rafmacalaba/data-use-mentions-extended huggingface dataset:
0.519)|(0.443)|(1.103)|(0.278)|(0.168)|
|Observations|510|1,045|1,020|1,007|1,047|

Notes: The estimations are contrasts between the matching differences of the indicator between the group of exposure and the comparison group. Variables for matching: ln of the provider’s height, eligibility score of PROSPERA in 1997 (linear and square), classification as poor household in 1997, gender, provider’s gender, provider’s age (linear and square), provider’s education, in 1997, two-months period when the transfers of PROSPERA were first received in the household of origin, number of persons in the household in 1997, and labor income of the household in 1997. Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations by propensity score matching based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

We find that the estimated positive effect of PROSPERA is driven by the groups in treatment villages who received the intervention in critical periods for building human capital. No effects were found in the group that had passed the critical periods of growth (Group 0, youth over 15 years of age in 1997).

30
specification which accounts for the interaction of the treated and Post period variable; iitt TT is the set of additional explanatory variable suspected to impact the level of nightlights radiance; and is an independent and identically distributed error term, clustered at the iitt administrative level 2, to avoid spatial autocorrelationccPPccT PPT TTPP _._ iitt εε One of the challenges in our analysis is the definition of conflict-affected areas. Conflict events in Mozambique are clustered geographically as such that it is readily ascertainable what regions are most impacted by these events, and thus defined as conflict-affected areas for the purpose of this study.

Nigeria, conflict-affected areas in Nigeria are not defined based on number of events alone but are based on the WBG FCS conflict classification. This classification uses publicly available data to annually assess countries, pinpointing those most affected by fragility and conflict. This method differentiates between territories experiencing _Fragility_ and/or _Conflict_ situations. Aligned with this definition, the study employs the following conflict indicators identified by the FCS index to delineate conflict-affected areas in Nigeria at the Local Government Area (LGA) scale:

> 10 This variable takes a value of 1 if _t_ = August 2020 to estimate the effect 1 month after the disaster, _t_ = November 2020 for the effect 3 months after the disaster, etc. and 0 otherwise.

8
more widely used data set is that of Barro and Lee (1993, 2000). This data set contains information on educational attainment for persons aged 15 years or older for many countries spanning the period 1960 to 2000 on a 5-year basis. There are 142 countries with at least one observation and 107 countries with complete information. Their data set is based on school attainment figures obtained from UNESCO and they estimate missing values using data on school enrollment rates and the structure of the population by age groups. More specifically, their fill-in procedure uses the gross enrollment rate, adjusted for repeaters. Barro and Lee claim that this measure reflects the inflows of new school graduates to existing educational stock more accurately than the

> 24For example, see Barro and Sala-i-Martin (1995), Barro (1997, 1999), Benhabib and Spiegel (1994), Gundlach (1995), Isham (1995), Krueger and Lindahl (2000), O’Neill (1995) and Temple (1999b).

> 25Note that the measure of the average years of schooling as a proxy for the productive human capital stock is not without its own deficiencies.  It will be seen later that our _actual_ measure of the human capital stock attempts to account for some of these deficiencies.
deciles. If lower-income households are more likely to reduce their high consumption rates of tobacco when faced with a price increase, they can naturally capture the highest benefits of the tax reform.

Similar studies have been conducted in the Europe and Central Asia (ECA) region, in countries that may be considered Georgia’s peers. Using the ECBA on Armenia, Postolovska et al. (2017) find that increasing the cigarette excise tax rate to 75 percent of the retail price would bring large health and financial benefits to Armenian households, with pro-poor impacts. They estimate that 88 thousand premature deaths, US$63 million in out-of-pocket medical expenses, 22 thousand poverty cases, and 32 thousand cases of catastrophic health expenditures could be averted with the price increase. The bottom 40 percent of the population would concentrate half of the adverted premature deaths and 27 percent of the prevented cases of poverty. In the Kyrgyz Republic, a 40 percent price increase on cigarettes could contribute to avert 55,000 tobacco-related premature deaths, US$1.3 million in OOP expenditures, US$3.8 million in government expenditure to treat tobacco-related disease, and 6,400 cases of poverty. The poorer quintiles would concentrate the highest relative benefits (Postolovska et al. 2018).

Available evidence from Georgia Total health care expenditures are particularly high in Georgia, estimated at GEL 2,871 million in 2016 (WDI 2016). The share of 8.4 percent of these expenditures in GDP in Georgia is comparable to the average for the higher-income OECD countries (which had average health spending of 8.9 percent of GDP in 2016) (OECD 2018). Moreover, out-of-pocket payments account for 56 percent of health expenditures or 4.7 percent of GDP in Georgia, despite large reductions over recent years.<sup>9</sup> Based on budget data from the IHS, health care accounted for 12.5 percent of the cash consumption expenditure by Georgian households in 2018 (Geostat 2019).

Some previous work has
samples suggest that persistence is about 33 percent higher for daughters, while the correct estimate from full samples is 45 percent. In contrast, the gender gap estimates based on IGC are similar across full (4.5 percent higher for daughters) and coresident (4.8 percent higher for daughters) samples. Thus, consistent with the cross-country comparisons discussed above, when working with coresident samples, it is preferable to use IGC as a measure of intergenerational persistence to understand gender gap in educational mobility.

# **5. Additional Evidence**

# **5.1 Alternative Age Ranges for the Children**

The age range used so far in Tables 1-3 is 13-60 years. This is motivated by the fact that the average schooling attainments in rural Bangladesh and India remain low in the survey years, so that a 13 years lower threshold may not be binding for most of the rural children. In Bangladesh data, the average years of schooling is only 4.43 years; for sons it is 5.5 years and for daughters 3.4 years. The average schooling in India is 5 years, and for sons it is 7 years and for daughters 3.7 years. To explore the sensitivity of the conclusions with respect to the age range of children, we estimate the IGRC and IGC across a number of different age ranges. For the sake of brevity, we report estimates from the following age ranges: ( _i_ ) 13-50 years, ( _ii_ ) 16-60, and ( _iii_ ) 20-69 years.

Since many children start first grade at age 6, a 13 years age cut-off implies 7 years of potential schooling as the minimum threshold in our sample (primary schooling is 5 years). The observed schooling attainment, however, may vary across 13 year old children for a variety of reasons. For example, children from poor households may start schooling later than usual, and they may also have to interrupt schooling because of negative economic shocks.<sup>22</sup> The
governance attributes and three informal (practice/process) attributes. This is shown in the reformulated version of the key summary table of the report shown below<sup>22</sup> .

**_The key point to note in Table 2 is that, with the exception of Indian gas and telecoms, all the scores on the informal attributes of regulatory governance are lower than the scores for the formal, legal attributes._** The scores for transparency are particularly low.

It is also worth recognizing:

- (i) _In retrospect, the NERA 1998 survey over-estimated achievements in developing effective regulatory frameworks in practice in the Asian countries considered_<sup>_23_</sup> .

The scores on the three informal accountability attributes would have been significantly reduced for the highest marked sectors if the study had been carried out 1-2 years later. This applies to Orissa electricity, Indian telecoms and (particularly) Pakistan electricity, the main apparent successes as seen in 1997-8. In retrospect, for all three cases, and particularly Pakistan electricity, too much weight was given in the survey to stated plans for the establishment and enhancement of regulation rather than to actual achievements. This shows the inherent risks of optimism bias in even the best surveys.

- (ii) _In retrospect, the degree to which the NERA survey captured aspects of regulatory practice was relatively limited._

The regulatory bodies, particularly the new, independent ones were, in 1998, simply too recently established to be have enough of a track-record of experience on which they could be appraised. In consequence, both the topic list in Table 1 above and the results are heavily weighted to aspects of the law and to legal obligations relative to actual regulatory practice. This is true even for aspects of regulatory practice such as Transparency and Predictability. This was unavoidable at the time but means that current and future studies should enhance the “what regulatory agencies <u>actually</u> do” aspect relative to the “what regulatory agencies are required by
>countries,<br>1961-2021|
|Secondary<br>educatio<br>completion rate|n<br>Percent of population that<br>completed<br>secondary<br> <br> <br> <br>Barro and Lee (2013); World<br>Development Indicators<br>179<br>countries,<br>1960-2020|
||education<br>in<br>percent<br>of<br>population in relevant age|
||group|
|Tertiary<br>educatio<br>completion rate|n<br>Percent of population that<br>completed tertiary education<br>in percent of population in<br>relevant age group<br>Barro and Lee (2013); World<br>Development Indicators<br>174<br>countries,<br>1960-2020|
|Secondary<br>educatio<br>enrolment rate|n<br>Percent of population of the<br>age group corresponding to<br>the level of education<br>World Development Indicators<br>193<br>countries,<br>1970-2020|
|Tertiary<br>educatio<br>enrolment rate|n<br>Percent of population of the<br>age group corresponding to<br>the level of education<br>World Development Indicators<br>192<br>countries,<br>1970-2020|
|Life expectancy|Years<br>UN<br>population<br>statistics;<br>UN<br>population projections<br>181<br>countries,<br>1985-2035|
|Fertility rate|Number of births per 1,000<br>UN<br>population<br>statistics;<br>UN<br>175<br>countries,|
||women<br>population projections<br>1960-2095|
|Employment|Number<br>Penn World Table
- 66 -

The former accounts for 10,223 employees, of which 369 at the central level and the rest at the local level. The latter accounts for 11,371 medical employees, all of which are local and of which 1,077 are contractual employees. Military employment data include conscripts (20,000), but do not, however, include personnel in paramilitary units, e.g., National Police (30,000 est.) and the Narcotics police (600).

Data on GDP at market prices is a 1993 estimate and is taken from World Tables 1995. Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing are from IMF Report No. SM/96/61 and relates to March 12,1996.

# Brazil

Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Military employment data include conscripts (132,000), but do not include personnel in paramilitary units, e.g., Public Security Forces (385,600), which are the state Military Police organizations (State Militias), under Army control and considered Army Reserve.

Chile Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994.

Data for central and non-central government and health are from Gary Reid (LA3PS). Civil Service Reform in Latin America: Lessons from Experience" and refers to 1990. Education employment has been essentially decentralized. Government finances education through system of vouchers which are given to public education teachers as well as teachers from some private institutions. Education figure, accordingly, is very small. Health employment is also a decentralized activity and offloaded to Local Govemments. State owned enterprise employment is also from the above mentioned study and relate to 1991. Military employment data include conscripts (31,000), but do not include personnel in paramilitary organizations
choice of title - agreed and the paper was eventually published (Alderman et al. 2006). Do we need a similar modification of agreed ethical guidelines for large scale and cluster based studies? This is not just a question for economic programs; the issue has been mooted in various health journals, sometimes in an expected benefit, expected risk framework (Osrin et al. 2009; Winkins et al. 1997). Nor is the issue of how scale influences informed consent raised only with controlled trials. Scale affects consent even in observational studies since a community leader is often the initial contact before individuals are approached for their consent. Whether the community leader represents the full interests of the population is often doubtful. In the SALDRU data collection mentioned above, much of the non-response from the initial sample was due to a landowner refusing access to his tenants or hired laborers.

In the case of a cluster-based trial, often a local official or community leader provides consent for the cluster. Indeed, in most observational studies, as well as many impact evaluations, the central government’s approval is considered binding on the community. Local officials are often relatively powerless to decline involvement in a survey undertaken when the central government is a partner in the study or when participation in an impact evaluation has been approved at a higher level. Similarly, households may feel compelled to follow the lead of the community leader.

In some cases individuals themselves can opt out by not taking up the service offered. This take-up provides important practical information and the difference between the intention to treat and the treatment on those taking up the treatment is an important aspect of the analysis. But in many cases all members of the cluster share some opportunities and risks. As communities are generally heterogeneous in their needs, the consent of one individual, even if this person was chosen by consent of the community, does not cover the risks for all individuals 25
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from equation 2.

tariffs reduce the skill premium.

# **4 Conclusion**

In this paper we examined the effect of FDI on wage skill premia in seven emerging economies. We matched information on FDI penetration from a project-level FDI data set with data on wages from household surveys by sector, location and year. This allowed us to estimate the effect of FDI on the skill premium across sectors within regions within countries. We found that FDI is associated with a higher probability of employment and higher wages for unskilled workers, relative to skilled workers, in 6 of the 7 countries. While our results contrast with the previous literature suggesting that FDI has effects similar to that of skill-biased technical change on increassing the skill premium, we noted that our results are in line with those of trade liberalization. Because our estimates are done at the country-sector-region level, we may also capture potential spillover effects (e.g. backward and forward linkages) of FDI within sector-regions. Our results suggest that globalization may thus have inequality-reducing qualities via FDI that should not be overlooked.

13
results for the United States in Table 3 are consistent with the overall results reported in Table 2. Import protection through TTBs is associated with a real appreciation of the US dollar, an increase in the US unemployment rate, and a decline in trading partner real GDP growth. Furthermore, while importer _j_ and exporter _i_ combined fixed effects were also included as part of the model estimates in Table 2, we did not report them to conserve space. We report one fixed effect estimate in Table 3 in order to highlight the policy-imposing economy’s relationship with China. In specification (1), the IRR of 22.66 is evidence that China was substantially more likely than the omitted trading partner (in this case, Australia) to face US import protection.<sup>23</sup> As has been well

> 23 The IRRs for the other trading partners are not reported in the table but are available from the authors upon request. In terms of scale, the estimated IRR for China in the US models, for example, is typically twice as large as that for the second-highest trading partner.

16
# **2.2 Literature on welfare impacts of food price changes**

As mentioned before, the impacts of food price changes have received much attention in the aftermath of the 2007-08 global price spike. While a complete review of these studies is beyond the scope of this paper, we focus here on studies that have examined this subject in Mozambique or other countries in Africa that offer a similar context. Overall, the short-term impact of earlier food price spikes in Africa has mostly been found to result in a net increase in poverty in most countries. However, there is a large variation in impacts across countries and within a country – across rural/urban areas, as well as across spatial regions. A second observation stemming from the literature is that relaxing the household supply and demand response assumption appears to have less of an effect on the results relative to the importance of dealing with differences in consumer and producer price rises.

The common approach has been to use CV calculations based on equation (2), with varying degrees of simplifying assumptions. Some studies have used the first-order approximation in Equation (3), identical to Deaton’s approach. For instance, Wodon et al., (2008) use household survey data for twelve West and Central African countries to examine the impact of the 2007-08 price rise. They present estimates of the impact on poverty based on the Deaton methodology under two different scenarios – first, when both consumers and producers face the same price rise, and second, when only consumers face the price hike. The upper-bound (results from the second scenario, considering only consumer price rises) of their poverty impact estimates, with a 50 percent increase in food price of staples, range from 1.8 percentage points in Ghana to 9.6 percentage points in Senegal. Their analysis indicates that the impact is mediated by the share of the food commodities undergoing price changes in the consumption expenditure of households as well as the degree of dietary diversity in food consumption. As expected, they
# **Table 1.The Determinants of Russian Firms’ Decision to Invest between 2011 and 2013**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|||<br>Firm|<br>implemented|<br>investment in 2|<br>011-2013,du|<br>mmy||
|Bribe incidence, BEEPS data|-0.12||-0.14||-0.36||-0.06|
||(0.27)||(0.27)||(0.29)||(0.33)|
|Bribe tax, mean, BEEPS data||0.69|0.69||0.36||0.62|
|||(0.56)|(0.55)||(0.63)||(0.50)|
|Bribe tax, variation, BEEPS data||||-0.11**|-0.14*|||
|||||(0.06)|(0.08)|||
|Raider attacks normalized by GRP||||||-0.07***|-0.07**|
|||||||(0.03)|(0.03)|
|Log GRP per capita, 2008|0.12|0.11|0.11|0.10|0.11|-0.01|-0.00|
||(0.08)|(0.09)|(0.09)|(
2019). As income decile shares do not follow a random path but tend to be persistent over time, we opt for a first di↵erence GMM estimator (Roodman, 2009). This class of models has been applied to small T/large N settings. For this reason, we collapse our data and form five-year time windows. In line with expectations concerning the central features of this model class (Wu, Luca, and Jeon, 2011), our point estimates become stronger in comparison to baseline (Table A10).<sup>12</sup> Overall, our results withstand a series of robustness checks giving us more confidence in the viability of our proposed mechanism.

# **5 Results: Mechanisms**

In this section, we present the results of the main mechanisms through which CBI is driving inequality. We start with an in-depth analysis of the policy changes and then present the results for anticipated economic outcomes.

## **5.1 Three Mechanisms**

First, a powerful way to compensate for the loss in monetary policymaking is to deregulate financial markets (Aklin and Kern, forthcoming). To capture such instances of financial deregulation, we rely on two indicators. Besides taking an aggregate financial liberalization index that ranges from 0 to 21, whereby higher values indicate less stringent regulatory practices, we complement our analysis using a measure for the degree of privatization of the banking industry. Whereas our aggregate measure captures the overall ‘aggressiveness’ of financial reform, our measure for the extent of the banking industry’s privatization proxies the degree to which policymakers remove barriers to competition among banks to stimulate financial activity and credit (La Porta, Lopez-de Silanes, and Shleifer, 2002; Epstein, 2016). The data come from Abiad, Detragiache, and Tressel (2010).

Second, we perform an analysis of whether CBI leads to changes in the governance of labor relations. For one, CBI constrains governments’ ability to inflate wages for restoring full employment (Thelen,