rafmacalaba/data-use-mentions-extended huggingface dataset:
centers, since in the sample they pay about 65% percent of center-reported prices.

> 17Correlation studies indicate that “the productivity of women in home production appears to increase as their education increases” (Schultz (1993), p 53) while fathers’ education seems substantially less important. Admittedly, the conclusion is more nuanced among the several studies that use arguably exogenous variation on maternal education to look at its effect on children in the short and long run (for a survey, see Black and Devereux (2010)). An example is Carneiro et al. (2013)’s finding that maternal education has a positive effect on children when they are 7 to 8 years old but a negative one when they are younger than 24 months old. In addition, the reduced form relationship between mother’s education and children outcomes estimated by these studies involves a variety of mechanisms such as labor market participation and assortative mating in the marriage market. At least the second one is clearly not independent of father’s education.

> 18In our sample, this is the maximum observed enrollment.

> 19For a survey on children-related transfers, see Fang and Keane (2004); for one focused on single-mother households, see Hoynes and Schanzenbach (2018).

> 20See Appendix C.2.3 for additional details. Transfers are scaled by location.

16
- 27 -

States manufacturing employment originates in labor intensive products and 24 per cent of industry shipments are composed of these goods. 18/ Such statistics imply broad based competitive pressures from developing country exporters in the 1990s.

Just as Table 7 showed certain industries appear vulnerable to increased exports from developing countries (based on the implications of factor proportions data), several of these industries also appear relatively "safe" as far as labor intensity is concerned. 19/ No labor intensive products occur in petroleum refining and coal (SIC 29), while chemicals (SIC 28) generates less than 2 per cent of cotal shipments and employment in labor intensive products. Aside from these sectors capital intensive production techniques seemingly prevail in processed food products, paper, printing, electrical equipment, transport equipment and instruments. In a recent extension of the NBER approach, Erzan and Yeats (1989) also determined that most service industries in the United States employ relatively high capital intensive methods as compared to all manufacturing activity.

- 18/ Employing the 1965 NBER labor intensity indices in connection with the 1963 Census of Manufact'aree allows one to compute related "exposure" indices for the earlier period. The results suggest t'aat in 1963 appoximately 46 per cent of United States manufacturing employment originated in labor intensive products and 23 per cent of all industry shipments were composed of these goods. In computing both the 1963 and 1982 employment and shipment ratios, labor intensive products were defined as items whose factor proportions indices were at least 10 per cent below the U.S. average.

- 19/ Petroleum refining was identified as one of the most capital intensive of U.S manufacturing industries (see Table 5), yet this industry should not be considered "safe" due to the special stituation that exists in some energy exporting developing countries. Saudi Arabia, for example, has been developing an increased export capacity in refined petroleum and petrochemical products and has both the financial reserves and access to international capital markets needed for these ventures.
born/not born in country|0.52|-5.62|-5.10|
|Books at home|0.11|0.01|0.12|
|**Total**|**3.11**|**-5.22**|**-2.11**|
|School characteristics||||
|Total enrolment, grade 8|3.95|-9.03|-5.08|
|Community size|-0.62|-1.21|-1.83|
|Proportion of disadvantaged students|-0.25|3.05|2.80|
|Ability grouping yes/no|-0.54|2.62|2.08|
|Remedial lesson in Math Yes/no|-0.20|-1.12|-1.32|
|Severe absenteeism in school yes/no|0.90|5.13|6.03|
|Shortage of buildings yeas/no|0.50|-0.17|0.33|
|Shortage of teachers yeas/no|-0.01|0.38|0.37|
|Index of good attendance|-1.38|-1.04|-2.42|
|<br>**Total**|**2.35**|**-1.39**|**0.96**|
|Teacher characteristics||||
|Teacher age|0.61|-2.94|-2.33|
|Teacher gender|0.55|1.04|1.59|
|Teacher years of experience|0.03|-14.51|-14.48|
|Teacher education|0.66|-4.64|-3.98|
|Teacher certificate yes/
expenditure distribution that would not be detected easily from a comparison of standard measures of inequality and polarization. Second and most important, the paper develops within the relative distribution framework a novel methodology to identify the drivers of distributional changes and quantify their impact on the welfare distribution; the main value added being it enables a very granular analysis of the distributional changes that an analysis based on standard inequality decompositions would not allow.

The paper is organized as follows. Section 2 discusses the data and presents the methodology. Section 3 provides the results. Section 4 concludes.

# **2 Data and methodology**

## **2.1 The Ghanaian household survey data**

The data used in this paper come from the Ghana Living Standard Survey (GLSS), a nation-wide survey conducted by the government-run Ghana Statistical Service that provides information for assessing the living conditions of Ghanaian households.

The GLSS has emerged as one of the most important tools for the welfare monitoring system in Ghana. It provides detailed information on approximately 200 variables, including several socioeconomic and demographic characteristics, and information on household consumption of purchased and home-produced goods as well as asset ownership. Each of the waves is organized into 4 modules, which are stored in the individual, the labor force, the household and the household expenditure files, for which survey questionnaires are readily available.

The Ghana Statistical Service has conducted six rounds of the GLSS since 1987, thereby providing over 20 years of comparable data. The second, third, fourth and fifth rounds were carried out, respectively, in 1988, 1991/92, 1998/99 and 2005/06. Recently, data for the sixth round of GLSS have also become available, so that the proposed case study paper will be one of the first studies using this data set. However, only the last four rounds, from 1991/92 (GLSS-3) to 2012/12 (GLSS-6), have been based on the same questionnaire and are therefore fully comparable.

The availability of comparable and extensive information
through burdensome regulation or attracting state attention (Leff 1964; Slinko et al. 2005). In this paper, however, we draw a distinction between differences in _levels_ of relatively well-ordered (and often centralized) and therefore predictable corruption – a predictable component of the cost of doing business – and _variation in experiences with corruption_ , which often results from decentralized, unconstrained (a.k.a. “administrative”) corruption and the

> 1 The former is measured by the 2012 Doing Business in Russia’s Regions data set, and the latter by the 2012 EBRDWorld Bank Business Environment and Enterprise Performance Survey (BEEPS) for Russian regions.

2
(0.00507)|
|Trade Openness|-0.0626**|-0.0134|-0.00708|-0.0417**|-0.0534*|-0.0144|-0.00726|-0.0312|
|_(% of GDP, logs)_|(0.0279)|(0.0165)|(0.00570)|(0.0204)|(0.0273)|(0.0163)|(0.00556)|(0.0198)|
|Financial Openness|0.0127**|0.00868***|0.000324|0.00349|0.0110**|0.00893***|0.000227|0.00173|
|_(Chinn‐Ito Index)_|(0.00547)|(0.00325)|(0.00110)|(0.00399)|(0.00540)|(0.00322)|(0.00109)|(0.00392)|
|_External Factors_|||||||||
|Foreign Growth|-0.000133|0.000226|-0.000556*|0.000252|0.000401|0.000217|-0.000695**|0.000926|
||(0.00158)|(0.000928)|(0.000317)|(0.00116)|(0.00155)|(0.000916)|(0.000311)|(0.00113)|
|VIX Index|0.0409|0.00229|-0.00472|0.0433**|0.0640*|0.00218|-0.00994|0.0715***|
||(0.0270)|(0.0160)|(0.00532
support. In urban areas of Indonesia and Korea, by contrast, fewer than 35 percent of men and 15 percent of women have access to pensions.<sup>17</sup> Another unique feature of pension eligibility in Korea lies with an imperfect correspondence between mandatory retirement age and age at which pension-eligible retirees may start receiving pension benefits. The National Pension scheme, the largest of the three main sources of pensions in Korea, does not begin paying benefits until age 60, yet a significant share of employees face mandatory retirement at age 55 (Cho and Kim, 2005). This imperfect correspondence likely increases incentives for those employees facing mandatory retirement to look for new career opportunities, including in the self-employed sector.

Across rural areas of China, Indonesia and Korea, both men and women remain actively employed until much later in their lives than urban residents. In rural areas of all three economies, agricultural production on the family farm continues to be a significant source of employment for older workers, and this is necessitated by the fact that rural residents tend to accumulate less wealth over their working lifetimes. In addition, older workers in rural China and Indonesia are far less likely to have access to pensions than their urban counterparts. Both the CHARLS and the 2005 Population Census suggest that, of rural residents over 60, roughly 5 percent of men and less than one percent of women have pension support. Similarly, in rural Indonesia, the Indonesia Family Life Survey (IFLS) shows that only 8 and 3 percent of men and women over 60 have access to pensions.

Rural Korea is a somewhat different matter. Efforts to bring rural residents into the National Pension scheme established in 1988 have led to pension coverage rates in rural areas that do not differ significantly from urban areas, indeed 34 percent of older rural men in the 2006 KLoSA wave report receiving pensions. Nonetheless the continued labor supply of rural men is viewed as an important factor contributing to high rates of economic activity among older Koreans (Lee 2009). In
Gianmaria and Vittadini, Giorgio, 2014. “Closedskew normality in stochastic frontiers with individual effects and long/short-run efficiency.” Journal of Productivity Analysis, 42(2), 123-136.

Edvardsen, Dag Fjeld and Førsund, Finn R., 2003. “International benchmarking of electricity distribution utilities” Resource and Energy Economics,Volume 25, Issue 4, October 2003, Pages 353-371 Feenstra, Robert C., and Inklaar, Robert and Timmer, Marcel P., 2015. "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182.

Felipe, Jesus and Usui, Noroi 2008. “Rethinking the Growth Diagnostics Approach: Questions from the Practitioner” ADB Economics, Working Paper Series, No. 132.

Foa, Roberto, 2013. “Household Risk Preparation Indices—Construction and Diagnostics.” World Bank, Washington, DC.

Greene, William, 2005. “Reconsidering Heterogeneity in Panel Data Estimators of the Stochastic Frontier Model” Journal of Econometrics, June 2005, Vol.126(2), pp.269-303.

Hausmann, Ricardo and Rodrik, Dani and Velasco, Andres, 2008 “Growth Diagnostics” in J. Stiglitz and N. Serra, eds., The Washington Consensus Reconsidered: Towards a New Global Governance, Oxford University Press, NewYork, 2008.

Intenational Monetry Fund (IMF), 2014 “World Economic Outlook” International Monetary Fund, Washington, D.C.

16
comparable with the poverty figures using the revised weights.

Table 1 shows descriptive statistics for selected economic and demographic variables, disaggregated by urban and rural areas. Our main variables of interest are total per capita income,<sup>2</sup> labor income, and poverty status. Total income and labor income are initially measured as individual income, with a reference period of the ’past month’, which is then aggregated to monthly income at the household level and finally divided by household size to obtain per capita income values. We convert monthly per capita income as well as the poverty lines (absolute and extreme) measured in local currency, Guaraní (PGY), into per capita income in 2017 US Dollars (PPP). Further, we spatially deflate incomes by the factor 0.897 for urban households to account for differences in price and income levels between urban and rural areas (Dirreción General de Estadística, Encuestas y Censos, 2012). Following from this, poverty is estimated by comparing monthly per capita incomes to the national poverty lines, separately for urban and rural areas. On average, over the period of 16 years, per capita labor income represented 75% of total income in urban, and 78% in rural areas. Moreover, a median income and labor income of $372.5 and $282.9 in urban and rural areas, respectively, which are considerably lower than the mean, points towards a skewed income distribution in Paraguay.

**Temperature and precipitation data.** The lowest level of geographical aggregation in the EPH is the district-level, representing the smallest political-administrative constituencies in Paraguay (Instituto Nacional de Estadística, n.d.). In total, there are 263 districts in Paraguay, including the capital city Asunción with population sizes from as low as 717 inhabitants in San Carlos, Concepción, to approximately 243,800 inhabitants in Ciudad del Este, Alto Paraná and 530,5000 in Asunción in 2023<sup>3</sup> (de
Townsend, A. C., & Bruce, D. A. (2010). The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution. International Journal of Remote Sensing, 31, 4459–4480. http://dx.doi.org/10.1080/01431160903261005 Ullah, S., Khan, M., & Yoon, S. M. (2021). Measuring Energy Poverty and Its Impact on Economic Growth in Pakistan. Sustainability, 13(19), 10969.

United Nations (2018). Policy brief 08. Interlinkages among energy, poverty, and inequality. Link: <u>https://sustainabledevelopment.un.org/content/documents/17480PB8.pdf</u> US Energy Information Administration. Residential Energy Consumption Survey. Link: <u>https://www.eia.gov/consumption/residential/data/2020/index.php?view=characteristics</u> World Bank (2022) “ECAPOV 3.0 Harmonization Guidelines” Zhao, N., Hsu, F., Cao, G., & Samson, E. L. (2017). Improving accuracy of economic estimations with VIIRS DNB image products. International Journal of Remote Sensing, 38, 5899–5918. <u>http://dx.doi.org/10.1080/01431161.2017.1331060</u> 39
# **4. Extensions**

In this section we consider how our results depend on three assumptions underlying the unobserved components model of the previous sections: that the disturbances are independent across indicators, that the mapping from unobserved governance into observed data is linear, and that the distribution of unobserved governance is normal. We find that the first two assumptions if anything overstate the precision with which govemance is measured. Relaxing the third assumption does not materially affect our results.

# **Correlated Disturbances**

In the previous section we assumed that the disturbances **Eo,k)** were independent across indicators. Intuitively, this assumption allowed us to attribute all of the observed correlation of scores across indicators to the common component of governance go), and hence permitted us to identify the portion of the variation in scores across countries within each indicator due to measurement error. A consequence of this assumption is that any indicator which is not very correlated with the others was interpreted as having a large residual variance.

Although useful, the assumption that the errors are independent across indicators may not be valid, for at least three reasons. First, in the case of polls of experts, it is possible that the perceptions of experts who rank countries on a particular indicator are influenced by their knowledge of countries' rankings on other indicators. Second, the errors in surveys of residents might be correlated across countries if residents of a particular country have a tendency to systematically overstate regulatory and govemance obstacles, due to a broad-based predisposition to report a worse situation than is objectively warranted.10 Finally, it is possible that perceptions of governance from various indicators are unduly influenced by a single event, such as a high-profile scandal which is not representative of the level of graft in that country.

10 See Kaufman and Zoido-Lobat6n (1999).

20
each case.<sup>12</sup> To estimate supply of infrastructure service _h_ , we used the actual stock of _h_ . We then calculated individual indicators of excess demand for each component of infrastructure services by taking the ratio of supply to “predicted” demand. This ratio gives therefore an indicator of adequacy between supply and demand; a fall in the ratio would indicate excessive pressure on existing infrastructure and therefore a deterioration in quality. Second, we calculated a “composite” excess demand indicator for each country. To do so we used the same procedure used by Hulten (1996) to calculate his quality index, that is, we standardized each of the three series (by subtracting the mean and dividing by the standard error) and calculated the unweighted, arithmetic average of the standardized series.

Continuous annual time series for all three of the infrastructure services referred to earlier were not available for the whole estimation period. For instance, data on roads (in terms of kilometers per capita) were available only since 1990. We therefore calculated the composite indicator with all the information available in the base period to begin with, and added additional series as they became available. Figure 4 shows the behavior of these series, which have been normalized to a value of unity in 1977, to facilitate comparisons across countries. Again, a three-year moving average is used, in order to smooth out spikes possibly associated to measurement errors. In contrast to our ICOR-based indicator, the results appear to suggest that quality improved in all three countries in recent years.

Differences in the behavior of our two quality indicators may appear problematic, given that there is no strong _a priori_ reason for choosing among them. We thus follow the same approach as Calderón and Servén (2004 _b_ ), who define several standard quality indicators (based on electricity losses, percentage of paved roads, and telephone faults, given that they use stocks of electricity, roads, and

> 12In their estimation of demand functions for infrastructure services based on panel data
-48 -

Average Government wages to per capita GDP: is meant to provide information on the condition of an average Central Government employee of a particular country in relation to the living standard of that country, as measured by the per capita GDP. It is therefore calculated by dividing the average wage by the per capita GDP estimate. This, however, sometimes fails to encapsule the real conditions of central government employees to the extent that certain non-wage benefits are not included in the estimate of the average central Govemment wage.

GNP per capita (in US Dollars) is from WB database and relates to the same year as GDP per capita in local currency.
of this [ITEM] within the household? PLEASE ONLY LIST ITEMS<br>CONSUMED WITHIN THE HOUSEHOLD AND EXCLUDE FOOD<br>CONSUMED OUTSIDE ASK THIS QUESTION FOR ALL<br>ITEMS, BEFORE MOVING ON TO THE NEXT QUESTIONS FOR ITEMS<br>WITH YES|Within the past 7 days, did any members of your household<br>eat/drink any of this [ITEM] within the household? PLEASE ONLY LIST<br>ITEMS CONSUMED WITHIN THE HOUSEHOLD AND EXCLUDE FOOD<br>CONSUMED OUTSIDE THE HOUSEHOLD.ASK THIS QUESTION FOR ALL<br>ITEMS, BEFORE MOVING TO Q2.|
|Over the past 30 days, did the household purchase or pay for any<br>[...]?|Over the past 30 days, did your household purchase or pay for any<br>[ITEM]?|
|List all the items in question 1 and<br>the owner of the asset in question<br>2. If more than one item, write a<br>description of the item below,||
|otherwise write only the code of<br>the item.|Does your household own any [item]? Only count items that are in<br>workingcondition|

Note: the table reports the original questionnaire for the non-monetary indicators used in the survey-to-survey imputation exercise. To ensure comparability between the GHS and NLSS survey, data on the non-monetary indicators needed for surveyto-survey imputations need to be collected in a comparable way and following the same methodology.

30
welfare in Mozambique (Arndt et al., 2008). The authors use data from the 2002/03 National Household Consumption Survey (Inquérito aos Orçamentos Familiare (IOF) 2002-03) which provides information on income from the sale of agricultural output and on own consumption levels but has no information on production of specific agricultural commodities. Therefore, in their analysis, they calculate NBR using share of the value of agricultural sales and own production (‘auto-consumption’) in total household income as proxy for production share and the ratio of total expenditure on all food items, including the value of own consumption, relative 10
PIP are standardized as far as possible but differences exist with regard to the method of data collection, and whether the welfare aggregate is based on income or consumption. PIP reports per capita household income or consumption reported in 2017 purchasing power parities. PIP directly uses survey micro data wherever available and supplements these with binned income data.<sup>4</sup> 3 In 2022, PovcalNet was replaced by PIP. The data in PIP feeds into the United Nations’ monitoring of the first target of the first Sustainable Development Goal, to end extreme poverty. Most of the data in PIP is based on the Global Monitoring Database, which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty. We use the September 2022 version of PIP. 4 For 156 of the countries, housing 73% of the world’s population, survey micro data are available. For an additional 8 economies (Australia; Canada; Germany; Israel; Japan; the Republic of Korea; Taiwan, China; and the United States), or 8% of the world’s population, grouped income data of 400 bins are available. We treat these bins as microdata. Finally, for China, United Arab Emirates, Algeria, and Trinidad and Tobago, constituting about 17% of the world’s population, only decile or ventile shares and the overall mean income or consumption is available. For these countries, PIP fits a General Quadratic Lorenz curve or a Beta Lorenz curve, chooses the one that gives the best fit, and uses it to recover a full distribution.

5