rafmacalaba/data-use-mentions-extended huggingface dataset:
with non-missing instrument and satisfaction variables, which are by 2-3% smaller. In the main equations country average satisfaction is imputed when missing. Marginal effects are based on fixed parts of the models and conditional on working as employee (i.e. not corrected for selection into paid employment). *, **, * -** marginal e **f** fects significantly different from zero at 12%, 10%, 5%, 1% level, respectively. _Source:_ Calculation with ESS data.

**Table 11 Satisfaction with the government and working without a contract in Eastern and Western Europe, 2004-2009 (Mixed-effects logistic regression: Marginal effects)**

_<u>% points</u>_

|**Country group**|**_East_**|**_West_**|
|---|---|---|
|(% employees without a contract)|5.12|8.36|
||Marginal effect(stan|dard error),%points|
|**How satisfied with thegovt.**(0-10 scale)|-0.20***_(0.07)_|-0.24***_(0.08)_|
|**Other controls**|As in|Table 9|
|LogL|-3867.11|-4590.40|
|# obs.[# countries: # regions]|22094[12:145]|20976[8: 98]|
|Region level random effects: s. d.(s.e.)|0.224*** _(0.058)_|0.173***_(0.052)_|
|LR test vs.logistic regression(conservative)|P = 0.0058|P = 0.0011|
|Exogeneitytests of_Satisfaction w_|_ith the Govt_.(robust,
For the omitted variable bias concern, the instrument partially helps.The instrument overcomes concerns of a special condition that emerged in a particular district-industry that favored female ownership (e.g., the inspirational female business leader, a favorable local government treatment) if such omitted factor is short-term in duration. That is, the instrument addresses omitted factors that are localized to the 2000-2005 period and do not extend back to the early 1990s. To the extent that an omitted factor was very long in duration and specific to a district-industry (e.g., a local women’s training institute that is specific to an industry), then the instrumental variable approach will not help if the factor was present in 1994 too.

Finally, the instrumental variables will help overcome measurement error in the agglomeration regressors. This measurement error is due to the sampled nature of our data and the imperfect design of our metrics. The instrumental variables can help relieve the standard downward bias in coefficient values that measurement error produces. On a related note, the instrumental variables approach can further help with potential concerns about measuring our entry rates and incumbent conditions from the same surveys, given that we do not observe the universe of Indian firms. The data split that we use of young firms and incumbents has been used by multiple researchers on India, and we have not identified any sampling biases in this approach. Nonetheless, the instrument variables approach will identify off of conditions from the 1994 survey to predict entry in 2000 and 2005.

Table 8 provides the first-stage results. The first two columns provide the first-stage results where we instrument for just the total incumbent employment in the district-industry and the count of female-owned incumbent firms in the district-industry. Columns 3-6 are the firststages from an extended specification where we also include the two Marshallian indices. The construction of the Marshallian indices in 1994 mirrors that in the later surveys. The lagged conditions strongly predict the incumbent conditions in 2000 and 2005, with the strongest elasticity for each agglomeration metric being its direct counterpart in 1994.
Table 2: Geographic concentration of economic activity by degree of formalisation

||**Nairobi**<br>**Mombasa**|**Rank r**|**egression**|
|---|---|---|---|
||in %|_α_|SE|
|Population overall|9<br>3|1.29|0.18|
|Population of cities & towns|31<br>9|0.85|0.01|
|Migration infows|29<br>6|0.59|0.04|
|Migration outfows|5<br>2|0.77|0.07|
|GDP|25<br>5|1.00|0.07|
|GDP w/o agriculture|33<br>7|0.97|0.05|
|GDP w/o non-market services|25<br>5|0.91|0.08|
|No. MSMEs|14<br>3|0.86|0.17|
|Employment in MSMEs|19<br>3|0.78|0.13|
|No. licensed MSMEs|18<br>3|0.73|0.09|
|Employment in licensed MSMEs|28<br>3|0.67|0.07|
|No. SMEs|37<br>3|0.58|0.06|
|Employment in SMEs|36<br>3|0.60|0.05|
|No. census establishments|36<br>4|1.10|0.12|
|No. frms census of industrial production|48<br>6|0.54|0.02|
|Sales census of industrial production|61<br>7|0.32|0.03|
|No
the log yearly earnings of US worker _i_ on a set of her productive characteristics using the CPS data:

where _εi_ is a standard error term with mean zero and variance _σ_<sup>2</sup> . We predict the expected ˆ earnings of a Mexican migrant, which we denote _wMX_<sup>_US_, using the estimated coefficients (</sup><sup>_βUS_)</sup> and her characteristics ( _Xj_<sup>_MX_</sup> ) from the ENOE data. When predicting migrant wages in the

> 3We use a Mexico PPP rate for 2018 of 7.17 based on Argente et al. (2023).

9
x||
|_1st Girl_*_Post1_ & _1st Girl_*_Post2_|x||x||x||x||x||x||
|_Xijt_|x|x|x|x|x|x|x|x|x|x|x|x|
|_1st Girl_ x Birth year FE||x||x||x||x||x||x|
|_Female_ x Birth year FE||x||x||x||x||x||x|
|_Female_ x State FE||x||x||x||x||x||x|
|_Female_ x Birth order FE||x||x||x||x||x||x|
|Birth order x State FE||x||x||x||x||x||x|
|State-specifc time trends||x||x||x||x||x||x|
|_Firstborn girl_ x State FE||x||x||x||x||x||x|
|_Firstborn girl_ x Birth order FE||x||x||x||x||x||x|

NOTES: This table reports investment effects for children of second- and higher-order birth order using the REDS sample, separately for Hindus and Muslims. Each column is from a separate regression. We control for _Female_ , _Firstborn girl_ , and fixed effects for birth year, birth order, and state in all columns. The vector _Xijt_ comprises mother’s age at birth, caste, educational attainment of child’s parents, and mother’s birth cohort. Breastfeeding results are based on the last two surviving births of a mother and restrict the sample to children above age 2. Vaccination and health expenditure results are based on all surviving children of a mother. Standard
-0.058***|
|||(0.009)||(0.016)|
|Kleibergen-Paap F stat.||17.692||19.635|
|<br>AR 95-CIs||[-0.111, -0.031]||[-0.400, -0.124]|
|Adjusted_p_-value|0.000|<br>0.000|0.001|<br>0.011|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|

32
|**Country**|**Survey(s)**|**Year(s)**|
|---|---|---|
|Ecuador|Encuesta de Condiciones de Vida|2006|
|Egypt|Household Budget Survey|2005|
|El Salvador|Encuesta de Hogares|2000-07|
|Estonia|Household Budget Survey|2000-04|
|Ethiopia|Income and Expenditure Survey|2000,04|
|Fiji|Income and Expenditure Survey|2002|
|Gambia|Integrated Household Survey|2003|
|Georgia|Household Budget Survey|2004-07|
||Surveyof Georgian Households|2000,03-04|
|Ghana|LivingStandard Survey|2005|
|Guatemala|Encuesta de Condiciones de Vida|2000,06|
|Guinea|Enquête sur le budget et l'évaluation de la<br>pauvreté|2002|
|Guinea Bissau|Inquéritopara Avaliação de Pobreza|2002|
|Guyana|Household Budget Survey|2007|
|Haiti|Enquête sur le conditions de vie|2001|
|Honduras|Encuesta de Hogares|2001-06|
|Hungary|Household Budget Survey|2000-04|
|Indonesia|Socioeconomic Survey|2000-08|
|Iran|Income and Expenditure Survey|2006|
|Iraq|Socioeconomic Survey|2006|
|Jamaica|LivingConditions Survey|2000-07|
|Jordan|Income and Expenditure Survey|2002,06|
|Kazakhstan|Household Budget Survey|2001-06|
|Kenya|Household Budget Survey|2004|
|Kiribati|Income and Expenditure Survey|2006|
|Kyrgyz Republic|Household Budget Survey|2000-07|
|Lao|Expenditure and Consumption Survey|2002,07|
|Latvia|Household Budget Survey|2000,02-04|
|Lesotho|Household Budget Survey|2002|
|Lithuania|Household Budget Survey|2000-06|
||LivingConditions Survey|2007|
|Macedonia|Household Budget Survey|2000,04,06|
||Income and Expenditure Survey|2003|
|Madagascar|Enquête auprès des ménages|2001|
|Malawi
.|23|21|17|
||(na,1)|..|(na,1)|(30,9)|(23,16)|
|All countries|55|26|38|24|32|
||(49,7)|(32,10)|(21,2)|(26,25)|(36,55)|
|**_Mainlines per Employee in..._**||||||
|Developing|66|107|110|108|98|
||(31,10)|(87,9)|(66,3)|(63,16)|(70,43)|
|Dld||146|441|192|215|
|eveope|..|||||
||..|(43,2)|(120,2)|(50,10)|(96,19)|
|All countries|66|114|242|141|134|
||(31,10)|(80,11)|(197,5)|(71,26)|(95,62)|

Source: Own calculations using ITU data. Countries were classified in developing and developed according to their 2001 GNI per capita. - Coverage for 2003 is not as good as the one for previous years. Thus, for some groups samples are too small to draw significant conclusions. - na means not applicable. Used when there is only one observation in the group, which makes impossible to calculate the standard deviation. - IRA refers to independent regulatory agency, and priv refers to the existence of private capital. - The price of a 3-min local call is expressed in 2000 US cents/10,000 GDPpc; while annual subscription fees are expressed in 2000 US dollars/10,000 GDP pc. - Phone faults correspond to reported faults
values (as opposed to physical units) and are estimated assuming a worldwide common production technology for each 2‐ digit industry.<sup>2</sup> The resulting TFP estimates represent the portion of output that is not explained by the amount of inputs utilized and, thus, unlike labor productivity results, TFP estimates are not expressed in monetary terms. TFP may be considered a better measure of efficiency than labor productivity, as it captures the level of efficiency at which all factors of production are utilized to produce output. However, due to the data requirements for the estimation of TFP, it can only be computed for establishments in the manufacturing sector (hence the lower “N” for TFP in Table 2).

Because average productivity figures can be strongly influenced by outliers in the sample, Table 2 below presents both the average and the median values for labor and total factor productivity. Of the seven countries covered, sales per worker are highest in Uruguay, as reflected in both the mean and median

> 2 For an explanation of the TFP estimation using the worldwide Enterprise Survey database see EBRD, EIB, World Bank (2016); page 16.

9
and hours worked in the household activity. We conducted the same analysis for Uganda, but the data from the MS 1996 did not permit a reliable comparison.

Income data is known to be of generally poor quality, but we use it here to complement individual level participation data and focus on the relative income differences by gender and sector rather than differences in magnitudes. We make the strong assumption that incomes are distributed according to the amount of time a person spends contributing to the household enterprise. This may not be the case, and thus we would overestimate incomes of the relatively powerless in the household. Despite this bias, we find that the income data tells an important part of the story. As Adams (1999) noted in a recent paper on non-farm eamings in rural Egypt, income data collection efforts should be strengthened if we want to better understand the determinants of growth by sector. We would add that it is especially needed to distinguish gender differences that are usually masked by household income and expenditure totals.

We use household headship as the main indicator of gender differences in this paper, mostly out of necessity, but also because of its qualities as an indicator. We use headship first because the poverty measures are derived from household level consumption data and second because the 1996 Uganda data has minimal information at the individual level. However, the strength of headship as a gender indicator is that it provides the best representation of women's general economic opportunities and circumstances at the household level. Since we have an economy that is composed of households which interact as collective units, rather than one in which individuals interact as purely independent agents, the differences among households as defined by the gender of their head can reveal a lot about different economic experiences. There are, of course, 8
**HUMAN CAPITAL, TANGIBLE WEALTH, AND THE INTANGIBLE CAPITAL RESIDUAL**

# **1. Introduction**

Both theory and intuition support the idea that income is the economic return on wealth. But this conception of income and wealth is apparently at odds with the figures that we can observe in the national balance sheets of the System of National Accounts (SNA).

Since the 1993 revision of the SNA, the national balance sheet accounts include both produced assets and the value of commercial natural resources, including land. According to wealth estimates published in _The Changing Wealth of Nations_ (World Bank, 2011), the ratio of produced wealth to GDP averages a factor of 2.6 across more than 120 countries in 2005. If we expand the asset accounts to include all tangible wealth, both produced and natural, then the ratio of wealth to GDP averages a factor of about 6.6.

The puzzle in all of this is that, if income is understood as the return on wealth, and the rate of return is assumed at 5%, then the total wealth of a nation should be on the order of 20 times GDP. Since there is no reason to believe that average rates of return on assets in these countries should be substantially higher than 5%, the obvious conclusion is that there are ‘missing’ assets, i.e. assets that are not measured in the SNA balance sheet accounts.

The most obvious missing asset in the SNA is human capital, which was measured indirectly as a component of the residual in the World Bank’s comprehensive wealth accounts (World Bank, 2006, 2011). Recent work at the OECD (Liu, 2011) has estimated the monetary value of human capital stocks for sixteen countries<sup>2</sup> over a range of years, which offers an opportunity to include these direct estimates of human capital into the World Bank framework of comprehensive wealth accounting.

The growth accounting literature also suggests that there must be other ‘missing
analysis.

## **5.4 Heterogeneity**

Next, we examine the effects of the riots by religion. The following equation identifies the effect of the riots by religion: where _Muslimi_ is equal to 1 if woman _i_ belongs to the Muslim community. The coefficient of interest, _γ_ identifies the additional effect of the riots on Muslim women’s marital outcomes relative to the outcomes of women of all other regions. We cluster all our standard errors at the state level or the state-year level in alternative specifications.

# **6 Results**

In this section, we discuss the effects of the Hindu-Muslim riots of Gujarat in 2002 on women’s age at marriage and their post-marital social and economic status.

## **6.1 Women’s Marriage Age**

Using data from NFHS-IV and specification 1, Table 3 presents the changes in women’s age at marriage, probability of child marriage, age at cohabitation, age at consummation, and age at first birth post the 2002 riots compared to the rest of India. Columns (1) and (2) suggest that women’s age at marriage declined by 0.33 years or 4 months after the riots, and the probability of marriage before the age of 18 increased by 4.4 percentage points or 14 percent compared with the rest of India or the control mean. Columns (3) and (4) in Table 3 indicate that the age at cohabitation and age at first sex also declined after the riots. The estimates in the first four columns of Table 3 are significant at the 1 percent level. According to column (5) of Table 3, while statistically insignificant, the effect of violence on women’s age at first birth is negative.

Figures 1(a) and 1(b) present coefficients using the event study specification in 2. Figure 1(a) suggests that the age at marriage decreased for women married after the Gujarat riots of 2002. Figure 1(b)
(0.021)|
|Observations|432|432|414|432|
|AR(1)|0.329|0.270|0.209|0.06|
|Sampleperiod||1986–|2009||

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ All columns report estimates using panel data estimators controlling for first-order autoregressive (AR(1)) disturbances. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include individual effects, year effects, and time trends by city. Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

31
.9, workers can move in a location with higher wages, lower consumption and housing prices depending on their initial preferences across the amenities that are offered across locations. Such assumptions smooth the movement of workers across locations, otherwise all workers would move to the locations with the highest purchasing power.

Transport costs are measured as a function of the travel time to reach other locations from the center of each location. The travel time, which is calculated using GIS network techniques, is the shortest time given all the possible routes in the road network (Figure A8 in the Appendix). Travel times are based on categories of roads from Open Street Map (primary, secondary, ter9
keeping a balanced portfolio. However, they could alternatively reflect money-chasing returns (or rushing to the exit) whereby asset managers increase their positions in countries with excess returns and cut their exposure in falling markets. To control for this possibility, we re-estimate our models in **Table 11** using Arellano-Bond panel estimator with external and internal instruments. As external instruments, we compute, for each country, capital flows to the rest of EM, the assumption is that fund flows, which are highly correlated across EM, can only affect

> 19 A digression should be made about currency markets. Because there is no data on currency flows to make comparable estimations, we use data from equity funds as a proxy. The assumption is that many professional investors go into equity markets using unhedged positions, so that currency and equity cross-border flows are closely correlated.

<u>15</u>
# 1.INTRODUCTION

This paper discusses the objectives and design of a proposed World Health Survey (WHS). It was prepared at the request of World Bank as a background document for a meeting of staff of the World Bank, World Health Organisation (WHO) and invited experts to discuss the possibility of a WHS.

The terms of reference (TOR) for this paper defined the WHS as a programme to sponsor studies of health in a broad range of developing countries. They identified a need for an information system that could measure the impact of public health programmes and health services and also improve overall allocation and targeting of health interventions to specific population groups. Three specific objectives for the proposed WHS were identified: (1) To improve knowledge of patterns and determinants of health, disease and mortality in developing countries; (2) To develop appropriate methodology for measuring the impact on health status of investments in health as well as other sectors; (3) Tc conduct appropriately-designed surveys in several countries (a minimum of 5 and maximum of 20) over a 5 year period.

The TOR specified that the paper should provide a rationale for a WHS and discuss methodological options for country health studies. It was emphasised that the paper should discuss possible approaches to a WHS rather than presenting a detailed proposal for the implementation of any single study design.

Consideration of the TOR has led us to identify a number of additional issues which need to be taken into account both in deciding whether to conduct a WHS and in determining the design of the country health studies: Firstly, to what extent is it reasonable to expect that the two primary objectives stipulated, namely (1) ascertaining Patterns and deterinants of disease and (2) demonstrating methodology for impact 5
Outflows from industry lead mostly to non-employment, but there are also significant flows from industry to service jobs. Flows from industry to services are twice as large as flows in the other direction. Given the size and diversity of the service sector, it is not surprising that this sector has the lowest outflow rates. Recorded outflows from the service sector are almost exclusively to non-employment.

**Changes between 1990 and 2000.** Appendix Table 16 presents changes in the labor market status of  the Latvian population between 1990 and 2000. The most mobile labor market sector appears to be agriculture. Indeed, farmers recorded both the highest probability of making transitions to unemployment and making transitions out of the labor force. Moreover, among those employed in farming in 1990, less than 40 percent were employed ten years later, and only 13.8 percent were found in the same activity.

Among those who worked in industry or services in 1990, the proportion still employed in 2000 is generally a little higher than it was for those farming in 1990. The majority of those employed in both years stayed in the same main sector. According to this measure of mobility, the least mobile group is public service employees.

Empirical evidence shows that there was substantial movement between sectors over the first decade of transition in Latvia. Employment declines in agriculture and industry have given rise to considerable unemployment. Job losers from these sectors frequently find it difficult to compete for jobs with younger labor market entrants. On average, persons, who were in the service sector already in 1990, experienced the best outcomes, while those who were employed in agriculture at that time faced the greatest difficulties.

# **B. JOB-FINDING PATTERNS**

**What makes a successful jobseeker?** We now examine factors with the greatest impact on job-finding prospects of the unemployed. For this purpose, multinomial regression analysis is applied to a sample of 1,676 respondents from the LFS 2002 who reported themselves to be unemployed one year prior to the survey. The estimated
which is more than two times larger than the increase in personal income of about 689 Taka.

Second, the null effects on household expenditures on men’s and boys’ assignable goods shown in Table 3 make a pure income effect less likely, though the estimates are noisy. The insignificant effect on men’s expenditures as a share of household income is only an eighth of the significant effect on women’s expenditures of 2.71 percentage points. And while girls’ expenditures increase significantly by 1.22 percentage points, the coefficient for the share spent on boys’ goods is noisy, small and negative. A Marshallian income effect resulting from the woman’s wage

> the survey or refused to be surveyed, such that the number of observations is slightly smaller for husbands than for the wives.

> 68I estimate Equation 1 with the same set of potential control variables, but include survey round fixed effects instead of month fixed effects in the algorithm. For time reasons, the high-frequency surveys asked about assignable goods expenditures for women and girls (and men and boys) combined, and only included questions about remittances to the wife’s family (not the husband’s). It also did not include questions about income, which is why the expenditure outcomes are expressed in Taka per month.

25
U.S. State Department page on FY2008 assistance operations in Macedonia: http://www.state.gov/ p/eur/rls/rpt/seedfy08/117521.htm.

World Bank Group Enterprise Surveys: www.enterprisesurveys.org World Bank Group, January 2010. ―Secured Transactions Systems and Collateral Registries‖, Chapter 1: Economic Rationale.

World Bank MENA SME Lending Survey 2010 39