believe that provision of jobs and subsidies is exactly what governments should be doing because markets cannot be trusted to address the injustices of poverty and inequality. Historical inequality in the distribution of economic endowments may explain why citizens develop norms of supporting policies that provide immediately verifiable private benefits, with insufficient attention to the longer run social costs associated with those policies. This interpretation is consistent with a strand of literature arguing that countries with historical institutions of greater inequality exhibit worse economic performance than those who history favored with more egalitarian institutions. For example, Sokollof and Engerman (2000) argue that historically unequal economic institutions in Latin America compared with those in the United States and Canada were responsible for the delay in adoption of inclusive political institutions, and this delay is associated with subsequent weak institutions and persistently worse economic outcomes. The authors provide evidence that exogenous variation in 31