rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Gender segmentation by establishment size

|Empl.|Share of fem<br>own|ale employee<br>ed establishm|s in female-<br>ents|Share of m<br>own|ale employee<br>ed establishm|s in male-<br>ents|Share of the<br>worker|avg. female e<br>s who are also|mployee's co-<br>female|Share of t<br>co-work|he avg. male<br>ers who are a|employee's<br>lso male|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Count|2001|2006|2010|2001|2006|2010|2001|2006|2010|2001|2006|2010|
|<br>|||||A. Unorga|nized and or|ganized manuf|acturing|||||
|0.5 - 1|0.971|0.989|0.999|0.948|0.962|0.996|0.976|0.986|0.998|0.982|0.982|0.997|
|1.5 - 3|0.838|0.895|0.884|0.736|0.763|0.820|0.641|0.713|0.708|0.814|0.830|0.869|
|3.5 - 5|0.570|0.670|0.733|0.805|0.832|0.879|0.541|0.580|0.634|0.878|0.897|0.926|
|5.5 - 9.5|0.295|0.242|0.464|0.850|0.839|0.855|0.501|0.543|0.548|0.914|0.909|0.915|
|10-19|||||||0.545|0.592|0.560|0.884|0.881|0.884|
|20-29||N/A|||N/A||0.573|0.558|0.584|0.848|0.870|0.798|
|30-49|||||||0.546|0.544|0.544|0.758|0.908|0.805|
|50-99|||||||0.634|0.499|0.497|0.828|0.928|0.789|
|100+|||||||0.779|0.791|0.709|0.867|0.895|0.809|
|Total|0.876|0.916|0.934|0.795|0.817|0.859|0.718|0.787|0.811|0.870|0.884|0.904|
|||||||B. Se|rvices||||||
|0.5 - 1|0.902|0.940|0.999|0.994|0.994|0.999|1.000|1.000|0.996|1.000|1.000|0.999|
|1.5 - 3|0.661|0.706|0.782|0.839|0.825|0.870|0.595|0.759|0.774|0.900|0.887|0.914|
|3.5 - 5|0.653|0.611|0.689|0.830|0.843|0.861|0.609|0.637|0.756|0.901|0.903|0.916|
|5.5 - 9.5|0.530|0.615|0.674|0.813|0.809|0.838|0.596|0.655|0.853|0.888|0.890|0.895|
|10-19|0.633|0.629|0.729|0.777|0.749|0.755|0.661|0.921|0.878|0.860|0.869|0.844|
|20-29|0.571|0.456|0.598|0.693|0.716|0.655|0.535|0.865|0.619|0.779|0.838|0.746|
|30-49|0.582|0.409|0.522|0.652|0.841|0.787|0.602|0.649|0.596|0.745|0.787|0.779|
|50-99|0.710|0.547|0.655|0.534|0.758|0.524|0.624|0.631|0.689|0.734|0.710|0.576|
|100+|.|.|0.150|0.372|0.556|0.812|0.799|0.506|0.568|0.827|0.705|0.844|
|Total|0.738|0.778|0.850|0.890|0.892|0.907|0.671|0.819|0.824|0.930|0.925|0.934|

Notes: See Table 1. Part-time employees are represented as one-half of a full-time employee.
Table 6: Correlations between indices of civil liberties and civil unrest

|_Civil liberties_|(all varia|_Civil unrest_<br>bles adjusted for popu|lation effects)'|
|---|---|---|---|
|_indicator_|Riots|Demonstrations|Strikes|
|Freedom House<br>(Civil)b<br>(1978-87)|.27<br>(.00001)|.17<br>(.0001)|.34<br>(.0001)l<br>l<br>l|
|Humana<br>(1982-85)|.06<br>(.34)|-.01<br>(.87)|.22<br>(.0002)|
|Media Pluralism<br>(1983-87)|.14<br>(.0011)|.24<br>(.0001)|.29<br>(.0001)|
|Freedom to<br>organize<br>(1983-87)|.30<br>(.0001)<br>___|.29<br>(.0001)|.36<br>(.0001)|

Notes: a) indicators of civil unrest per capita adjusted for total population size as described in footnote 28; b) Annual values from 1978-87. The other three indices are single values extrapolated to cover the listed time period; c) p-levels in parenthesis. Source: Authors' calculations Table 7 uses the same regression base specification as above and shows that there is, if anything, a modest positive effect of various indicators of civil unrest on project returns. In both of the specifications without regional dummies, the number of riots, protest demonstrations, and political strikes are _positively_ and significantly related to the rate of return. Projects apparently do better in environments with greater civil strife when civil liberties are not included as a determinant (e.g. a coefficient of 0.56 in specification A for

- 23 -
- Yao, Y.. 2015. Beijing Consensus. In _The Wiley Blackwell Encyclopedia of Race, Ethnicity, and Nationalism._ _<u>https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118663202.wberen577.</u>_

- Yao, Y., M. Zhang. 2015. Subnational Leaders and Economic Growth: Evidence from Chinese Cities. _Journal of Economic Growth_ 20(4), 405-436.

- Zhang, W. 2019. The China model view is factually false. _Journal of Chinese Economic and Business Studies,_ DOL: 10.1080/14765284.2019.1663696.

- Zhu, X. 2012. “Understanding China’s Growth: Past, Present and the Future.” _Journal of Economic Perspectives_ 26(4), 103-124.

``` 28 ```
34 Relationships among Them", World Bank Staff Working Paper No. 686, Washington, D.C., 1984.

13. Heller, Peter S., "Interactions of Childhood Mortality and Fertility in W. Malaysia: 1947-1970", Discussion Paper No. 57, University of Michigan, September 1976.

14. Hobcraft, John, John McDonald and Shea Rutstein (1983), "Child-Spacing Effects on Infant and Early Child Mortality " Paper presented at the Annual Meeting of the Population Association of America, Pitcsburgh, Pennsylvania.

15. Huffman, Sandra L., "Child Spacing: for Maternal and Child Health", Mothers and Children, Bulletin on Infant Feeding and Maternal **NL;U.L.LUvI,** VU-. **LI.** 1, ***, A1 L1L-A.J** _O ._ 16. Hull, Terence, and Valerie Hull. "The Relation of Economic Class and Fertility: An Analysis of Some Indonesian Data." Population Studies, Vol. 31 (March 1977), pp. 73-87.

17. Mcgreevey, William and Nancy Birdsall, The Policy Relavance of Recent Social Research on Ferility, Occasional Monograph Series, No. @ Washington, D. C. : Interdisciplinary Communications Program, Smithsonian Institution, 1974.

18. Mosley, W. Henry and Lincoln C. Chen, Child Survival. Strategies for Research, Supplement to Vol. 10, 1984.

19. Nerlove, M. and T. Paul Schultz. "Love and Life Between the Censuses: A Model of Family Decision Making in Puerto Rico, 1950-1960," Rand RM6322 AID, 1970.

20. Nortman, Dorothy and John Bongaarts, "Contraceptive Practice Required to Meet a Prescribed Crude Birth Rate Target: A Proposed Macro Model (TABRAP) and Hypothetical Illustrations", Demograpby, Vol. 12, No. 3, Aueust 1975.

21. Pebley, Anne R. and Sara Millman, "Birthspacing and Child Survival", International Family Planning Perspectives, Vol. 12, No. 3, September 1986, pp 71-79.

22. Preston, Samuel H., "Causes and Consequences of Mortality Declines in Less Developed Countries During the Twentieth Century", Conference on Population and Economic Change in Less Developed Countries, September 30 - October 2, 1976, National Bureau of Economic Research.

23. Preston, Samuel H. (ed.), The Effects of Infant and Child Mortality on Fertility, Academic Press, New York, 1978.

24. Schultz, T. Paul, nEvaluation of Population Policies: A Framework for Analysis and its Application to Taiwan's Family Planning Programw,
In (7), denotes the joint distribution of _y_ and _X_ , _g_ denotes the conditional distribution of _y_ on _X_ , and denotes the joint density of the covariates in the vector _X_ .<sup>12</sup> If the joint density of the observable covariates _X_ in a particular survey for country _j_ is written , then our first proposed estimate for a test-score distribution (density) corrected for sample selection on observables is given by: Where (9) Equation (9) is simply the ratio of the density of fifteen year-olds whose observed characteristics _X_ take certain values, in the ancillary household survey (HH), to the density of fifteen year-olds with the exact same observed characteristics in the PISA survey. is a re-weighting function exactly analogous to that used by DiNardo, Fortin and Lemieux (1996) to construct counterfactual income densities in their study of inequality in the US. Whereas DiNardo et al. use the ratio of densities across different years (of the same survey), we use the ratio of densities across different surveys (for the same year). To the extent that test-taking (i.e. being in the PISA sample) is correlated with observed covariates in X, the counterfactual distribution in (8) should correct for the corresponding selection bias.<sup>13</sup> In practice, this procedure was implemented by partitioning both the PISA and the ancillary household survey into cells with identical values for three observed covariates: gender, mother’s education, and father’s occupation, with the latter two variables classified as in Section 2.<sup>14</sup> The ratios of densities in each cell in these partitions were used to construct the reweighting function (Equation 9), and both the S.D. and the IOp measures were computed over the counterfactual density of scores given by (8).

This procedure assumes that selection into the PISA sample is fully explained by observable variables, such as gender and family background. While such variables are likely to play a role in selection, it is also likely that other, unobserved variables do too. Within the set of girls, with mothers with no formal education and fathers who work in agriculture, for example, it is possible that a higher proportion of high-ability students than low-ability students stay in school long enough to enter the PISA sample. This kind of selection would imply that equation (8) may overstate the achievement of those students who are counterfactually “brought back into” the sample: simple re-weighting effectively assigns all those out-of-sample students the same scores

> 12 The triple integral notation is short-hand for integrating out every element of _X_ , so that there are as many integrals as there are elements in the vector of covariates common to both surveys. As it happens, in our application that dimension is three.

> 13 The superscript SO stands for selection on observables.

> 14 Surveys were thus partitioned into 24 cells. Given the sample sizes reported earlier, particularly for Turkey’s HBS and, to a lesser extent, Mexico’s ENIGH, it was not possible to further refine the partition by using additional covariates.

13
sold to Petrobras, the Brazilian state-owned oil firm has only one relatively small refinery (5% of total refining capacity in Argentina) 600km south of Buenos Aires.

> Figure 1 presents the evolution of market shares for premium<sup>8</sup> gasoline of the

> largest four firms and a category "other" that aggregates both small branded firms (Sol Petr6leo, Refinor, Rhasa) and unbranded retail outlets. It is important to highlight that the SDCyC policy recommendations were made in 2000. Up to that year<sup>9</sup> market shares were remarkably stable, with the only exception of the "other" firms that doubled their market

> share from _5_ to 10%. The growth in market shares of the latter group has been accompanied by an impressive growth in retail outlets. Table 1 shows the evolution of

> retail gasoline outlets in Argentina. The biggest change in the quantity of retail outlets

> since deregulation is the fall in the quantity operating under the RepsolYPF flag, matched by an almost equivalent increase in the quantity of unbranded outlets. Looking at figure 1

> and table 1, it should come as a surprise the increase in RepsolYPF's market share during 2002 and the parallel fall in the market share of "others". As we explain in the Policy evaluation section this change was caused by the devaluation of the Argentine currency

> that has made the new policies adopted for this market very difficult to evaluate.

> 8 The Secretariat of Energy established that premium gasoline has to have more than 93 RON while regular gasoline can have between 83 and 93 RON. In Argentina the ratio of premium to regular gasoline consumption is more than 2.

> **9** 1995 is the first year with reliable information. From 1991 to 1994 there are no comprehensive price and quantity time series data.

6
Studies of India similarly suggest that expectations have become better anchored in recent years. For example, Asnani et al (2019) analyze the inflation expectations of households and find that inflation expectations have become better anchored during the inflation targeting period; in particular, there is only limited spillover from food inflation to food and non-food inflation expectations in the inflation targeting period.<sup>38</sup> The RBI has been conducting its Inflation Expectations Survey of Households (IESH) since 2005, recording survey respondents’ perceptions of current inflation and expectations of inflation three months and one year ahead. The survey records both qualitative and quantitative responses. It was conducted quarterly (viz., Mar, Jun, Sep and Dec) until March 2014. At that point two additional rounds in May and November were added to align it with the bi-monthly monetary policy review cycle.

The RBI has also been conducting a survey of professional forecasters since the second quarter of 2007-08, drawing responses from forecaster with both financial and non-financial institutions. Initially, the survey was conducted at quarterly frequency, but this was changed to bi-monthly in 2014-15. The survey collects annual quantitative forecasts for two financial years (the current year and next year) and quarterly forecasts for five quarters (the current quarter and next four quarters). We analyze how the inflation expectation series for India has changed since the implementation of inflation targeting. For the analysis below, we use both the household and professional forecaster series averaged at quarterly frequencies.<sup>39</sup> We use the CPI inflation expectations of professional forecasters, and compare household and professional forecasts with CPI inflation.<sup>40</sup> Both professional forecasts and households’ expectations of inflation declined with the shift to IT. Even so, household expectations of inflation consistently exceed actual inflation, and the deviation has not declined. Figure 7 shows that the average of professional forecasts has been close to actual inflation, while household expectations have often exceeded actual inflation. In the last few years, and particularly since the shift to IT, expected inflation has declined, in line with the decline in actual inflation. However, households’ expectations have declined less than actual inflation and continue to be higher (by about 3 percentage points).

> a statistically significant reduction in the response of inflation expectations (12-month-ahead) to shocks in oil prices and observed inflation. 38 Benes et al. (2017) and Patra and Ray (2010) similarly found that lagged inflation, as well as current and lagged changes in fuel and food prices significantly affected inflation expectations prior to inflation targeting. 39 We restrict our analysis to a quarterly frequency in order to have comparable results across sectors. 40 We use the CPI combined inflation series from 2012 onwards and prior to that the CPI-IW. Professional forecasters’ expectations are for the CPI-IW prior to 2014, and for the combined series from 2014 onwards.

22
# **4 Results**

The sub-sections below present (a) the results comparing publicly available “top-down” population estimates with each other and with the census; (b) the predictive accuracy of satellite imagery-based indicators for census density, using LASSO-based Poisson regression models; and (c) out-of-sample density predictions based on a model using the survey (HIES), and comparing their accuracies with other estimates.

## **4.1 Validation of population products**

The validation exercise indicates that publicly available population count estimates are accurate at the sub-district level but not necessarily at the village level. At the sub-district level, except LandScan and WorldPop 2010, the _R_<sup>2</sup> for all other products with the census are above 0.9 (panel A in Table 4). At the village level, panel B in Table 4 shows that WorldPop 2015 has the highest association with the census ( _R_<sup>2</sup> of 0.988), followed by Facebooks HRSL (0.841), GHSL (0.672), GPW 2015 (0.595), GPW 2010 (.404), WorldPop 2010 (0.107) and LandScan (0.0006) in that order. These results qualitatively hold if we use correlation coefficients as a measure of association (see supplementary tables S2 and S3). Clearly, the products using the most recent census and using higher resolution imagery-based covariates are performing better than the others. For example, GPW and WorldPop 2010 estimates use the 2001 census for redistribution, and hence it is not surprising that they perform poorly in comparison to the 2012 census.<sup>18</sup> However, even the best performing products may themselves become inaccurate with the passage of time as their source data in the census becomes outdated.<sup>19</sup> 18 Similarly, an earlier unpublished version of Facebook’s population data which we worked with, only yielded a correlation coefficient of 0.5 with the 2012 Sri Lankan census at the village level because Facebook initially used the 2001 Sri Lankan census for calibration. Their estimates were later updated by using the most recent census, which now gives an _R_<sup>2</sup> of 0.841 and a correlation coefficient of 0.917.

> 19 We also assess the consistency of four publicly available built-up area measures, namely, GUF, GUF+, GHSL, and Facebook, against each other, and the validate the built-up area estimates for the 55 sub-districts by Engstrom et al. (2017) against Facebook’s. All estimates of built-up area at the village level are reasonably consistent with each other. At the national level, the _R_<sup>2</sup> between estimates from Facebook and each of the first three sources are 0.76 or greater (supplementary Table S4). In the 55 sub-districts, the _R_<sup>2</sup> between Facebook estimates and those from Engstrom et al. (2017) is also high at 0.794 (supplementary Table S5). These patterns also hold if correlation coefficients are employed for the analysis (see supplementary tables S6 and S7).

10
# **The Transformative Effects of Privatization in China:**

**A Natural Experiment Based on Politician Career Concern**<sup>1</sup>

Zhangkai Huang Tsinghua University Jinyu Liu Univ. of International Business and Economics Guangrong Ma Renmin University of China Lixin Colin Xu World Bank Keywords: privatization, career concern, politicians, productivity, China. JEL Code: D22, D23, L29, H19, P31, P39 1 Zhangkai Huang: School of Economics and Management, Tsinghua University, Beijing 100084, China (e-mail: huangzhk@sem.tsinghua.edu.cn); Jinyu Liu: School of Banking and Finance, University of International Business and Economics, Beijing 100029, China (e-mail: jinyu.liu@uibe.edu.cn); Guangrong Ma: China Financial Policy Research Center, School of Finance, Renmin University of China, Beijing 100872, China (e-mail: grma@ruc.edu.cn); Lixin Colin Xu (the corresponding author): World Bank, the Research Group, 1818 H Street, N.W., Washington, DC 20433 (e-mail: lxu1@worldbank.org). Word count: 15294.

We thank Daniel Xu for excellent editorial service. The results and opinions present in this paper are our own, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
Table 4. Wealthier households were less likely to experience reduction in wage or business income relative to previous period, though this is not significant everywhere <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br>wage/business income<br>Share of households with<br><!-- End of picture text --> Note: The reference period for looking at changes in income are since the previous round. The exception is the first round, for which the reference period is pre-pandemic. Results from an OLS regression are shown. A probit regression produces qualitatively similar results. Income is not available for PNG.

Source: HFPS 4. Potential long-term effects of the pandemic: Coping mechanisms, food insecurity and access to education Consistently, poorer households have been more likely than wealthier households to rely on coping mechanisms that may inhibit recovery such as selling scarce assets or livestock, crediting purchases, delaying payments, and borrowing (Figure 10). Across countries, households in the bottom 20 were on average 16 percentage points more likely than those in the top 20 to rely on such methods of coping.

22
**Table 4: Impact of winning the lottery and of migration on applicant labor and income**

||(1)<br>Control|(2)|(3)<br>ITT|(4)|(5)|
|---|---|---|---|---|---|
||group|ITT|(Controls)|ITT(IPW)|IV|
|ihs(Total hours worked)|8.350***<br>(0.0294)|-0.0320<br>(0.0470)|-0.0626<br>(0.0503)|-0.0616<br>(0.0488)|-0.109<br>(0.0863)|
|FDR q-value|||[0.022]|||
|ihs(Hours in wage work)|5.050***|1.490***|1.225***|1.219***|2.130***|
||(0.126)|(0.163)|(0.173)|(0.169)|(0.277)|
|FDR q-value|||[0.001]|||
|ihs(Hours in farming)|2.877***|-1.474***|-1.354***|-1.344***|-2.337***|
||(0.106)|(0.131)|(0.136)|(0.134)|(0.216)|
|FDR q-value|||[0.001]|||
|ihs(Hours in self-employment)|2.644***|-1.203***|-1.097***|-1.105***|-1.893***|
||(0.118)|(0.145)|(0.149)|(0.146)|(0.241)|
|FDR q-value|||[0.001]|||
|ihs(monthly income in 2015)|9.659***|0.570***|0.596***|0.592***|1.022***|
||(0.0390)|(0.0524)|(0.0561)|(0.0547)|(0.0937)|
|FDR q-value|||[0.001]|||
|ihs(income last month, direct)|9.205***|0.520***|0.437***|0.433***|0.758***|
||(0.0901)|(0.124)|(0.131)|(0.129)|(0.219)|
|FDR q-value|||[0.001]|||
|ihs(monthly income, computed)|11.83***|0.593***|0.536***|0.519***|0.957***|
||(0.0691)|(0.0912)|(0.0963)|(0.0977)|(0.164)|
|FDR q-value|||[0.001]|||
|ihs(monthly inc, computed -- p10)|11.88***|0.714***|0.657***|0.649***|1.133***|
||(0.0673)|(0.0854)|(0.0897)|(0.0869)|(0.147)|
|FDR q-value|||[0.001]|||
|ihs(hourly income, computed)|3.757***|0.406***|0.406***|0.409***|0.726***|
||(0.0290)|(0.0422)|(0.0433)|(0.0426)|(0.0710)|
|FDR q-value|||[0.001]|||
|<br>ihs(hourly income, computed -- p10)|3.789***|0.484***|0.483***|0.483***|0.837***|
||(0.0288)|(0.0401)|(0.0415)|(0.0402)|(0.0645)|
|FDR q-value|||[0.001]|||
|<br>Index: Labor and Income|0|0.514***|0.517***|0.526***|0.892***|
||(0.0331)|(0.0458)|(0.0468)|(0.0453)|(0.0739)|

_Note:_ This table shows the impact of winning the lottery (T1) and of migration on labor and income of the applicant estimated using Equation (2). The first column shows the control group mean. Column (2) shows the ITT estimates with only Upazila indicators, Column (3) adds other controls (applicant height, age, religion, and parental education). Column (4) presents the estimate of the reduced form with inverse-probability-weights (IPW) estimated using the controls. Column (5) presents the 2-SLS estimates of the impact of migration. The rows indicate the outcome variables. _ihs_ refers to the inverse-hyperbolic sine transformation of the variables. The bottom most outcome is a covariance-weighted index of all other outcomes with positive values representing increased income and labor supply. Standard errors, reported in parentheses, are clustered at the union level. Column (3) also presents the (False Discovery Rate) FDR adjusted q-values that adjust for multiple-hypotheses testing. *:p>0.1; **: p>0.05; ***: p>0.01 40