rafmacalaba/datause-encoder-data huggingface dataset:
expenditures for dirty fuels. Poor households in these countries may be more likely to gather firewood themselves than to purchase fuel, which could explain the results.

**Poor households are provided with lower quality infrastructure and energy services.** Among households with connections, there is also evidence in the region that the income poor are somewhat more likely to be affected by the low quality of services, although the differences are slight.  Aggregate data for the ECA region on the reliability of water supply systems and electricity supply for the lowest and highest income quintiles are presented in Figure 4.20.  In both the capital cities and in secondary cities, higher income households have somewhat more reliable water services than do lower income households; however, virtually no difference exists for electricity services.

**Figure 4.20. Reliability of water and electricity by quintiles in capital cities and other urban areas** (% of households)

<!-- Start of picture text --> Q1 Q5<br>80<br>60<br>40<br>20<br>0<br>Capital Other Urban Capital Other Urban<br>Potable w ater 24 hours per day Electricity 24 hours per day<br>%<br><!-- End of picture text --> Source: see Table 2.1 Note: Capital city data are based on 6 ECA countries; other urban data are based on 8.

Looking at a more disaggregated level, there is also evidence of disparities across countries in the extent to which poor household are disproportionately affected by lower service reliability (Figures 4.21 and 4.22). For instance, in capital cities, the gap between the poorest and the richest was greatest in Albania and Bosnia, but there was little difference between the poor and rich in Armenia, Bulgaria and Kazakhstan. In Georgia, higher income residents in the capital were actually a bit worse off, which likely reflects the overall lack of progress on water reform in Tbilisi and the city’s aging water system.

The differences between the highest and lowest income quintiles in secondary cities show a stronger pattern with high income quintiles benefiting from more reliable services in all countries except Albania and Armenia where reliability is about the same.

38
The literature clearly demonstrate _(cf_ Beny and Cline, 1979; Binswanger and Rosenzweig, 1986; Binswanger and Kinsey, 1993; Binswanger and Elgin, 1992; Binswanger _et al,_ 1993) that family farms are generally more efficient and superior to other types of farming because of the way in which labor relations are organized. Family farms are by definition farms where the owner is the operator and where his/her family provides the large bulk of the regular labor requirements throughou **t** he year. While the definition of family farms does not exclude the hiring of other people, especially in a part-time capacity when related to seasonal labor, it tends not to rely too much on such behavior. In addition, in countries where capital is relatively scarce and expensive, the relationship between labor and capital should reflect this. Overemphasizing modernization, restructuring, mechanization and other similar concepts implying the use of more capital to labor than that dictated by economic realities should be discouraged. This all implies farm sizes on the smaller side of the spectrum rather than larger sizes for family farms.

# **Structure Of** South **African Agriculture: Issues Related To Size**

## _Farm Sizes in South Africa_

Farm sizes in South Africa began to increase in the 1950s and continued to increase until the 1980s. After steadily increasing until 1971, black farm employment began to decline. Consequently, it can be argued that scale efficiencies appeared after 1950, and in particular after 1970, and were a main factor behind the steady decline of employment in agriculture (Van Zyl _et al,_ 1987). Agriculture was the only major economic sector that experienced an absolute decline in employment between 1951 and 1985 -- despite the fact that wages were rising at a slower rate in agriculture than in other sectors. This history suggests that in South Africa, a number of interventions in the markets for land, labor and capital produced a structure of incentives which induced scale efficiencies, in particular since the 1970s.

From the beginning of the century until the 1950s, the number of farms and the total area cultivated increased, but the average farm size declined. After 1950 this trend is reversed; and farm size grew consistently, accelerating in the 1970s before leveling off in the late 1980s. Because the cultivated area remained the same, the number of farms declined -- from 116 848 units in 1950 to 62 084 units in 1990 (RSA, 1994). The pattern seems to continue until the late 1980s, although there is some evidence of an increasing differentiation in farm sizes below the 100-hectare minimum which (in some areas) defines a farm in official statistics (World Bank, 1994).

8
**Table 16: Household Expenditure on Energy Products (Million LYD)**

||**Gasoline**|**Diesel**|**Electricity**|**LPG**|**Kerosene**|**Total**|
|---|---|---|---|---|---|---|
|Quintile_1|28.9|0.7|29.9|5.7|0.3|65.4|
|Quintile_2|34.9|0.7|34.0|5.9|0.5|76.0|
|Quintile_3|36.1|0.6|34.2|5.7|0.5|77.1|
|Quintile_4|36.2|0.6|33.1|5.5|0.6|76.0|
|Quintile_5|33.6|0.7|35.8|5.2|0.6|75.9|
|Total|169.6|3.3|167.1|28.0|2.5|370.5|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

Compared to other countries in the North Africa region, the share of expenditure on energy products in Libya is more homogeneous across quintiles. This corroborates the result we found in analyzing food subsidies, namely that the income distribution in Libya is comparatively more flat, with lower inequality, compared to other countries in the region. Particularly striking is the distribution of gasoline and diesel expenditure. The poorest quintile of households spends on gasoline 85% of what the richest quintile spends and twice as much for diesel. Indeed, data on car ownership from the household survey confirm that most households in Libya own at least one car and that the share of non-owners, 25.8 percent (Table 18, first column), is rather homogeneously distributed across quintiles. This finding, which is atypical for countries at similar levels of per-capita income, is likely explained by the very low cost of gasoline and the availability of cheap old cars.<sup>20</sup>

**Table 17: Share of Energy Expenditure in Total Household Expenditure (%)**

||**Gasoline**|**Diesel**|**Electricity**|**LPG**|**Kerosene**|**Total**|
|---|---|---|---|---|---|---|
|Quintile_1|1.57|0.04|1.62|0.31|0.01|3.55|
|Quintile_2|1.53|0.03|1.49|0.26|0.02|3.32|
|Quintile_3|1.40|0.02|1.33|0.22|0.02|2.99|
|Quintile_4|1.32|0.02|1.21|0.20|0.02|2.77|
|Quintile_5|1.09|0.02|1.16|0.17|0.02|2.47|
|Total|1.35|0.03|1.33|0.22|0.02|2.96|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

**Table 18: Percentage of Households that Own Cars (by quintile and number of cars)**

||0|1|2|3|4|5|Total|
|---|---|---|---|---|---|---|---|
|Quintile_1|6.25|12.17|1.32|0.17|0.08|0|20|
|Quintile_2|4.64|13.54|1.51|0.29|0.03|0|20|
|Quintile_3|4.87|13.62|1.26|0.21|0.04|0|20|
|Quintile_4|4.69|13.86|1.16|0.24|0.04|0.01|20|

20 Anecdotal evidence suggests that the stock of cars in Libya is quite old, many low-income people drive run-down cars and keep doing so given cheap gasoline and no alternative transportation means.

21
affect the time women spend in home-based work any more than it does for those working outside of the household (the exception is pre-school children in Brazil).

# **VI. Conclusions**

This paper has shown that many of the findings on home-based work from qualitative studies can be supported by large, randomly sampled quantitative surveys. Women are more likely to be home-based workers. This is due to their roles as mothers, wives, and women. Wives select themselves into this type of work in order to better balance home and market work, but sacrifice higher wages in exchange; children seem to have little impact on hourly wages or hours worked for women. Women with low levels of education, who are likely to subscribe to more traditional gender roles and have fewer exit options, also tend to enter this type of work, perhaps in order to preserve their own and their husband’s gender identities. Women in home-based work also are more likely to be part-time workers, allowing them to hold multiple jobs, a common phenomenon among poor women (Geldstein 2001, Chant 1993, Benería and Roldón 1987), and perform household duties. Men with low levels of education, spouses, or children, on the other hand, are not more likely to be home-based workers, have less of a wage penalty, and have work shifts that are more similar to men who do not work out of their homes.

The paper has also shown that home-based workers are a sub-set of the labor force for whom labor market opportunities are scarce or expensive. Women with families, uneducated women, and the elderly do not have the time flexibility (women) or the skills and mobility (older workers) to work outside the home. Instead, home-based work offers perhaps the best form of market work in which they may engage given their constraints (Beneria and Roldon 1987).

23
||**Table 10:**|**Trade Openn**|**ess and Indust**<br>**(s**|**ry Labor Dem**<br>**tandarderror**|**and for White**<br>**s in parenthes**|**Collar Worke**<br>**is)**|**rs in Colomb**|**ia (1980-91**|**)**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Ln L t-I|Ln WBt|Ln WBt-1|Ln WWt|Ln WWt- 1|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test<br>|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|Trade Policy<br>Specification(b)|0.340(*)<br>(0.066)|-0.096<br>(0.090)|0.086<br>(0.071)|-0.484(*)<br>(0.098)|0.136(***)<br>(0.080)|_0.035(**)_<br>(0.015)|0.007<br>(0.010)|--|0.340|0.892|22992<br>(1916)|
|Variable* Tariff Rate|0.001|-0.001|0.0004|-0.002|0.001||0.006|0.892||||
||(0.003)|(0.006)|(0.005)|(0.005)|(0.004)||(0.009)|||||
|Variable* Average Tariff|-0.002|0.001|0.0005|0.004|-0.002|||0.632||||
||(0.002)|(0.006)|_(0.005)_|_(0.005)_|(0.004)|||||||
|Variable* Non-Tariff-|0.0002|0.001|-0.0003|-0.0003|-0.0001|||0.778||||
|Barriers|(0.0002)|(0.001)|(0.001)|(0.001)|(0.001)|||||||
|Variable* Real|-|0.0004|-0.001|0.001|0.00003|||0.164||||
|Exchange Rate|_0.0005(***_<br>|(0.001)|(0.0005)|(0.001)|(0.001)|||||||
||)<br>(0.0003)|||||||||||
|Wald Test: p-value(c)|0.072|0.918|0.519|0.204|0.705|||||||
|Trade Flows|0.296(*)|-0.111|0.264(**)|-0.361(**)|0.058|0.068(*)|0.017|--|0.130|0.983|22992|
|Specification(d)|(0.074)|(0.161)|(0.131)|(0.147)|(0.131)|(0.018)|(0.012)||||(1916)|
|Variable* Import Ratio|0.0003<br>(0.001)|0.003<br>(0.007)|-0.018(*)<br>(0.006)|0.001<br>(0.006)|0.012(**)<br>(0.006)||0.000<br>(0.006)|0.068||||
|Variabie* Export Ratio|-0.003|-0.029(**)|-0.012|0.036(*)|0.004||-0.005|0.096||||
||(0.002)|(0.013)|(0.020)|(0.012)|(0.017)||(0.015)|||||
|Variable* Real|0.0001|0.002|0.001|-0.002|-0.001|||0.375||||
|Exchange Rate|(0.0004)|(0.002)|(0.001)|(0.002)|(0.002)|||||||
|Wald Test: p-value(c)|0.496|0.097|0.028|0.005|0.056||0.940|||||
|<br>Price-Cost-Margin<br>Specification(e)|0.200(*)<br>(0.067)|0.087<br>(0.068)|0.084<br>(0.075)|-0.323(*)<br>(0.086)|0.190(**)<br>(0.084)|0.038(*)<br>(0.013)|0.012<br>(0.010)|--|0.875|0.936|22968<br>(1914)|
|Variable* Price Cost|0.003(**)|-0.004(***)|<br>-0.002|0.0007|-0.004||0.022(*)|0.004||||
|Margin|(0.001)|(0.002)|(0.002)|(0.003)|(0.003)||(0.007)|||||

Notes: GMM estimates with first-differenced data. Time dummies were included but are here omitted. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and further lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Wald Test of Joint Significance of the variables interacted with a given openness variable. (b) Additional Instruments: second and further lags of the tariff rate. (c) Wald Test of Joint Significance of openness variables interacted with a given variable. (d) Additional Instruments: second lags of import and export ratios. (e) Additional Instruments: second and further lags of price-cost-margins.
distance from the attacks. The difference between exposed and control households is the timing of their interview in relation to when an attack occurred. However, fieldwork of Wave 2 is likely to have followed a geographical pattern or strategy when conducting interviews, due to logistical considerations. Thus, exposed and control groups and unlikely to be random but determined by the data collection schedule, which implies that households interviewed before and after the incidents are likely to be different. Yet, we find that they do not differ much in terms of observable characteristics. Exposed and control groups are relatively comparable or balanced on key observable dimensions (Table 14 in the Appendix).

Table 6: OLS estimates for the effect of terrorist attacks against civilians considering Wave 2 households from Mogadishu interviewed before and after the incidents

|Log of core|consumption (<br>|per capita defla<br>|ted)<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Exposed/control|-0.215*<br>(0.107)|-0.458***<br>(0.117)|-0.458***<br>(0.142)|-0.458***<br>(0.124)|
|District fixed effects|Yes|Yes|Yes|Yes|
|Characteristics of household & head|Yes|Yes|Yes|Yes|
|Dwelling characteristics|Yes|Yes|Yes|Yes|
|Standard errors|Sampling<br>weights|Clustered<br>by district|Clustered<br>by PSU|HAC|
|Observations|180|180|180|180|

Source: Authors’ calculations based on the SHFS and ACLED. Note: Estimated coefficients from an OLS regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, gender and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Drought affected status and humanitarian assistance are not included due to the lack of variation in the data within Mogadishu. Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

We estimate a linear regression model using ordinary least squares (OLS). The coefficient of interest corresponds to the exposed or control dummy variable capturing the effect of terrorist incidents. Our specification includes district fixed effects to account for time-invariant unobservable factors. Also, there is a small risk that time-varying unobservables could affect the estimates as all interviews were conducted within a 6-week period. This alternative strategy produces similar results for Mogadishu, which are also robust to the inclusion of clustered standard errors and HAC standard errors (Table 6). The OLS estimate suggests a decline in core consumption of 22% attributable to attacks that occurred during data collection of Wave 2. Our main DiD estimate from the preferred specification (-33%) lies within the 95% confidence interval of this OLS point estimate.

22
# **Abstract**

This paper addresses three areas of the rural labor market: employment, labor wages and agriculture producer incomes. Findings show that the poor allocate a lower share of their labor to farm sectors than the nonpoor do, but still around 70 percent work in agriculture, and the vast majority of rural workers are engaged in the informal sector. When examining nonfarm employment in rural Argentina, findings suggest that key determinants of access to employment and productivity in nonfarm activities are education, skills, land access, location and gender. Employment analyses show that women have higher probability than men to participate in rural nonfarm activities and they are not confined to low-return employment. Moreover, workers living in poorer regions with land access are less likely to be employed in the nonfarm sector. There is strong evidence that educated people have better prospects in both the farm and nonfarm sectors and education is a particularly important determinant of employment in the better-paid nonfarm activities. Labor wage analyses reveal that labor markets pay lower returns to poorer than to richer women and returns to education are increasing with increased level of completed education and income level. Moreover, nonfarm income and employment are highly correlated with gender, skills, household size, and education. This analysis also shows a rather heterogeneous impact pattern of individual characteristics across the income distribution, but education is important for all levels of income. Agricultural producer income analyses reveal that producers’ income is monotonically increasing with land size and with completed education level, and positively correlated with road access and use of electricity, fertilizer, and irrigation. Finally, farms operated by women are slightly more productive than farms operated by men.

2
# **The Great Proletarian Cultural Revolution, Disruptions to Education, and Returns to Schooling in Urban China**

# **1.  Introduction**

One consequence of the ideological furor of China’s Great Proletarian Cultural Revolution, initiated by Chairman Mao Zedong in 1966 and lasting until after his death in 1976, was the widespread disruption of formal education in urban areas. The extent of these disruptions differed across cohorts, across time, and across cities, depending on how zealously new policies were interpreted and implemented locally.<sup>1</sup> As a result, for no other reason than the unlucky location and timing of their birth, the schooling of many Chinese citizens was delayed or cut short, with long-term consequences for occupational attainment and earnings.

In this paper, we estimate the economic returns to schooling in urban China by relying on an identification strategy that exploits a particular feature of how exogenous supply shocks to education caused by the Cultural Revolution affected individual educational attainment. Analyzing data from a survey of workers conducted in five large Chinese cities in 2001, we first show that the Cultural Revolution was a great equalizer of educational access. Prior to and after the Cultural Revolution, children’s educational attainment in urban China was highly correlated with the educational attainment of their parents--a nearly universal finding throughout the world. However, among city-cohorts affected during the Cultural Revolution years, children’s educational attainment became much _less_ correlated with that of their parents (see also Deng and Treiman, 1997) and _more_ correlated with whether parents held administrative positions.

> 1See Pepper (1996) for case studies that describe differences across localities.

2
- <mark>Letta, Marco, Pierluigi Montalbano, and Richard SJ Tol. (2018). "Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Tanzania."</mark> _<mark>World Development</mark>_ <mark>, 112: 1332.</mark>

- Mathiassen, Astrid. (2009). “A Model Based Approach for Predicting Annual Poverty Rates without Expenditure Data”. _Journal of Economic Inequality_ , 7:117–135.

- Mathiassen, Astrid, and Bjørn K. Getz Wold. (2021). "Predicting poverty trends by survey-tosurvey imputation: the challenge of comparability." _Oxford Economic Papers_ 73, no. 3: 1153-1174.

- <mark>Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(2), 87-106.</mark>

- <mark>Naudé, W. and  Vinuesa, R. (2020). Data, global development, and COVID-19.</mark> _<mark>W</mark> IDER Working Paper 2020/109_ .

- Ravallion, Martin. (2016). _The economics of poverty: History, measurement, and policy_ , New York: Oxford University Press.

- Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew Dabalen. (2015). "Data deprivation: another deprivation to end." _World Bank Policy Research Paper no. 7252_ , World Bank, Washington, DC.

- <mark>Sheahan, Megan, and Christopher B. Barrett. (2017). "Ten striking facts about agricultural input use in Sub-Saharan Africa."</mark> _<mark>Food Policy</mark>_ <mark>, 67: 12-25.</mark>

- <mark>Snijders, Tom AB, and Roel J. Bosker. (1994). "Modeled variance in two-level models."</mark> _<mark>Sociological Methods & Research</mark>_ <mark>, 22(3): 342-363.</mark>

- Stifel, D. and Christiaensen, L. (2007) “Tracking Poverty over Time in the Absence of Comparable Consumption Data”. _World Bank Economic Review_ , 21, 317-341.

- Tanzania’s National Bureau of Statistics. (2011). _Basic Information Document—National Panel Survey 2010-11_ .

- Tarozzi, Alessandro. (2007). “Calculating Comparable Statistics from Incomparable Surveys, With an Application to Poverty in India”. _Journal of Business and Economic Statistics_ 25, no. 3:314-336.

- UNESCO-UIS/OECD/EUROSTAT. (UOE). (2020). _Data collection on formal education— Manual on concepts, definitions and classifications_ . Montreal/ Paris/ Luxembourg.

- United States Census Bureau. (2017). Current Population Survey, Imputation of Unreported Data -

- Items.  Accessed on the Internet on May 24, 2021 at <u>https://www.census.gov/programs surveys/cps/technical-documentation/methodology/imputation-of-unreported-data-items.html</u>

34
|**Gaining all type of work experiences**|4972|0.0440|0.2052|0<br>1|2322|0.0349|0.1835|0<br>1|2650|0.0521|0.2222|0<br>1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Understanding the meaning of life**|4972|0.0603|0.2381|0<br>1|2322|0.0633|0.2436|0<br>1|2650|0.0577|0.2333|0<br>1|
|**Gain self-esteem**|4972|0.1185|0.3232|0<br>1|2322|0.1016|0.3022|0<br>1|2650|0.1332|0.3399|0<br>1|
|**Living in rural areas**|4972|0.4055|0.4910|0<br>1|2322|0.3876|0.4873|0<br>1|2650|0.4211|0.4938|0<br>1|
|**Living under the poverty line**|4972|0.1961|0.3971|0<br>1|2322|0.2046|0.4035|0<br>1|2650|0.1887|0.3913|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Table A3. Descriptive statistics by** **<u>gender and age group</u>**

||**Young te**|**enagers**|||**Young ad**|**ults**|||**Prime ag**|**ed**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Men||Women||Men||Women||Men||Women||
|**Variable**|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|Obs|Mean|
|**wage**|187|5.924|129|5.868|463|5.963|240|5.850|588|5.992|289|5.886|
|**women**|878|0|830|1|913|0|802|1|859|0|690|1|
|**lhours**|187|3.620|129|3.667|463|3.675|240|3.608|588|3.659|289|3.580|
|**gensec**|878|0.300|830|0.304|913|0.060|802|0.087|859|0.052|690|0.074|
|**secgen**|878|0.517|830|0.499|913|0.670|802|0.638|859|0.558|690|0.530|
|**specsec**|878|0.017|830|0.024|913|0.083|802|0.122|859|0.122|690|0.119|
|**univ**|878|0.007|830|0.016|913|0.136|802|0.100|859|0.204|690|0.204|
|**pwe2**|878|3.860|830|0.020|913|6.543|802|0.792|859|11.029|690|4.378|
|**youngteen**|878|1|830|1|913|0|802|0|859|0|690|0|
|**youngadult**|878|0|830|0|913|1|802|1|859|0|690|0|
|**youngold**|878|0|830|0|913|0|802|0|859|1|690|1|
|**wengaged**|878|0|830|0.039|913|0|802|0.082|859|0|690|0.107|
|**mengaged**|878|0.013|830|0|913|0.058|802|0|859|0.088|690|0|
|**wmarried**|878|0|830|0.030|913|0|802|0.190|859|0|690|0.490|
|**mmarried**|878|0.011|830|0|913|0.081|802|0|859|0.384|690|0|
|**wdivwid**|878|0|830|0.001|913|0|802|0.009|859|0|690|0.042|
|**mdivwid**|878|0|830|0.000|913|0.002|802|0|859|0.012|690|0|
|**mchild**|878|0.014|830|0|913|0.057|802|0|859|0.314|690|0|
|**wchild**|878|0|830|0.020|913|0|802|0.146|859|0|690|0.467|

47