rafmacalaba/datause-encoder-data huggingface dataset:
42 neonatal mortality showed signs of decline during the second half of the 1970s and fluctuated between 50 and 55 per 1.000 during the HP LaserJet Series IIHPLASEII.PRSed between 30 and 40 per 1,000 during the 1970s, moving up to a higher plateau during the 1980s. Postneonatal mortality among this group of families shows more noticeable changes, more than doubling between 1977-81 and 1982-86.

Mortality trends among the rural poor and the urban poor are presented in figures 17 to 19. Among the rural poor, neonatal mortality declined during the 1970s, and during the 1980s it fluctuated around the same levels reached in 1977-81. Postneonatal mortality for the group increased somewhat between the middle of the 1970s and the early 1980s, and showed no signs of decline during the first half of the 1980s.

Among the urban poor during the 1980s, neonatal mortality remained at the levels reached in 1977-81. Postneonatal mortality declined during the second half of the 1970s, but showed some signs of increase during the 1980s.

The descriptive analysis suggests that changes in child health between the expansionary period and the economic crisis-adjustment period varied from one socioeconomic group to another. In general, infant mortality either increased or remained at the same levels between the two periods in each socioeconomic group. Postneonatal mortality underwent more important changes than neonatal mortality, increasing markedly in urban areas, particularly Abidjan, and among families with limited savings. We reach stronger statements based on the regression results, where we attempt to control for the effects of confounding variables.
modifies local rainfall and temperature averages. Hence, the resulting projections from the various global climate models were tailored to different regions in Mexico by Climate Change Knowledge Portal at a resolution of 50 x 50 km approx. Baseline data on daily temperature and precipitation employed for the projections cover the period from 1950 to 2000, and projections for average daily temperature and precipitation are for 2030 âĂŞ 2039.

To determine how greenhouse emission will affect changes in precipitation and temperature, climatic models need to be combined with emission models that predict the amount of manmade greenhouse emissions<sup>8</sup> . The Third IPCC Assessment Report accepts several emission models. We choose A2 which assumes a business as usual scenario (i.e., same trend changes in economic growth, use of fossil fuels and population growth).

Annual temperatures are expected to rise between 0.49 and 2.46 degrees Celsius in 2030-2039, compared to the historic average from 1950-2000. The largest increases in temperature are expected in coastal areas, including the Yucatan Peninsula and the southern region of the Gulf of Mexico, comprising the states of Quintana Roo, Yucatan, Tabasco, Campeche and Veracruz. In the Pacific, the states of Sonora, Sinaloa, Nayarit, Guerrero and central Jalisco exhibit the greater increases in temperature. Central Mexico appears less warm than coastal regions; although some desertic areas in Hidalgo and Queretaro display temperature increases (see Fig. 1).

Annual rainfall will change between -169 mm and +57mm. However, as Fig. 2 shows, almost the entire country will be facing a drier world by 2030-39. Desertic areas in states like Chihuahua, Nuevo Leon, Zacatecas and San Luis will become drier, as will the states of Chiapas, Tabasco, Veracruz and Puebla.

# **2.4 Poverty**

The integration of income and consumption data from household surveys with census data has enabled the creation of municipal poverty maps in Mexico<sup>9</sup> . We rely on such information for 2000 and 2005, using income poverty levels in three officially-defined (until 2011) alternative

> Fourth Assessment Report.

> 8Information about the basic characteristics of the emissions scenarios used can be found at the SRES Emissions scenarios. http://sdwebx.worldbank.org/climateportal/index.cfm.

> 9Briefly, poverty mapping involves, first, discovering relationships between household characteristics and the welfare level of households as revealed by the analysis of a detailed living standards measurement survey; and second, applying a model of these relationships to data on the same household characteristics contained in a national census to determine the welfare level of all households in the census. The resulting estimates of household welfare and poverty derived from the census are spatially disaggregated to a much higher degree than is possible using survey information (Elbers et al., 2004; Bedi, Coudouel and Simler, 2007).

7
Table A3: Balance of baseline outcomes and covariates for those completing the medium-term survey

||_Mean_|_Mean_|_Mean_|_Norm.Dif._|_Norm.Dif._|_Dif=0_|_Dif=0_|
|---|---|---|---|---|---|---|---|
||Drama<br>N=212<br>(1)|Document.<br>N=200<br>(2)|Control<br>N=207<br>(3)|Drama<br>vs.Control<br>(4)|Document.<br>vs.Control<br>(5)|_(p-value)_<br>Drama<br>vs.Control<br>(6)|_(p-value)_<br>Document.<br>vs.Control<br>(7)|
|**Panel A: Outcomes**||||||||
|Baseline index|0.161|0.076|0.131|0.040|-0.075|0.562|0.287|
|Father determines sex|0.316|0.335|0.309|0.010|0.039|0.880|0.578|
|Stricter control daugthers|0.423|0.513|0.457|-0.048|0.079|0.496|0.269|
|Women should be virgin|0.715|0.724|0.681|0.052|0.066|0.477|0.374|
|Justify beating if unfaith|0.250|0.286|0.262|-0.019|0.039|0.792|0.593|
|Women wear whatever|0.980|0.941|0.944|0.137|-0.007|0.055|0.920|
|Ban kitchen during period|0.273|0.305|0.337|-0.098|-0.049|0.178|0.512|
|Tell anyone if friend beat|0.918|0.879|0.948|-0.085|-0.174|0.249|0.021|
|Climate change is a threat|0.670|0.700|0.720|-0.077|-0.031|0.267|0.661|
|Work in the future|0.708|0.750|0.705|0.003|0.071|0.960|0.312|
|**Panel B: Controls**||||||||
|Age (years)|20.816|20.960|20.894|-0.029|0.024|0.680|0.731|
|English self-assess (0-10)|5.552|5.775|5.681|-0.030|0.022|0.663|0.754|
|Survey in english|0.443|0.505|0.401|0.061|0.148|0.380|0.035|
|Female|0.264|0.265|0.295|-0.048|-0.047|0.487|0.506|
|Primary|0.042|0.065|0.043|-0.004|0.067|0.959|0.340|
|Secondary|0.382|0.415|0.478|-0.138|-0.090|0.047|0.200|
|University|0.542|0.500|0.449|0.132|0.072|0.057|0.307|
|HH-head primary|0.208|0.160|0.179|0.052|-0.035|0.456|0.615|
|HH-head secondary|0.354|0.315|0.295|0.089|0.031|0.197|0.657|
|HH-head university|0.344|0.420|0.391|-0.069|0.041|0.320|0.557|
|Hindu|0.844|0.810|0.826|0.035|-0.029|0.616|0.675|
|Muslim|0.099|0.120|0.130|-0.070|-0.022|0.315|0.751|
|Christian|0.014|0.005|0.005|0.068|0.002|0.325|0.981|
|Sikh|0.014|0.045|0.014|-0.002|0.127|0.977|0.072|
|General caste|0.557|0.595|0.541|0.022|0.077|0.750|0.273|
|OBC caste|0.269|0.235|0.251|0.028|-0.027|0.681|0.704|
|SC caste|0.151|0.135|0.169|-0.035|-0.067|0.614|0.339|
|Student|0.651|0.720|0.633|0.027|0.132|0.700|0.060|
|Employed|0.137|0.120|0.116|0.044|0.009|0.522|0.899|
|Self-employed|0.113|0.045|0.077|0.086|-0.095|0.211|0.174|
|Live in Delhi|0.571|0.530|0.575|-0.006|-0.064|0.932|0.364|
|Member of organization|0.160|0.170|0.135|0.050|0.068|0.470|0.332|
|Currently dating|0.307|0.275|0.343|-0.055|-0.104|0.428|0.138|
|Married|0.042|0.050|0.058|-0.050|-0.025|0.469|0.723|
|With sisters|0.703|0.740|0.686|0.026|0.084|0.709|0.229|
|Daily freq. social media|15.924|14.896|16.721|-0.050|-0.113|0.492|0.126|
|Daily freq. watch videos|2.368|2.296|2.496|-0.089|-0.138|0.200|0.050|
|Male friend beating|0.160|0.205|0.106|0.113|0.194|0.104|0.006|
|Female friend beated|0.132|0.210|0.135|-0.007|0.140|0.924|0.047|

Notes: Table shows sample means at baseline for respondents who completed the medium-term survey. The normalized difference in columns 4 and 5 is the difference in the sample means of treatment and control groups divided by the square root of the sum of the sample variances.

28
protect more capital or skill-intensive products. It arises from the conclusions in the numerical literature on trade and poverty that the poor would benefit from trade reforms because the structure of protection is biased against goods they consume/produce—for example, the contributions in Hertel and Winters (2005). Clearly the need for a relatively comprehensive dataset is pressing.

- **Actual/potential impact of trade liberalization on wages** . The high levels of aggregation used in household surveys (2- or 3-digit ISIC) may not be fine enough to detect worker reallocation across firms within the same industry in response to trade liberalization. This leads Goldberg and Pavcnik to call for empirical firm/plant level studies that explore the income distributional effects of trade reforms by analyzing the impacts of reform on firms belonging to the same 3- or 4-digit ISIC sector, as reflected for example in the compositional changes of their output (quality upgrading or other forms of greater differentiation of their production). Information on relative (productivity-adjusted) labor costs would help identify sectors/firms that may be confronted with more serious adjustment costs post reforms. These exercises could also be augmented with information on additional operating costs related to the “quality” of the business environment, of the sort generated by the World Bank’s Investment Climate Research (World Bank 2005a) and _Doing Business_ (World Bank 2005b).

- **Inter-sectoral mobility, entry/exit across sectors** . Borjas and Ramey (1995) found that the effect of trade on the labor market depended on market structure of industries. Barriers to entry and exit will clearly have a bearing on labor market responses to further trade and investment liberalization. Capital/financial market distortions or inefficiencies will affect the ability of firms to expand/enter. These variables may be more important than the labor market. To a large extent such factors have already been studied, but perhaps not from a labor market adjustment perspective.

- **Beyond manufacturing** . The manufacturing sectors are the focus of the lion’s share of research on the effects of trade on employment/wages, in both developing and developed countries. However, most employment in both sets of countries is elsewhere. In OECD countries services account for 70+ percent of turnover and employment, whereas

26
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33
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**2.b H  =** **_e_**<sup>**0.13s**</sup>

Suppose for the moment we choose the first form for average human capital, (2.a). Following numerous other authors, we divide through by the labor force to obtain an equation explaining per capita output (y = Y/L) as a function of physical capital stock per worker (k = K/L), average schooling per worker **s** over time, and TFP per worker (a = A/L). Taking the natural logarithm and differentiating with respect to time (or differencing for estimation purposes) we obtain:

# **3.** _dln_ y/ _dt_ = _dln_ a/ _dt_ + a _dln_ k/ _dt_ + (1-a) **s** _ln_ (1.13)

This functional form has the advantage that it is stated in growth rates, independent of units (currency units or manpower headcounts) and independent of conversion factors between capital, output, and labor. One then finds the rate of change in per capita TFP over time by re-arranging this equation, subtracting from the growth of output the shareweighted growth rates of capital per worker and the presumed growth in worker effectiveness as measured by average years schooling.

## **Comparison With Deterministic TFP Estimates by the World Bank**

Sandeep Matajan (2003) has recently developed robust estimates of Solow-residual total factor productivity and the other related variable for numerous countries. These are found on the World Bank’s intranet. These estimates are shown below, in Figure A-4.

To compare these estimates with those generated in this paper, Figure A-5 presents the Mahajan estimates in index form. These are readily comparable with the indices created for this paper found on Figure 10. The major difference is that this paper focuses on per worker productivity, capital, TFP, etc. To make the Mahajan estimates more comparable, one might subtract the difference of logs of the labor force (labor force growth) from Mahajan’s capital stock growth rate and TFP growth, dragging down estimated TFP. In any event, the TFP indices created for this paper and the indices created from these World Bank sources have the same general profile.

The World Bank spreadsheet includes a summary for Cote d’Ivoire, presented in Figure 14 These also are not in per-worker form. They are quite like the estimates in this paper: output, physical capital and TFP grow strongly in the 1960s and 1970s, and weakly thereafter. In particular they grow more slowly than the labor force, so would be resoundingly negative (like ours) if put in per-worker terms. Human capital grows strongly throughout (like ours), as the labor force grows, and increases in quality.

26
analysis, with many people crossing the contact line routinely for daily needs. With these characteristics, we can implement the procedure developed in the previous section.

# **3.1 Background**

Ukraine’s Eastern regions have historically been home to the country’s industrial core, including coal mining, metallurgy, and chemical industries. Before World War I, these regions produced more than three quarters of the pig iron and coal output of the Russian Empire. Under the Soviet industrialization programs, Donbas became the most heavily settled region of Ukraine, attracting people from elsewhere in Ukraine and from other parts of the union.<sup>6</sup> After the collapse of the Soviet Union, however, the region’s industrial infrastructure saw very limited modernization. To offset the eroding competitiveness, the industry was increasingly granted subsidies in key inputs like electricity, gas, and coal. Nonetheless, with dissipating favorable external conditions after the Global Financial Crisis in 2008, which include a slowing demand for steel and increasing modern production capacity in previous export markets of Ukraine, and as the subsidy component of energy inputs shrank after disputes with Russia, the challenges faced by the region’s aging economy grew. By 2013, Donetsk and Luhansk still had relatively larger populations and economies than other regions. However, they were losing people to other parts of the country (net out-migration), partially because of social challenges (i.e., high crime rates, alcoholism, and drug abuse) and environmental problems (pollution from mining and industry).

The onset of the conflict in 2014 changed the economic and demographic characteristics of these regions dramatically. Before the onset of the war in 2022, about 38 percent of the combined territories of Donetsk and Luhansk oblasts were outside the Ukrainian government’s control (less than 4 percent of total Ukrainian land), which was demarcated by a 457 kilometer line of contact. This division imposed additional obstacles to economic activity, especially the provision and transportation of industry inputs and outputs like coal and steel. Despite the hostilities, however, migration was not prohibited. Ukrainians routinely crossed the contact line for family visits, shopping, and using ATMs for withdrawing pensions. Overall, there were about 1.4 million IDPs registered by the country’s Ministry of Social Policy between 2014 and 2019, close to half of whom remained displaced within the territories of Donetsk and Luhansk oblasts, and the other moved elsewhere within Ukraine.

> 6For a detailed analysis of the social and economic conditions in Donbas before and after the onset of the conflict in 2014, see World Bank, 2021.

9
dimension of the cell until an adequate number is reached (i.e. first we substitute countrylocation-size bins, then country-sector-size)<sup>16</sup> .

This captures the broader environment in which the firm operates and allows the individual firm’s own contribution to the average to be excluded. The approach also has the benefit of not losing those observations where a firm did not answer all the individual investment climate questions.<sup>17</sup> In constructing these measures, we actually take two steps before entering the averages into the regressions, with the second one being particularly important if endogeneity concerns are to be addressed. An issue is how the investment climate indicators are calculated and matched to firms in the regression. This requires differentiating between a firm’s current size – that can be important in the conditions they report facing now – and their initial size – and the average conditions faced by firms of the size they were initially. Simply giving firms the average value of firms in their current size category is giving them the ex post conditions not the initial conditions. To justify this, one has to assume that the conditions faced by firms have remained constant over time, regardless of how they may have grown. The alternative we present assumes instead that conditions faced by firms are likely to vary as they grow. Rather, we make the less restrictive assumption that, within a location and sector, conditions facing firms of a certain size now are the same as the conditions facing the same sized firm in the earlier period. For example, we can use the information on conditions facing micro firms now to measure what micro firms faced three years before.

Thus, in the first step, we use the information that firms report about investment climate conditions they face in the current period. While it would be desirable to have the conditions they faced in the initial period three years ago, this information was not collected. However, we also recognize that firms that have recently changed size may report very

> 16 If we were to keep always the original 4 dimensions same country-location-sector-size cell there are approximate 5,000 observations that are lost and results do not change substantially.  We also used different minimum cell-size requirements (4-10 firms) and found results were robust.  Results presented use the cutoff of at least 4 firms in a cell.

> 17 This approach is very similar to using location-sector-size dummies as instruments (except that the firm’s own value is not excluded in this calculation, the number of observations averaged in a cell may be very small, and the additional observations cannot be recovered if a single investment climate variable is not available.). The test of over-identifying restrictions could not be estimated using the full specification due to the large number of dummies and instruments.  However, in specifications not using a full set of sector-survey interaction dummies, the restrictions could not be rejected at the 0.3 level.

17