rafmacalaba/datause-encoder-data huggingface dataset:
19 In any case, excluding the self-employed focuses more narrowly on differences among the wage earning population that constitutes the group affected by minimum wages, and yields an analysis more comparable to the analysis conducted for firms. In this vein, table 11 presents estimates of formal sector wage effects excluding the self-employed. It shows that formal sector differences are much larger, especially for male full-time workers, and more similar wvith respect to gender and labor market status. As a guide, the estimates suggest that formal sector workers earn an 18 to 27 percent premium for work in the formal sector (with greater returns for females).

While the household data provide no value based information on total compensation, they do provide discrete information about the nonwage benefits workers receive at their job. Specifically, individuals are asked whether they receive profit sharing payments, Christmas or other bonus, vacation leave, medical assistance, or housing credits in their current job. This information can be incorporated into the analysis in order to better understand whether the wage premiums estimated above are upper or lower bounds on the true compensation effects. Specifically, the data allow one to estimate the conditional probability of receiving each type of benefit (or a package of these benefits) controlling for characteristics of the individual (education and experience), and job (occupation, industry, and region of the country) and conditional on formal sector status. Probit estimates for each of the non-wage benefit items and packages of benefits (not presented here) all produced positive signed coefficients on the formal sector variable that were significant at greater than 99 percent confidence levels in all cases, suggesting that if anything, the large measured wage advantages estimated here may underestimate the true compensation advantage to formal versus informal sector work in Mexico.<sup>28</sup> If the sizable differences between formal and informal sector pay can be taken as long run effects, then the data provide evidence of a Mexican labor market that is segmented between high paying formal and informal sector jobs, since wages, non-wage benefits, and compliance with minimum wage laws are all lower in the informal sector. As modeled in Section 1, the larger is the (skill-adjusted) gap between the respective standard errors for schooling are .051, .044, and .066; for age they are .131, .151, and .187. For female workers the differences are much larger-- .007, .014, and .022 for In income; .057, .062, and .101 for schooling; and .131, .219, and .292 for age.

28The only exception to this pattern were the individual probit estimates for medical benefits, which yielded negatively and statistically significant coefficients on the formnal sector dummy variable.
Global market shares have been declining...

<!-- Start of picture text --> 110 110<br>Export Market Share<br>100 (1996=100) 100<br>=<br>90 _o* 90<br>\<br>80 % 80<br>*.<br>70 “s<br>‘ 70<br>sy<br>60 Values ‘s 60<br>eee== Volumes my<br>50 50<br>1996 1998 2000 2002 2004 2006 2008<br>...and the real exchange rate appreciated.<br>145 145<br>REER Based on ULC and CPI<br>135 (1999=100)<br>F France (ULC) 135<br>= |i aly (ULC)<br>—— Germany (ULC)<br>115 fF teens italyGermany (CPD(CPI) 115<br>105 --"<br>ee<br>a.eee ae te led<br>we<br>95 tal «<br> a 28<br>85 85<br>2000 2002 2004 2006 2008<br>_..but the exports started to recover following<br>asharp decline ...<br>15 15<br>10 Exports<br>(Year-on-year percentchange) 7 10<br>5 Weck<br>‘ 9<br>0 = 0<br>~~<br>“10 ‘ 41 710<br>“15<br>-20 — Value , Fy 7<br>2 Volume F/ -20<br>-25 ws -25<br>-30 -30<br>2007q1 9 2007q4 9 2008q3 2009q2<br><!-- End of picture text -->

<!-- Start of picture text --> _.as labor costs have been relatively high...<br>7 7<br>ULC of the Total Econamy<br>6 (Percent change) ‘<br>é<br>ra<br>3 4 3<br>Fi<br>f<br>1 a / 4<br>--<=- =<br>* ’<br>-1 —— France ‘“\ Fd -1<br>=<<=- Germany —<br>——= fialy<br>-3 -3<br>2000 «©2002 «2004 «= 2006 )=—s 2008<br>Nominal exports fell sharper than than for peers...<br>20 20<br>Nominalexport growth<br>(Year-on-year percent change)<br>10 10<br>eee<br>|<br>|<br>-10 10<br>20 @Jan-Dec 2007 20<br>= an-Liec -<br>g@ Jan-Dec 200 98<br>-30 -30<br>Italy Germany Spain France<br>...with trade balances moving sideways.<br>6 6<br>Trade Balance<br>(Billions)<br>—— Goods<br>4 moene Services 4<br>2 2<br>0 AN \ ~\n | 0<br>| \ '<br>oi 2<br>-4 -4<br>Jan-07 Jul-O7 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10<br><!-- End of picture text -->

Nominal exports fell sharper than than for peers...

Sources: Istat; OECD; Eurostat; Bank of<sup>Italy; andIMFstaff estimates.</sup>
**Figure 22 : Poverty Rate and Real Per Capita GRP, Russian Federation**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>3 4 5 6 7<br>Real per capita GDP (logarithmic scale)<br>North-Western Federal Region Central Federal Region Southern Federal Region<br>Volga Federal Region Ural Federal Region Siberia Federal Region<br>Far East Federal Region North-Caucas Federal Region Fitted values<br>Poverty Rate (% of population below poverty line)<br><!-- End of picture text --> _Source_ : Rosstat.

0
an ordinal sense only: a higher score indicates that, for example, a given sector is more tradable in our sense than another sector.

# **3.2.1 Analysis by Sector**

We first present tradability indices by sector (Table 4). As indicated in the data section, we use an aggregated classification corresponding to EBOPS macro-sectors because data on services become increasingly unreliable as they are disaggregated. As can be seen from the table, the two most tradable services (i.e., those with the highest index scores) are other business services and travel.<sup>9</sup> It is important to recall at this point that our trade data only cover transactions recorded in the EBOPS statistics, i.e. Mode 1 and some elements of Mode 2. This result does not therefore mean that these services are the most tradable across all modes, only that they are the most tradable EBOPS services. With this restriction in mind, the result is not surprising. Business services include those activities associated with business process offshoring, which have enjoyed strong growth in recent years thanks to developments in ICTs. The increasing reality of North-South services offshoring, using hubs such as India and the Philippines, is proof of the relatively high level of tradability of these services. Travel, on the other hand, is a very large sector in our dataset, accounting for 23% of total trade value, which is usually traded via Mode 2 (movement of the consumer). The conclusion that these two sectors are highly tradable therefore accords with both the data and prior expectations.

The next group of sectors is composed of: personal, cultural, and recreational services; communications services; and computer and information services. Based on the difference in index scores with the first group of sectors, these sectors can be considered as moderately to highly tradable. The cases of communications and computer services again accord well with intuition, because technological change over recent years has made at least some elements of these activities highly tradable across borders. In particular, the rise of ICTs in combination with the relaxation of traditionally restrictive regulatory strategies has given rise to new types of trade in telecommunications services, such as voice over

> 9 Again, we stress that we do not put a probabilistic interpretation on the average index numbers: they are interpreted in terms of rank ordering only.

21
# **Table 14 – Global banks: Adverse reform spillovers affecting cross-border lending directly to local entities in EMDEs**

_Please indicate the regulatory reforms which contributed to a significant reduction or tightening of your institution’s activities in emerging markets and developing economies (EMDEs), particularly those that are jurisdictions not required to implement the reforms (you can select more than one reform area)._

|**N = 4**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing with**<br>**OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't know**|
|---|---|---|---|---|---|---|---|---|
|For local long-term investment<br>purposes|3|1|2|2|0|0|1|0|
|To local banks|3|1|2|2|0|0|1|0|
|To<br>local<br>non-financial<br>corporates|4|1|2|2|0|0|0|0|
|To the local sovereign|2|1|2|1|0|0|2|0|

38
Table 2: OLS results for Horn of Africa

||Agriculture|Manufacturing|Services|
|---|---|---|---|
|Paved road|-0.0398<sup>~~∗~~</sup>|0.0221<sup>~~∗∗~~</sup>|0.0188|
||(-2.24)|(2.87)|(1.34)|
|Internet|-0.0135|0.0247|-0.0313|
||(-0.21)|(0.50)|(-0.67)|
|Road + Internet|-0.0134|-0.0230|0.0622|
||(-0.20)|(-0.45)|(1.29)|
|Road + Electricity|-0.232<sup>∗∗</sup>|0.0758<sup>∗∗</sup>|0.161<sup>∗∗</sup>|
||(-9.63)|(6.07)|(8.68)|
|Year + Country FE|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|
|R-squared|0.459|0.326|0.398|
|N. of observations|1887|1887|1887|

_t_ statistics in parentheses + _p_ < 0 _._ 10, ∗ _p_ < 0 _._ 05, ∗∗ _p_ < 0 _._ 01

**By share of agricultural employment** Table A6 in the appendix confirms the spatial heterogeneity of outcomes across districts in Ethiopia. Results are reported for the first quartile of employment in agriculture (49 percent of employment in agriculture), the second quartile (75 percent of employment in agriculture), and the third quartile (90 percent of employment in agriculture). Access to roads reduces the share of employment in agriculture only in the districts with an existing low level of agricultural employment. The combined impacts of road and electricity in reducing the share of agricultural employment is the largest for the second quartile, districts with around 75% of employment in agriculture.

**By distance to a main city** Changes in the shares of manufacturing and services employment associated with access to infrastructure vary with distance to large towns. Tables A7 and A8 in the appendix report the estimation results for the share of manufacturing employment across quartiles of distance to a town of at least 50,000 inhabitants for Ethiopia. They show that a combined access to roads and electricity is associated with a reduction in manufacturing employment share but a large increase in services employment in the most isolated locations.

# **4.2 Instrumental Variables**

In this section, we use an instrumental variables identification strategy to deal with the potential endogeneity in the placement of the infrastructure. We instrument both roads and electricity, and the interaction terms.

We instrument electrification and access to a paved road. Regarding electrification, the instrumental variable relies on four assumptions. First, electricity generation must be connected to demand, which comes mostly from the main cities. Second, the sources of energy generation 11
Table 7. Linear regression model estimates for the aggregate production function parameters (dependent variable: - <u>log output per worker log emissions per worker)</u>

||skills measure<br>not rescaled|skills and<br>capital measure<br>rescaled|
|---|---|---|
|log emissions per worker (negative)|0.24***|0.24***|
||(0.04)|(0.03)|
|log skills per worker x (log capital per worker - log emissions per worker)|0.07<br>(0.09)|0.07***<br>(0.003)|
|log capital per worker - log emissions per worker|0.30<br>(0.46)||
|log skills per worker|-0.37<br>(1.01)||
|Industry fixed effects included|yes|yes|
|Constant|4.07|5.63***|
||(4.83)|(0.19)|
|Observations|317|317|
|R-square|0.96|0.96|

Standard errors denoted in parentheses. Statistical significance at the 1, 5, and 10 percent levels denoted as ***, **, and *, respectively.

_Estimating the skill premium_ In steady state, the skill premium, h, is constant. This was estimated for each country using the PIAAC data set with a simple, univariate linear regression model with earnings as the dependent ww variable and literacy skills, rescaled as described above, as the independent variable. This estimate of h is used to determine the country-specific capital augmenting technology factor, , by setting such that equation (2) holds. KK,ii ww AA KK,ii AA _Estimating the skills cost function_ In equilibrium, individual cognitive skills is determined by h 0 1α 2α 3 where and are set to one and zero, respectively, which is cognitive skills h(θθ, kk) = ww (γγ + γγ (kk) + expressed as a linear function of the individual’s endowment and endowment squared; aa bb 3 is the γγ (kk)<sup>2</sup> + γγ θθ) γγ γγ mean zero error term. This function was estimated using PISA for each country, with the following γγ θθ rescaling of the variables. First, each plausible value of reading achievement was rescaled such that their means equaled the mean of the skills per worker for the country, calculated from the 21
_10_ Table 1—: Summary Statistics

|**Variable**|**Mean (SD)**|
|---|---|
|Annual average dry-bulb temperature (<sup>0</sup>C)|29.11 (0.72)|
|Annual average WBT (<sup>0</sup>C)<br>|24.62 (1.84)<br>|
|Annual average WBT (<sup>0</sup>F)<br>|75.28 (3.55)|
|Deviation in average dry-bulb temperature (<sup>0</sup>C)<br>|0.04 (0.06)|
|Deviation in average WBT (<sup>0</sup>C)<br>|0.62 (0.47)<br>|
|Deviation in average WBT (<sup>0</sup>F)|33.10 (0.85)|
|Sales per worker (US$, 2009)|71,730 (240,491)|
|Ownership status||
|Domestic (%)|90|
|Foreign (%)|10|
|Firm size: categorical||
|Small(_<_20) (%)|47|
|Medium(20-99) (%)|34|
|Large(100 and over) (%)|19|
|Firm size: continuous<br>|74.44 (179.01)<br>|
|Permanent workers (%)|95.30 (11.68)|
|Temporary workers (%)|4.70 (11.68)|
|Skilled workers (%)|71.16 (30.72)|
|Unskilled workers (%)|28.84 (30.72)|
|Firm age (year)|18.76 (15.72)|
|Exporter status||
|Non-exporter (%)<br>|85|
|Exporter (%)|15|
|Road infrastructure (within 25km radius)||
|Highways (km)|66.18 (114.58)|
|Primary roads (km)|291.52 (365.83)|
|Secondary roads (km)|325.94 (372.01)|
|Tertiary roads (km)|426.39 (446.90)|
|Population density|5,248.32 (6,889.85)|
|PM2.5: dif between 1998 and 2019|-2.72 (13.15)|
|Broad sector||
|Manufacturing (%)|55|
|Services (%)|45|
|Region||
|Africa (%)|20|
|East Asia and Pacifc (%)|11|
|Europe and Central Asia (%)|31|
|Latin America and the Caribbean (%)|20|
|Middle East and North Africa (%)|10.0|
|South Asia (%)|9.2|
|Number of observations = 141,815||

(Sabater, 2019).<sup>10</sup> The C3S provides a comprehensive reanalysis dataset of var-

> 10Historical temperatures and relative humidity can be obtained from the European Union’s C3S Climate Data Store. We downloaded the data on November 25, 2022.
Figure 4: Distribution of sum of first 7,600 draws: `invnorm(uniform())` versus `rnormal()` <!-- Start of picture text --> invnorm(uniform()) vs rnormal() in Stata 10.1<br>Seeds 1/100000; 7600 observations<br>−.05 −.025 0 .025 .05 −.05 −.025 0 .025 .05<br>Mean of 7600 draws from invnorm(uniform()) Mean of 7600 draws from rnormal()<br>SD:.01143 (theory: .01147) SD:.01656 (theory: .01147)<br>Kurtosis 2.99 (theory: 3); Skewness 5.3e−03 (theory: 0) Kurtosis 1.64 (theory: 3); Skewness .037 (theory: 0)<br>40 40<br>30 30<br>20 20<br>Density Density<br>10 10<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> invnorm(uniform()) vs rnormal() in Stata 12.1<br>Seeds 1/100000; 7600 observations<br>−.05 −.025 0 .025 .05 −.05 −.025 0 .025 .05<br>Mean of 7600 draws from invnorm(uniform()) Mean of 7600 draws from rnormal()<br>SD:.01143 (theory: .01147) SD:.01143 (theory: .01147)<br>Kurtosis 2.99 (theory: 3); Skewness 5.3e−03 (theory: 0) Kurtosis 3.01 (theory: 3); Skewness −2.0e−03 (theory: 0)<br>40 40<br>30 30<br>20 20<br>Density Density<br>10 10<br>0 0<br><!-- End of picture text --> Histograms of the distribution across 100,000 initialization seeds of the mean of the first 7,600 normal draws in Stata. Upper panel shows results using Stata 10.1; lower panel shows Stata 12.1. Left histograms show results using `invnorm(uniform())` ; right histograms show `rnormal()` .

14
first and the second effect. Finally, indirect evidence supporting this hypothesis is also provided by the fmding that there are close-to-zero firm level labor tumover rates (defined as min(separation, hiring]lemployment). An average rate of 0.2 per cent, invariant to the local unemployment rate, suggests that firms do not, or cannot, exploit the large pool of unemployment to replace their poorly performing workers.

(iv) Persistent regional differences; looking at local unemployment rates we find a surprisingly stable rank order for smaller regions. Regressions using variables on infrastructure, wealth, educational level, degree of industrialization and land quality from 1990 are more and more successful in explaining regional variation as we move closer to 1994. The regional differences seem to be rooted, to a great extent, in characteristics inherited from the past and hence difficult to change.

(v) Rising recurrent unemployment; a simplified vision of transition-induced unemployment, in which temporary joblessness is generated in a process of relatively fast transition from collapsing (state) to emerging (private) sectors is partly challenged by observations on labor market flows. These indicate that transitions occur mainly in the form of job-to-job flows; unemployment appears persistent (indicating that the main streams of transition avoid the pool of unemployment) and there is now evidence of an apparent rise in recurrent unemployment. Figure 20 likely underestimates the proportion of reentrants as many do not register because of lack of benefit eligibility, while others returning to unemployment after a short spell of employment had actually remained throughout on the register and were not counted as reentrants. Repeat spells are partly accounted for by temporary lay-offs in agriculture but preliminary estimations by labor office officials and researchers suggest the growing importance of intense mobility between unemployment and the low wage, short tenure end of the private sector. If we consider that recurrence shows up on the inflow as well as on the outflow side of unemployment, the rate of mobility between fte steady jobs of a 'primary segment' and the pool of unemployment seems even less intense and decreasing.

The policy implications of points (i)-(v) are more-or-less clear but do not promise any quick help for the unemployed. They include targeted ifrastructural and job creation programs in regions with very high unemployment; but all such policies are likely to have fairly extended lags. More generally, but subject to the regional and other constraints that characterize current 35
<!-- Start of picture text --> Figure 6.1: Welfare Loss - Rural Poor (8% increase)<br>0 X XxX<br>“60<br>~-100<br>0<br>9<br>95-150<br>~<br>-200<br>-250 %<br>15-120. 125—=t«—«i138=s135C 11S<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6.2: Welfare Loss - Rural Non-Poor (8% increase)<br>0 XK OK<br>200<br>a<br>2 -400<br>6 x<br>°x %<br>600 x<br>x<br>800<br>x<br>:<br>@ 8 "4 8 "6<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6.3: Welfare Loss - Urban Poor (8% increase)<br>04 x xy x<br>50<br>100<br>><br>9 450<br>8<br>= -200<br>&<br>-250<br>-300<br>-350 %<br>12 13 14 15<br>Log Food Expenditure<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6.4: Welfare Loss - Urban Non-Poor (8% increase)<br>07 x xm x<br>-200<br>400<br>3<br>w<br>8fo} -600<br>& ‘<br>x<br>-800 x<br>x<br>-1000 x<br>x<br>12 13 14 15 16<br>Log Food Expenditure<br><!-- End of picture text -->
Policy Research Working Paper 10071

# **Abstract**

Using fine-grained spatial data and a dynamic spatial general equilibrium model, this paper assesses the magnitude of mobility frictions in Latin America as well as the effects of their reduction on spatial development in the region. The results suggest that in most Latin American countries, migration frictions calibrated based on spatially differentiated initial utility are on average smaller and less dispersed than those obtained assuming uniform within-country initial utility. A reduction in trade costs due to optimal investments in road infrastructure in most Latin American countries increases the present discounted value of real per capita income on average in the region by 15.1 percent. This effect is larger than the effects obtained with static quantitative trade models because of substantial dynamic gains. By contrast, a reduction in migration entry costs in the most productive and more populous locations in the Latin American countries has a negligible effect on the present discounted value of the region’s real per capita income, reflecting the relatively small dispersion in domestic migration frictions and their relatively low levels in top locations. In both counterfactuals, the welfare increases are significantly larger than the increases in real per capita output because the reductions in mobility frictions allow people to relocate to areas with better amenities and therefore derive higher utility. These results suggest that trade costs, not migration barriers, represent a major constraint to the efficient spatial distribution of economic activity and growth in Latin America.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 b.conte@unibo.it or eianchovichina@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