rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **5.  Estimating the challenge**

―Growth with equity‖ is a contemporary development goal espoused by governments and international organizations (for example, World Bank 2005). In theoretical terms one can assume that social welfare ( _SW_ ) consists of two components: efficiency ( _EF_ ) and equity ( _EQ_ ), as in the following social welfare function:

# (10) _SW = f (EF, EQ)_

This function could be specified as:

# (11) _SW = (Y/P)_<sup>_α_</sup> _(1- GINI)_<sup>_β_</sup>

where ( _Y_ / _P_ ) is per capita income, _GINI_ is the common inequality index and α and β the value of the weights one puts at the efficiency and equity components. Assigning such weights implies a value judgment that is best left to voters and politicians. The education challenge in this framework is how particular education policies (for example, greater coverage of primary education) are conducive or not to changing the values of per capita income and income distribution in a way that promotes social welfare.<sup>4</sup>

# **Data and sources**

The following data were used in our simulations:

- Historical national accounts data from Maddison (2010)

- Investment in education data from the World Bank on line indicators (EdStats)

- Education data from Barro and Lee (2010) and World Bank indicators

- Micro returns to education from Psacharopoulos and Patrinos (2004)

- Gini coefficients from the Word Bank indicators

Although all measures of education are used in this paper, our central education variable is the average years of schooling of the population aged 15-plus. This choice is dictated both by the availability of a solid comparable database spanning 60 years (1950-2010), and the fact this measure is a summary of educational development encompassing all levels (Barro and Lee 2010). This measure of education also links to an extensive body of
Bank. 2020. _Poverty and Shared Prosperity: Reversals of Fortune._ Washington, DC: World Bank. World Bank. 2022. _Global Economic Prospects: Slowing Growth, Rising Risks._ Washington, DC: World Bank.

- Yonzan, N., C. Lakner, D. G. Mahler, R. A. C. Aguilar, H. Wu. 2020. _The impact of COVID-19 on global poverty under worsening growth and inequality._ Washington, DC: World Bank. https://blogs.worldbank.org/opendata/impact-covid-19-global-poverty-under-worsening-growthand-inequality.

- Yonzan, N., A. Cojocaru, C. Lakner, D. Mahler, and A. Narayan. 2022. The impact of COVID-19 on poverty and inequality: Evidence from phone surveys. World Bank Development Data Blog. https://blogs.worldbank.org/opendata/impact-covid-19-poverty-and-inequality-evidence-phonesurveys.

- Zeufack, A.G., C. Calderon, G. Kambou, M. Kubota, V. Korman, C.C. Canales, and H.E. Aviomoh. 2021. Africa’s Pulse, No. 23.April _._ Washingto D.C.: The World Bank.

- World Health Organization (WHO), 2022. Africa’s COVID-19 vaccine uptake increases by 15%. <u>https://www.afro.who.int/news/africas-covid-19-vaccine-uptake-increases-15</u>

36
with household survey data on detailed income and expenditure patterns to estimate these first order welfare effects for 54 low and middle income countries. With estimates of the welfare effects of import tariff liberalization for each household, we study the aggregate gains from trade (as in Arkolakis, Costinot and Rodriguez-Clare, 2012) and the distribution of the gains from trade (as in Porto, 2006). Using

> 1See also Artuc, Lederman and Porto (2015), Caliendo and Parro (2015), Melitz and Redding (2015), Arkolakis, Costinot, Donaldson and Rodriguez-Clare (2015), and Caliendo, Dvorkin and Parro (2018).

> 2See also Nicita, Olarreaga and Porto (2014), Faber (2014), Goldberg and Pavcnic (2007), Topalova (2010), Kovak (2013), Autor, Dorn and Hanson (2013), Dix-Carneiro and Kovak (2017).

1
. There are expected to be 70-100 national consultations, open meetings involving international and interdisciplinary perspectives, spaces for online input from ordinary people, a global survey of poor people, and inter-governmental working groups on the Sustainable Development Goals (“The Post-2015 Development Agenda,” UNDESA presentation, Democratic Governance and Human Rights: Criteria and measurement proposals for a post-2015 development agenda OHCHR/UNDP Expert Consultation, 1314 November 2012, New York). But these processes are somewhat distinct from the point being made here, which is that national political institutions (legislatures, courts etc) control political salience, and that the processes for the formulation of MDGs 2.0, although participatory in many important respects,  have not given a central place to those institutions.

16
(iv) “The mandatory nature of certain standards differs from the international practice of voluntary standards.’ This is a significant problem since mandatory requirements for products not subject to these requirements in export markets, limit that capacity to adapt to market needs in addition to imposing direct compliance costs.

Although Maliszewska et al., (2008) conducted a survey of Armenian exporters, they were not able to obtain estimates of their costs of compliance with EU standards. In the case of Ukraine, however, such estimates are available based on the survey of 500 Ukrainian firms that export to the EU. Those results shown in Jakubiak et al., (2006), and we shall base our estimate for Armenia on the Ukrainian data, with an adjustment for Armenian circumstances that we discuss below.

On average, the Ukrainian respondents reported that 13.9 percent of their production costs in the prior year were due to the costs of compliance with EU norms, regulations or product quality standards. One of the more significant expenditures in this regard was testing

> 35 The term ―National Quality Infrastructure‖ denotes the complete public and private infrastructure required to establish and implement the standardization, metrology, inspection, testing, certification, and accreditation services needed to prove that products and services meet defined requirements, whether demanded by authorities or the market.

100
be suitable to capture local property markets where online advertisements are not frequently used and might, therefore, weaken the generalization of the results to localized markets.

Second, the price data collected are concentrated in countries’ biggest business cities and urban centers. This is not surprising, since urban centers are the place where most new housing units are being built, responding to the accelerated urbanization rates currently observed in EMDEs. Further, urban dwellers are more likely to formally transact their property and to use online sources to sell or buy properties. Given the diversity of urbanization across EMDEs, the level of geographical disaggregation differs significantly. Disaggregated data for geographical areas beyond the major business city might not be sufficiently large to provide price estimations beyond the largest urban area. Due to data limitations beyond the biggest business cities and the absence of location markers on many housing listing websites in EMDEs, highly complex and spatially heterogenous housing markets cannot fully be delineated.

Third, the data listed on real estate websites include newly developed properties and the secondary housing market, which might bias house price estimations. In addition, some new housing developments are sold as investment projects, often tailored towards foreign investors. These properties are usually sold in bulk, i.e., entire apartment complexes containing several apartment units. Accounting for this potential bias, we conduct careful, multivariate outlier removal. In addition, we differentiate between property types (apartments and houses) and provide distinct price estimates for both property types.

Fourth, the final transaction price is likely to be different from the listed price, which often appears to be the price ceiling that precludes the possibility of sales at higher prices (Horowitz 1992). Furthermore, the listed prices advertised online represent the user-inserted price, which could include either the appraised 19
power, households tend to consume better calories. Quality of food is as important as the quantity of food. When a women’s decision making power increases, there is no significant difference in per capita calorie availability, after controlling for household expenditures. However, families eat more calories from foods like fruits and vegetables and less from grains. This is to say that when a woman has more decision-making power, households tend to consume better calories. Women’s decision-making power on children’s education is quite robust. In households where women have greater decision-making power, the share of household income spent on education and children’s school enrollment is significantly higher, particularly for girls. The fact that women’s decision-making power is positively associated with nutrition and child schooling is important in policy design. When deciding who should receive welfare benefits, with the aim to increase household well-being, it is necessary to understand the effect of power distribution within the households at which the aid is directed. To mitigate the impact of the 2008 food crisis, the government of Pakistan initiated the Benazir Income Support Program (BISP) to provide a safety net for the poor. This national cash transfer program provides Rs. 1,000 per month to ever married women of eligible families. The rationale for transferring cash directly to women relies on two assumptions: (1) as in other countries, giving cash directly to women can not only increase the total household income, but also increase women’s bargaining power as the cash transfers are perceived to be women’s additional income, and (2) higher women’s decision power is associated with higher human development investment within households. This study tests the second assumption using the existing data to understand the relationship between women’s bargaining power and some human development indicators, including nutrition and child education in Pakistan and the finding supports at least one aspect of the design of the program: transferring cash directly to women.

16
_Social Capital, Household Welfare and Poverty in Indonesia_ 48 These results suggest that households benefit from local associations because of their direct participation in the association's activities and that this is independent from the overall amount of social capital in the village.

This conclusion, however, needs to be treated with caution because of the possibility of omitted-variable bias, in case the regression does not control for all relevant factors. We try to tackle this question in two ways. As a first approach, we added to the basic specification of Table 5 a dummy variable for each village (minus one omitted village), under the assumption that these variables capture all omitted relevant village factors. This raises the R-squared from 0.25 to only 0.32, suggesting that the amount of possible omitted-variable bias is fairly limited. More importantly, it changes the coefficients of the social capital variables only very slightly and leaves their significance pattern unaffected. This suggests that our finding of significant social capital effects at the household level is not the result of omitted variables which could capture the social capital effects at the village level.

A second and more interesting approach is to include information on the village's past community activities. Our village data files include information on the major development projects which were undertaken in the sample villages over the past 10 years, and whether the community was actively involved in the design, funding and implementation of the project. On that basis, we constructed an index of past community involvement for each village (ranging from 0 to 3), and added this variable to the basic model. This index was much higher in the richest ten villages (1.62) than in the poorest ten villages (1.37). We also included a set of village-level control variables pertaining to the village's location (upland/lowland, distance to nearest market city), and internal
Use of the two alternative pseudo-maximum likelihood estimators does not fundamentally change the Poisson results. In columns 2 and 3, we find that importer and exporter logistics performance both have positively signed and 1% statistically significant coefficients. Interestingly, the magnitude of both semielasticities is noticeably larger than with Poisson, about double in each case. The interaction terms display the same pattern of signs as in the Poisson estimates: the importer interaction term has a positive and 1% statistically significant coefficient, while the exporter interaction term has a negative coefficient in both cases, but it is only statistically significant for the negative binomial model. As with Poisson, the relative magnitude of the interaction effect is much stronger on the import side than on the export side. This finding, combined with the statistical insignificance of the exporter interaction coefficient in the gamma estimates, reinforces our conclusion that it would be inappropriate to put too much weight on the unexpected negative sign in the baseline Poisson results.

In light of the continued use of the OLS estimator in the literature, column 4 presents results using that method. The pattern of signs and significance of the coefficients of interest is the same as for the Poisson estimates. These results are presented for comparative purposes only, however, since they suffer from two major defaults. First, observations for which trade is equal to zero cannot be included in the estimation sample, which reduces the sample size by about half relative to Poisson. Second, the Park-type test proposed by Santos Silva and Tenreyro (2006, equation 11) strongly rejects the adequacy of the OLS log-linearization of the gravity model (prob. = 0.000).

Another common gravity model estimator is the Heckman sample selection model (Helpman et al., 2008), which, unlike OLS, allows for the presence of zero entries in the bilateral trade matrix.<sup>6</sup> Results for the outcome (trade intensity) and selection (trade propensity) equations are in columns 5-6. We use Doing Business data on the time
9 distribution of income. Fortunately, an approach is available that does just this. Developed in the public finance and income redistribution literature in the early 1990s [8], the approach was used to measure equity in health care finance in several OECD countries in the latter part of the 1990s [6, 7]. This section outlines the approach, and the next section applies it data on out-ofpocket payments from Vietnam.

# _DECOMPOSING REDISTRIBUTIVE EFFECT_

The redistributive effect of a tax-i.e. the impact of a tax on the distribution of incomedepends on four key factors [8]. The same reasoning can be applied to health care payments. In the case where income inequality is measured using the Gini coefficient, redistributive effect, _RE,_ is simply the difference between the Gini coefficient for prepayment income _Gpre,_ and the Gini coefficient for postpayment income, _Gp,,,._ This has been shown by Aronson et al. to be equal to: (2) _RE = V-H-R,_ where _H la_,GF(X)_ and _RGy p -Cx p_ An explanation of these various indices and their interpretation follows.

# _VERTICAL INCOME REDISTRIB UTION-THE ROLE OF PROGRESSIVITY_

The term V in eqn (2) represents _vertical_ income redistribution-the change in income inequality that would have been brought about by health care payments if everyone at each prepayment income level had paid the same amount towards health care. V depends on two things, one of which is _K._ This is Kakwani's [9] index of progressivity, but computed on the assumption that at each income level everyone spends the same amount on health care. Kakwani's index, illustrated in Fig 3, is defined as twice the area between the prepayment Lorenz curve, _Lpre,_ and the payment concentration curve, _Lpay,_ the latter being the graph of the cumulative share of health care payments against the cumulative share of the population, ranked by prepayment income. Equivalently, K is the difference between the payment concentration index (defined
Table 4. AGOA and exports to US and other markets

|<br>Data at country-HS<br>year level (includ<br>Dependent|<br> <br>6-digit-destination-<br>ing zeros) is used<br>variable is:|
|---|---|
|Log|<br>Log|
|(imports + 1)|(imports + 1)|
|(1)<br>WITS/COM|(2)<br>TRADE data|
|GSP LDC * Africa<br>0.225***|0.273***|
|(14.38)|(12.94)|
|GSP LDC * Non-Africa<br>0.066***|0.133***|
|(4.09)|(6.68)|
|AGOA Non-LDC<br>0.113***|0.178***|
|(7.79)|(8.31)|
|AGOA Non-apparel<br>0.139***|0.155**|
|(3.55)|(2.75)|
|AGOA apparel<br>0.169***|0.201***|
|(3.95)|(4.23)|
|Treatment group-specific time trends<br>Yes|Yes|
|Country-product-destination fixed effects<br>Yes|Yes|
|Country-destination-year fixed effects<br>Yes|Yes|
|Product-destination-year fixed effects<br>Yes|Yes|
|Country-product-year fixed effects<br>No|Yes|
|clustered by HS 6-digit product *** ** and<br>Observations<br>52,866,235|* indicate significanc<br>50,891,350|

Notes: Robust t-statistics in parentheses, clustered by HS 6-digit product. ***, **,
# **1 Introduction**

The low price of oil along with onerous government spending and excessive international debt have pushed the Republica Bolivariana de Venezuela, once one of the richest countries in Latin America, in to one of the worst economic and social crises of modern day. Economic activity has been contracting with GDP (PPP) shrinking at a rate of 35% in 2019 according to United Nations data. The inflation rate hit 929,797% in 2019 (IMF, 2019), while 89% of the population has been estimated to be living in poverty (ENCOVI, 2018). Health conditions have worsened with an average Venezuelan losing 10 kilos of body weight in 2017 and infant mortality rising to 26 per 1000 live births in 2016 from 14.6 per 1,000 live births in 2010. An average of 89 homicides were reported per 100,000 inhabitants in 2019, which is almost three times the rate of countries that are at war (World Bank, 2019a). These worsening economic and social conditions generated an unprecedented exodus of Venezuelans in search of a better future and ability to avail basic human rights. The Venezuelan refugees and migrants living abroad increased by about six times between 2015 and 2019 as Table 1 suggests. The net migration rate in the Republica Bolivariana de Venezuela, was about –15.69 per 1000 population in 2019, suggesting more than 7% of the population of the Republica Bolivariana de Venezuela, has fled the country since 2014. Although the United States and Spain were the most significant hosts of Venezuelans<sup>1</sup> traditionally, 80% of the Venezuelans who were displaced after 2014 were hosted by Latin American countries. Colombia and Peru have been the most significant hosts followed by Chile, Ecuador, the United States and Brazil. As of October 2020, Colombia has about 1.7 million of the Venezuelan refugees and migrants, which is after about 60,000 to 100,000 Venezuelans returned back to reunite with their families during the COVID-19
<!-- Start of picture text --> Chronic poverty Non-poor in both rounds<br>~a<br>©9<br>nN ~z<br>; ar<br>I SE ©<br>7 ~.<br>Pa ak ne ae Pad q } Pi ik ab 2 A) Fis<br>es §HM € £a €ee gSaf £ ¢ £a £ €a Bgee *ay oy<br>Moving out of poverty Moving into poverty<br>at<br>ACa<br>eS ey<br>o —OO—————E—_—V o Eee<br>-s a -* £a os sca2 *Csa #os #ay¢ #+ a2 #be #C]2 =CJth<br>_—_ Bounds ® Point estimate a +/- two standard deviations<br>Shares are based on synthetic panel estimates for household heads aged between 25 and 60 in the first round of each interval.<br>The poverty line used is RM 527: note that the vertical axis scales vary across graphs.<br><!-- End of picture text -->
three groups of developing countries in the MENA, SA and LAC regions. With minor exceptions, these groups are clearly identifiable on the estimated curve, and countries within these groups present common features and a similar FLFP to the inequality gradient. While there is an extensive literature on the dis-equalizing impact of FLFP in developed countries, little has been written about developing countries, and to our knowledge this is the first empirical estimation of an inverted U- shaped relationship between FLFP and inequality.

To strengthen our novel findings, we replicate these macro trends using household- and individuallevel data. While micro-data are scarcer than macro-data, we were able to confirm the results from the macro analysis. Overall, the impact of FLFP is dis-equalizing in the developing countries under consideration, and the lower the FLFP the higher the impact. This is because the FLFP rate of return of the last two deciles is higher than that of the bottom two – although participation is higher among the top deciles. This shows that the increase in FLFP would have a strong pro-inequality impact, if not compensated with measures that favor women in the bottom deciles. Among developed countries, the Scandinavian and the Euro-Mediterranean show a pro-equality impact as FLFP returns in the lower deciles are higher than those <mark>in the higher ones,</mark> while in the remaining group of European countries the dis-equalizing effect is prevalent.

In conclusion, while there is almost unanimous consensus that increasing FLFP levels is an important developmental outcome, particularly in countries where FLFP levels are low and with high levels of gender discrimination – the translation of this general principle into effective policies becomes much more complicated. In certain contexts, fostering FLFP without considering its distributional impact can have potentially negative consequences in terms of inequality. There are two good reasons why policy makers should consider this an important piece of information when formulating policies to increase FLFP levels.

First, these policies often require the usage of public resources, e.g. tax breaks or subsidies for companies employing women, financing of nurseries and
Liberia’s gender education gap were to mirror that of OECD countries. The gender hierarchy in educational achievement in the OECD has recently reversed, where more (young) women than ever are attaining formal education (OECD, 2015). If the women-tomen divergence found in the OECD would be achieved in Liberia, GDP would be 8.65 percent higher.

**Figure 1: Selected macro-economic changes due to complete convergences of the education gender gap (in USD, growth in %)**

<!-- Start of picture text --> $5,000<br>4,388<br> $4,500 85%<br>62.4%<br> $4,000<br>65%<br> $3,500<br>45%<br>31.8%<br> $3,000<br> $2,500 25%<br>8.4%<br>1.8%<br> $2,000 5%<br> $1,500 1,254 -15.9% -18.4% 1,262<br>-15%<br> $1,000 709<br>545 600<br>-35%<br> $500<br> $- -55%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text -->

> 19 To allows for the 5-year cohort projections, the Barro & Lee 2013 data were used. The value is slightly lower than the more recently measured (2016 HEIS) estimates presented in the introduction section.

> 20 The sample adopted is the one for which countries were available in the latest estimates of the dataset and excluded: Comoros, Djibouti, Eritrea