rafmacalaba/datause-encoder-data huggingface dataset:
# **Section I. Introduction**

Nearly 20 years after enacting major market-oriented reforms, India faces new opportunities and challenges in sustaining high growth, promoting shared prosperity, and eliminating extreme poverty. India has the potential to sustain high growth thanks to demographic dividend. But the slow pace of job creation can transform the demographic dividend into demographic disaster ([Bloom (2011)]). A majority of jobs in the manufacturing sector is created in the unorganized/informal<sup>5</sup> segment of the economy ([Government of India (2012)], [Ghani et al. (2011a)]). The most prevalent view of the informal sector is that it is stagnant and produces “bad” jobs (i.e. highly insecure, low wage jobs).

But what is not well known is the interaction between formal and informal sectors. Do firms in organized sector outsource their work to informal sectors to remain cost competitive? What are the product market, input market and other linkages between the organized and unorganized segments? The answer to these questions is important because on the one hand labor-intensive tasks being outsourced from organized sector can cause intense cost competition and a downward spiral of wages in the unorganized sector ([Meagher (1995)]). On the other hand, product market, input market and fiscal linkages between the organized and unorganized segments can bolster employment and productivity in each individual segment. Additionally entry into the unorganized sector may be voluntary ([Maloney (2004)]). The exact nature and magnitude of linkages and spillovers between the organized and unorganized sector has huge policy implications for whether policies should focus on the organized/formal or unorganized/informal sectors.

Another important concern for policy makers is the fact that the manufacturing sector in India has been accused of producing “jobless growth”. Large scale manufacturing in India tends to be relatively skill- and capital-intensive ([Chamarbagwalla and Sharma (2011)]) and most employment generation occurs in small (organized and unorganized) ancillary plants that are

> 5 In this paper we will use the word unorganized interchangeably with informal. Our definition is based on the definition used by the National Sample Survey organization that includes in the unorganized sector all economic units that have less than 10 employees (and using power) or those that have less than 20 employees (and don’t use power) and which are not required to register with the government.

2
These results may reflect the strength of _benevolent sexism_ in Morocco: that is, “a set of interrelated attitudes toward women that are sexist in terms of viewing women stereotypically and in restricted roles but that are subjectively positive in feeling (for the perceiver)” (Glick and Fiske 1996, 491). One manifestation of this is a valorization women’s traditional roles while emphasizing that women deserve help at home from supportive partners. In prior work, I found that 94% of Moroccans (equally among men and women) agreed or strongly agreed with the statement that “men should guard and cherish women,” while 76% of women and 73% of men agreed or strongly agreed that “men should sacrifice to provide for the women in their lives” (Barnett 2022).<sup>19</sup> Other recent research has found that while hostile sexist beliefs have declined substantially in the MENA region, benevolent sexist beliefs remain prevalent and are often called upon to justify expansions in women’s formal rights (Jones, Mitchell, and Martin 2021; Maitner and Henry 2018). The strength of benevolent sexism may account for both the relative interest in “equalizing” household contributions (and men’s interest in emphasizing the extent to which they currently contribute) co-existing with the continued preference men hold for women not working, where feasible. Thus, while openness to more equal divisions of household labor could bode well for women’s potential labor market participation, such expressions of interest in supporting women’s work may be conditional on women’s continued embrace of distinct gender roles. Alongside structural obstacles to women’s employment, the combination of benevolent sexism and commitment to the male breadwinner model may jointly form an equilibrium that makes shifting either women’s labor force participation or the actual distribution of household labor especially challenging.

The results also suggest that what may matter most for altering the distribution of labor is not abstract attitudes toward gender roles, but men’s reactions to their own life circumstances and how these intersect with conceptions of masculinity. Research from other contexts has shown that high-earning women may compensate for their violation of gender role norms by taking on a disproportionate share of household labor (Bittman,

> 19. These figures are not reported in the dissertation but are calculated from responses to the telephone survey described therein.

32
For this specification,

- captures gender differences in callback rates for darker-skinned applicants

- • is the gender differences in callback rates when the applicant is lighter-skinned αα

- captures gender differences in callback rates for darker-skinned applicants

- • is the gender differences in callback rates when the applicant is lighter-skinned • is the marginal increase in the difference of the gender callback rates for lighter-skinned jj αα+ ββ

- is the gender differences in callback rates when the applicant is lighter-skinned

- • is the marginal increase in the difference of the gender callback rates for lighter-skinned jj

- applicants versus darker-skinned ones. jj ββ

Table 7 presents the results. It indicates that there are no differences in callback rates between females and males by the applicant’s skin color or region of residence. However, women who are veiled are less likely than nonveiled women to receive a callback. This causal effect is substantially large, at about 8.5 percentage points (30 percent).

# **Table 7 Effects of applicant characteristics on gender differences in callback rates**

||Coefficient|Standard<br>error|P-value|[95% con<br>interval]|fidence|
|---|---|---|---|---|---|
|_Skin color_||||||
|Difference in gender callback rates when<br>applicants are lighter-skinned|0.022|0.030|0.46|-0.037|0.082|
|_Veiled or nonveiled_||||||
|Difference in callback rate between veiled and<br>nonveiled women|-0.085***|0.022|0.000|-0.128|-0.041|
|_Region of residence_||||||
|Difference in callback rates for male and female<br>applicants from Ariana|0.022|0.031|0.47|-0.038|0.083|
|Difference in callback rates for male and female<br>applicants from Ben Arous|0.015|0.029|0.60|-0.042|0.073|
|Difference in callback rates for male and female<br>applicants from Mennouba|0.055|0.040|0.17|-0.023|0.133|
|Difference in callback rates for male and female<br>applicants from Tunis|0.021|0.022|0.35|-0.022|0.064|

_Note_ : The panel on skin color reports the results from equation (3), where the callback dummy was regressed on the full interaction between the female dummy and the lighter-skinned applicant dummy. Only the coefficient on the interaction term between the female dummy and the lighter-skinned applicant dummy is reported in the table ( ). In the panel on veiled or nonveiled, the callback dummy was regressed on a veiled female dummy and the four major iijj dummies and solely for the subset of female applicants, following the approach in equation (1). Only the coefficient ββ on the veiled female dummy is reported in the table ( ). In the panel on region of residence, the callback dummy was regressed on the interaction terms between the female dummy and each of the four region dummies, following the ii approach in equation (2). Only the coefficient on the interaction term between the female dummy and the region ββ dummy is reported in the table ( ). * ** *** δδiijj _p_ < 0.10 _p_ < 0.05 _p_ < 0.01.

31
|Appendix2:Instrumentingequationfortrave<br>(dependent variableisthelogoftravel time betw|ltime<br>een<sup>wardandtown)</sup>|||
|---|---|---|---|
||unit|coef.|t-stat|
|Foottraveltimebetweenwardandtown|log|0.677|174.12|
|Wardcharacteristics||||
|Areaofdistrictinwhichwardislocated|log(squarekm)|0.202|21.65|
|Totalarablelandareaofdistrict|log(ha)|-0.145|-12.82|
|Meanelevation|meters|0.000|14.02|
|Standard deviationofdistrictelevation|meters|-0.000|-6.71|
|Centralregion|yes=1|-0.229|-24.06|
|West region|yes=1|-0.281|-26.65|
|Mid-westregion|yes=1|-0.219|-14.90|
|<br>Far-westregion|yes=1|-0.199|-11.98|
|Towncharacteristics||||
|Areaofdistrictinwhichwardislocated|log(squarekm)|0.067|5.34|
|Distancetonearestnavigablepartofriver|log(km)|0.048|7.28|
|Totalarablelandareaofdistrict|log(ha)|-0.005|-0.30|
|Meanelevation|meters|0.000|8.18|
|Standard deviationofdistrictelevation|meters|-0.000|-6.35|
|Centralregion|yes=1|-0.150|-11.40<br>|
|Westregion|yes=1|-0.186|-15.86|
|Mid-westregion|yes=1|-0.172|-9.55|
|<br>Far-westregion|yes=1|-0.082|-4.20|
|Intercept||-1.780|-9.37|
|Numberofobservations||9187||
|R-squared||0.8443||

Source: foot travel time computed from GIS data correcting<sup>for elevation.</sup> Other variables as in Appendix 1. Eastem region<sup>is the omitted region.</sup> 46
micro-processes that lead to the emergence of the typical network characteristics of big data are not well understood (Snijders, Matzat, and Reips 2012).

# **4. The Indian Labor Market, Skills Development and Babajob**

India has made impressive progress in economic growth and poverty reduction over the past few decades. India’s gross domestic product (GDP) grew at an average rate of 7.3 percent per year between 2007 and 2012. This contributed to a substantial decline in the incidence of poverty, and an estimated 138 million people rose above the poverty line during the period. Despite its robust growth, India still faces major challenges in improving worker productivity and ensuring that the supply of workforce skills is consistent with labor market demand.

The Indian labor force is large, mostly informal and relatively young. India’s population reached 1.295 billion in 2014, including 497 million workers.<sup>3</sup> The size of the labor force has been expanding at an annual net growth rate of 4.2 million for the past 10 years. The labor force participation rate is 54 percent, with a relatively high participation rate among men (80 percent) and low rate among women (26 percent). Only 16 percent of the labor force is engaged in wage employment (18 percent of male workers and 12 percent of female workers), and a large majority work in the informal sector. Moreover, 54 percent of the country’s population is under 25 years of age. While the country’s relatively young population has the potential to yield significant demographic dividends, ensuring sufficient employment opportunities for a young workforce also presents a critical challenge for the government. India faces a lack of highly trained workers and a large share of unskilled youth. As a result, job creation and skills development are critical priorities. To address these challenges, the government launched the National Policy for Skill Development and Entrepreneurship in 2015, and its 12<sup>th</sup> Five-Year Plan set a goal of training 400 million workers by 2022.

India’s national labor market data are mainly collected through National Sample Surveys, some rounds of which have dedicated sets of employment questions. The Central Statistical Organization also conducts an Annual Survey of Industries and quarterly employment surveys designed to track industrial and workforce development trends in selected economic sectors. External surveys, such as the International Finance Corporation’s Enterprise Surveys, periodically analyze India’s business environment, and especially its formal sector (IFC 2014). Sample surveys of households and industries are typically conducted by the government and have traditionally served as the primary data source for understanding labor market trends and skills demand in India.

> 3 World Bank World Development Indicators website.

6
<!-- Start of picture text --> Figure 1: Absolute and Relative Word Count for 'equality/inequality' in World Bank Documents<br><!-- End of picture text --> <!-- Start of picture text --> Absolute Number of Keywords<br>10000-<br>7500-<br>5000-<br>7)<br>6<br>%Swo = .2500-<br>><br>go °<br>fe) Relative Frequency: Number of Keyword per 10000 Words<br>ae)<br>g¢<br>§<br>2 3<br>2-<br>1-<br>a + wo © OD NWN tS © DBD DN SF to 806 o uy +t © DBD DOD NUN +t DD D&D ©<br>ind Ld ~ ~ Ld ie] oO eo © eo cor) for) or) a D> So So So So So = = bad = = N<br>cor]a oD— cor]— cor]— oD— cor]= paleo oD= oD_ oo- oD- D— for]- fo)= aD- ra]o rot]o nNo nNo nNoO nNOo nNoO oON SoN oN SoN<br>Year<br><!-- End of picture text --> Note: the top graph refers to the absolute number of times the term ‘equality/inequality' appears in all documents. The bottom graph refers to relative frequency. Both use a 5-year moving average. Data source: World Bank, Open Knowledge Repository. 16573 documents downloaded between July-September 2021
**Table A.21:** Alignment with Citizens’ Preferences, by Social Distance to Village Government

|||**Bureauc**|**rat survey**||**Villag**|**e head survey**|**Citize**|**n survey**|
|---|---|---|---|---|---|---|---|---|
||**Offcials/**|**citizens agree on:**|**Complain**|**ts received about:**|**Village he**|**ad takes action on:**|**Citizen**|**Perceptions**|
|**Village Services:**|Priority|Worst-quality|Priority|Worst-quality|Priority|Worst-quality|Access|Quality|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||||**Panel A: F**|**irst-Degree Connec**|**tion**||||
|New village head|0.113|0.288<sup>_∗∗∗_</sup>|0.192<sup>_∗∗∗_</sup>|0.074|0.098|0.212<sup>_∗∗_</sup>|0.046|0.130|
||(0.110)|(0.114)|(0.063)|(0.084)|(0.071)|(0.112)|(0.040)|(0.109)|
|Observations|1067|1067|1067|1067|443|443|2647|2646|
|Control mean|0.72|0.40|0.86|0.77|0.96|0.86|0.76|-0.071|
||||**Panel B: Sec**|**ond-Degree Conn**|**ection**||||
|New village head|0.167<sup>_∗_</sup>|0.242<sup>_∗∗_</sup>|0.117<sup>_∗∗_</sup>|0.147<sup>_∗∗_</sup>|0.098|0.111|0.055|0.143|
||(0.109)|(0.138)|(0.067)|(0.076)|(0.071)|(0.086)|(0.048)|(0.117)|
|Observations|1067|1067|1067|1067|443|443|2207|2207|
|Control mean|0.70|0.42|0.86|0.75|0.96|0.89|0.79|-0.018|
|||**Pan**|**el C: Third-**|**Degree or More Co**|**nnection**||||
|New village head|0.173<sup>_∗_</sup>|0.244<sup>_∗∗_</sup>|0.069|0.139<sup>_∗_</sup>|0.121<sup>_∗∗_</sup>|0.194<sup>_∗∗_</sup>|0.100<sup>_∗∗_</sup>|0.276<sup>_∗∗∗_</sup>|
||(0.119)|(0.139)|(0.060)|(0.072)|(0.067)|(0.107)|(0.053)|(0.122)|
|Observations|1046|1046|1046|1046|436|436|3994|3993|
|Control mean|0.69|0.37|0.87|0.75|0.96|0.90|0.80|-0.022|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in columns 1–4, village heads in columns 5–6, and citizens in columns 7–8. In columns 1 through 6, the outcomes are identical to those in columns 1–6 of Table 5. In columns 7–8, the outcomes are identical to those in columns 2–3 of Table 7. Each panel looks at a different subsample of citizens with varying degrees of social proximity to the village government, namely: citizens with first-degree (direct) connections in Panel A, second-degree connections in Panel B, and third- (or higher) degree connections in Panel C. See Section 5 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.
**Table   II:   Fee-­‐School   Quality   Relationship   and   Impact   on   Perceptions**

||**Fee**|**s (Year 1)**|<br>**Per**|<br>**ception**|
|---|---|---|---|---|
||||**Year 1**|**Year 2**|
||(1)|(2)|(3)|(4)<br>|
|School Score|369.2***|316.3***|0.216***|-0.0279|
||(95.07)|(107.2)|(0.0239)|(0.0347)|
|School Fee||||0.000129***<br>|
|||||(2.26e-05)|
|Baseline Perception<br>||||0.228***<br>(0.0365)|
|Report Card||||0.00798|
|||||(0.0364)|
|Report Card * School Score<br>||||0.114**<br>(0.0438)|
|Basic Infrastructure Index<br>||54.93*<br>(33.08)|||
|Extra Infrastructure Index||141.1*|||
|||(79.67)|||
|Controls|Village|Village Fixed Effects|Village|Village|
|Observations|289|289|610|588|
|R-squared|0.337|0.137|0.116|0.315|
|Baseline Depvar(mean)|1184.360|1184.360|3.288|3.275|

# _Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level

This  table  presents  results  on  the  association  between  school  fees  and  quality,  and  some  findings  on  perception  of  school  quality.  Columns  1  and  2  show  the relationship  between  school  characteristics  and  school  fees  for  private  schools;  there  are  303  private  schools  in  our  sample,  but  we  have  fewer  observations due  to  missing  data.  The  dependent  variabes  in  Columns  3  and  4  are  constructed  by  taking  the  average  of  all  parental  perceptions,  ranked  of  a  five  point scale,  for  a  given  school.  This  ensures  schools  are  equally  represented  (one  observation  per  school).  Column  3  shows  the  correlation  between  school  test score  and  parental  perception  in  year  1.  Column  4  considers  perception  in  year  2  (open  schools  only).  We  have  fewer  than  800  schools  in  Columns  3  and  4 because  we  restrict  to  only  those  household-­‐school  combinations  where  we  have  perceptions  data  for  both  rounds.  All  regressions  have  standard  errors clustered  at  the  village  level,  and  include  district  fixed  effects.  All  regressions  include  baseline  of  outcome  variable  as  a  control  as  well  as,  where appropriate,  additional  village  controls  (village  wealth  [median  monthly  expenditure],  number  of  households  in  village,  Herfindahl  index  of  schools  in  village, and  percent  of  adults  [>24]  literate  in  village).  Baseline  Depvar  (Mean)  displays  the  baseline  mean  for  the  sample  for  all  outcome  variables.

35
# **Notes**

1 UN-Habitat 2010.

2 As in John Dunne: No man is an island entire of itself; every man is a piece of the continent, a part of the main.

3 The literature on firm dynamics and industrial policy has also focused on this problem. For example, is it more desirable to subsidize small firms or large firms? See Restuccia and Rogerson (2008).

4 The online appendix is available at www.princeton.edu/~erossi/RethinkingCitiesCh14Appendix.pdf.

5 Glaeser and Gottlieb 2008.

6 Kamal-Chaoui and others 2009, 9.

7 See www.princeton.edu/~erossi/RethinkingCitiesCh14Appendix.pdf 8 World Bank 2009, 221.

9 World Bank 2009.

10 Kamal-Chaoui and others 2009.

11 Of course, given that people will move as a result of the policy, the ex-post aggregate efficiency may be different.

12 The numbers for 2005 are slightly different than those for figure 3. Because we want to use a constant sample of cities across all years, the number of cities analyzed here is a bit lower. 13 Meza and Quintin 2007.
Uganda, Mathiassen (2013) also finds imputation-based poverty estimates to accurately track the true poverty rates in most cases.

In the same spirit, another approach is to combine a household expenditure survey and a more recent labor force survey to impute consumption into the latter and subsequently to estimate poverty. This approach has been implemented for Mozambique by Mathiassen (2009). Douidich, Ezzrari, van der Weide, and Verme (2013) similarly take advantage of an almost identical design between the household expenditure survey and the LFSs in Morocco to impute poverty rates in the latter and find very encouraging results.

Among all these cited studies, however, only the three most recent studies by Christiaensen et al. (2012), Mathiassen (2013), and Douidich et al. (2013) offer validation for their estimates against the true poverty rates before extending their analysis to the years without consumption data. It is worth noting that all these validation studies restrict their analysis to surveys of the same design, but none of these studies explicitly discusses this assumption that their studies rely on.<sup>7</sup> Missing data imputation, however, does not appeal to economics researchers alone. The few existing studies in economics appear to have been developed independently of a much more established literature on missing data imputation in statistics. Starting with the seminal work on imputation methods by Rubin in the late 1970s (Rubin, 1977, 1978), imputation methods have steadily become counted among the main tools of a professional statistician. Government agencies such as the U.S. Census Bureau regularly use imputation to fill in important missing data on various statistics for income (Census Bureau, 2014a) and labor (Census Bureau, 2014b).

> 7 A recent study that uses the ELL approach for poverty imputation for Sri Lanka by Newhouse et al. (2014) is an exception. It finds that differences in sampling design can undermine the accuracy of survey-to-survey predictions. Another study by Dabalen et al. (2014) imputes poverty estimates from one household survey round to another round for Liberia but does not provide validation due to missing consumption data in the latter.

9
<!-- Start of picture text --> Stunting prevalence (% under-5)<br>60 © High performer<br>© Low performer<br>Other<br>; © Philippines<br>50 Papua New Guinea Tigor-Leste © Regional comparator<br>Pakistangemen2. PDR éigeniaGyatemala<br>40<br>igdonesia<br>Fallippines<br>30<br>Viet °<br>e<br>20 Nam laysia<br>10 Armenia Thailand:<br>o Tonga en and Hefcegovina<br>Republic 0° Jordan Fill Oc” China<br> of Moldova State of Palgstine Samoa Quamaica<br>0 Ukraine Belarus<br>USD 250 500 1,000 2,000 4,000 8,000 16,000 32,000<br>GDP per capita (Constant 2010 USD) [log scale]<br><!-- End of picture text --> Source: UNICEF, WHO, The World Bank Group: Joint child malnutrition estimates. Notes: 'High performers' denotes countries with GDP per capita in the USD 1,000 to 5,000 range whose national stunting rates are below 10 percent. 'Low performers’ denotes countries in the same GDP per capita range whose national stunting rates are above 40 percent. Data correspond to the year with the most recently available stunting estimate for each country (the mean year for all estimates shown is 2013), the oldest estimate is from 2001 (Czech Republic) and the most recent estimates are from 2016. The line shown is based on a locally weighted scatterlpot smoothing algorithm (loess) to more clearly show the average relationship between stunting and average income per capita levels across countries.