rafmacalaba/datause-encoder-data huggingface dataset:
so that the weight on each a� is w�/�w� �⋯�w��.<sup>17</sup> Clearly, C takes on values between 0 and 1.

A key challenge is how to discern the relative importance of the different attainments in order to aggregate up the counts. One overarching structure for doing so is found in the nested method of Alkire and Foster (2011), which extends Atkinson’s (2003) “equal importance” construction to subdimensions and variables. For example, in the case of statistical capacity, it could be argued that the CS dimension should be divided into two equally important subdimensions, one for censuses and a second for surveys. Likewise, variables that reside within a given subdimension (or dimension if it has no subdimensions) should be selected to have roughly equal contributions within the group. A nested approach helps to account for the relative values of variables, subdimensions and dimensions in a fashion that is coherent with the concept being measured.<sup>18</sup>

# _Useful Properties_

Once w has been set and the counting index C has been defined, it offers four useful properties we examine below. First, C is additively decomposable by subsets of variables; second, C is additively decomposable by subsets of countries (or regions). Third, C can be constructed using

> 17 Although the entries of _w_ will typically sum to 1, this general definition allows the sum to exceed or fall short in line with different approaches to counting indices. This generality is also convenient in defining sub-indices that use only a subset of the values in _w_ .

> 18 Other empirical methods can be employed to infer the importance of individual variables, such as applying principal component techniques to obtain weights from the principal component combination that explains the most variation. This approach has the benefit of letting the data speak for themselves. However, estimation results are completely dependent on the timing and coverage of the data set used to calibrate the weights, which raises hard-to-answer questions: How far back in time should the data extend? What should be done when new data come online? Can an outside indicator of what is being measured actually be found? As such, empirical methods may yield data-oriented weights that totally ignore variables conceptually considered to be important. See also Decancq and Lugo (2013) for a discussion of empirical methods for finding weights.

17
# FIGURE 13. POSTNEONATAL MORTALITY TRENDS BY SAVINGS GROUP IN COTE D'IVOIRE, 1970-86 (LS1S 1985/1986)

<!-- Start of picture text -->
-I<br>_*  Low savings<br>All groups<br>- *  ~~Medium savings<br>*  High savings<br>UU  'l_  4*-<br>Year (1)<br><!-- End of picture text -->

NOTE: (1) PNMR 5 - Year Moving Average
# Missing Women and India’s Religious Demography<sup>_∗_</sup>

Vani Borooah<sup>_†_</sup> Quy-Toan Do<sup>_‡_</sup> Sriya Iyer<sup>_§_</sup> Shareen Joshi<sup>_¶_</sup>

## **1 Introduction**

Two features of India’s demography have recently received a great deal of attention. The first is gender bias - the small number of females compared to males (Visaria, 1971; Dyson and Moore, 1983; Sen, 1992, 2001; Kishor, 1993; Das Gupta, 2005). According to the 2001 Census, India has 933 females for every 1000 males, which implies that as many as 35–37 million women in India may be “missing” (Dr`eze and Sen, 1996; Klasen, 1994; Agnihotri, 2000; Sen, 2003; Oster, 2005).<sup>1</sup> The second feature is that demographic variables in India vary sharply by religious group. Fertility and the population growth rate for example, are higher among Muslims than Hindus (Basu 1997; Jeffery and Jeffery 1997; Iyer 2002; Dharmalingam and Morgan, 2004).<sup>2</sup> In the literature on Indian demography, both these issues have received enormous attention, but they have typically been studied independently of each other. The adverse sex

> _<u>∗</u>_ For helpful discussions we are grateful to Robert Barro, Eli Berman, Edward Glaeser, Timothy Guinnane, Monica Das Gupta, Partha Dasgupta, Larry Iannaccone, Siddiq Osmani, Abusaleh Shariff, Chander Velu, David Voas, and to participants at the American Economic Association 2007 meetings in Chicago, the ‘Religion, Economics and Culture’ 2004 Annual Meetings in Kansas City, Missouri, and the 2005 ESRC seminar on ‘Demography and Religion’ in Lancaster, UK. We are also grateful to Sarina Siddhanti for excellent research assistance, We are especially grateful to the Editor and two anonymous referees for their invaluable comments.

> _†_ Faculty of Economics and Politics, University of Ulster, Northern Ireland. vk.borooah@ulst.ac.uk

> _‡_ Development Economics Research Group, The World Bank. qdo@worldbank.org

> _§_ Corresponding author: Faculty of Economics and St. Catharine’s College, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DD, United Kingdom. Tel: 44 1223 335257; Fax: 44 1223 335475; Email: Sriya.Iyer@econ.cam.ac.uk

> _¶_ The School of Foreign Service, Georgetown University. shareen.joshi@gmail.com

> 1The estimate of the number of missing women is based on comparisons with Europe and North America which have 1050 females per 1000 males.

> 2An often-cited figure from the National Family Health Survey conducted in 1998-99, shows that the Total Fertility Rate (TFR) for Hindus and Muslims was 2.8 and 3.6 respectively.

1
**Table 3. Survey Methods’ Impacts on the Reporting of Labor Market Outcomes**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|LOA|0.040*|0.043**|
||(0.023)|(0.022)|
||[0.078]|[0.047]|
|ESR|-0.001|-0.012|
||(0.024)|(0.023)|
||[0.972]|[0.601]|
|Observations|2,480|2,480|
|Outcome (Control|||
|Group)Mean|0.571|0.670|

_Notes:_ This table shows the estimated effects of including the List of Activities (LOA) module or enforcing self-reporting (ESR) when indicating employment or work status. We present the estimated coefficients from Equation (1). Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. All estimations include strata fixed effects and control by variables selected using a doubleLASSO procedure (Table A3 in the Appendix shows the variables selected). Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 30
# **3 Data**

## **3.1 Annual Survey of Industries (ASI) Data**

Our primary dataset consists of firm-level surveys from the Annual Survey of Industries (ASI). The period of coverage for each ASI survey is the accounting year that ended on any day during the fiscal year: the 1985-86 survey (which we refer to as the 1985 survey) refers to the factory’s accounting year that ended on any day between April 1, 1985 and March 31, 1986. We obtained firm-level data for all available years between 1985 and 2004. Data were not available for 1995. In addition, the way in which input data were collected and made available for the years 1996 and 1997 did not make it possible to construct certain key variables for those two years that were consistent with the other years. Therefore, we restrict our analysis to the firm-level data for the remaining 17 years between 1985 and 2004 (1985 through 1994 and 1998 through 2004).

The sampling universe for the ASI is all firms that are registered under Sections 2m(i) and 2m(ii) of the Factories Act, as well as firms registered under the Bidi & Cigar Workers Act, and a number of utility and service providers. We include only manufacturing firms in our analysis. All firms that have 20 or more employees (10 or more employees if a power source is used) are required to register.<sup>3</sup> The sampling frame is derived from the registry list of each state’s Chief Inspector of Factories, and all but four small states are covered.

The ASI divides firms into two sectors - the “census” sector, in which firms are surveyed every year, and the “sample” sector, in which firms are sampled every few years. Between 1985 and 2004, the inclusion of firms in the census and sample sectors, as well as the sampling strategy, changed several times. To ensure that our results are representative of the population of firms, we apply the sampling multiplier weights that are provided for each firm in the population analysis (but not the panel analysis).

Each unit surveyed is generally a factory; however, if an owner has two factories in the same

> 3Although firms with fewer than 10 employees are not required to register under this act, and therefore should not appear in the sampling universe, a significant fraction of firms report fewer than 10 employees. These firms may be registered for various reasons, including the possibility that they used to have more than 10 employees but shrank; that they plan to grow in the future; and that registering may be a signal to creditors or other business partners.

9
# **Table 12 : Expenditure types  that would have been cut had remittance not been received  (percent of recipient reporting )**

||**Cash**<br>**savings**|**Education-**<br>**related**|**Health**<br>**related**|**Food**<br>**consumption**|**Housing-**<br>**related**|**Purchase**<br>**of**<br>**consumer**<br>**durables**|**Investment**<br>**in business**|**Livestock**<br>**purchase**|**Others**|**N**|
|---|---|---|---|---|---|---|---|---|---|---|
|All regions|19.2|8.1|3.4|43|10.2|4.3|6.4|2.3|9.1|235|
|Dhaka|||||||||||
|region|17.2|4.3|2.6|53.5|9.5|0.9|5.2|2.4|9.5|116|
|Chittagong|||||||||||
|region|29.6|14.1|5.6|38|4.3|1.4|5.6|0|12|71|
|Other|||||||||||
|regions|8.3|8.3|2.1|25|20.8|16.7|10.4|4.8|4.8|48|

**Table 13: Determinants of migration status of households**

||**Estimated coefficient**|**t-statistic**|**p-value**|
|---|---|---|---|
|Area of homestead owned ten years ago|0.005|1.16|0.245|
|Area of agricultural land owned ten years ago|0.001|1.7*|0.089|
|Dummy variable=1 if household owned non<br>agricultural business 10 years ago|-0.125|-0.66|0.508|
|<br>Number of adult males|0.340|3.15**|0.002|
|Number of adult females|0.256|2.26**|0.024|
|Dummy variable=1 if household is female|0.786|4.33**|0|
|Dummy variable=1if household head is a wage<br>laborer|-0.344|-1.4|0.162|
|Number of female adults with primary education|-0.128|-1.22|0.222|
|Number of male adults with primary education|0.093|1.02|0.306|
|Education level of head of household|-0.015|-0.63|0.529|
|Education level of spouse of head of household|0.067|2.94*|0.003|
|Maximum education among adults in household|0.055|1.61|0.107|
|Dummy variable=1if household owns fan|0.130|0.69|0.493|
|Dummy variable=1if household owns iron|-2.502|-2.57**|0.01|
|Dummy variable=1if household owns TV|0.281|1.04|0.298|
|Dummy variable=1if household owns sowing<br>machine|-0.530|-0.95|0.344|
|Dummy variable=1if household owns pounder|0.061|0.14|0.886|
|Dummy variable=1if household owns fishnet|-0.906|-1.8*|0.072|
|Dummy variable=1if household owns plough|0.260|0.53|0.599|
|Distance to nearest railwaystation|-0.004|-1.3|0.195|
|<br>Distance to nearest town|-0.132|-0.98|0.327|
|Distance to nearest bank branch|-0.001|-0.11|0.914|
|Dummy variable=1 if Dhaka region|-0.074|-0.38|0.701|
|<br>Dummyvariable=1 if Chittagongregion|0.088|0.44|0.661|
|N=439. Asterisk(*)and double asterisk(**)denote|variables significant at 5% and 1|0% respectively.||

32
Table 12: Effects of forecast errors and forecast growth on worker compensation and worker composition: persistent versus transitory demand shocks

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:|log<br>monthly|log hourly<br>wage|log<br>monthly|log (1 +<br>overtime|log (1+<br>other pay)|log total<br>hours|share with<br>a degree|person FE|
||wage||base wage|pay)|||||
|Weighted forecast error|||||||||
|- transitory shocks|0.0100|0.0107|-0.0010|0.0328|0.0516|-0.0134|0.0068|-0.0555**|
||(0.0070)|(0.0070)|(0.0060)|(0.0370)|(0.0448)|(0.0154)|(0.0047)|(0.0245)|
|- persistent shocks|0.0115**|0.0108**|0.0113**|0.0204|0.0011|0.0466***|0.0037|-0.0374|
||(0.0054)|(0.0054)|(0.0046)|(0.0169)|(0.0209)|(0.0081)|(0.0026)|(0.0230)|
|Weighted forecast growth|||||||||
|- transitory shocks|-0.0041|-0.0048|-0.0099***|0.0645***|0.0485*|-0.0013|-0.0046|-0.0114|
||(0.0043)|(0.0042)|(0.0037)|(0.0223)|(0.0279)|(0.0107)|(0.0030)|(0.0155)|
|- persistent shocks|0.0048*|0.0044|0.0025|0.0234**|0.0048|0.0168***|0.0009|-0.0130|
||(0.0028)|(0.0028)|(0.0023)|(0.0111)|(0.0136)|(0.0042)|(0.0014)|(0.0126)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|12631|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|6012|
|Adj. R<sup>2</sup>|0.0213|0.0233|0.0565|0.0224|0.00960|0.0610|0.0243|0.0489|
|RSS|472.1|471.2|248.3|15008|16662|1709|105.2|1546|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
expansion by SSA'. The model was simulated from 1992/93 through to 2000/01 in order to provide base forecasts of price, production and consumption in the cotton and non-cellulosic fibers sectors. This required that values for all the exogenous variables be evaluated for each year to the end of the simulation period. Annual percent growth rates in income and population for each region were used to project the levels of these variables for each year between 1992/93 and 2000/01. Exchange rates and consumer price indexes were set at their 1991/92 values, thus assuming that purchasing power parity was maintained over the long-run and projected prices are in 1991/92 constant US$ terms.

The analysis is presented in terms of how world cotton market projections (for the period 1992/93-2000/01) change under alternative scenarios about SSA cotton exports. The results of two simulations of the model are reported in Tables 6 and 7. The results from !he base simulation (referred to as 'Base' in the tables) indicate that SSA cotton exports will grow from 775,000 tons in 1992/93 to 900,000 tons in 2000/01, an increase of about 1.8% annually<sup>2</sup> . In the second simulation (referred to as 'Shock' in the tables), it was assumed that SSA maintains the same growth in the 1990s as it did in the 1980s. That is, exports are assumed to expand at 7.9% annually in the forecast period.

In order for exports to expand at the accelerated rate, production would have to reach 1 million tons by 2000/01, an increase of 524,000 tons over the base production projection. The larger exports do cause the world price of cotton to be lower than they otherwise would be (see Table 7). In the base simulation the world cotton price is projected to increase at 2.7% p.a., reaching US¢162/kg by 2000/01 (prices are in 1990 constant US$ terms). With the accelerated exports, the price is projected to increase at a rate of only 2.2% p.a., reaching USc156/kg by the end of the decade. On average, the price difference is USc3/kg over the simulation period. With the lower world prices, cotton production in nonSSA countries is reduced 0.72% on average, equivalent to about 138,000 tons, while world mill consumption is increased by about 35,000 tons (up 0.2%). As a result of lower production, exports from non-SSA countries decline on average by 86,000 tons (down 2.1 %). Finally, lower export volumes and prices result in lower export revenues received by non-SSA developing countries, 3.7% lower on average in real terms over the simulation period.

# 'The model is fully documented in Coleman and Thigpen (1991).

2The substantial reduction in growth rate results from recent stagnation in the cotton sectors of many francophone countries, especially Burkina Faso, CBte d'Ivoire, and Mali. As well, high growth rates during the 1980s reflect low base level exports in the early 1980s.

12
# **Table 11: Receiving SMS vs. Reporting Changing Work Patterns**

||C<br>|hange Wor<br>|k Patterns= <br>|1; Do not=<br>|0<br>||C<br>|hange Wor<br>|k Patterns= <br>|1; Do not=<br>|0<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)||(6)|(7)|(8)|(9)|(10)|
|SMS primary||||||Gov. SMS past||||||
|source|0.200***|0.185***|0.178***|0.039***|0.039***|week|0.090**|0.111**|0.011|0.206***|0.203***|
||(0.013)|(0.012)|(0.012)|(0.010)|(0.010)||(0.042)|(0.044)|(0.043)|(0.041)|(0.041)|
|Urban||0.063***|0.061***|||Urban||0.067***|0.064***|||
|||(0.012)|(0.012)|||||(0.012)|(0.012)|||
|Ln(income pc)||0.042***|0.043***||-0.004**|Ln(income pc)||0.044***|0.045***||-0.004**|
|||(0.002)|(0.002)||(0.002)|||(0.002)|(0.002)||(0.002)|
|Constant|0.437***|0.297***|0.225***|0.479***|0.492***|Constant|0.478***|0.326***|0.247***|0.484***|0.496***|
||(0.006)|(0.009)|(0.034)|(0.002)|(0.006)||(0.005)|(0.009)|(0.034)|(0.001)|(0.006)|
|Month control|No|No|Yes|Ind. FE|Ind. FE|Month control|No|No|Yes|Ind. FE|Ind. FE|
|Observations|10,963|10,963|10,963|10,963|10,963|Observations|10,963|10,963|10,963|10,963|10,963|
|R-squared|0.027|0.063|0.120|0.002|0.002|R-squared|0.000|0.041|0.100|0.005|0.006|
|Unique individuals||||2,153|2,153|Unique individuals||||2,153|2,153|

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 17
Table 8: Difference between New and Official Growth Measures

||Absolute|diference|Underes|timation|Overest|imation|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||||difere|nce_>_0|difere|nce_<_0|
|Voice and accountability|-0.18***|-0.11**|-0.083|-0.094|0.30***|0.12*|
||(0.053)|(0.043)|(0.079)|(0.083)|(0.11)|(0.061)|
|Statistical capacity|-0.014***|-0.017***|-0.017***|-0.018***|0.0099*|0.015***|
||(0.0034)|(0.0030)|(0.0037)|(0.0040)|(0.0049)|(0.0044)|
|Voice and accountability _×_ COVID||-0.56***||-0.11||0.96***|
|||(0.13)||(0.29)||(0.31)|
|Statistical capacity _×_ COVID||0.016||0.013||-0.032|
|||(0.016)||(0.012)||(0.020)|
|Agriculture share _×_ COVID||0.020||-0.0054||-0.0053|
|||(0.014)||(0.035)||(0.029)|
|Industry share _×_ COVID||0.032***||0.067**||0.010|
|||(0.012)||(0.029)||(0.011)|
|Service share _×_ COVID||0.074***||0.12**||0.015|
|||(0.024)||(0.046)||(0.017)|
|Constant|2.11***|2.01***|2.73**|2.76**|-1.60*|-1.94**|
||(0.61)|(0.53)|(1.22)|(1.14)|(0.85)|(0.75)|
|Date fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|1965|1965|887|887|1078|1078|
|Adjusted _R_<sup>2</sup>|0.11|0.12|0.100|0.11|0.14|0.18|
|Number of countries|76|76|76|76|76|76|

_Note_ . The sample includes 76 EMDEs between 2013Q2 and 2020Q4. COVID is a time dummy that equals 1 between 2020Q1 and 2020Q4 and 0 otherwise. Throughout, standard errors are in parentheses. _∗p <_ 0 _._ 10, _∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01.

where _zk,i,t_ is year-on-year nighttime light growth in region _k_ in country _i_ at time _t_ . **_x_** _k,i_ is a row vector of average nighttime lights per capita and population density.<sup>25</sup> **1** _{t∈_ COVID _}_ is a time dummy indicating the COVID-19 period. _αi_ are country fixed effects and _δt_ are time fixed effects. _ξk,i,t_ is the residual term. **_β_**<sup>_sub_</sup> 0 and **_β_**<sup>_sub_</sup> 1 are column vectors of<sup>26</sup> Column (1) in table 9 shows that higher average nighttime lights per capita are associated with faster growth of nighttime lights over the estimation period. This suggests that regions within countries diverged and spatial inequality rose. The result strengthens when we control for the COVID-19 period (see column (2)) because more

> 25Neither is time-varying as we use the average for nighttime lights per capita and population density in 2015. Both are in logs.

> 26A table of summary statistics is included in appendix J.

25
**Trrade P61i;y WPS 629**

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Table 55

|Dependent variable: Fisc|al adjustm|ent when ne|eded based|on Escola|no et al.(2|014)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus<br>structural as % GDP|-0.005<br>(0.005)|-0.013**<br>(0.005)|-0.005<br>(0.006)|-0.011**<br>(0.005)|-0.001<br>(0.010)|0.018<br>(0.026)|-0.027**<br>(0.013)|-0.029**<br>(0.013)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.530***||-1.204**|-1.100**|-1.166**|-1.167**|-1.185**|
|||(0.489)||(0.490)|(0.483)|(0.491)|(0.485)|(0.506)|
|Inflation (t-1)||-0.190**||0.455|0.434|0.465|0.460|0.396|
|||(0.083)||(0.298)|(0.299)|(0.294)|(0.291)|(0.307)|
|U.S. Interest rate||-0.031*||-0.055**|-0.055***|-0.058***|-0.058***|-0.056***|
|||(0.015)||(0.021)|(0.020)|(0.020)|(0.020)|(0.021)|
|**Political factors**|||||||||
|Gini|||0.006|0.007|0.006|0.013*|0.006|0.006|
||||(0.005)|(0.004)|(0.004)|(0.007)|(0.004)|(0.004)|
|Rule of law|||0.021|0.104**|0.095**|0.101**|0.066|0.104**|
||||(0.039)|(0.046)|(0.047)|(0.045)|(0.064)|(0.045)|
|Margin of Majority|||-0.067|0.009|0.032|0.032|0.025|-0.313|
||||(0.120)|(0.126)|(0.131)|(0.131)|(0.130)|(0.233)|
|Election's year|||-0.014|-0.020|-0.019|-0.019|-0.020|-0.018|
||||(0.032)|(0.032)|(0.031)|(0.031)|(0.032)|(0.032)|
|**Interactions**|||||||||
|emes = o,|||||-||||
|EMEs * Deviation|||||-0.017||||
||||||(0.011)||||
|Gini * Deviation||||||-0.001<br>(0.001)|||
|Rule of law * Deviation|||||||0.004<br>(0.004)||
|Majority * Deviation||||||||0.034*<br>(0.020)|
|Constant|0.238***|0.485***|-0.053|-0.159|-0.129|-0.377|-0.004|0.046|
||(0.051)|(0.094)|(0.323)|(0.255)|(0.261)|(0.330)|(0.313)|(0.291)|
|Observations|619|607|541|531|531|531|531|531|
|R-squared|0.002|0.041|0.007|0.054|0.059|0.058|0.057|0.058|
|Number of ifscode|67|67|60|60|60|60|60|60|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors c|lustered by|country in|parenthese|s|||||
|***p<0.01,**p<0.05,*|p<0.1||||||||

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