rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 9. The investment equation by firm size (in capital stock) and firm age: Non-State Domestic Only**

|||**By size**<br>||
|---|---|---|---|
||**Small**|**Medium**|**Large**|
|ln(GDP per capita)|-0.101**|0.001|0.010|
||(-2.009)|(0.037)|(0.361)|
|ln(city pop)|-0.014|0.026|0.041|
||(-0.297)|(0.983)|(1.529)|
|ln(firm age)|-0.105***|-0.037**|-0.017|
||(-2.842)|(-2.408)|(-1.449)|
|Loan|0.309***|0.166***|0.047|
||(5.200)|(7.331)|(1.150)|
|Trade credit|0.237|0.024|0.042|
||(1.495)|(0.338)|(0.746)|
|UCA|0.048***|0.042***|0.029**|
||(4.010)|(4.748)|(2.345)|
|sales/lagged K|0.009***|0.011**|0.007|
||(4.310)|(2.540)|(1.377)|
|CF/lagged K|0.103***|0.140***|0.252***|
||(3.448)|(3.456)|(6.047)|
|Lagged sale growth|0.031*|0.001|0.034***|
||(1.901)|(0.149)|(3.257)|
|Tobin’s Q|0.364*|0.255**|0.066|
||(1.886)|(2.342)|(0.817)|
|Number of observations|2,170|4,021|1,573|
|Adjusted R2|0.082|0.077|0.075|
|p-value for the test of equality of<br>CF/lagged K for OLS|Small=medium:0.44|4; small=large:0.004; medium=l|arge:0.046|
|p-value for the test of equality of<br>Loan for OLS|Small=medium:0.022|; small=large:0.0001; medium=l|arge:0.009|
|p-value for the test of equality of<br>trade credit for OLS|Small=medium:0.21|9; small=large:0.233; medium=l|arge:0.839|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.

50
- 43 -

# Table 16: MEANS AND STANDAR **D** EVIATIONS OF EARNINC' ;,UNCTION VARIABLES

|Variables|Mean|SD|
|---|---|---|
|ln(W1)|11.12|1.09|
|ln(W2)|11.27|1.14|
|Years of Schooling|6.97|5.45|
|Years of Primary Schooling|4.24|2.66|
|Years of Secondary Schooling|2.33|2.68|
|Years of Higher Schooling|0.41|<br>1.41|
|Years of Work Experience-|9.19|7.91|
|Current Occupation|||
|Years of Work Experience-|1.52|3.45|
|Previous Occupation|||
|Years of Secondary VTE|0.86|<br>1.62|
|Years of Post-Secondary<br>VTE|0.08|0.43|
|Years of Teacher Training|0.13|0.54|
|Years of Apprenticeship<br>Training|0.71|<br>1.77|
|CEPE|0.22|0.42|
|BEPC|0.05|0.21|
|Secondary<br>VTE Diploma|0.22|0.42|
|Post-Secondary<br>VTE Diploma|0.04|0.19|
|Teacher Diploma|0.04|0.21|
|Other Diploma|0.07|0.25|
|Sex (1-<br>female)|0.19|0.39|
|Non-Ivorian|0.17|0.37|
|Seasonal Job|0.03|0.17|
|Number of Cases|500|<br>-|
- Greenwood B.M., Greenwood A.M., Bradley A.K., Tulloch S., Hayes R. and Oldfield F.S.J. (1987b) Deaths in infancy and early childhood in a well vaccinated, rural, West African population. Ann Troy Paediatr 7, 91-99.

- Grosse R.N. (1980) Interrelation between health and population: Observations derived from field experiences. Soc Sci & Med 14C, 99-120.

- Grubb G.S., Fortney J.A., Saleh S., Gadalla S., el-Baz A., Feldblum P. end Rogers S.M. (1988) A comparison of two cause-of-death classification systems for deaths among women of reproductive age in Menoufia, Egypt. Int J Enidemiol 17 (2), 385-91.

- Kachirayan M., Radhakrishna S., Ramakrishnan R., Ramanathan A.M. and Sreenivas V. (1983) Study of registered deaths in Madras city. Indian J Med Res, 77, April, 564-73.

- Kalter H.D., Gray R.H. and Black R.E. (1962) Validation of a postmortem interview method to ascertain selected causes of death in children in The Phillipines. Int J Epidemiol (in press).

- Kircher T., Nelson J. and Burdo H. (1985) The autopsy as a measure of accuracy of the death certificate. N Enal J Med 313 (20) 1263-9.

- Kroeger A. (1983) Health interview surveys in developing countries: A review of the methods and results. Int J Epidemiol 12 (4) 465-481.

- Muller A.S. and van Ginneken J.K. (1989) Morbidity and mortality at Machakos, Kenya, 1974-1981: a policy-oriented study of health in rural communities incorporating a pertussis vaccine trial. In Disease and Mortality in Sub-Saharan Africa. Ed. R.G. Feachem and D. Jamison. Oxford: Oxford University Press (in press).

- PAHO. (1988) Health Profiles, Brazil, 1984. in Epidemiological Bulletin 9 (2) 6-12.

- Preston S.H. (1976) Introduction: Nature of the data base, in Mortality Patterns in National Populations. London: Academic Press Inc.

- Ties Boerma J. (1987) Maternal mortality in sub-Saharan Africa: Levels, causes and interventions. Paper presented to the IUSSP seminar on mortality and society in sub-Saharan Africa, October 19-23. Yaounde, Cameroon.

- Timaeus I. and Graham W. (1989) Measuring aduit mortality in developing countries: A review and assessment of methods. CPS Research Paper 88-4 London School of Hygiene and Tropical Medicine.

- United Nations. (1983) Estimation of adult survivorship probabilities from information on orphanhood and widowhood, in Indirect Techniaues for DemoQraPhic Estimation. Manual X. New York: United Nations.

30
# Sex-Selective Abortions, Fertility, and Birth Spacing<sup>_∗_</sup>

## Claus C P ̈ortner

Department of Economics Albers School of Business and Economics Seattle University, P.O. Box 222000 Seattle, WA 98122 work@clausportner.com www.clausportner.com &

## Center for Studies in Demography and Ecology University of Washington

February 2015 JEL: J1, O12, I1 Keywords: India, prenatal sex determination, censoring, competing risk

> _∗_ I am grateful to Andrew Foster and Darryl Holman for discussions about the method. I owe thanks to Shelly Lundberg, Daniel Rees, David Ribar, Hendrik Wolff, seminar participants at University of Copenhagen, University of Michigan, University of Washington, University of Arhus,<sup> ̊</sup> the Fourth Annual Conference on Population, Reproductive Health, and Economic Development, and the Economic Demography Workshop for helpful suggestions and comments. I would also like to thank Nalina Varanasi for research assistance. Support from Danish Trust Fund resources in the World Bank and the University of Washington Royalty Research Fund is gratefully acknowledged. Partial support for this research also came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant, 5R24HD042828, to the Center for Studies in Demography and Ecology at the University of Washington. A prior version of this paper were presented under the title “The Determinants of Sex-Selective Abortions.”
Table 1: Aggregate Poverty Measures for Alternative Poverty Lines, 1984-87 'a

|Poverty<br>measure|Poverty<br>line|Sector|1984|1984<br>St.|1987|1987<br>St.|Significant<br>difference?|Percentage<br>* decline<br>|
|---|---|---|---|---|---|---|---|---|
|||||error||error|t=|(1984-87)|
|Headcount|11000|Urban|12.08|0.26|7.32|0.21|14.35|39.40|
|index (Z)(a=0)||Rural|39.43|0.26|26.80|0.23|35.77|32.03|
|||Total|33.02|0.21|21.65|0.18|40.86|34.39|
||15000|Urban|28.04|0.36|21.17|0.33|14.21|24.50|
|||Rural|64.46|0.26|54.31|0.26|27.50|15.75|
|||Total|55.91|0.22|45.55|0.22|33.16|18.53|
|Poverty|11000|Urban|2.68|0.07|1.25|0.05|17.70|53.36|
|gap ratio(a=1)||Rural|10.32|0.09|5.29|0.06|46.37|48.74|
|(Z)||Total|8.52|0.07|4.22|0.05|51.63|50.47|
||15000|Urban|7.31|0.12|4.67|0.09|14.21|36.11|
|||Rural|21.63|0.12|14.84|0.10|44.30|31.39|
|||Total|18.27|0.10|12.15|0.08|49.91|33.50|
|Preferred|11000|Urban|0.92|0.03|0.33|0.02|15.61|64.13|
|measure(Q=2)||Rural|3.86|0.05|1.57|0.02|44.50|59.33|
|(xlOO)||Total|3.17|0.03|1.24|0.02|49.38|60.88|
||15000|Urban|2.78|0.06|1.50|0.04|17.84|46.04|
|||Rural|9.57|0.07|5.51|0.05|47.20|42.42|
|||Total|7.97|0.06|4.45|0.04|52.79|44.17|

# Note: i) Ordinary CPI deflator for the poor used throughout; spatial deflatur gives very similar results.

- ii) *t = (Pa(1<sup>9 87</sup> ) - Pa(198<sup>4</sup> ))/se(Pa(l<sup>9 87</sup> ) - Pa(1984)). All differences between the poverty measures over the two years are statistically significant at the one percent level. (Calculations based on Kakwani's (1989a) standard errors.)
The rest of the paper is structured as follows. The next section reviews the existing work on the determinants of remittances, identifies some of their limitations and describes how the present paper can contribute to the current literature. Section 3 provides a brief description of the data employed for the empirical analysis and specifies the estimation models. Section 4 presents the estimation results. The final section summarizes the findings and draws out some policy implications.

# **2. Literature on Determinants of Remittances**

The existing literature on the determinants of remittances is largely based on microeconomic analyses. Lucas and Stark (1985), for instance, provided a pioneering work on the potential motives of migrant workers to remit home on the basis of the data drawn from the National Migration Study of Botswana. They consider several hypotheses for motivations to remit, ranging from pure altruism to pure self-interest. In the case of pure altruism, migrants send part of their income to their home countries to increase the well-being of family members by providing them with additional income. As for the case of pure self-interest, on the other hand, they remit in order to purchase durable goods and invest in housing, land, or business at home. In addition to these two cases, they propose tempered altruism or enlightened self-interest as a complementary alternative based on the view that remittances are part of a self-enforcing contractual arrangement between migrant and family, that are of mutual benefit. The migrant adheres to the arrangement as long as it is in his or her interest to do so, which may be either altruistic or more selfseeking (Lucas and Stark, 1985). Much of the existing work based on the micro data follows this approach (e.g. Aggarwal and Horowitz, 2002; Ilahi and Jafarey, 1998).

A macro-level analysis of remittances is, on the other hand, less established. Aggregate remittance flows are likely to reflect the underlying microeconomic considerations that determine individual decisions to remit (El-Sakka and McNaab, 1999). As a result, some of the macroeconomic variables that have been examined include those concerning the economic conditions of home country as well as investment variables such as interest rate 2
46 Graph 20 Stock Market Prices and Investment <!-- Start of picture text --> 3.5 64000 3.5 800000<br>330 tok iC ndx :60000 30 pfi c dex Itsto* 700000<br>g2.5. .56 0 ~ 2.5. 80O<br>20 .52000 E 2 0 600000i5<br>E 1.5- \ I / , / t .4aooo06 X 1.s 5 ,^, .400000O<br>S e 1.0t V I i / , J 44000 S o 1.0 | U / Grossfixedcapitaltonna5on 300000<br>,, 0.5- 90 91 92 93 94 95 96 97 98 99 00' 01 o Grossfixed caoptalforrnabon - 0.5 9091929394i9596 9798 99 00 01- 0<br>6 SO 110 9 2.8 360000<br>, GIossfi i G piclforKnationic x<br>6 5f e 'k i 00ff 2.4. /\ ' 320D00<br>1~~~~~~~7 /<! 520 'V 20o x<br>i 8 1 eJ 2.02 w z ff 60 i200000<br>so pfice index''<br>0 f0 e .' Gross fied capi1J forlma6o 1600D0<br>90 91 92 93 94 95 96 97 98 99 00 01 90 9i 92 93 94 95 96 97 98 99 00 Di<br><!-- End of picture text -->

Sources: Stock market price indices are from EMDB database from S&P. Gross fixed capital formation: Argentina, Ministerio de Finanzas y Obras Publicas; Chile, Central Bank of Chile; Colombia, DANE; and Mexico, INEGI via HAVER database.
# Figure 5

<!-- Start of picture text --> REVENLIE FROM ONEY CREATION A D TOTAL DEFICIT OF THE CPS<br>( as v OF CDP )<br>105.,. t.... ... . .<br>1 5 . . . . . . . . . . . . .. .<br>1969 1965 1979 1975 1989 19b'<br>I .... DEFT<br><!-- End of picture text --> With the correction described above the series of the revenue from money creation obtained from the monetary data shows a clear correlation with the series of the deficit of the CPS obtained from the fiscal data. Both series are shown in Figure 5. Almost systematically, the Deficit of the Consolidated Public Sector (DEFT from column 5 in Table 4) exceeds the Revenue from Money Creation. The difference between both series can be taken as an approximation to the part of the total deficit that was financed through the issuing of interest earning debt. The generation cf public debt should have been larger than this amount on the account that the Central Bank had its own quasifiscal deficit that also had to be financed. While we have the FIEL data -in Table 4, we do not feel it is comparable with our series because that part of what FIEL calls quasifiscal expenditures may actually be indirect financing of the CPS deficit through the variety of mechanisms that were previously discussed. The FIEL data also considers the changes of Reserves as part of the quasifiscal expenditures.

18
EV buyers by country by year, (5) non-financial incentives including green plate, free parking, and HOV lane privilege for EVs by country by year, (6) sales of heavy-duty EVs by country by year, (7) unit labor cost index in construction sector by country by year, and (8) socio-economic variables by country by year. The 13 countries include Austria, Canada, China, France, Germany, Japan, Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, and United States, accounting for 95% of world EV cumulative sales. Note that most developing countries are – excluded from the sample due to extremely low EV penetration for example 0.14% for India and 0.1% for Brazil in 2020.<sup>6</sup> We merge these data into a panel data set of 3,980 observations at the level of vehicle model by country by year. Table 1 presents the summary statistics of the data.

**Empirical framework** We define a vehicle model as a brand-model combination (e.g., Telsa Model 3). Let _c_ index a country, _k_ index a model, and _t_ index a year. We specify the following baseline equation for the analysis:

where _qckt_ is the sales of EV model _k_ in country _c_ and year _t_ . _Pckt_ denotes tax-inclusive price of a vehicle. _Sckt_ denotes the total subsidies that consumers are eligible for. The first term measures the actual acquisition cost (price minus subsidies) and _β_ 1 measures consumer sensitivity to the acquisition cost. The implicit assumption is that consumers respond to vehicle purchase price and subsidies similarly on a dollar-to-dollar basis. The assumption could be violated if the subsidies are not as salient as prices or the pass-through of the subsidies is not complete (Busse et al., 2006; Chetty et al., 2009). National-level EV subsidies in our study are of considerable size and widely publicized. Recent research points to complete pass-through in consumer subsidies for alternative fuel vehicles (Sallee, 2011; Muehlegger and Rapson, 2018). Li et al. (2021) shows that consumers respond to EV prices and purchase subsidies similarly in China.<sup>7</sup> From a practical standpoint, while it is possible to separately estimate consumer responses to prices and subsidies as in Li et al. (2021), our data only include consumer subsidies at the national

> 6In India, all most all electric vehicle ownership constitutes two-wheelers and three-wheelers, which are not the focus of this paper (Punditz (2021)). In Brazil, EV sales of popular EV brands (e.g. Nissan Leaf and Tesla Model 3) were less than 50 units in 2019. In the recent decade, even the gasoline vehicle per capita ownership in major developing countries is below that of major developed countries (Li et al. (2020)).

> 7We drop 2019-2020 data for China in our analysis due to the heterogeneous impact of the pandemic across countries, especially between China and other countries.

5
We extend this literature in several ways. First, we assess the correlation of mortality to national income and how that has changed over time. Second, we update the analysis of mortality changes in the era of HIV/AIDS. Third, we go beyond “extreme mortality” events and assess how adult mortality changes as a result of conflicts more generally. And last, by using the socioeconomic characteristics of the female respondent as a proxy for the socioeconomic background of her siblings, we analyze how these trends and variations in adult mortality differ with socioeconomic status, including an analysis of the variation over time in the gradient.

# **2) Data**

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere). The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region’s population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population—even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as “representative” of the rest of the world, or even the rest of the developing world.

DHS data are attractive for two main reasons. First, these are surveys that have been carried out systematically using similar protocols and questionnaires.<sup>1</sup> No other collection of nationally representative datasets this large has such a degree of cross-country comparability. Second, the surveys include a “sibling mortality” module from which we can derive mortality

> 1 DHS data collection and cleaning are typically carried out in a partnership between MeasureDHS and a local implementing partner with funding from USAID.  By monitoring quality at all stages, MeasureDHS ensures that the protocols meet quality standards, resulting in extremely reliable data.  More information in the DHS is available at <u>http://www.measuredhs.com.</u>

3
# **G Estimates of IGM using secondary education**

**Table A7:** Country-Level Estimates of Educational Intergenerational Mobility

|||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|---|
|mobility / N|census years|upward|upward|downward|downward|N|N|
|age range||19-25|20-25|19-25|20-25|19-25|20-25|
|Trinidad and Tobago|1970,1980,1990,2000,2011|.58|.579|.077|.072|51,140|21,370|
|Peru|1993,2007|.493|.504|.059|.049|348,429|220,485|
|Jamaica|1982,1991,2001|.458|.455|.138|.149|49,411|11,400|
|Saint Lucia|1980,1991|.42|.408|.084|.139|2,694|168|
|Cuba|2002,2012|.362|.381|.221|.205|84,252|123,515|
|Chile|1970,1982,1992,2002|.358|.369|.164|.148|431,534|129,491|
|Argentina|1970,1980,1991,2001,2010|.344|.358|.213|.198|1,276,838|447,347|
|Panama|1960,1970,1980,1990,2000,2010|.338|.351|.186|.174|97,802|31,532|
|Bolivia|1976,1992,2001,2012|.326|.338|.21|.2|211,870|62,971|
|Venezuela|1971,1981,1990,2001|.292|.297|.236|.233|636,479|130,603|
|Ecuador|1974,1982,1990,2001,2010|.289|.3|.194|.176|429,008|105,824|
|Costa Rica|1973,1984,2000,2011|.287|.296|.205|.19|122,731|40,554|
|Dominican Republic|1981,2002,2010|.257|.278|.243|.216|197,241|54,451|
|Brazil|1960,1970,1980,1991,2000,2010|.249|.268|.231|.203|12,610,650|1,718,702|
|Colombia|1973,1985,1993,2005|.242|.255|.2|.171|1,152,288|160,657|
|Mexico|1970,1990,2000,2010|.24|.252|.226|.202|3,392,481|506,282|
|Uruguay|1963,1975,1985,1996,2006,2011|.237|.24|.334|.31|130,248|34,931|
|Paraguay|1962,1972,1982,1992,2002|.206|.218|.143|.12|146,601|15,782|
|El Salvador|1992,2007|.185|.201|.224|.193|100,588|17,559|
|Haiti|1971,1982,2003|.157|.162|.425|.398|133,746|10,397|
|Guatemala|1964,1973,1981,1994,2002|.122|.131|.195|.169|278,412|19,341|
|Honduras|1974,1988,2001|.093|.096|.383|.371|121,155|11,281|
|Suriname|2012|.077|.114|.367|.334|4,413|1,318|
|Nicaragua|1971,1995,2005|.033|.043|.338|.309|118,929|15,611|
|mean / total||.277|.287|.221|.205|22,128,940|3,891,572|

Notes: Columns (1) and (2) give upward-IGM estimates. They reflect the likelihood that children, aged 19-25 and 20-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Columns (3) and (4) give downward-IGM estimates. They reflect the likelihood that children, aged 19-25 and 20-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. Columns (5) and (6) give the number of observations used to estimate the country-specific IGM statistics (children whose parental education is reported in the censuses). Countries are sorted from the highest to the lowest level of upward IGM in the 19-25 sample (column (1)). “mean” gives the unweighted average of the 24 country-estimates.

53
# **Table A25:  World Bank Country Policy and Institutional Assessments (PIA)**

|**Data Provider**<br>The World Bank.<br>**Description**<br>Multilateral development bank headquartere|d in Wa|shingto|n, Unit|ed Sta|tes||||
|---|---|---|---|---|---|---|---|---|
|**Website**<br>www.worldbank.org|||||||||
|**Data Source**<br>Country Policy and Institutional Assessment|s||||||||
|**Type**<br>Expert Assessment|||||||||
|**Respondents**<br>World Bank country economists subject to c<br> <br>|entralize|d revie|w for|compar|ability||||
|**Frequency**<br>Annually since 1978|||||||||
|**Coverage**<br>World Bank client countries|||||||||
|**Public Access**<br>Since 2005, only for countries eligible f<br>Development Association.|or conce|ssional|lendi|ng fro|m the|Intern|ational||
|<br>**Description**<br>Indicators on 16 dimensions of policy and|institutio|nal pe|rforma|nce.|Respon|ses are|coded||
|on a 6-point scale. CPIA indicators are<br>countries.|used t|o alloc|ate co|ncessi|onal le|nding|across||
||2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Political Stability and Lack of Violence**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|Management of external debt|X|X|X|X|X|X|X|X|
|Management of development programs|..|..|X|X|X|X|X|..|
|Quality public administration|X|X|X|X|X|X|..|..|
|Budget management|X|X|X|X|X|X|X|X|
|Efficiency of revenue mobilization / public expenditures|X|X|X|X|X|X|X|X|
|**Regulatory Quality**|||||||||
|Business regulatory environment|X|X|X|X|X|X|X|..|
|Factor and products markets|..|..|..|X|X|X|X|X|
|Trade policy|X|X|X|X|X|X|X|X|
|**Rule of Law**|||||||||
|Property rights|X|X|X|X|X|X|X|..|
|**Control of Corruption**|||||||||
|Transparency, accountability and corruption in public sector|X|X|X|X|X|X|X|..|
|**Country coverage**|**136**|**134**|**135**|**136**|**136**|**136**|**136**|**131**|
|**Year of Publication**|**2006**|**2005**|**2004**|**2003**|**2002 **|**2000**|**1998**|**1996**|

63