rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 4: Returns to Schooling and Growth, Figure 5: Returns to Schooling and<br>1992-2002 Unemployment, 1992-2002<br>15.0 25<br>10.0<br>20<br>5.0<br>15<br>0.0<br>10<br>-5.0<br>5<br>-10.0<br>-15.0 0<br>Returns (%) Real GDP p/c Growth Rate Returns (%) Unemployment rate (May)<br><!-- End of picture text --> Also, the returns to schooling increased while the rate of unemployment increased (Figure 5); as well as when the unemployment rate was decreasing. The returns to schooling increased even when real wages declined (Figure 6). Further, the returns increased while overall schooling levels increased and schooling inequality decreased (Figure 7). This provides an initial hint that the returns may be driven by increased demand for skilled labor.

<!-- Start of picture text --> Figure 7: Returns to Schooling and Real<br>Wage, 1992-2002<br>14 5<br>12<br>4<br>10<br>3<br>8<br>6<br>2<br>4<br>1<br>2<br>0 0<br>Returns (%) Real wage (hourly pesos)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 8: Returns to Schooling and Schooling<br>Inequality, 1992-2002<br>14 0.50<br>0.45<br>12<br>0.40<br>10 0.35<br>0.30<br>8<br>0.25<br>6<br>0.20<br>4 0.15<br>0.10<br>2<br>0.05<br>0 0.00<br>Returns (%) Coefficient of Variation (schooling)<br><!-- End of picture text --> 16
direct impact on the MWP decision. Causal effects of migration could be identified by a variation in the instrumental variables since the effect of this variation is entirely channeled through the household’s migration decision. We used information from the 2001 Nepal Census Data (Central Bureau of Statistics 2003) to construct two instrumental variables: the proportion of internal and abroad migrants in a ward (village) in 2001. We argued that the women’s decisions to participate in market work in 2004 should not be directly affected by the extent of migration networks in 2001, after controlling for the then current conditions on the local labor market. Studies of the Nepali labor market indicate that female and male labor markets are segmented and the rates of female paid employment are only marginally affected by changes in labor market conditions for males (Acharya 2003, Bhatt and Bhattarai 2006).

The proportion of migrants in a ward in 2001 could be interpreted as a proxy for the ward-level networks that help future migrants to migrate (e.g., Carringon, Detragiache, and Vishwanath 1996; Munshi 2003). When in the host country, Nepali migrants develop extensive social and information networks that link them with relatives and friends in the home country (Yamanaka 2000). Such networks lower the cost of migration for the population of a ward. Indeed, in Nepal, migrants tend to follow their covillagers and migrate to the same destinations (Thieme 2006). They also are likely to fill a similar niche in the labor market. The identification strategies that use migrant networks as instruments for migration decisions were applied by Woodraff and Zenteno (2001) and McKenzie and Rapoport (2005) in studies of migration in Mexico.

# _Explanatory variables_

Our theoretical model guided us on selecting the explanatory variables for the empirical specification. Conceptually, these variables can be grouped into four categories. The first group of variables contains those characteristics of a woman that determine her market productivity: her age (experience) and educational level dummies. The second group includes variables that could affect the home productivity of household members. Among such variables are household demographic composition and the size of the land plot owned by the household. The third group includes variables describing ethnicity, religion and household non-wage income. The household non-wage income is a sum of all 13
García, S., A. Harker, and J. Cuartas. 2019. “Building Dreams: The short-term impacts of a conditional cash transfer program on aspirations for higher education." _International Journal of Educational Development_ 64: 48–57.

Genicot, G., and D. Ray. 2017. “Aspirations and Inequality.” _Econometrica_ 85(2): 489–519.

- ———. 2020. “Aspirations and Economic Behaviors.” _Annual Review of Economics_ 12: 715–746.

- Gershenson, S., C. M. D. Hart, J. Hyman, C. Lindsay, and N. W. Papageorge. 2022. “The Long-Run Impacts of Same-Race Teachers.” _American Economic Journal: Economic Policy_ 14(4): 300–342.

- Gortazar, L., C. Hupkau, and A. Roldán-Monés. 2024. “Online tutoring works: Experimental evidence from a program with vulnerable children”. _Journal of Public Economics_ (232) 105082.

- Goux, D., M. Gurgand, and E. Maurin. 2017. “Adjusting your dreams? The Effect of School and Peers on Dropout Behaviour.” _Economic Journal_ 127(602): 1025–1046.

- Guyon, N., and E. Huillery. 2021. “Biased Aspirations and Social Inequality at School: Evidence from French Teenagers.” _Economic Journal_ 131(634): 745–796.

- Heller, S. B., A. K. Shah, J. Guryan, J. Ludwig, S. Mullainathan, and H. A. Pollack. 2017. “Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago”. _Quarterly Journal of Economics_ 132(1): 1–54.

- Huillery, E., Y. Algan, A. Bouguen, C. Chevallier, and A. Charpentier. 2023. “The Role of Mindset in Education: Evidence from a Large-Scale Field Experiment in Disadvantaged Schools”. _Revise and Resubmit at the Economic Journal_ . March 21 version, available at: <u>https://www.dropbox.com/s/d5qpoe8ww01a0rr/Huillery_Bouguen_etal_2023_EJRRrevis ed.pdf?dl=0</u>

- Islam, A., K. Sungoh, E. Masood, N. Prakash, S. Sabarwal, and D. Saraswat.  2022. “Does GoalSetting Pulls Off or Stops Short? Evidence from an at scale Experiment in Tanzania.” ~ -

- _Manuscript_ . April 6 version, available at: <u>https://users.monash.edu/ asaduli/pub/Goal Setting.pdf</u>

- Janzen, S. A., N. Magnan, S. Sharma, and W. M. Thompson. 2017. “Aspirations Failure and Formation in Rural Nepal.” _Journal of Economic Behavior & Organization_ 139(2017): 1–25.

- Jayachandran, S. 2018. “Think Positive, Climb Out of Poverty? It Just Might Work”. _The New York Times Economic Views Columns_ . July 13. Available at: https://nyti.ms/2mylA04

- Jensen, R. T. 2010. “The (Perceived) Returns to Education and the Demand for Schooling” _Quarterly Journal of Economics_ 125(2): 515–548.

- ———. 2012. “Do Labor Market Opportunities Affect Young Women's Work and Family Decisions? Experimental Evidence from India”. _Quarterly Journal of Economics_ 127(2): 753–792.

- Johnson, H. C., D. Pinzón, K. Trzesniewski, T. Indrakesuma, R. Vakis, E. Perova, N. Muller, S. De Martino, and D. Catalán. 2020. “Can teaching growth mindset and self-management at school

30
|No Job Status<br>(Unemployed and Out<br>of the Labor Force)<br>Formal Wage<br>Earners<br>Informal Wage<br>Earners<br>Self Employed|(1)<br>(2)<br>(3)<br>(4)<br>Log Inspected Firms per firm in city<br>0.001<br>0.01<br>0.002<br>-0.006<br>[0.001]<br>[0.001]***<br>[0.001]*<br>[0.001]***<br>Observations<br>5240<br>5240<br>5240<br>5240<br>Log Inspected Firms per firm in city<br>0.065<br>0.062<br>0.018<br>-0.097<br>[0.028]**<br>[0.021]***<br>[0.017]<br>[0.032]***<br>Observations<br>5,240<br>5,240<br>5,240<br>5,240<br>Panel A: OLS<br>Panel B: IV|Standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Panel A reports the estimated coefficients of the regression of the city variables reported at|the top of each column on the log of the number of inspected firms per firm in the city, controlling for state level dummy variables and several city level characteristics. Panel B reports|the instrumental variable estimates using city distance to the nearest labor office interacted with the number of state level inspectors per firm in the state as an instrument for|enforcement. City level characteristics include distance to the nearest labor office, its square and interactions with state level measures of institutional quality, distance to the state|capital city, its square and interactions with the number of inspectors per firm in the state and interactions with state level measures of institutional quality, city transportation costs, its|square and interactions with the number of inspectors per firm in the state and interactions with state level measures of institutional quality, city altitude, city latitude and city longitude.|It also includes the city level controls for income per capita, population, average schooling and share of urban population in 1991, 1980 and 1970.|
|---|---|---|---|---|---|---|---|---|
# C.1.7 Biofuels Sector

In the model baseline we dene the rst-generation biofuels as a grain-based ethanol and the second generation biofuels as cellulosic biomass-to-liquid diesel obtained through Fischer-Tropsch gasication. The values for biofuels conversion rate and cost for ethanol and biomass-to-liquid diesel are taken from Taheripour and Tyner (2011). Following Winston (2009) we adjust the quantity of rst generation biofuels produced by 0.7 to match the energy content of liquid fossil fuels. The elasticity of substitution of second generation biofuels feedstocks for xed factor technology and their value shares in CES function (B.24) are calculated based on MIT-EPPA model (Paltsev et al. 2005, Tables 12 and 13, p.40 ).

The change in factor-specic technological progress is described by the following equation: The rate of factor-specic technological change, _κφ,_ is highly uncertain and is an important contribution to the uncertainty in projected deployment in second generation biofuels (Creutzig et al. 2012). Following the economic literature on biofuels modeling (Popp et al. 2011, Wise and Calvin 2011) we set the rate to 0.5 percent. In section E.1 we perform a sensitivity analysis with respect to a higher value of the rate of factor-specic technological change.

# C.1.8 Energy Sector

We obtain the initial values for total consumption of liquid fossil fuels and rst generation biofuels from EIA (2010b, p. 24, Table 3). We set the initial consumption of second generation biofuels close to zero. The elasticity of substitution of fossil fuels for rst generation biofuels is based on Hertel, Tyner and Birur (2010) econometric estimates for the US biofuel industry over the 2001-2008 period. The technology of energy production, and the value shares of biofuels and fossil fuels in energy production in 2004 are calibrated as described in Rutherford (2002).

The growth in the energy eciency is described by the following equation: where the parameters _θ_ 0<sup>_e_and</sup><sup>_κe_reecttheinitiallevelandannualgrowth</sup> 19
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.5 Infrastructure endowments by demographic, political, and geographic variables**

||Popul<br>den<br>|ation<br>sity<br>|Urbani<br>|zation<br>|Govern<br>|ance<br>|Con<br>stat<br>|flict<br>us<br>|Coas<br>|tline<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|Sector and measure|Low|High|Low|High|Poor|Good|Yes|No|No|Yes|
|_Transport_|||||||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|11|131|51|21|45|54|12|62|16|65|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|273|2,829|1,144|308|354|1,819|212|1,378|218|1,492|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|79|315|155|112|178|126|135|158|148|154|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,878|4,065|2,585|2,178|1,532|3,631|1,927|2,762|1,174|3,183|
|_Information and communication technolo_|_gy_||||||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|21|56|33|28|10|51|15|36|14|50|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|92|133|82|321|68|128|44|115|49|131|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.2|4.1|2.7|3.8|2.6|3.0|1.0|3.3|1.3|3.5|
|_Energy_|||||||||||
|Electrical generating capacity (MW per<br>1 million people, 2003)|79|49|46|410|47|92|39|79|40|83|
|Access to electricity<br>(% of households with access, 2004)|18|17|16|49|15|21|10|21|9|22|
|_Water and sanitation_|||||||||||
|Water<br>(% of households with access, 2002)|59|72|62|77|63|64|52|67|61|64|
|Sanitation<br>(% of households with access, 2002)|34|39|34|48|35|35|27|38|32|36|

_Sources_ : As for table 2.1.

While the preceding analysis is suggestive, it does not take into account the high correlations that exist among these different variables, and with income. Indeed, correlation coefficients between these different indicators of “difficulty” range in absolute value between 0.3 and 0.6. In order to isolate the effect of individual factors we must perform a multivariate regression analysis that looks at all of the effects simultaneously.

As described in equation (1) above, we perform simple OLS cross-sectional analysis for each category of infrastructure. Many control variables are considered—among them income per capita; demographic measures (population density, urbanization, urban growth rates, and ethnic fractionalization); measures of the quantity and quality of public spending on infrastructure (proxied by governance and infrastructure aid per capita); measures capturing the geographical and cultural heritage of the country (including language group, location, and natural resources); and indicators of the structure of the economy (such as the share of exports, agriculture, and manufacturing in GDP). In addition, an Africa dummy is used to see if any specific disadvantages are associated with the continent as a whole.

12
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41. Fiscal Reforms. Public expenditure and taxation policy is an area where social considerations could receive explicit attention. There is often substantial scope for helping or protecting the poor through shifts in the composition of public spending and revenue. While public expenditure cuts may be unavoidable, across-the-board reductions can be avoided. Available resources can instead be allocated to priority investments in infrastructure and the social sectors; and to finance operation and maintenance expenditures, while increasing the efficiency of existing programs. The incidence of such reallocations on poor groups can be analyzed and taken into account. Improving the efficiency and equity of economic infrastructure that is particularly relevant to the productive activities of the poor, and of investments in human capital development, may not only protect the poor against adverse impacts, but may actually help them to respond to the new incentives generated by the adjustment program. Indonesia provides an example of effective public expenditure shifts in the process of fiscal adjustment to the oil price decline (see Box 5). The SDA Box 6 Changing Public Expenditure priorities: **Indonesia** Indonesia presents a model of what can be done in th- way of appropriate shifts in spending. Over **the** 1982-8S to 1987-88 **period, real** public spending declined by 17 percent. However, recurrent spending grew by 6 percent **and** transfers to **the** provinces by 29 percent, **despite** a near doubling of real Interest payments. This was facilitated by **a major** reduction in development spending and a r **al** decline in **the wage** bill. Within **development spending, there were major** shifts out of import-intensive Industry and mining projects **and** into human resources. **The net** effect was to **raise the** domestic and employment content of public **spending and** ensure **adequate** resources for **the** maintenanco and **selective** expansion of social and economic Infrastructure. Indonesia's starting position was also relatively favorable -- it had a low public debt burden and a relatively compliant civil service.
# **Rising Income Inequality in China: A Race to the Top**<sup>*****</sup>

Xubei Luo The World Bank, East Asia PREM, <u>xluo@worldbank.org,</u> Nong Zhu INRS-UCS, University of Quebec, <u>nong_zhu@ucs.inrs.ca.</u>

## **JEL Classification:** O15, O53, P36

**Key words:** Income growth, Inequality, Poverty, China

> * We would like to thank Chorching Goh for constructive brainstorming, and Deepak Bhattasali, Caryn Bredenkamp, Shaohua Chen, Jean-Jacques Dethier, Deon Filmer, Sudarshan Gooptu, Gillmore Hoefdraad, Yukon Huang, Russell Pittman, Qi Ye, and Adam Wagstaff for useful comments and participants at the sixth International Conference on the Chinese Economy (Clermont-Ferrand, France) and the Economists’ Forum 2008 (World Bank, Washington DC) for fruitful discussions.
communists. We estimate using the 3SLS method, and adjust the standard errors based on Angrist and Pischke (2009, chapter 4). The results are in columns (2) to (4) in Table 8.

The fraction of borderlines reset by the Mongols, as before, is negatively related to ruggedness on the border. The part of border ruggedness related to Mongolian demarcation, again as before, has a negative effect on the famines. Finally, the part of famine outcome driven by border ruggedness that is due to Mongolian demarcation has a negative impact on the number of deaths in the communist force. Increasing the number of famines in the prefecture by one standard deviation (0.192) would raise subsequent log war casualties by 1.06, or roughly 30% of the standard deviation of the outcome, a pronounced effect. The positive relation between famines and subsequent Communist war casualties suggests that state failure in providing basic livelihood allowed the CCP to successfully mobilize local residents for its cause.

# **6.3. Spatial Autocorrelation**

A concern is that the correlation between famines and the local CCP war casualties is only the result of spatial autocorrelation. That is, if nearby prefectures have similar famine likelihood and local CCP activities due to other economic, cultural or demographical reasons, then it is likely that the peak famine areas correspond to areas with high CCP war casualties, leading to a positive correlation between the two variables. We therefore conduct the Moran test following Kelly (2019). In particular, we check whether there is any autocorrelation among the residual terms from the cross-sectional regression. The z-value of the test is 0.759, responding to a p-value of 0.224. The null hypothesis of no spatial autocorrelations is not rejected.

# **6.4. Soldiering for Livelihood or Ideology?**

We have argued that famines changed local people’s ideology to the point of joining the CCP army. But another possibility is that local people, when poor and starved, just wanted to join _any_ army to make a living and stop starving—soldiering, after all, should provide a better livelihood than farming in places that tend to have famines. To consider this possibility, we examine whether famine-laden prefectures during the warlord era also witnessed more local 27
The purpose of our method is not to estimate the causal effect of wealth or gender on competency but rather their association. PISA only measures the competency of students who are enrolled in school and not those who drop out or were never enrolled. It is likely that students with lower cognitive achievement are more likely to drop out than those with higher cognitive achievement and that this likelihood decreases as wealth increases.

If this is true, students of less wealthy households included in the sample are above average for that level of wealth; the estimated association between wealth and competency for the population of 15 year olds may be higher than that found in our analysis.

Additionally, by including grade level and whether the school is in a rural location, the association of wealth through these channels is ignored. For example, less wealthy students may be forced to start at a later year than wealthier students; consequently the impact on achievement of wealth through this is not captured. Also, households in rural areas have a lower average level of wealth than those in non-rural areas; controlling for rural eliminates the role of wealth between urban and rural areas in competency. Excluding grade and rural area in our model would, therefore, increase the association of competency with wealth.

# 4.  Wealth Inequity

This section applies the preceding model to estimate the extent of wealth inequity in six Latin American countries, Argentina, Brazil, Chile, Colombia, Mexico, and Uruguay, as 24
_Table 11: Determinants of applicant being invited for an interview, conditional on applicant’s CV clicked_

||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|---|
|Female|0.0269*|0.0241|0.0234|0.0225|0.0247|0.0234|0.0085|
||(0.0151)|(0.0181)|(0.0181)|(0.0180)|(0.0181)|(0.0180)|(0.0224)|
|Accounting||||<br>-0.0552**||<br>-0.0780***|<br>-0.0763***|
|||||(0.0280)||(0.0252)|(0.0250)|
|Marketing||||<br>-0.0845***||<br>-0.0770***|<br>-0.0763***|
|||||(0.0260)||(0.0246)|(0.0245)|
|IT||||-0.0773***||-0.0259|-0.0238|
|||||(0.0274)||(0.0353)|(0.0352)|
|Istanbul Asia|||||||<br>-0.0508**<br>(0.0225)|
|Ankara|||||||<br>-0.0082<br>(0.0286)|
|Female * Istanbu|l Asia||||||0.0639**<br>(0.0267)|
|Female * Ankara|||||||<br>-0.0268<br>(0.0311)|
|Female * Acct||||-0.0384*<br>(0.0203)||||
|Female * Mrkt||||0.0139<br>(0.0196)||||
|Female * IT||||<br>0.0321<br>(0.0204)||||
|Female * Total a|pplication size|||||||
|<br>Vacancy and CV|<br>sectors match||-0.0292||-0.0274|-0.0413|-0.0410|
||||(0.0195)||(0.0195)|(0.0275)|(0.0274)|
|Experience (in m|onths)|0.0003|<br>0.0003|0.0001|<br>0.0002|<br>0.0001|<br>0.0001|
|||(0.0016)|(0.0016)|(0.0015)|(0.0016)|(0.0015)|(0.0015)|
|Age|-0.0278**|<br>-0.0306*|<br>-0.0303*|<br>-0.0299*|<br>-0.0290*|<br>-0.0288|<br>-0.0292*|
||(0.0138)|(0.0176)|(0.0176)|(0.0175)|(0.0176)|(0.0175)|(0.0176)|
|Objective beauty||0.0176|0.0174|0.0163|0.0187|0.0167|0.0125|
|||(0.0276)|(0.0275)|(0.0275)|(0.0276)|(0.0275)|(0.0275)|
|Subjective beauty||<br>0.0024|<br>0.0035|<br>0.0077|<br>0.0027|<br>0.0069|<br>0.0073|
|||(0.0128)|(0.0127)|(0.0126)|(0.0127)|(0.0126)|(0.0126)|
|Worked in one fi|rm only|<br>0.0000|<br>-0.0001|<br>-0.0041|<br>0.0007|<br>-0.0043|<br>-0.0028|
|||(0.0140)|(0.0140)|(0.0139)|(0.0140)|(0.0140)|(0.0139)|
|Total application|size (100)||||0.0019|0.0022|0.0022|
||||||(0.0016)|(0.0016)|(0.0016)|
|Constant|0.8957**|0.9354**|0.9392**|0.9757**|<br>0.8963**|<br>0.9407**|<br>0.9716**|
||(0.3693)|(0.4280)|(0.4281)|(0.4237)|(0.4292)|(0.4262)|(0.4277)|
|N.obs.|3469|3469|3469|3469|3469|3469|3469|
|R-squared|0.005|0.005|0.006|0.014|0.007|0.015|0.018|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ***, **, and * indicate significance at the 1%, 5% and 10% level, respectively.
CONALEP graduates work in the primary sector, the extraction (mining) sector or the electricity and gas sectors. With respect to overall patterns of employment, considering both sector and labor market status, the results for the CONALEP group are very similar to those obtained for the control groups. An important feature, however, is that CONALEP offers careers that are demanded in the manufacturing and service sectors. Due to the ENE98 limitations, it is not possible to assess in detail the type of job obtained by the individual. However, the CONALEP graduate tracer survey allows us to infer whether there is congruency in the CONALEP graduate professional profile. Among the employed CONALEP graduates, more than half reported that they were working in the occupational category congruent with their field of specialization. Close to 70 percent of employed graduates consistently reported that CONALEP training or specialization was "very useful" or "useful" in their current occupation. This high rate of congruency might be comparable to the high rate among apprentices in Germany, but it is significantly higher than in other developed countries (OECD 1997).

30