rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: Estimated Wage-bill Share Equations allowing Cross-Country Correlations State  Manufacturing  1990-98 using three digit industries for 22 states.**

|**Dep. variable:**Change in<br>non-manual wagebill share|||**1990-98**|||
|---|---|---|---|---|---|
|d Log capital/value added|**0.0343**|**0.0343**|**0.0339**|**0.0338**|**0.0342**|
||(.0067)|(.0067)|(.0067)|(.0068)|(.0066)|
|d Log value added|**0.0285**|**0.0284**|**0.0282**|**0.0281**|**0.0285**|
||(.0084)|(.0084)|(.0084)|(.0085)|(.0083)|
|d non-manual wage-bill<br>share for US in 1960s|0.130<br>(.243)|||||
|d non-manual wage-bill<br>share for US in 1970s||0.104<br>(.208)||||
|d non-manual wage-bill<br>share for US in 1980s|||-0.097<br>(.256)|||
|d non-manual wagebill share<br>for middle income in 1970s<br>d non-manual wagebill share<br>for middle income in 1980s||||-0.006<br>(.015)|0.015<br>(.011)|
|Constant|0.0007|-0.0007|-0.0002|-0.0003|-0.0002|
||(.0011)|(.0011)|(.0014)|(.0012)|(.0014)|
|**R**<sup>**2**</sup>|0.10|0.10|0.10|0.10|0.11|
|**Observations**|495|495|495|495|495|

**Note:** Figures are annualized changes for three-digit manufacturing industries in twenty two state groups. Heteroskedasticity-consistent standard errors reported in parentheses. Coefficients significant at the 5% level are in bold face. Regressions estimated by weighted least squares with wage-bill shares averaged over the period as weights. See equation (4) in text.

The answer is negative. There is only weak evidence of a correlation with U.S. skill upgrading in the same industries in the 1960s and 1970s and a weak negative correlation with the U.S. pattern in the 1980s. Interestingly enough, the Indian pattern of skill upgrading in the 1990s is also not strikingly similar to that in middle-income countries either, showing a weak positive 27
the correlation between their inflow and outflow loadings equals 0.73 for the global factor and 0.88 for the group-specific factor. Among emerging countries, the correlation between the global factor inflow and outflow loadings is much smaller (0.27). However, given the absence of a group factor for the outflows of emerging countries, perhaps a more meaningful statistic is the correlation between the sum of the global and group factor loadings of their inflows, on the one hand, and the global factor loadings of their on the other. That statistic a more outflows, equals respectable 0.47.

On the other hand, the loadings on the global and group factors are negatively correlated, which suggests that, to some extent, they play interchangeable roles in capturing countries’ exposure to common shocks – although the negative correlation is larger in the case of emerging-country inflows (for which the correlation equals -0.64) than for advanced-country inflows (-0.16) or outflows (-0.34).

Overall, the estimated model does a good job at capturing the comovement of gross capital flows. Figure A.1 in appendix A shows that the estimation residuals appear virtually uncorrelated across countries, while the within-country correlation of inflow and outflow residuals is consderably reduced relative to that in the original data. Further, the model succeeds at removing the strong dependence found in the data, as shown by the exponents of cross-sectional dependence of the residuals reported in Table A.4, which lie well below unity for the full sample as well as the two country groups.<sup>24</sup>

# **4.3 How important is the international financial cycle?**

The fact that the group and global factors are mutually orthogonal by construction allows a straightforward decomposition of the variance of gross capital flows into the shares attributable to their global, group, and idiosyncratic components. This helps assess the quantitative role of common 24Additionally, panel unit root and stationarity tests reported in Table A.5 in appendix A provide strong indication that the residuals are stationary: for each country group and flow direction, an Im-Pesaran-Shin test clearly rejects the null that all residual series contain a unit root, while a Hadri test fails to reject the null that all residual series are stationary. These results lend support to the validity of the factor model estimates.

17
crime variables available (total homicides rate, drug and non-drug related homicides) on a yearly basis.

# Other Sources of Municipal Level Data

We have also gathered data on aggregate figures of public expenditures, literacy rates, and police expenditures at the municipal level in Mexico. The data on public expenditures was obtained from the State and Municipal System of Databases (SIMBAD) produced by the National Institute of Statistics, Geography, and Information (INEGI). The data on literacy rates (our proxy for human capital) is also obtained from public figures made available by INEGI, as is the data on public spending on police.

# **5. Estimation Strategy**

The relationship between income inequality and crime can be described by the following equation: Where _i_ indexes a municipality in Census/Count year _t, y_ is a local crime rate indicator such as total murders per 100,000 inhabitants, _Gini_ is the Gini coefficient at the municipality level, and the coefficient β indicates the estimated effect of income inequality on local crime rate. _X_ contains a set of time-varying municipality characteristics, such as the share of the population that is poor, the percentage of rural households, local public expenditures per capita, police expenditures per capita and median household income. The term εit captures the unobserved determinant of local crime rates, which depends on a permanent component μi and a transitory component ωit.

Pooling four cross-sectional data from 1990, 2000, 2005 and 2010 for each municipality, we estimate: This first-difference specification absorbs the permanent component of the error term (μi). The coefficient of interest (β) indicates the relationship between changes in the Gini coefficient and changes in crime rates within a municipality over time, holding constant changes in median income and basic demographics.

Equation (2) is not sufficient to establish a causal relationship between income inequality and crime. The income distribution may affect crime through a number of channels such as lower social capital, higher returns to criminal activity, low mobility, higher distress, etc. However, higher crime rates may affect local inequality by diminishing the stock of physical capital and development of human capital, by raising segregation and eroding social capital, by affecting the capacity of local 12
<!-- Start of picture text --> Generator (y/n)<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>AFG<br>LKA<br>IND<br>MD V<br>BGD<br>PAK<br>BTN<br>NPL<br>6 7 8 9 10<br>Log(GDP p.c.) at PPP<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> 65
legal reform in this area may result in women working in more family‐friendly jobs that offer more flexibility but are associated with lower pay. Early evidence of unintended consequences of equal‐pay legislation is presented by Pike (1985), who finds that equal pay legislation in the United Kingdom was associated with a decline in female employment in the manufacturing sector.

# **6. Conclusions**

While several individual studies have illustrated how legal gender discrimination is related to economic outcomes, the literature has been missing a global picture of legal inequalities – contrasting the experience of countries where women are given the same rights under the law as men with that of countries where the legal gender gap is large, and examining the evolution of gender equality over time. This paper fills these gaps. First, we present an overview of the WBL database, highlighting the differences between countries and regions, and chart the development of gender equality over time. We find that, across the world, women still face by legal gender discrimination across multiple domains. The level of legal discrimination is particularly onerous in some highly‐populated countries and, therefore, restricts economic opportunities for a very large number of women. Having documented the extent of and trends in legal discrimination between men and women, we then study the relationship between these legal restrictions and women’s labor market outcomes. Focusing on female labor force participation, panel estimations of data from 190 economies over 50 years reveals that a more level legal playing field between men and women is associated with more women participating in the workforce; there is also some indication from the data that better rights for women are associated with a lower gender pay gap.

22
use of ICT also increased their productivity.

As Figure 2 shows, firms that exhibited changes in their ICT use above the median showed much higher increases in sales-per-worker. Furthermore, as shown in the second panel of the figure, this is the case even when we analyze the residuals of a regression of the change in sales over firm size, age, investment in fixed assets per worker and the share of white-collar workers. That is, even controlling for firm characteristics, firms that increased their use of ICT became more productive.

Figure 2: Mean change in sales/worker 2008-2012 (a) _Mean change in sales/worker_ <!-- Start of picture text --> Computers−per−worker **<br>Share of labor with internet<br>Share of labor with computer ***<br>−.1 0 .1 .2 .3<br>Low increase in ICT use High increase in ICT use<br><!-- End of picture text --> (b) _Mean change in sales/worker controlling for firm characteristics_ <!-- Start of picture text --> Computers−per−worker **<br>Share of labor with computer ***<br>Share of labor with internet<br>−.2 −.1 0 .1 .2<br>Low increase in ICT use High increase in ICT use<br><!-- End of picture text --> Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI. Differences in means significant at the: * 10% level, ** 5% level, *** 1% level Note: In panel (a) the means of the differences in ln(sales/worker) are shown for each ICT-use group. In panel (b) we take the residuals of a regression of the difference of ln(sales/worker) over size dummies, age dummies, ln(investment in fixed assets/worker) and the share of white-collar workers.

6
15

# **MDG 5: Improve Maternal Health**

_Target 6: Reduce by three quarters, between 1990 and 2015, the maternal mortality ratio._

**_A. Current Situation:_ maternal mortality remains high.** Maternal mortality stood at 747 per 100,000 live births in 2000. It is high notably in the rural and amongst the poorest segments of the population, where access to childbirth and attendance to obstetrical urgencies is low. In 2007, only 61 percent of births were attended by qualified health personnel, while the remaining 39 percent were assisted by a relative, an unqualified person, or without any type of assistance (GIRM 2007b). High-risk pregnancies, hemorrhages during delivery, anemia, as well as cesarean intervention are the principal causes of prenatal and maternal deaths.

**_B. Trends and Likelihood:_ a low to medium probability of achieving MDG5.** Between 1990 and 2000, maternal mortality decreased from 930 to 747 deaths for 100,000 live births. Recent data – not available at the time of the analysis – show in 2007 maternal mortality stood at 684 per 100.000 live births (GIRM 2007b). Also, the proportion of births attended by skilled personnel increased from 57 percent in 2001 to 61 percent in 2007. If these trends are sustained and accelerated, the country’s low probability of attaining the MDG target could increase (Figure 10).

**Figure 10. MDG 5: Current Situation and Trends**

<!-- Start of picture text --> 1000<br>930 912 893 875 857<br>840 839 820 802 784 765 747<br>712<br>678<br>680 643<br>608<br>574<br>539<br>504<br>520 469<br>435<br>400 380<br>360<br>360 Past performanceGovernment plan 340 320 300<br>Proyected status quo<br>MDG-4<br>200<br>1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Source:_ Authors’ calculations.

**_C. Financial Needs_ : 0.7 percent of GDP on average per year.** The financing gap to reach this goal is the second widest after poverty reduction (MDG1). The cost of achieving this target for the period 2008-2015 amounts to 0.9 percent of GDP on average per annum, corresponding to US$ 163.2 million in total. Once the allocation of government expenditure is taken into account, the country needs an additional total financing of 0.7 percent of GDP on average per year (Figure 11), which translates into US$ 124.4 million in total (Table 8). For a detailed, yearly cost breakdown, see Annex 1.
- 16 -

Table 2.8s Fuel Shares in Variouu Industries in O.B.C.D. Europe in 1988

|Coal<br>S|and Other<br>olid Fuel|Oil|Gas|Electricity|Energy shares<br>in Total<br>Industry|
|---|---|---|---|---|---|
|Total|22.7|33.4|21.6|22.2|100.0|
|Iron and Steel<br>Chemical|64.7|8.0|12.0|15.3|18.5|
|Feedstocks<br>other Chemicals<br>Non-ferrous|0.0<br>11.9|83.0<br>26.0|17.0<br>32.0|0.0<br>30.0|17.1<br>16.2|
|Metals<br>Non-metallic|6.6|16.8|13.2|63.3|3.8|
|Minerals<br>Transport|28.6|30.8|26.8|13.8|10.7|
|Equipment<br>machinery<br>|4.1<br>4.5|17.0<br>26.2|31.1<br>32.8|47.7<br>36.5|2.1<br>5.2|
|Mining/Quarrying<br>Food/Tobacco<br>Parar/Printing<br>Wood/|6.1<br>8.9<br>29.5|33.2<br>32.6<br>16.6|14.6<br>32.3<br>18.6|45.7<br>26.2<br>35.1|1.0<br>6.5<br>7.2|
|Wood Products<br>|37.0|15.9|5.3|41.5|1.2|
|Construction<br>|20.0|61.9|3.5|14.6|1.6|
|Textiles/Leather|6.1|34.4|23.7|36.0|2.7|

Sources As table 2.1.

which bring about change. in industrial structure will bring about changes in aggregate fuel mix, but such cdianges are not likely to be brought about by a desire to alter the overall pollution characteristics of an economy apart from exceptional eases. The projected decline of certain heavy industries in Eastern Europe is being welcomed as a positive contribution to reducing the very high levels of pollution in certain areas (Hughes (19911).

Comparison of the disaggregated industrial data for 1988 in table 2.8 and that for 1970 shown in table 2.9 does allow an insight into the possibilities of fuel swltching for a specific type of technology. However it must be noted that even for industrial data disaggregated into 13 sectors there will still be some changes due to the particular product composition changing within a given sub-sector.
29 'traditional estimates based on a CRTS technology in autos yielded velfare costs of about $10 billion. In the most reasonable representation with IRTS, pure profits, internationally mobile capital and endogenous conjectures (between fixed and our particular assumption about endogenous conjectures) then the estimate of the welfare costs of the VER are reduced by about $1 billion or 10 percent.

It is also instructive to note that the costs per job protected in the auto sector (at the expense of employment elsewhere) are very high, ranging from $164,000 to $296,000 per job per year. Alternatively, obtaining the ratio of the welfare benefit calculations to the adjustment costs, proxied by the present value of the lifetime earnings losses of displaced auto workers displaced by VER removal, there are between 14 and 26 dollars of benefits for every dollar of worker adjustment costs saved.<sup>24</sup> Finally we have illustrated the use of industrial policies to exploit scale economies and to counteract wage distortions. The results under IRTS call for caution regarding the use of industrial policy because the optimal policy may shift from a subsidy to a tax depending on behavioral assumptions.
ten of which involved foreign banks. The period between the Tequila Crisis in late 1994 and the beginning of our sample witnessed even greater consolidation (see World Bank 1998).

Argentina experienced the most mergers and acquisitions in the late 1990s. However, Chile, Colombia, and Peru have also undergone a process of consolidation. In Colombia and Peru, there were seven mergers or acquisitions, four of which involved foreign banks. Chile had six such transactions, half involving foreign banks. Argentina also led the way with seven _de novo_ entrants, compared to two for Peru and none in either Chile or Colombia. In our empirical analysis, we control for the effects of mergers, acquisitions, and _de novo_ entry on the share and growth rate of lending to small businesses.

The ratio of banking sector assets to GDP provides another indication that Colombia and Peru have a lower level of financial intermediation than Argentina and Chile (see Figure 2). Both for Colombia and Peru that ratio hovered near forty percent, and was declining at the end of our sample period. To some extent, these dips were due to Colombia's intemal conflict and banking crisis and to Peru's scandals at the end of the Fujimori administration. By way of comparison, in Argentina the ratio of banking sector assets to GDP went from 48 percent in 1998 to 56 percent in 2000. Owing to the hyperinflation of the late 1980s, Argentina started the 1990s with a very low ratio of banking sector assets to GDP. However, with the exception of the Tequila Crisis, that ratio has grown relatively steadily since the adoption of the Convertibility Plan in 1991, which brought about price stability. Chile's banking development occurred earlier. By 1996, banking sector assets were nearly 140 percent of GDP, and that figure had grown to over 180 percent by 2000. It is true that much of the Chilean banks' assets are in assets other than loans, and that banking assets relative to GDP may overstate their relative level of development. However, Chile holds a sizable advantage over the other three countries on other measures of financial development such as the ratio of private credit to GDP.<sup>2</sup> The evidence on foreign penetration also indicates that Chile and Argentina differ from Colombia and Peru (see Figure 3). In Colombia, foreign banks comprise forty percent of the total number, but they account for only a quarter of banking sector loans. In Peru, over half the banks are foreign-owned, but they are responsible for forty percent of total system loans. There is a key difference between the two 5
Ethnic polarization and the duration of civil wars

# _6. The effect of ethnic polarization on the duration of civil wars_

The arguments exposed above indicate that ethnic polarization may be a powerful propagation mechanism for civil wars and, therefore, have an important influence on the duration of civil wars. We are going to use initially the definition of civil wars of Uppsala/PRIO25 (see the Appendix for a precise definition). This data set includes wars that cause at least 25 yearly deaths. Our database contains 117 civil wars corresponding to 74 countries during the period 1960-99. We consider that a war has finished if there are at least two years of peace before a new civil wars breaks out. Following this definition we have 42 countries with only 1 civil war; 24 countries with 2 civil wars; 5 with 3 wars and 3 with 4 wars. The mean duration is 7.15 years. In 1999 there were 20 ongoing civil wars.

As a preliminary indication we can look at the survivor functions of civil wars in highly polarized countries versus countries with a lower level of ethnic polarization. To separate these two groups we use the median value of ethnic polarization<sup>10</sup> . Figure 2 shows the Kaplan-Meier survivor function for countries with a degree of ethnic polarization above the median versus a degree of polarization below the median. Notice that this only provides a preliminary indication since we have not included any covariate. The Kaplan-Meier estimator is a nonparametric procedure, alternative to the life tables, to obtain survivor functions.

Figure 2 shows that the survivor function of the countries with a high degree of ethnic polarization dominates almost everywhere the function for lower ethnic polarization. Since the failure time in this case is the end of a civil war and the time at risk is the period of civil war the dominance of the survivor function of highly polarized countries implies that the duration of wars is longer in those countries.

There are several alternatives to deal with the analysis of duration in a parametric set-up. The most important factor is the effect of the parametric distribution on the censured observations. Collier et al (2004) use a piecewise<sup>11</sup> exponential model to overcome the restrictions implied by the simple exponential model (in particular, a constant hazard function). However, most of the literature uses a Weibull model when running a parametric estimation (for instance Fearon (2004), Gates and Strand (2004) in their basic table and DeRouen and Sobek

> 10 The appendix describes the data on civil wars and the explanatory variables used in the duration models of this section.

> 11 The choice of the pieces of the exponential model is somehow arbitrary.

14
Table 9 <u>OLS Models of Average Crown Cover and Number of Seedlings per Hectare</u>

|||CrownCover|(%)|S|eedlings per hec|tare|
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model3|Model 1|Model 2|Model3|
|CFdummy|0.057||0.001|-0.500||-0.420|
||(0.50)||(0.01)|(2.68)***||(2.42)**|
|CanIDFUGyear|-0.134|-0.105||0.194|-0.069||
||(1.11)|(0.96)||(1.04)|(0.41)||
|NDVI_1990|1.949|1.946|1.956|4.156|4.305|4.132|
||(2.46)**|(2.46)**|(2.46)**|(3.98)***|(3.97)***|(4.04)***|
|Totalforestarea|0.000|0.000|0.000|0.001|0.001|0.001|
||(1.70)*|(1.77)*|(1.56)|(2.41)**|(1.97)*|(2.54)**|
|Hills|0.073|0.081|0.074|0.631|0.574|0.635|
||(0.54)|(0.59)|(0.53)|(2.76)***|(2.33)**|(2.76)***|
|Altitude|-0.000|-0.000|-0.000|-0.000|-0.001|-0.000|
||(0.14)|(0.08)|(0.11)|(2.27)**|(2.48)**|(2.31)**|
|Slope|0.011|0.011|0.011|0.001|-0.004|0.000|
||(2.30)**|(2.34)**|(2.24)**|(0.06)|(0.47)|(0.01)|
|Fire|0.307|0.314|0.350|0.038|-0.036|-0.023|
||(2.96)***|(2.92)***|(3.30)***|(0.21)|(0.19)|(0.14)|
|Foresttype|-0.304|-0.281|-0.282|-0.882|-1.073|-0.915|
||(1.18)|(1.10)|(1.13)|(2.48)**|(3.01)***|(2.54)**|
|Forestperhh|-0.008|-0.007|-0.007|-0.045|-0.051|-0.047|
||(0.36)|(0.32)|(0.28)|(0.73)|(1.06)|(0.82)|
|HHsinfug|0.000|0.000|0.000|-0.000|-0.000|-0.000|
||(2.21)**|(2.31)**|(2.19)**|(4.51)***|(4.37)***|(4.28)***|
|Migrationrate|-0.574|-0.541|-0.705|1.360|0.957|1.505|
||(0.53)|(0.50)|(0.63)|(0.83)|(0.57)|(0.93)|
|Constant|3.037|2.991|2.931|9.195|9.532|9.358|
||(5.99)***|(5.86)***|(5.57)***|(11.33)***|(12.11)***|(11.88)***|
|_R_<sup>2</sup>|0.28|0.28|0.28|0.41|0.38|0.41|
|_N_|129|129|129|122|122|122|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; Dependent Variables in Logs, Robust t Statistics in Parentheses

The four metrics represent very different measures of forest condition, but only the 1990 NDVI results in positive effects on all four measures, including sequestered carbon. The base vegetative cover therefore appears to be a particularly important determinant of contemporary forest health, with a 10% increase in 1990 NDVI correlated with 15% to 40% increases in forest quality. Larger forests by area also have more carbon and higher levels of all other forest quality measures except trees per hectare. Forests controlled by larger forest user groups have more trees per hectare and percent crown cover, but fewer seedlings per hectare, probably reflecting

**26 |** P a g e