rafmacalaba/datause-encoder-data huggingface dataset:
Determinants of teacher hourly earnings

|Dependent variable log of ho|urly earnings<br>**Sample: All**<br>**(public and **|**paid workers**<br>**private sector**|<br>**s)**<br>**Sa**<br>**ser**|**mple: Teachers and**<br>**vants (public secto**|**other civil**<br>**r)**|
|---|---|---|---|---|---|
|Teacher|23.42<br>(16.66)**|||-18.70<br>(-4.64)**||
|Teacher primary school||46.94|||-12.58|
|||(23.13)**|||(-2.91)**|
|Teacher not primary school||<br>4.98<br>(3.01)**|||<br>-23.47<br>(-5.86)**|
|Civil servants (excluding|||46.72|||
|<br>teachers)|||(9.34)**|||
|<br>Age|7.43|7.33|<br>7.59|14.26|13.53|
||(44.43)**|(43.95)**|(45.31)**|(18.6)**|(17.58)**|
|Age square|-0.08|-0.08|-0.08|-0.12|-0.11|
||(-36.05)**|(-35.7)**|(-36.58)**|(-13.91)**|(-13.07)**|
|Male|34.18|34.40|31.82|6.52|6.76|
||(41.84)**|(42.2)**|(39.57)**|(2.94)**|(3.06)**|
|Urban|20.46|20.91|18.99|3.02|4.39|
||(27.27)**|(27.89)**|(25.56)**|(1.38)|(1.98)|
|Educ. Diploma I and II|<br>80.73|<br>71.92|<br>104.22|<br>29.27|<br>28.02|
||(30.01)**|(27.23)**|(39.08)**|(8.82)**|(8.51)**|
|Educ. Academy/Diploma III|93.56|95.36|101.04|23.90|27.65|
||(39.04)**|(40.06)**|(42.02)**|(5.6)**|(6.35)**|
|Educ. University/Diploma IV|101.09|107.40|113.19|31.30|37.26|
||(59.18)**|(61.19)**|(68.03)**|(10.7)**|(11.95)**|
|Constant|6.30|0.03|6.30|5.52|5.63|
||(221.31)**|(222.45)**|(220.57)**|(36.26)**|(36.84)**|
|Observations|<br>38,431|<br>38,431|<br>38,431|<br>3,616|<br>3,616|
|R squared<br>_Source: _World Bank staff estimat|.31<br>es.|.32|.32|.30|.31|

_Source:_ World Bank staff estimates. _Note:_ Robust t-statistics reported in parentheses. ** denotes significance at the 1 percent level. Earnings are defined as wage salary in cash plus wage salary in kind. Hourly earnings are calculated on the basis of average monthly earnings, divided by hours reported in the main job in the past week x 4 39
potential emissions reduction from that appliance under the mitigation scenario would be smaller as a large potential would already be realized in the baseline. The study also argue that the costs of existing stock of appliances should be treated differently for those already in operation from those available in the market but not yet used.

The literature of marginal abatement cost curve analysis is rich because of the hundreds of analyses carried out by academia, research institutions and industries. The purpose of the analysis is to indicate the level of GHG mitigation potentials and corresponding marginal abatement costs of various measures and options. However, it is important to note that the potential may not be realized automatically even if the associated marginal cost is negative. This is because there exist a large number of technical, financial and institutional barriers inhibiting their implementation. Therefore, the indicative potential might be unrealistic from the perspective of their realization. To estimate more realistic GHG mitigation potentials, some experimentation would be needed to understand the likely penetration rate of adoption of energy efficient and clean technologies. For example, households could be surveyed to understand whether or not they replace their existing energy utilizing appliances if they know that they save more money over the years (in present value terms) than their investment for the replacement. If they are not interested, what incentive would they require to do so? Those experiments could tell what level of incentives would be needed to realize the GHG mitigation options. The costs of these incentives should be accounted for to calculate the true marginal cost of a GHG mitigation potential. Expanding marginal cost analysis incorporating the costs of incentives for their realization would be an area of future expansion of this study.

# **References**

Asian Development Bank (ADB). 1998. Asia Least Cost GHG Abatement Strategies: Summary Report, ADB, Manila.

Bloomberg (2010). A Fresh Look at the Costs of Reducing US Carbon Emissions, Bloomberg New Energy Finance, New York.

Cervigni, R., J. A. Rogers, and I. Dvorak (eds.) 2013. Assessing Low-Carbon Development in Nigeria: An Analysis of Four Sectors. World Bank, Washington, DC.

22
exception of Denmark and Switzerland (1960-80), Hong Kong (1971-91), Venezuela (1961-81) and Trinidad and Tobago (1970-90). The STARS data diskette provides additional data for other countries which do not appear in the book.

Male LFPs were used because female LFP ratios tend to rise with income per capita levels, adding a dimension to the trends not found in the male ratios. In fact, these trends are important for the finances of the pension system since they can increase the contributor base even when the working age population remains relatively constant. The complicated financial and distributional implications of these long-term changes are not discussed in the book but warrant further research.

With the exception of LFP rates for Uruguay, which are taken from U.S. Bureau of the Census (1993) all data were calculated using the International Labor Office's "Yearbook of Labor Statistics" for several different years. The methodology and sources for individual country data are included in that publication. The "change" columns refer to the absolute percentage point change between LFP in the earlier and later periods. The decline of LPF among males over 55 years of age is well documented, especially in the industrialized world, but little empirical evidence exists as to the role of public pension schemes on this trend.

# C. Duration of Retirement

Figure 4.5 and Appendix Table A.10 present data on the expected duration of retirement at official retirement age in several countries by income per capita groupings using the Purchasing Power Parity definition from Table 30 of the 1992 World Development Report. This indicator is based on life expectancies at retirement age and therefore apply only to those who survive to the official retirement age. The life expectancy data are mostly taken from the 1990 UN Demographic Yearbook (1992) and are based on country studies for years ranging from 1978 to 1990.

The actual expected duration of retirement is actually higher in almost all of these countries because the average effective retirement age is lower than the official retirement age. Different policies regarding early retirement across countries would lead to different results but data on the effective 53
**Table 4. Regressing income and consumption adequacy on isolation and household characteristics**

||||**Subjective a**|**dequacy of:**|||
|---|---|---|---|---|---|---|
|**Isolation**|**Food**|**Clothing**|**Housing**|**Schooling **|**Health care**|**Income**|
|Travel time to nearest market (log)|-0.188|-0.124|-0.153|-0.205|-0.254|-0.048|
||(5.87)**|(3.93)**|(4.89)**|(5.95)**|(7.93)**|(1.49)|
|Urban population within 2 hrs travel time|0.853|0.636|1.372|0.867|1.206|0.832|
||(4.20)**|(3.20)**|(6.76)**|(3.82)**|(5.79)**|(4.20)**|
|Population Density (per sqkm)|0.0006|0.0004|-0.0004|0.0002|0.0002|0.0000|
||(5.53)**|(3.39)**|(4.04)**|(1.51)|(1.72)|(0.27)|
|**Household consumption and controls**|||||||
|Consumption expenditures (log)|0.591|0.517|0.233|0.339|0.164|0.352|
||(9.96)**|(8.83)**|(4.06)**|(5.25)**|(2.80)**|(5.89)**|
|Value of assets (log)|0.161|0.127|0.105|0.109|0.098|0.121|
||(10.55)**|(8.64)**|(7.37)**|(6.36)**|(6.70)**|(7.64)**|
|Household size (log)|-0.397|-0.345|-0.090|-0.336|-0.109|-0.198|
||(4.93)**|(4.35)**|(1.16)|(3.71)**|(1.35)|(2.48)*|
|Share of adult females|0.101|0.186|0.030|0.270|0.110|0.032|
||(0.40)|(0.76)|(0.12)|(0.86)|(0.44)|(0.13)|
|Share of children 6 and under|-0.308|0.086|-0.515|0.079|-0.379|-0.360|
||(1.13)|(0.32)|(1.96)|(0.25)|(1.39)|(1.33)|
|Share of youths aged 7 to 20|-0.257|-0.259|-0.538|0.107|-0.268|-0.592|
||(1.17)|(1.20)|(2.57)*|(0.42)|(1.22)|(2.77)**|
|Share of elderly 65 and above|-0.362|0.389|0.079|0.307|-0.188|0.144|
||(1.26)|(1.37)|(0.28)|(0.81)|(0.65)|(0.51)|
|Age of household head|-0.017|-0.002|-0.015|-0.037|-0.017|0.004|
||(1.55)|(0.22)|(1.33)|(2.97)**|(1.55)|(0.38)|
|Age of household head squared|0.000|0.000|0.000|0.000|0.000|0.000|
||(1.59)|(0.21)|(1.30)|(2.81)**|(1.36)|(0.66)|
|Female head dummy|-0.053|0.003|0.037|-0.088|0.007|-0.068|
||(0.57)|(0.04)|(0.40)|(0.87)|(0.07)|(0.70)|
|**Prices**|||||||
|House rental premium in district (predicted)|0.058|0.079|0.183|0.078|0.158|0.099|
||(1.91)|(2.59)**|(5.86)**|(2.35)*|(5.19)**|(3.03)**|
|Median wage rate in ward (log)|-0.248|0.007|-0.065|-0.064|0.039|0.077|
||(4.90)**|(0.15)|(1.31)|(1.17)|(0.77)|(1.49)|
|Median rice price in ward (log)|-0.281|-0.027|0.114|-0.101|-0.013|-0.167|
||(2.66)**|(0.26)|(1.10)|(0.84)|(0.12)|(1.57)|
|**Regional and belt dummies**|||included but|not shown|||
|Intercept|-5.798|-6.773|-4.172|-3.709|-3.453|-5.427|
||(9.16)**|(10.74)**|(6.78)**|(5.25)**|(5.55)**|(8.48)**|
|Number of observations|3050|3048|3045|2451|3030|3041|
|**Normalized coefficient of travel time to neares**|**t market**||||||
|α2/α1|0.319|0.240|0.66|0.61|1.55|0.137|
||(4.74)**|(3.38)**|(2.88)**|(3.64)**|(2.50)*|(1.39)|

Estimator is ordered probit. Absolute value of t statistics in parentheses: * significant at 5%; ** significant at 1%
(NAS).<sup>2</sup> (Given certain assumptions, one can derive standard poverty measures from the mean and a suitable inequality measure.) We use NAS data in some aspects of our estimation methods, notably in dealing with the fact that different countries do their surveys at different dates, and we want to line them up in time to a common reference date. However, we do not let the NAS data override the survey mean when both are known. In other words, we use the survey at the survey date. In this respect we follow the standard, though not universal, practice in the literature on poverty measurement.

Advocates of replacing the survey mean by the NAS estimate of national income or consumption per capita argue that household surveys underestimate mean income or consumption, due to deliberate under-reporting and selective compliance with random samples. However, it is not clear that the NAS data can provide a more accurate measure of mean <u>household</u> welfare than the survey data that were collected for that purpose. And, even acknowledging the problems of income underreporting and selective survey compliance, there can be no presumption that the discrepancies between survey means and the NAS aggregates (such as private consumption per person) are distribution neutral; more plausibly the main reasons why surveys underestimate consumption or income would also lead them to underestimate inequality.<sup>3</sup> Furthermore, the NAS-means method is clearly unacceptable when doing an urban-rural split of global poverty measures, allowing for cost-of-living (COL) differences, since neither the inequality measures nor the NAS means would then be valid.

Also following past practice, “poverty” is assessed here using household per capita expenditure on consumption.<sup>4</sup> The measures of consumption (or income, when consumption is 2 Examples include Bhalla (2002) and Sala-i-Martin (2006). 3 For example, Banerjee and Piketty (2005) attribute up to 40 percent of the difference between the (higher) growth of GDP per capita and (lower) growth of mean household per capita consumption from household surveys in India to unreported increase in the incomes of the rich. Selective compliance with random samples could well be an equally important source of bias, although the sign is theoretically ambiguous; Korinek et al. (2006) provide evidence on the impact of selective non-response for the US. 4 The use of a “per capita” normalization is standard in the literature on developing countries. This stems from the general presumption that there is rather little scope for economies of size in consumption for poor people. However, that assumption can be questioned; see Lanjouw and Ravallion (1995).

3
programming in which investors were provided with a standardized scoring system and questions when assessing each other. Overall, this suggests that VilCap is a conservative setting to test whether systematizing inquiry can reduce gender disparities in evaluation.

Investors in the control group evaluated startups represented by all male founders lower over the course of the program, from a baseline zscore of 0.12, which was reduced to 0.07. As demonstrated in Figure 3, investors very slightly increased their evaluation of startups represented by females on average from -0.09 to -0.07 on the zscore. This suggests that the Vilcap program itself acted to reduce gender disparities, largely by reducing zscores for startups with all male teams. Even so, startups with female founders received directionally lower zscores than startups with all male teams at the end of the program. By contrast, in the treatment group, the baseline score was already closer for startups represented by male founders (0.04) and female founders (0.01) because investors had been treated at baseline (prompted to ask about risk and reward). Even given this baseline difference, there were additional effects of the systematizing inquiry bundled treatment. Treated investors evaluated startups represented by all males lower over the course of the program (from 0.04 to -0.07), and evaluated startups represented by female founders higher over the course of the program (from 0.01 to 0.11). This effect was driven by changes in how startups with female founders were evaluated. Effects in the treatment group were larger than those observed in the control group, and were driven by increases in scores to startups with female founders.

As shown in Table 5, regression analysis showed similar results. Treated investors using Vilcap’s systematizing inquiry evaluation template scored startups represented by females lower than startups represented by males at the beginning of the program but not significantly so. Investors treated systematizing inquiry scored startups with all male teams lower than control group investors, 26
money supply. These variables also return close to their baseline values as the effects of the shock fade away.

> We now turn to poverty and income distribution indices based on our fictitious household survey data. The impact and short-run effects of the termsof-trade shock generally result in reductions in consumption-based poverty headcount indices. There is a decrease in the poverty headcount index of the agricultural nontraded sector, but the largest decrease occurs in the agricultural traded sector. The decrease in the latter index is expected, given the 10 percent increase in the world price of the exported agricultural good and the existence of fixed real wages in that sector. The decrease in poverty in the rural nontraded sector is mainly due to higher wages, because increasing nontraded agricultural goods demand leads to increasing labor demand in that sector. Because there are only smaller changes in consumption poverty headcount indices for the remaining sectors, there is a negative net effect on the economy-wide consumption poverty index in the short run. In the long run, however, there is only a slight decrease in the poverty index for the

> agricultural traded and nontraded sectors (given the size and temporary nature of the shock), which leads to a slight decrease in the consumption-based

> poverty headcount indices for the rural sector and the economy as a whole.

> Poverty headcount indices based on disposable income depict similar results: there is a relatively strong decline in the economy-wide poverty index in the short run, and a small decrease in the long run.

Consumption-based poverty gap indices indicate that poverty decreases in the agricultural traded and nontraded sectors and in the urban informal sectors in the short run. Given these decreases, the economy-wide poverty gap index also decreases despite an increase in poverty in the formal unskilled labor group. In the long run, there is also a decrease in the poverty gap index, although its magnitude is much smaller. A similar picture emerges when income-based poverty gap indices are used instead: the economy experiences a decrease in poverty in the short run, which reverses itself in the long run.

> In summary, a temporary terms-of-trade shock has relatively unambigu-

> ous effects on poverty. Both headcount and poverty gap indices indicate that declines in poverty rates in the short run are reversed in the longer run.

With regard to income distribution indicators, the Gini coefficients generally indicate short and long run declines in income inequality, whereas the Theil indices shows short- and long-run increases in income inequality. Longrun effects are generally much smaller than short-run effects. Again, this is not surprising, given the temporary nature of the shock.

65
TABLE A2: 2SLS ESTIMATES OF FOOD AID ON CONFLICT BY FARM BILL ERA

|VARIABLES<br>|(1)<br>Any<br>Conflict|(2)<br>Any<br>Conflict|(3)<br>Any<br>Conflict|(4)<br>Any<br>Conflict|(5)<br>Any<br>Conflict|
|---|---|---|---|---|---|
|_Panel A: Pre-1985_||||||
|U.S. wheat aid (tonnes)||||||
|- from FAO|0.00218|0.00331|0.00327|0.00397|0.00357|
||(0.00182)|(0.00294)|(0.00291)|(0.00322)|(0.00286)|
|Observations|1,460|1,460|1,460|1,460|1,460|
|R-squared|0.43882|0.20773|0.25901|0.12822|0.24196|
|KP F-Stat|2.050|2.078|1.813|2.140|2.138|
|_Panel B: 1985 to 1996_||||||
|U.S. wheat aid|0.00097|-0.00140|-0.00162|-0.00415|-0.00386|
|(tonnes) - from FAO|(0.00744)|(0.00241)|(0.00206)|(0.00465)|(0.00408)|
|Observations|1,384|1,384|1,384|1,384|1,384|
|R-squared|0.60715|0.63300|0.63867|0.34016|0.41052|
|KP F-Stat|0.106|0.968|0.901|0.700|0.790|
|_Panel C: Post-1996_||||||
|U.S. wheat aid|0.00457|0.01265|0.00567|0.00368|0.00281|
|(tonnes) - from FAO|(0.00301)|(0.02416)|(0.00600)|(0.00322)|(0.00258)|
|Observations|1,245|1,245|1,245|1,245|1,245|
|R-squared|0.56814|-0.43313|0.52204|0.65228|0.69256|
|KP F-Stat|3.219|0.256|1.141|2.384|2.527|
|Controls (for all panels):||||||
|Country FE<br>|Yes|Yes|Yes|Yes|Yes|
|Region-year FE<br>|Yes|Yes|Yes|Yes|Yes|
|US real per capita GDP x avg.<br>prob. of any US food aid|No|Yes|Yes|Yes|Yes|
|US Democratic president x.<br>avg. prob. of any US food aid|No|Yes|Yes|Yes|Yes|
|Oil price x avg. prob. of any US<br>food aid|No|Yes|Yes|Yes|Yes|
|Monthly recipient temperature and<br>precipitation|No|No|Yes|Yes|Yes|
|Monthly weather x avg.<br>prob. Of any US food aid|No|No|Yes|Yes|Yes|
|<br>Avg. US military aid x year FE|No|No|No|Yes|Yes|
|Avg. US economic aid x year FE|No|No|No|Yes|Yes|
|Avg. recipient cereal imports x<br>year FE|No|No|No|No|Yes|
|Avg. recipient cereal||||||
|production x year FE|No|No|No|No|Yes|

Notes: An observation is a country and a year. The sample includes 125 non-OECD countries for the years 1971-1984 (panel A), 1985 to 1996 (Panel B), and 1997 to 2006 (Panel C). Coefficients are reported with standard errors clustered at the country level in parentheses. In these shorter panels, collinearities arising when adding country characteristics cause fixed effects for several countries to be dropped, leading to the change in r-squared from column 4 to 5. Data are from the NQ replication file.

39
<u>Table 9. Openness, Technological Innovation and the Demand for Skills: Robustness Alternative Samples</u>

||<br>Cambodia|<br>China|<br>Indonesia|<br>Malaysia|Philippines|South Korea|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Exporter|-0.0664*|0.121***|-0.0992**|-0.0814**|-0.064|-0.0698*|-0.151***|-0.102**|
||[0.0388]|[0.0417]|[0.0397]|[0.0394]|[0.0395]|[0.0394]|[0.0482]|[0.0464]|
|Foreign Ownership|0.213***|0.139***|0.236***|0.213***|0.229***|0.207***|0.258***|0.0965**|
||[0.0415]|[0.0450]|[0.0426]|[0.0434]|[0.0423]|[0.0418]|[0.0513]|[0.0466]|
|Technological Innnovation|0.266***|0.167***|0.270***|0.277***|0.243***|0.268***|0.308***|0.285***|
||[0.0342]|[0.0387]|[0.0351]|[0.0353]|[0.0349]|[0.0345]|[0.0402]|[0.0402]|
|Baseline Firm Characteristics?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry-Size Fixed Effects?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|5,949|4,114|5,515|5,657|5,485|5,791|4,681|4,640|
|Rsquared|0.28|0.31|0.26|0.28|0.29|0.27|0.27|0.28|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Dependent variable is the logarithm of the share of workforce with more than the secondary education. Table reports the results for the baseline specification (column 5 in table 5) but restricting the sample to exclude one country at the time (excluded country reported at the top of each column). All the variables are defined in table A1 in the appendix.
52 government sought to promote cotton productlon on the collective ejidos, In La Laguna ejldatarlos enjoyed akmost as much access to credit as private farmers (Eckstein et al,1978:61). Many of these studles were conducted under the auspices of the Centro de Investigaciones Agrarlas, a government dependency with an kuplicit brief to defend the land reform (Reyes Osorlo et al,1974; Ecksteln,1 966).

115. A survey conducted In 1967 (Eckstein et al,1978:60-84) sought to distinguish the effects of different forms of land tenure and farm scale on various measures of productivity, using a sample of 208, consisting of small and large private farmers, Individual ejido farmers and collective ejido farmers. The collective ejidos were divided Into three groups according to the Ejldo Bank's evaluation of their creditworthiness: "good" (15 percent of all collective ejidos), "regular" (70 percent) and "bad" (15 percent). Cotton yields per hectare were found to be highest on large private farms, followed In descending order by "good" ejidos, small private farms, Individual ejidos, "regular" ejidos and "bad" ejldos (Table 19).

116. The measurements of gross productivity (value of output per unit of input expenditure) present a complex picture. The "good" ejidos rank the highest In terms of land and total factor productivity; the large private farms rank highest In terms of labour productWity; and the Individual ejidos occupy first place In terms of capital productivity. The least creditworthy collective ejidos (the groups "regular" and "bad") have the poorest overall record. It Is hard to draw any fIrm conclusion from these data because they do not control very carefully for variations In farm scale; this Is a significant ommission because In Mexico there Is some evidence that delivery of subsidized capital Inputs has been blassed In favour of large farms (Cornia,1985).
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- World Bank. 1984. World Development Report. oxford University Press, New York.

- World Bank. 1986. Pooulation Growth and Policies in Sub-Saharan Africa. A World Bank Policy Study, Washington, D.C.

- World Bank. 1987. "Bangladesh: Promoting Higher Growth and Human Development." Report No. 6616, Washington, D.C.

- World Bank. 1988. "World Development Indicators." World Develo0ment Reoort 128. Oxford University Press, New York.

- World Bank OED. 1987. "The Jengke Triangle Projects in Malaysia." An Operations Evaluation Study. Washington, D.C.

- World Bank OED. 1988. "Cotton Development Programs in Burkina Faso, Cote d'Ivoire and Togo." An Operators Evaluation Study. Washington, D.C.

- World Bank. 1989a. "Kenya: The Role of Women in Economic Development." Washington, D.C.

- World Bank. 1989b. "India: Poverty and Gender." Yellow Cover Draft, Washington, D.C. (Restricted).

- World Bank. 1989c. "Pakistan: The Role of Women in Economic Development." White Cover Draft. Washington D.C. (Restricted).

- Youssef, N.H. and C.B. Hetler. 1983. "Establishing the Economic Condition of Women-Headed Households in the Third World: A New Approach." In Buvinic and others, ads. Women and Poverty in the Thirld World. Baltimore: Johns Hopkins University Press.