rafmacalaba/datause-encoder-data huggingface dataset:
the LMFS nor with roughly equal wages in the private and state sectors **12*** The very high labor costs among small state firms may possibly be explained by the use of shell entities, already alluded to above.

Ability to pay is clearly important, at least in governing the bonus component of wage income. However, given the size and distribution of loss-makers, what evidence, if any, exists for predatory wage setting ? Value subtracting firms are not only numerous (c.6000 in 1992) but -- see Table 11 -- also appear to experience above average wage and bonus increases. The reductions in employment are large for both categories of value subtractors -- between 47-58% declines for 1992 over 1991, but very striking, is the large margin in wage increases over other better-performing fims. For finns with sales below wage costs, nominal wages increased by over 70% on average as against around 20% for other firms. Further, bonuses relative to wages are high. For finms with sales below wages, the average wage bill was rou ghly equal -to sales, while bonuses were about 80% of both variables **13** Appendix 1 concentrates on wage and employment decisions in firms held by the AVRt and further indicates the very weak or absent association of wage changes to measures of financial performance.

On relative skill wages, the picture is of a decompression of the inherited wage arrangement. At an economy-wide level, for exaample, white collar wages grew by 31% while blue collar wages only grew by 23% in 1992. And the earnings functions reported below (Table 12) using the Hungarian Household Panel yield not only a conventional set of returns to education but also a clear increase in returns to education for 1993 over 1992. The result is robust and statistically significant across wages, earnings and income measures and points to non-trivial changes to relativities and the likely growing inequality in the wage and income distribution. It is striking that the coefficients on the education variables are both larger and more significant than either sector or ownership variables.

With respect to relative branch wages, 2 digit industry level data show smnall variation in wage changes in 1993 (but this may, as noted above, camouflage quite large intra-branch variation). The stability of relative branch wages cannot be laid at the door of an incomes ' As reported in Commander et al (1994).

u Part of this may be severance payments to workers but this **cam** account for the geneal pattern.

25
**Table A12. Associations between SCP Quality Determinants and Labor Market Outcomes** **_Using Program-Level Data for all Survey Countries_**

|_Ddt ribl_|_Formal employment_|_Log Wages_|
|---|---|---|
|_epenen vaae:_|(1)|(2)|
|_Infrastructure_|||
|Has enough equipment or tools for practice|0.0702**|0.0200|
||(0.0352)|(0.0167)|
|_Training and curriculum_|||
|Teaches numerical competencies|0.133***|0.0561***|
||(0.0394)|(0.0189)|
|Offers remediation classes during the program|0.0525*|0.0275*|
||(0.0317)|(0.0146)|
|Offer non-class-based remediation||-0.0257*<br>|
|||(0.0152)|
|Employment outcomes or employers' requests are very|||
|important to update program|0.0678||
||<br>(0.0425)||
|HEI perception of labor market is very important to update|<br>||
|program|0.0400|0.0395**|
||(0.0356)|(0.0179)|
|Enrollment trends are very important to update program||-0.0169<br>(0.0130)|
|Offers credits for longer degrees||<br>0.0615***<br>(0.0207)|
|_Link with productive sector_|||
|Collect data on employment or employers' satisfaction more|||
|than once per year|0.0483*||
||(0.0292)||
|Industry has agreements to train faculty|-0.0488||
||(0.0320)||
|Industry lends or provides equipment to program for student|||
|<br>training|-0.0266||
||(0.0286)||
|Program has staff assigned to collect grads' employment data|0.0576*||
||(0.0313)||
|HEI provides job market information||0.0126|
|||(0.0188)|
|HEI has an employment center|0.0830***|<br>0.0252|
||(0.0311)|(0.0161)|
|_Faculty_|||
|Percent of <40 years old faculty|-0.00175***||
||(0.000501)||
|Percent of faculty with 5yrs+ industry experience|0.00101**||
||(0.000441)||
|Percent of faculty that are women||-0.000683**|
|||(0.000324)|
|Percent of faculty with BA degree||0.000469*|
|||(0.000250)|
|Class planning is very important in faculty evaluation|0.0470<br>(0.0293)||
|Classroom observation is very important in faculty evaluation||0.0157|
|||(0.0137)|
|Number of faculty (log)||<br>0.0229**|
|||(00109)|
|Research skills are very important when hiring faculty||.<br>0.0207<br>(00160)|
|_Other Practices_||.|
|<br>Update or review admin data more than once per year|0.0468||
||(0.0302)||
|Percent of governing body that belongs to current students|<br>-0.000798|-0.000614*|
||(0.000678)|(0.000318)|
|Percent of governing body that belongs to private sector||0000367|
|||.<br>(0.000349)|

85
the estimated income per capita was less than 1/3 of minimum wage (net of taxes), then the household members were deemed eligible for the Green Card.

Step 3: The local committee decided whether or not qualifying applicants (according to steps one and two above) were indeed to be admitted to the Green Card program. Even if the household was formally eligible, the committee was able to use its discretion not to provide the card if evaluated the person/s not as poor. The household members who qualified each obtained an individual green booklet which registered all interactions with the health system.

This hybrid targeting mechanism, combining central criteria with local knowledge and assessment, resulted in a progressive roll-out of the program during expansion.

# **3. Data and Variables Used**

We use two main data sources in our Green Card assessment: the Turkey Household Budget Surveys (HBS) from six consequent years (2003-2008) and the Turkey Welfare Monitoring Survey Baseline dataset (collected in May 2009).

HBS data sets are cross-sectional household surveys collected annually by the Turkish Statistical Institution (TURKSTAT). Their main purpose is to provide estimations of household expenditure levels for various categories and to monitor poverty developments in the country. The samples are nationally representative, varying in size but large throughout (25,764 households in 2003, about 8,600 from 2004-2008). The data provide consistent information over time, capture households‘ access to health insurance as well as health expenditures in various sub-categories.

The HBS contains a number of variables which allow for an assessment of the Green Card program. First, it records, for each individual household member, access to various forms of health insurance such as the formal social security health insurance, private plans, or the Green Card program. We can then use nominal household per capita expenditures to assess the targeting and coverage outcome of the Green card program.<sup>3</sup> Health expenditures are recorded in line with the international COICOP standards.

> 3 Ideally, we would prefer to use real consumption at the household level with both spatial and adult equivalence adjustments. But due to lacking regional identifiers in the household dataset, we can only compute nominal per capita expenditures to define population deciles.

5
exception of Mauritius, Botswana and Lesotho, while all six countries displayed in Figure 1.6 had a consistently declined trend in the share of labor employed in the agriculture, the share was still over 70 percent in 2010. When this evidence is combined with the trend in manufacturing, including in Mauritius, Botswana and Lesotho, the absence of structural transformation is striking.

**Figure 1.6: Still to catch – stalling structural transformation in select African countries: trends in** **<u>the share of employment in agriculture and manufacturing</u>**

<!-- Start of picture text --> Share of Agricultural Employment in Total Share of Manufacturing Employment In Total Employment<br>Employment (%) (%)<br>100%<br>14% 25%<br>90%<br>12%<br>80% 20%<br>70% 10%<br>60% 15%<br>8%<br>50%<br>40% 6% 10%<br>30% 4%<br>20% 5%<br>2%<br>10%<br>0% 0% 0%<br>1980 1985 1990 1995 2000 2005 2010 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Botswana Ethiopia Kenya Botswana Ethiopia Kenya<br>Lesotho Madagascar Malawi<br>Lesotho Madagascar Malawi<br>Uganda Tanzania Mauritius<br>Mauritius Uganda Tanzania<br>Source: Constructed from UNCTAD, UNIDO and ILO<br>Share of agricultural employment in total (%) Share of Manufacturing Employment in Total (%) Share of Manuf Emp in Total = Mauritius (%)<br><!-- End of picture text --> To some, economic growth rates of over 6 – 9 percent in many mineral-rich Sub-Saharan African countries in the decade preceding the current financial crisis seem to suggest that structural transformation is not necessary for catch-up. This contention is erroneous. Typically, agricultural workers earn only subsistence wages and are the poorest. If the share of agricultural employment is any indicator, then the case for structural transformation seems the strongest for resource-rich countries where the predominance of mining stymies the shift of labor from agriculture to manufacturing and services. As an example, until 2000, in the copper-dominated Zambian economy, only 28 percent of the labor force was employed in industry and services; the remaining 72 percent was in agriculture. In the minerals and cocoa-dominated Ghanaian economy, in 1990, the share of employment in manufacturing was merely 9 percent; in 2009, it had declined to 8 percent (UNSTAT 2011). Although data limitations do not permit better comparisons, it is clear that the agricultural sector continues to host an overwhelming majority of the labor force in resource-rich Sub-Saharan African countries.

In what is often termed the third Kuznets‘s fact, structural transformation is characterized by a redistribution of the population from rural to urban areas during. Growth in both manufacturing and services, which are invariably in urban locations, leads to a decline in the share of the rural 10
dimensions besides bonding and bridging<sup>7</sup> , and 2) that people face a trade-off in enjoying social relationships, either within their kinship and existing ties, or across new social ties.

## [TABLE 3]

# **5 Bridging, bonding and life satisfaction**

In this section, we turn to the life satisfaction equations which we use to try determining how bonding and bridging attitudes relate to happiness. We first use the pooled world sample with the disaggregated proxies for bonding and bridging attitudes, assuming these variables as two separate concepts (Table 4). We then use the constructed proxies of the locus of socializing. We assume bonding and bridging attitudes as two sides of the same variable and test these proxies on the world sample and across groups of similar countries (Tables 5, summarized in Table 6).<sup>8</sup> Table 4 shows that all bonding indicators are positively and significantly correlated with life satisfaction, and with a sizeable coefficient as compared to the standard controls. The most important indicators are ‘importance of family’, ‘importance of religion’, and ‘trust in institutions’. But also ‘importance of work’ and ‘child obedience’ are positively and significantly correlated with life satisfaction. The positive correlations of the bonding indicators used with life satisfaction are not new results with respect to those in the literature (see section 2). However, our previous analysis and the estimations in Table 4 show in more detail how different and interlinked components of bonding can affect life satisfaction.

The results for the bridging indicators are more mixed. We find ‘trust in people’ to be positive and significant, ‘homosexuality is justifiable’ negative and significant, while ‘child independence’ and ‘child imagination’ are non-significant. Whereas the finding on trust is not new, the finding on tolerance of homosexuality is rather surprising. The proportion of homosexuals in the population has been used in some studies as a proxy for bridging attitudes in the composite ‘creativity index’, which, in its turn, has been

> 7For example, Woolcock (2001) considers ‘linking social capital’, besides bonding and bridging social capital.

> 8The choice of world regions rather than single countries is dictated by the number of observations available given the type of equation. Table 2 in annex provides the full list of countries and number of observations for each world region.

16
Okuyama, Yasuhide, 2003, “Economics of Natural Disasters: A Critical Review,” working paper West Virginia University.

Oxfam, 2005, “Making the Case for Cash: Humanitarian Food Aid Under Scrutiny,” Oxfam Briefing Note.

Roland, Gerard and Thierry Verdier, 1999, “Transition and the Output Fall,” _Economics of Transition_ , Vol 7(1), pp.

Runyan, Rodney C. 2006. “Small Business in the Face of the Crisis: Identifying Barriers to Recovery from a Natural Disaster”, _Journal of Contingencies and Crisis Management_ 14(1): 12-26.

Schultz, T.W. 1975. “The Value of the Ability to Deal with Disequilibria”, _Journal of Economic Literature_ 13(3): 827-46.

Skoufias, Emmanuel. 2003. “Economic Crises and Natural Disasters: Coping Strategies and Policy Implications”, _World Development_ 31(7): 1087-1102.

Smith, Stanley K and Christopher McCarty, 1996, “Demographic Effect of Natural Disasters: A Case Study of Hurricane Andrew,” _Demography_ Vol 33(2), pp. 265-275.

Strömberg, David, 2007, “Natural Disasters, Economic Development and Humanitarian Aid,” _Journal of Economic Perspectives_ , Vol. 21(3), pp. 199-222.

Yang, Dean. 2008. "Coping with Disaster: The Impact of Hurricanes on International Financial Flows, 1970-2002" _The B.E. Journal of Economic Analysis & Policy_ , 8(1) (Advances), Article 13.

32
Table 10. How would the changes in education quality implied by PISA from 2000 to 2012 affect the marginal effect of an increase in carbon price?

||Note: value|of 1 implies n<br>is ind|o difference in<br>icated for a dif|marginal effect<br>ference from 1|and statistical<br>not 0|significance|
|---|---|---|---|---|---|---|
||Scenario 2a:<br>supply implie|Same skills b<br>d by changes|ut different elas<br>in quality from|ticity of skill<br>2000 to 2012|Scenario 2<br>skills and e<br>skills suppl<br>changes in<br>2000 t|b: Different<br>lasticity of<br>y implied by<br>quality from<br>o 2012|
||Emissions|Output|Skills|Wage gap<br>(log diff.)|Emissions|Output|
|Average of|0.9999|1.0005|1.0004|0.2324|0.996|0.9906|
|ratios|(0.0014)|(0.0631)|(0.0095)|(11.783)|(0.0109)|(0.3104)|
|Belgium|1.0002<br>(0.0007)|0.9985<br>(0.0276)|1.0032<br>(0.0083)|1.6657<br>(0.6586)|1.004<br>(0.0106)|0.9994<br>(0.0459)|
|Czech|1.0002|1.0037|1.0027|0.6893|1.0033|1.005|
|Republic|(0.0007)|(0.0952)|(0.0079)|(0.2707)|(0.0072)|(0.1025)|
|Denmark|0.9999|0.9997|0.998|0.8471|0.9974|0.9998|
||(0.0007)|(0.0337)|(0.008)|(0.2956)|(0.0078)|(0.0516)|
|Spain|0.9991<br>(0.0009)|1.0039<br>(0.008)|0.9894<br>(0.0078)|0.6195**<br>(0.1235)|0.9883*<br>(0.0098)|1.0067<br>(0.02)|
|Estonia|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Finland|0.9951***|1.0204*|0.9575***|-0.3115***|0.9333***|1.0621|
|France|(0.0049)<br>1.0003|(0.2833)<br>0.9981|(0.0083)<br>1.0037|(0.3895)<br>0.6902**|(0.0591)<br>1.0043|(1.5171)<br>0.9968|
||(0.001)|(0.0124)|(0.0084)|(0.1385)|(0.0078)|(0.0242)|
|United|<br>0.9989***|<br>1.0281*|<br>0.9458***|<br>0.6199*|<br>0.9355***|<br>0.9248|
|Kingdom<br>Greece|(0.0018)<br>1.0034|(0.4839)<br>0.9991|(0.0095)<br>1.0134|(0.1984)<br>1.0243|(0.0393)<br>1.0117|(3.1876)<br>0.9969|
||(0.0105)|(0.0111)|(0.0213)|(0.3658)|(0.0172)|(0.0101)|
|Italy|1.0009|0.9976|1.0084|1.7298***|1.0086|0.9966|
|Netherlands|(0.0011)<br>0.9968***|(0.0049)<br>1.0058*|(0.0095)<br>0.9604***|(0.4326)<br>-3.8441|(0.0098)<br>0.941***|(0.0086)<br>0.9734|
||(0.0036)|(0.504)|(0.0098)|(120.07)|(0.0477)|(0.8435)|
|Norway|<br>0.9999|<br>1.0004|<br>0.9996|<br>-1.6975|<br>0.9995|<br>0.9999|
||(0.0012)|(0.0401)|(0.0083)|(74.485)|(0.0092)|(0.0411)|
|Poland|1.0043***|0.95***|1.1222***|0.7566|1.1253***|0.9265***|
||(0.0038)|(0.1108)|(0.0192)|(0.2842)|(0.0702)|(0.1355)|
|Slovak|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Republic|||||||
|<br>Slovenia|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|

29
tions, we conducted a number of robustness checks (see Appendix D). One potential concern is that the results are valid only for the selected bandwidth. Figure D.10 illustrates that the effects of FMLN territorial control on the main outcomes are robust to different choice of bandwidths between 0.1 and 4 km. Second, in Tables D.4- D.6 in the Appendix, the main results are presented using alternative RD polynomials (constant, linear, and quadratic); using additional bandwidth options; and varying the kernel choice. Lastly, we estimate our main model using the OLS estimation approach (see Table D.7). Overall, the results are robust to all these alternative specifications.

Figure 4. Effects of Guerrilla Control on the Arcsine of Night Light Luminosity Over Time <!-- Start of picture text --> Year<br>92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13<br>0<br>-.05<br>-.1<br>-.15<br>-.2<br>Effect on Night Light (Arcsine)<br>-.25<br><!-- End of picture text --> _Notes:_ This figure shows the coefficients obtained from the estimation of equation 1 for each year between 1992 and 2013. The gray coloring illustrates 95 percent confidence intervals. The estimates shown include up to 400 fixed effects. The figure illustrates the coefficients of each yearly estimation from 1992 to 2013. Overall, the effect of guerrilla control on night light luminosity is negative and stable over time.

# **V.B.2**

One relevant concern regarding our empirical strategy is that since FMLN territories were defined using altitude as the main geographic feature for the borders, the results may reflect some socioeconomic characteristic associated with higher-altitude areas rather than rebel control. As shown above, there are no statistical differences in variables that measure economic productivity and state capacity at baseline in areas later controlled by the FMLN.

Nevertheless, we conducted a placebo exercise by selecting pairs of neighboring census tracts in 24
28 Agricultural raw materials, like fibers, are key inputs for many labor intensive industries like textiles and clothing where Africa should have a comparative advantage in production and export. The cost raising impact on major inputs must constitute an important disincentive to local production for export.<sup>4</sup> Second, it is widely recognized that one of Africa's most pressing social problems concerns the extent and level of rural poverty in Africa, and how it can be alleviated. Import barriers, like high tariffs and other trade control measures on products like fertilizers, pesticides and other agricultural chemicals clearly have the potential to act as a major constraint to the expansion of agricultural output which could improve living conditions and income in Africa.

# VI. Summary and Policy Implications

In the mid-1950s sub-Saharan Africa accounted for 3.1 percent of global exports, yet by 1990 this share had fallen to 1.2 percent. From a policy perspective the reasons for this decline are of major importance. Some views hold that external protection in OECD markets was an important contributing factor. If so, the solution to Africa's trade problems requires a liberalization of industrial countries' trade barriers. An alternative view is that Africa's marginalization was primarily due to inappropriate domestic policies that reduced the region's ability to compete internationally. If true, changes in Africa's own policies are of paramount importance if the adverse trade trends are to be reversed.

This study finds that a major and extensive loss of Africa's international competitiveness played a key role in its decline in world trade. If Africa had merely retained its 1962-64 OECD market shares its exports now would be 75 percent ($11 billion) higher. Africa's marginalization in world trade is also 24Empirical evidence clearly shows that structural adjustment and trade policy reforms can make a significant improvement in African countries ability to compete internationally. For example, the World Bank (1994, Box Table 1.3 lists sub-Saharan African countries that implemented trade reforms in the 1980s and early 1990s. As a result of these policy changes the reformers were able to re-coup some of their lost market shares. By 1993 the imports shares of the non-reforming African countries were 64 percent below their 1962-64 levels while those for the reformers were 46 percent lower.
**Table 5.1: The effect of relative grade on test scores at age 15 (Mexico, Republic of Korea, Slovakia, Thailand)**

Note: Regressions in columns (1) and (5) are simple OLS regressions of test scores on grade. Regressions in columns (2) and (6) regress scores on Assigned Relative Grade, as a function of the discontinuity produced by birth month and the date cutoffs for school entry. Regressions in columns (3) and (7) use Assigned Relative Grade to instrument for grade. Regressions in columns (4) and (8) instrument in the same way, but include only the sub‐sample around the discontinuity cutoff.

While each of the analyses presented in this subsection has potential drawbacks, together they suggest that learning trajectories have a plausibly local linear trend, especially across the grades that we are interested in, and that this is true for multiple specifications and tests. Moreover, on balance the results suggest that the calculated slopes are consistent with a learning trajectory that starts at birth rather than at age 6—meaning that the _LAYS_ adjustment ratio is really a measure of learning productivity of a _society_ , and not just its schools.

# **_5.2 Evidence on schooling quality derived from labor market returns_**

Second, we examine whether labor-market evidence is consistent with the LAYS assumption and implications. As noted above, the _LAYS_ measure shows that the stock of schooling—once adjusted for quality of learning—in some countries is much lower than the standard measures indicate. Of course, this is true not just for societies, but also for the individuals in those societies. But this throws a wrench into the standard calculations of returns to education. The private Mincerian return to schooling is typically calculated to be between 8 34