rafmacalaba/data-use-mentions-extended huggingface dataset:
samples that are not nationally representative (typically, in the context of randomized control trials) or is based on incomplete data on the various forms of savings (see Karlan et al. (2014) for a literature review). For India, we are not aware of any other nationally representative survey that collects detailed data on various forms of savings, except REDS.<sup>27</sup> Information on various types of savings allows us to examine the changes in the entire savings portfolio of a household rather than just one form of saving; we can thus capture substitution across different modes of savings.

Table 1 shows that per capita savings in financial institutions is the biggest component of the annual flow of per capita household savings. This is true even for the annual stock of savings; however, unlike the flow measure, the magnitude of the stock of jewelry, livestock, and other durable

> 24Among various questions related to spending at the time of marriage, 2005 IHDS also reports ‘average cash’ given as a gift at the time of the daughter’s marriage. The average cash reported is equal to INR 18,209 (USD 289).

> 25Saving in financial institutions comprise savings in commercial banks, private banks, post office, chit funds, self-help groups, and co-operative societies or banks.

> 26These comprise investments in the stock market, mutual funds, and life insurance.

> 27The Debts and Investment Schedule of the National Sample Survey of India collects household data on assets and liabilities but does not include a roster of household members, which is essential for our empirical strategy.

8
9 data from rural Gansu and Inner Mongolia are used. Both are agriculture based, lagging, inland provinces in western China. Gansu is the poorer of the two. It is mountainous and characterized by growing circular migration. Rural Inner Mongolia is somewhat richer and ethnically more diverse, with livelihoods still firmly anchored in agriculture. The data were collected by the National Bureau of Statistics of the Government of China as part of the monitoring and evaluation system for the World Bank supported Western Poverty Reduction Project.<sup>20</sup> Fifteen project counties were sampled (8 in Inner Mongolia and 7 in Gansu) and within each sample county, 10 villages were sampled in the ratio of 6 project villages to 4 nonproject villages. Within each sample village, 10 households were sampled randomly, yielding a sample of 800 households in Inner Mongolia and 700 in Gansu. Households were surveyed annually between 1999 and 2004. All data on household consumption, income and loans were collected using the daily diary method, with the exception of the baseline year 1999, when annual recall was used. To ensure comparability in the consumption data, the study is confined to the 2000-2004 panel. Data on household characteristics, e.g. demography, education, and assets were collected every year in December using the recall method.<sup>21</sup> As before, income is coded into earned and unearned income. Earned income includes wage income from temporary migration to urban areas, wage income from participating in off-farm wage-earning activities locally, and income from family business. Farming, forestry, fishery, animal husbandry, construction, transportation, restaurant and other services are all considered as family business. Unearned income includes remittances, gifts and other transfers. Most earned income is derived from family businesses (78% in Gansu and 86% in Inner Mongolia) (Tables 3 and 4).

Less than half of the households have wage income. In both provinces, households have on average between 300 and 400 (1999) Yuan unearned
“Refusal” values for the key variables of the number of permanent full-time workers, the total value of annual sales, or the estimated bribery payment, were removed. This process yielded a sample of 812 enterprises.

Tables 1 and 2 report the weighted summary statistics of bribery in Türkiye. The first column in the tables indicates enterprise size, which shows whether the statistics belong to small enterprises (enterprises with 5-99 employees) or big enterprises (more than 100 employees). Small plants constitute 95.1% of the sample. The second column of Table 1 shows the percentage of enterprises that have encountered a bribery request during a public transaction in the recent years. Of the enterprises that have done public transactions in recent years, 1.8% were asked to pay an informal payment or gift when they applied for a license, permit or during inspections. Among the small enterprises, 1.8% were asked to pay a bribe whereas, 2.9% of the big enterprises reported that they were asked to pay a bribe. The third

> 4This indicator is also being used by the U.N. Sustainable Development Goals (See SDG 16.5.2).

6
as 13th in the world, just behind Singapore and Hong Kong SAR, China in East Asia. In this section we evaluate the recent performance of Malaysia’s trade in goods with special emphasis on the distinction between modern and traditional exports. First, we consider goods exports based upon the technological classification of the OECD as discussed above in the defining modern and traditional service sections. Thus, we use commodity-level trade data from the UN COMTRADE database to classify Malaysia’s exports into 4 categories based upon technological intensity. In Figure 7b, we compare the relative growth in high-technology goods exports to low-technology and resource-based exports for several countries during 1995-2006. Although the level of growth in Malaysia’s exports are slightly lower than reference countries such as Singapore and Korea, Malaysia records strong growth of over 8.5 percent annually in high-tech exports. In fact, its growth in these modern exports over the past decade has been stronger than that of Singapore, which had relatively higher growth in lower-technology commodities. An alternative perspective of the technological intensity of trade is shown in Figure 7c. Here we plot a time-series of the share of high-technology exports to total goods exports for the period 1990-2006. What is immediately striking is the high share of advanced,

> 14 China, refers to Mainland China. Unless otherwise noted, data for China do not include data for Hong Kong SAR, China; Macao, China; or Taiwan, China.

16
service quality, we show that the correlation holds not only for the aggregate score but also for a majority of individual sub-components of the index. The results become stronger if we use the first principal component of individual health indicators.

Finally, we provide evidence consistent with the mechanism that higher political integrity influences bureaucratic productivity through improved oversight and facilitation by district councilors who are elected at the frontlines of health service delivery. Among the three types of politicians surveyed - local councillors, District Chairperson and nonelected Contenders -, it is the integrity of councillors, who are supposed to monitor and facilitate delivery, that is robustly correlated with several indicators of health service delivery.

# **4.1 Overall correlations**

To investigate whether characteristics of politicians and bureaucrats in Uganda’s districts are robustly correlated with the quality of service delivery, we start by presenting simple graphs showing how the DLT Health Scores and average traits of local policy actors covary in the sample of 75 districts in Figures 6 and 7. To account for broad differences in income across districts, we regress the DLT scores on district poverty rates, and present the scatter plot of estimated residuals against selection traits.

The first finding is that, among politicians, integrity is the only robust predictor of variations in DLT between districts: a 1 s.d. increase in politicians’ average Integrity Index is correlated with a 0.3 s.d. increase in the residual of health score. The only other trait of politicians that is marginally correlated with health scores is Altruism. Importantly, personality traits like those measured by the Big Five Index and Public Sector Motivation are not correlated with quality of service delivery.

Among bureaucrats, the scatter plots indicate no correlation between average integrity and quality of service delivery. Three traits of bureaucrats are significantly correlated with DLT scores: districts where bureaucrats are more risk-averse and more altruistic, and Districts where bureaucrats’ PSM is lower, also have higher scores.<sup>26</sup> .

> 26The correlation between Risk-Aversion and
# II.2 Measures of potential output growth

The literature has largely focused on three methods of estimating potential growth: a production function method, time-series filters, and analysts’ growth forecasts .

Production function method. The production function approach represents potential output as a function of the fully utilized capital stock, fully employed labor force, and technology as measured by TFP. For analytical convenience, the production function is often assumed to have a particular form, known as Cobb-Douglas. Potential TFP growth is estimated as the predicted value of a parsimonious panel regression of five-year averages of trend TFP growth on lagged per capita income relative to the advanced-economy average (to proxy convergence-related productivity catchup), education and demographic indicators, and trend investment (annex A). Potential labor supply is estimated as the population-weighted aggregate of predicted values of age- and gender-specific labor force participation rates from regressions on policy outcomes and cohort characteristics, business cycles, and country effects. The potential capital stock is assumed to match the actual capital stock.

Time-series filtering methods. These methods employ univariate or multivariate filters. Univariate filters involve estimates of trend output using only GDP data series (annex B). Multivariate filters use the empirical relationship between GDP and other variables (such as inflation, unemployment rates, commodity prices or financial variables) to help distinguish short-run deviations of output from trends (annex C). The database in this study employs the following five univariate filters: the Hodrick-Prescott filter, the BaxterKing filter, the Christiano-Fitzgerald filter, the Butterworth filter, and a filter based on an unobserved components model. An additional multivariate filter uses financial variables and commodity prices, a Phillips curve relationship, a Taylor rule, and Okun’s law.

Growth forecasts. This method is applied using two sets of long-term (five-years-ahead) growth forecasts, from Consensus Economics and the IMF’s World Economic Outlook database (annex D). These forecasts are based partly on models used by the analysts and partly on the analysts’ judgement. Judgment can play an important role during
allocational effects of trade reform on income risk, has additionally argued that increased foreign competition following trade reform will increase the elasticity of the goods and the derived labor demand functions. If a higher demand elasticity translates any given shock into larger variations in wages and employment, lower trade barriers may lead to increased individual income risk.<sup>4</sup> On the other hand, it has also been suggested that the world economy is likely to be less volatile than the economy of any single country, which leads to goods prices that are more stable worldwide than in any single autarkic economy. This opens up the possibility that greater openness may reduce the variance in individual incomes. Thus, theoretically, the openness-volatility relationship is ambiguous, that is, the theoretical literature does not offer a strong prior on the sign or magnitude of this relationship.<sup>5</sup> In this paper, we study _empirically_ the effects of trade policy on individual income risk using the following approach. For each industry (sector), we use longitudinal data on individual earnings to estimate time-varying parameters of individual income risk using a methodology that follows the approach taken by the extensive empirical literature on labor market risk.<sup>6</sup> More specifically, we focus on the variance of (unpredictable) changes of individual income as a measure of income risk, and carefully distinguish between transitory and persistent income shocks. The distinction between transitory and persistent income shock is important since workers can effectively “self-insure” against transitory shocks through borrowing or own savings, which implies that the effect of these types of shocks on workers’ consumption and welfare are quite small (Aiyagari (1994), Heaton and Lucas (1996), Levine and Zame (2002)).

> 4While Rodrik (1997) appears to have in mind mostly aggregate volatility, it is easy to see that his arguments equally apply to individual income volatility if there are idiosyncratic shocks to firm-level productivity.

> 5Clearly, this sign-ambiguity does not extend to
females. The sign of the coefficient is as expected because, as previously mentioned, overweight and obesity consume health capital which is in this case reflected in the expected returns.

Controlling for accumulated capital, measured by height and years of schooling, and for the context variables that determine labor participation, no statistically significant differences are observed in the expected income for indigenous persons.

Other results, consistent with the results of the mid-term evaluation, indicate that individuals who migrated from their villages of origin have on average higher incomes: 47.3% higher for males and 20.3% for females, in comparison to those who remained in their villages of origin. We have no data on the population that migrated to the United States or to parts of Mexico other than major cities. Thus, this estimate reflects only the migration to places captured in the ENCEL 2017 data. We thus expect this estimate to underestimate the effect of migrating on income, as those who migrate to the United States likely have the highest income and are excluded from this analysis.

Despite this, the estimator reflects that the return to accumulated capital in health and education can be enhanced in presumably more favorable contexts, that is, those with less _marginalization_ (poverty) and isolation than the villages of origin. This same logic is reflected in the estimations of the selection model (columns 2 and 4), where having migrated from one’s place of origin reflects a greater probability of entering the labor market, especially for females.

> 17 The mobility in additional height for male individuals of the group with greater exposure in critical periods is of 2.4 cm. In an equivalent manner, the mobility in additional years of schooling is 3.53 years. For females the calculation is equivalent.

> 18This considers the average income of the population and adds the average gains by height and schooling.  Pesos reported in the collection period of ENCEL 2017.

32
004)|(0.005)|
|N|83,961|58,596|318,555|293,190|173,146|550,075|524,710|404,666|

NOTES: This table presents estimates of equation (5). The outcome is an indicator of mortality. _EFMRst_ is computed as the difference of the female-male sex ratio at birth between firstborn girl and firstborn boy families, averaged for each state and year cell, and is computed using our version of the NFHS data. The specification includes household characteristics, state-female fixed effects and year-female fixed effects. Twin indicator and mass media exposure index are not included in the controls, and regressions are unweighted. Standard errors clustered at the state level are reported in parentheses. *** 1%, ** 5%, * 10%.

Table A.25: Replacing post indicators in our specification with _MFRst_

||||Children b|orn within:|||
|---|---|---|---|---|---|---|
||20 ye|ars before su|rvey|10 ye|ars before s|urvey|
|Died within:|1 month|12 months|5 years|1 month|12 months|5 years|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**A. Our estimates**|||||||
|_Firstgirl_ x _Female_ x _Post_1|-0.00564|-0.00691|-0.0153*|-0.0115|-0.0205*|-0.0254**|
||(0.00457)|(0.00607)|(0.00768)|(0.00769)|(0.0103)|(0.0107)|
|
presents the framework and explains the methodology, section V analyzes the results, in general, by firm size and examines training by gender, and section VI offers conclusions.

> 2 Hellerstein and Neumark (1999) use the same methodology to test for gender discrimination in Israel. Hellerstein and Neumark (2004) use the 1990 Decennial Employer-Employee Dataset (DEED), a more recently constructed matched employer/employee data set for the United States that contains detailed demographic information on workers (most notably, information on education). Using the same methodology, they use the new to update and expand on previous findings.

3
# **iii. The wage bill: Price versus quantity**

Changes in the public wage bill can be due to variations in wage and/or changes in the public employment level. We observe a positive correlation between the three measures of public employment (as share of the labor force) and size of the wage bill (as percentage of GDP), with the General Government and Public Sector aggregations showing the clearest association (Figure 3).

_Figure 3: Public employment (% labor force) and compensation to employees (% GDP)_ <!-- Start of picture text --> 0 .05 .1 .15 .2 .25 0 .2 .4 .6<br>General Government Central Government<br>30 30<br>20 20<br>CE (% GDP) CE (% GDP)<br>10 10<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 .2 .4 .6 .8<br>Public Sector<br>30<br>20<br>CE (% GDP)<br>10<br>0<br><!-- End of picture text --> Source: ILO Stats and IMF A variance decomposition of the wage bill into the variance of its components (employment and wages) may be a useful starting point.<sup>4</sup> The decomposition is done using country-year level data (80 countries) 4 The variance of the wage bill is equal to the variance of employment, plus the variance of wages (proxied by total wage bill divided by public employment) and twice the covariance between both components: V i )) = V i )) + V i )) + 2 ∗C i i )) Where i corresponds to the wage bill in constant 2011 international dollars, i is general government employment (in
by elderly and youth dependency rates, as well as GDP per capita growth rates. The savings function specification follows Loayza, Schmidt-Hebbel, and Serven (2000) with different coefficients for developed and developing countries. In the case of China and the Russian Federation, we target projections of investment or savings rates up to 2030 from World Bank regional reports. Since government and foreign savings are exogenous, investment is savings driven. The last closure determines the external balance. We fix the foreign savings and therefore the trade balance, hence changes in trade flows result in shifts in the real exchange rate.

This modeling framework allows to incorporate the complex interactions of productivity differences at the country, sector or factor level, shifts in demand as income rises, as well changes in comparative advantage and trade flows following trade liberalization. The applied multiregional dynamic CGE model accounts simultaneously for interactions among producers, 5
and vice versa for fever. The "index" is just the mean of these two variables. An equivalent method is used to generate an "index" for the ratio for no treatment of children with fever or ARI.

# _Description of data_

The list of variables and their detailed descriptions, the number of non-missing values, their means and standard deviations across all observations, are in table Al-1. The list countries, year of the surveys, sample sizes (i.e. the number of women interviewed), and the types of data available by country are given in table Al-2. In addition, the summary statistics for each ofthe indicators in the data are given in Appendix 2, Table A2- 1.

# _Income data_

The data used for income across different countries are from the Penn World Tables 5.6 (PWT) and the variable used is the real per capita GDP per capita expressed in 1985 international dollars (i.e. these are purchasing power adjusted quantities). For countries which do not have data up until the date of the survey, the data are extrapolated from the last two years for which actual data exist.

The income data for states India are derived from Government of India's 1993-94 Economic Surve (Government of India, 1994) which reports state level per capita net State Domestic Product for 1991-92. These are "converted" into 1985 international dollars and scaled for the difference between net State product and GDP, using the conversion implied by the comparison of the (weighted) average Indian net state product to the Indian real GDP per capita from the PWT.

The income data for the provinces of Pakistan are derived from household expenditures per capita from the 1991 PIHS. The Province level per capita expenditures are "converted" into 1985 international dollars and scaled for the difference between per capita expenditures and GDP per capita, using the conversion implied by the comparison of the average Pakistani household per capita expenditures to real GDP per capita from the PWT.

31
Mean con17.5 14.6 tract value 50.9 62.4 6.0 4.6 13.8 _Notes: All values in million US dollars._ The contract values captured by politically connected supervisors vary significantly among sectors (Table 2). Water works exhibit the most contracts (86), followed by the transport and education sectors. In these sectors, 19, 12, and 21 different contractors won at least one contract, of which nine, four, and four are coded as PCF1. In total, supervisors received contracts amounting to $213 million, much of which has been captured by PCF1 consultants. Consultancy contract values in the solid waste and irrigation sectors, for example, have been captured almost entirely by PCF1 supervisors. Such high levels of concentration of contract values contrast with measurements of market competition. The HerfindahlHirschmann Index (HHI), a widely used indicator for industry competitiveness, indicates that the markets for consultancies in the transport, and water works sectors would be competitive, despite that 76% and 73% of projects are captured by PCF1s.<sup>6</sup> PCF2s only play a very minor role in contract allocation.

The allocation of contracts for design consultants exhibits a similar degree of concentration for the solid waste and irrigation sectors. In these sectors, PCF1 designers designed 99% of all contract values. The water works sector, by contrast, has a lower degree of concentration of connected designers.

Table 2: Market competition among sectors

|**Supervision**|**Transport**|**Water works**|**Solid waste**|**Irrigation**|**Education**|**Other**|
|---|---|---|---|---|---|---|
|HHI|2,159|996|4,287|9,171|3,874|895|
|