rafmacalaba/datause-encoder-data huggingface dataset:
4 channels which influence investment behavior may work in opposite direction. (See, for example, Chhibber and Shafik, 1992.) Furthermore, as argued in Dornbusch (1990), investors are likely to adopt a wait and Bee attitude and there is a possibility that an economy get trapped in an inferior equilibrium where the transition from stabilization to growth does not materialize.

# 2.3. Effects on **Aagreqate SuDDlV**

In addition to the demand related effects identified above, there are a number of supply-side channels which influence the impact of a devaluation on aggregate output.

The increase in the price of intermediate inputs is the most important channel through which a devaluation can be contractionary. As a devaluation makes these inputs more expensive, in domestic currency, the marginal cost of producing nontradable increases. (See, Taylor, 1983.) Labor is an essential cost of production and the effects of a devaluation on aggregate supply are largely a function of what happens to real wages in each sector.

Finally, the structuralist school (see, for example, van Wijnbergen, 1983) has also emphasized that a devaluation can be contractionary if financing costs for labor and imported inputs (which arise because suppliers often need to borrow to pay for factor inputs -labor and intermediate goods- until they get paid for their products) rise as a result of an increase in nominal interest rates.

We now turn to a simple model to illustrate, in the case of a typical primary commodities exporter less developed country, the effects identified above. Although the model developed below is fairly general in its applicability, the research was motivated by the January 1994 devaluation of the CFA franc and the economic structure underlying the construction of the model corresponds to that of a typical CFA country.

# 3 Devaluation and Aggregate OutRut Model

# 3.1 Structure of the model

The model developed in this section is similar in spirit to that of Devarajan and de Melo (1987), and Edwards (1989). Consider a small open economy characterized by three sectors:
**TABLE 5. MARGINAL PRODUCTIVITY DIFFERENTIALS AND RELATIVE WAGES FOR TRAINING BY GENDER**

|_Variable_|_Producti_<br>_Differen_|_vity_<br>_tial_|_Relati_<br>_Wag_|_ve_<br>_Reject_<br>_e_<br>_i_<br>_i_<br>_o_<br>_H_<br>λ<br>φ =<br>:|
|---|---|---|---|---|
|_Ho: coefficient=1_|||||
|Men within non-trained workers|1.97<br>(0.15)|*****|1.54<br>(0.04)|*****<br>*****|
|In-house training within women|1.14<br>(0.17)||1.23<br>(0.08)|*****|
|External formal training within women|1.25<br>(0.29)||1.23<br>(0.13)|*****|
|In-house training within men|1.28<br>(0.07)|*****|1.14<br>(0.03)|*****<br>*****|
|External formal training within men|1.55<br>(0.10)|*****|1.24<br>(0.05)|*****<br>*****|
|Adjusted R<sup>2</sup>|0.90||0.96||
|Observations|6,662||||

_Notes:_ Calculations based on ENESTYC 2001. Standard errors are shown in parenthesis. * denotes significance at 5 percent level. The Wald statistic was used to test the equality of the estimated wage and productivity differential. Reference group: women without training.

As can be seen in the table, among workers that received no training, men are 97 percent more productive and earn 54 percent higher wages. These results are consistent with what was previously found on the effects of gender, even among those workers that have received no training. In the same way, the results for education and experience are practically the same as we had before. This shows that our model is robust to the assumptions that we made.

With respect to training, there is no evidence that women who receive either inhouse or external formal training are more productive than those without training. However, even though they are paid 23 percent more, we cannot reject the equality of the wage and productivity differentials. This means that both trained women and their employers share the benefits equally. As for men, those with in-house training are 28 percent more productive, and those with external formal training, are 55 percent more productive. Respectively, men earn 14 percent and 24 percent higher wages. In this case, there is significant evidence that the productivity differential is larger than the wage differential. The results are very similar to the ones we had before relaxing the assumptions.

18
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 45 bootstrap method, based on the bootstrap procedure for finite populations introduced by Gonz ́alez-Manteiga et al. (2008). This method is described below.

# **Bootstrap procedure for MSE estimation:**

1. Fit model (5.1) to the sample data **y** _s_ = ( **y** 1<sup>_′_</sup> _s_<sup>_, . . . ,_</sup><sup>**y**</sup> _Ds_<sup>_′_)</sup><sup>_′_,obtaining</sup> ˆ

estimates of the model parameters, **_β_**<sup>ˆ</sup> , _σu_<sup>2,and</sup><sup>_σ_ˆ</sup> _e_<sup>2.</sup> 2. Generate bootstrap area effects as follows: Generate, independently of _u_<sup>_∗_</sup> 1<sup>(</sup><sup>_b_)</sup> _, . . . , u_<sup>_∗_</sup> _D_<sup>(</sup><sup>_b_),bootstraperrorsasfol-</sup> lows: Then, generate a bootstrap population (or census) of the response variable through the model, 3. Define the census vector of response variables for area _d_ , **y** _d_<sup>(</sup><sup>_b_)</sup> = ( _yd_<sup>(</sup><sup>_b_</sup> 1<sup>)</sup><sup>_, . . . , y_</sup> _dN_<sup>(</sup><sup>_b_)</sup> _d_<sup>)</sup><sup>_′_, and calculate from it the indicator of interest,</sup><sup>_δ_</sup> _d_<sup>(</sup><sup>_b_)</sup> = _hd_ ( **y** _d_<sup>(</sup><sup>_b_)),</sup><sup>_d_= 1</sup><sup>_, . . . , D_.</sup> 4. For the original sample _s_ = _s_ 1 _∪· · ·∪sD_ , let **y** _s_<sup>_∗_(</sup><sup>_b_)</sup> = (( **y** 1<sup>_∗_</sup> _s_<sup>(</sup><sup>_b_))</sup><sup>_′, . . . ,_(</sup><sup>**y**</sup> _Ds_<sup>_∗_(</sup><sup>_b_))</sup><sup>_′_)</sup><sup>_′_</sup> be the vector containing the bootstrap observations whose indices are in the sample, i.e., it contains the variables _ydi_<sup>_∗_(</sup><sup>_b_),</sup><sup>_i∈sd_,</sup><sup>_d_=</sup> 1 _, . . . , D_ . Fit the model (5.1) again to the bootstrap sample data **y** _s_<sup>(</sup><sup>_b_)</sup> and obtain the bootstrap EB predictors _δ_<sup>ˆ</sup> _d_<sup>_EB∗_(</sup><sup>_b_)</sup> , _d_ = 1 _, . . . , D_ .

5. Repeat steps 2–4 for _b_ = 1 _, . . . , B_ , and obtain the true values, _δd_<sup>_∗_(</sup><sup>_b_)</sup> , and the corresponding bootstrap EB predictors, _δ_<sup>ˆ</sup> _d_<sup>_EB∗_(</sup><sup>_b_)</sup> , for each area _d_ = 1 _, . . . , D_ , and for each bootstrap replicate, _b_ = 1 _, . . . , B_ .
12 ;.

CMEA played a more important role for Armenia, Belarus, Ukraine and Moldova. Geographical proximity and cultural links account for the high share of the EFTA (mainly Finland and Sweden) in exports of the Baltic republics, and China in shipments originating in some Asian republics (Kazakhstan, Kyrgyzstan, and Uzbekistan). However, it appears that distance was not always a major determinant of trade volumes. For example, despite the geographical proximity of European CMEA markets, their share in the exports of the Baltic republics was much lower than that for most other republics, while India's share was considerably larger (see Appendix Table 5).

What were the major external export items of the republics in 1990? Table 4 draws on data compiled by the Goskomstat to provide an indication. For each republic, the ten largest exports - as measured by their shares in all shipments (excluding inter-republic trade) - are identified. Since exports of most republics were highly concentrated, Table 4 covers a high share of their total trade, i.e., between 67% (Latvia) to 99% (Tajikistan) of total exports. Petroleum products were among the top export earners, not only for the net energy exporters (Azerbaijan, Russia, Kazakhstan and Turkmenistan), but also for five other republics. Exports from the three Baltic republics and Moldova were clustered in agricultural products. Industrial raw materials and lightly-processed, resource-intensive products accounted for an important share of Soviet exports. These mainly originated in Russia, Ukraine, Belarus, Moldova and, to a lesser extent, Lithuania. For some republics there was a very high export concentration. For instance, non-ferrous metals accounted for 62% of Kyrgyzstan's exports and 79% of Tajikistan's exports, cotton products comprised 65% and 55%, respectively, of exports originating in Turkmenistan and Uzbekistan. Similarly, petroleum products accounted for around 62% of Azerbaijan's exports, for 47% of Georgia's exports and 41% of Lithuania's exports.

# IV. Tariff Barriers Facing FSU Exports

How widespread are pre-Uruguay Round tariff barriers facing FSU exports, and on which products is their incidence highest? Table 5 shows the average duties on broad categories of goods
<mark>Kling, J. R., Liebman, J. B., & Katz, L. F. (2007). Experimental analysis of neighborhood effects.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(1), 83-119.</mark> <mark>Kugler, A. D., & Rojas, I. (2018).</mark> _<mark>Do CCTs improve employment and earnings in the very long-term? Evidence from Mexico</mark>_ <mark>(No. w24248). National Bureau of Economic Research.</mark> <mark>Lee, D. S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects.</mark> _<mark>The Review of Economic Studies</mark>_ <mark>,</mark> _<mark>76</mark>_ <mark>(3), 1071-1102.</mark> <mark>Parker, S. W., & Vogl, T. (2018).</mark> _<mark>Do conditional cash transfers improve economic outcomes in the next generation? Evidence from Mexico</mark>_ <mark>(No. w24303). National Bureau of Economic Research.</mark> Rodríguez-Oreggia, Eduardo and Samuel Freije (2012). Long Term Impact of a Cash- Transfers Program on Labor Outcomes of the Rural Youth in Mexico. CID Working Paper No. 230.

<mark>Saavedra, J. E., & García, S. (2012). Impacts of conditional cash transfer programs on educational outcomes in developing countries: a meta-analysis.</mark> _<mark>RAND Labor and Population Working Paper Series, WR-921-1</mark>_ <mark>.</mark> <mark>Vaz, A., Pratley, P., & Alkire, S. (2016). Measuring women’s autonomy in Chad using the Relative Autonomy Index.</mark> _<mark>Feminist Economics, 22</mark>_ <mark>(1), 264-294</mark> 23
originated by other banks (Basel Committee, July 2016); (iii) counterparty credit risk, whereby banks take on exposures (usually to other banks) through financial transactions such as derivatives and sale and repurchase agreements (Basel Committee, April 2014); (iv) capital and margin requirements on exposures through non-centrally cleared derivatives (Basel Committee and IOSCO, March 2015); and (v) liquidity, where the Net Stable Funding Ratio (NSFR) makes it more expensive for a bank to make longer-term (more than one-year maturity) loans, because these have to be matched by more stable – generally longer-term and more expensive – funding.

In addition, the capital and liquidity frameworks are applied differently across jurisdictions. For example, a foreign bank may be subject to higher risk weightings (in particular for sovereign and central bank exposures within an EMDE) at a group consolidated level than at the level of its EMDE subsidiary. Similarly, there may be differences at subsidiary and group consolidated levels in terms of which assets qualify as high-quality liquid assets for the purpose of meeting the Liquidity Coverage Ratio (LCR). This may discourage banks from holding EMDE assets, with a detrimental impact on EMDE sovereign debt issuance and trading. This in turn may pose challenges for broader capital and money market development.

While beyond the scope of this paper, other developments may also have a disproportionate impact on EMDEs, including the tougher approach to anti-money laundering, the application of sanction regimes and a tougher approach to counter-terrorist financing more generally. The FSB is already undertaking a major study of why many foreign banks have pulled back sharply from correspondent banking and of what might be done to mitigate this (FSB, December 2016). In addition, the World Bank conducted a global survey in 2015 to gather data on the extent and drivers of termination of correspondent accounts and banking services to money remitters,<sup>13</sup> and ongoing work is aimed at collecting more information on possible knock-on effects of derisking on emerging markets located in different regions, including in Africa, the Middle East, Latin America and South-east Asia.

> 13 See World Bank (2015).

14
26 We have constructed a simple model to evaluate the effects of changing risk premiums and guarantees on the cost of capital. Table 2 provides one estimate of how costs of capital have changed for a typical transport infrastructure project.

**Table 2 Effects of Increased Risk on Cost of Capital**

||**FROM:**|**TO:**|
|---|---|---|
|**Risk-free rate**|6.00%|6.00%|
|**Debt risk premium**|**5.00%**|6.00%|
|**Project risk premium**|**1.00%**|2.00%|
|**Country risk premium**|**2.00%**|4.00%|
|**Regulatory risk premium-debt**|**1.50%**|3.00%|
|**Regulatory risk premium-equity**|**1.50%**|**3.00%**|
|**Equity Risk premium**|**5.50%**|**8.00%**|
|**Asset beta**|0.75|0.75|
|**Debt beta**|**0.00**|**0.00**|
|**Equity beta**|**1.88**|1.50|
|**Debt as % Capital**|60.00%|50.00%|
|**Equity as % Capital**|**40.00%**|**50.00%**|
|**Marginal tax rate**|**30.00%**|**30.00%**|
|**Cost of Debt (pre-tax)**|**16.50%**|**21.00%**|
|**Cost of Debt (after-tax)**|**11.55%**|14.70%|
|**Cost of Equity**|**20.81%**|**27.00%**|
|**Weighted Average Cost of Capital (pre-tax)**|**18.23%**|**24.00%**|
|**Weighted Average Cost of Capital (after-tax)**|**15.26%**|**20.85%**|

Source: Author estimates.

The table reflects the higher risk factors for country and currency risks, as well as increases in risk premiums required by investors for more uncertainty about transport infrastructure traffic levels and revenue generation capabilities. Regulatory risk premiums have risen as a result of being unsure whether pricing increases and adjustments for exchange rates and inflation will be forthcoming in a timely manner. The higher equity percentage also is shown. Overall, the events since 1997 are estimated (in a general sectoral sense) to have caused the cost of debt to rise to as much as 15 percent (after-tax) while costs of equity may be as high as 27 percent. Overall, this leads to a weighted average cost of capital of 24 percent pre-tax, 21 percent after-tax. In conversations with investment and commercial bankers, common rules of thumb are that transport infrastructure projects in 1999 might require 30 percent equity returns, with costs of capital in the 20 percent to 22 percent range. These notions are broadly consistent with the above crude estimates.21 Thus, recent financial market conditions might have pushed up all-in financing costs by as 5 percent or more. We also can place this increase in monetary terms. Suppose we have a $100 million dollar project with a 25 year life. An increase in the cost of capital from 15 percent to 20 percent would require _additional_ payments to investors of just under $5 million per year.

21 Of course, all of these will vary by project.
Since the GDP growth of the destination countries is exogenous to the local labor market conditions the interacted term is likely to be exogenous as well. I report results from both IV1 and IV2 in the main specication. Despite the binary endogenous variable, I use the linear 2SLS estimation strategy as suggested by Angrist and Pischke (2009). Because of the binary endogenous variable, the conditional expectation function (CEF) associated with the rst-stage might be nonlinear. One can use a nonlinear rst-stage and use the predicted probabilities as an instrument in a gardenvariety 2SLS as suggested by Angrist and Pischke (2009) and Wooldridge (2010) to avoid `'forbidden regression in the second step. However, this requires making distributional assumption of the rststage CEF. In contrast, with the linear 2SLS, one need not worry whether the rst-stage is linear (Angrist and Pischke, 2009).<sup>11</sup>

# 5 Results

A linear estimation of equation (2) using IV1 (equation 3), and IV2 (equation 4), are presented in Table (A.2) columns (1) and (2) respectively. Coecients on both the instruments are highly correlated with the household's migration decisions. Adults in VDCs with higher proportions of migrants in 2001 were more likely to live in a household with at least one international migrant in 2011. Interestingly, there is a positive relationship between local unemployment rate and illiteracy rate. The negative correlation between the migrant outows and the local market conditions results in downward biased OLS estimates of the impact on the labor market outcomes (Foged and Peri, 2013).

The OLS estimates of specication (1) are presented in columns (3) and (4), the 2SLS using IV1 are presented in columns (5) and (6) and IV2 are presented in columns (7) and (8) of table (A.2). The dependent variables are weekly hours supplied for wage-employment and self-employment. I use all the adults, both employed and non-employed in wage and self-employment. Since 0 hours are observed for non-employees, there is no problem of sample selection. The estimated eects are a combination of intensive and extensive margins. Columns (3), (5) and (7) are estimated wage labor supply equations while (4), (6) and (8) are self-employment labor supply equations.

Overall, the estimates of _β_ 2 s, coecients on X's in equation (1), are comparable across the

> 11 With a linear estimation, one would be worried about predicted probabilities from the rst-stage not being within 0 and 1. Assuredly, all the predicted values in the estimated models are within the range.

12
# **Table 8**

**Regressions of Monthly Earnings (In) Full Time, Male, Salaried Workers General and Vocational School Completers Israeli Census, 1983 (n=13879)**

||Direct M|atchings|Wider|Matchings|
|---|---|---|---|---|
|Independent<br>Variables|Coefficient|t-Statistic|Coefficient|t-Statistic|
|YRS.SCH|0.017|1.34|0.017|1.29|
|EXP<br>|0.034|3.52|0.034|3.49|
|EXP<sup>2</sup>|-0.0008|7.32|-0.0008|7.31|
|EXP*YRS.SCH|0.001|1.79|0.001|1.82|
|WEEKS(ln)|0.312|16.58|0.312|16.57|
|HOURS (in)|0.312|9.60|0.313|9.64|
|ETHNIC|-0.131|13.59|-0.129|13.43|
|Economic Sector|:||||
|IND|0.072|2.40|0.069|2.30|
|ELECT|0.260|6.55|0.261|6.59|
|COMM|-0.028|0.86|-0.027|0.84|
|FIN|0.128|3.82|0.130|3.88|
|TRANS|0.073|2.29|0.074|2.32|
|PUB|-0.029|0.94|-0.026|0.83|
|PRIV|-0.072|1.99|-0.017|1.96|
|CONST|0.004|0.13|0.003|0.08|
|Occupation:|||||
|ACAD|0.283|3.70|0.279|3.65|
|TECH|0.360|7.99|0.361|8.01|
|MANAG|0.525|11.65|0.480|10.68|
|CLER|0.215|4.90|0.211|4.82|
|SALES|0.224|4.87|0.199|4.35|
|SERV|0.212|4.60|0.208|4.53|
|UNSKILL|0.134|2.60|0.134|2.61|
|SKILL|0.173|4.03|0.174|4.07|
|Certification:|||||
|P.CERT|-0.001|0.07|-0.001|0.05|
|S.CERT|0.063|3.18|0.064|3.22|
|BAG|0.113|4.66|0.116|4.81|
|VOC.M.|0.096|6.84|0.081|6.27|
|VOC.U.|-0.003|0.27|0.013|1.05|
|Intercept|6.940|31.49|6.959|31.57|
|R2|0.173||0.173||

|- 35-|
|---|
demographic and geographic groups most in need of help to find employment.

Third, understanding worker and job flows can help policymakers determine the kinds of policies that might best foster employment growth. Unlike in developed countries, younger Mexican firms in all periods tend to have lower percentage employment growth than older firms. To the extent that smaller firms and younger firms are more susceptible to poorly functioning credit markets,<sup>6</sup> these results provide microeconomic evidence consistent with Tornell, Westerman, and Martínez (2004) who suggest that Mexico’s relatively slow growth is related to a lack of credit. The finding that women participate less in firm births may be an indication that women have less access to credit than men. Since a large majority of workers are employed in small firms, policies that ease credit during credit-driven recessions may help smooth job creation and destruction along the business cycle.

The format of the rest of the paper is as follows. In the next section, we describe how we created our worker- and job-flows data set from social-security records in Mexico. In section 3, we describe the methodology we use to calculate worker- and job-flows statistics. In section 4, we present the statistics. We include results separated by establishment age, establishment size, gender, and employee age. In section 5 we add some final concluding remarks.

# **2. Creation of a Job- and Worker-Flows Data Set for Mexico**

The raw data come from the Mexican Social Security Institute ( _Instituto Mexicano del Seguro Social,_ or _IMSS_ ), which is the agency that manages the social-security accounts for all private-sector tax-registered workers in Mexico. Since filing with the IMSS has been used as a criterion for formal sector participation,<sup>7</sup> the data can be thought of as a census of formal-sector establishments in the private sector. The IMSS uses its own 4-digit industry classification system 3
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Due to this huge weight for NIT, the correlation of WBI with NIT is .96; it is not surprising therefore that results in Table 8 for WBI closely mirror those for NIT, but with the signs reversed.

Corruption in public procurement, as measured by two BEEPS questions, has a near zero correlation with the TI and WBI indexes. The WEF question on “irregular payments” needed to obtain public contracts is a component of both the TI and WBI indexes, so it is moderately correlated with them. But correlations of both indexes are far higher for the other four WEF “irregular payments” questions related to administrative corruption. These may be the most noteworthy findings from this exercise, as graft in public procurement receives more publicity than any other aspects of corruption. Media reports on procurement fraud in a country are often accompanied by references to its TI ranking. Corruption in procurement - as reported in firm surveys - has little to do with rankings on TI and WBI, however.

Factor analysis is an alternative approach for analyzing the content of broad corruption indicators. A factor analysis of 12 BEEPS variables (all but the first five listed in Table 8) yields two significant factors (i.e., with eigenvalues exceeding one) that together explaining 85% of the variation in the data. One of these is clearly identifiable as a “state capture” factor: the three variables on unofficial payments to influence legislation and rules all load most heavily on it (Q41j, Q44a, Q44b). The second factor reflects administrative corruption: variables loading most heavily on it include payments to obtain business licenses (Q41b), to deal with fire and building inspections (Q41e), and to deal with taxes and tax collection (Q41g). Adding one of the more broadly-defined perceptions measures to these 13 in a factor analysis, we can observe which of these 42