rafmacalaba/datause-encoder-data huggingface dataset:
Based on the classification in table 2, whose construction is explained in the subsection on measuring labor market flexibility, we create a dummy variable called “FLEX” which takes a value of 1 for states with flexible labor market institutions (and 0 otherwise).

Our variable on delicensing, which we call “delicensed”, is defined as the share of manufacturing output accounted by delicensed industries. First, we determined delicensed industries based on Aghion et al. (2005), who use industrial policy statements, press notes, and notifications issued by the central government to identify when various 3-digit manufacturing industries were delicensed.<sup>12</sup> We then calculated the output share of these industries within each 2-digit industry for each state in each year.

Another policy variable we use is the real per capita development expenditure. Development expenditure here includes expenditure on education, public health, water supply, sanitation, relief from natural calamities and food subsidy.

# **4.2 Methodology**

Our basic measure of productivity in this paper is labor productivity, which is real net value added divided by the number of workers. This measure of productivity is regressed on our policy and institutional variables, and their relevant interactions.

We extend our analysis of productivity to total factor productivity. Our methodology in investigating the determinants of total factor productivity follows the general Cobb-Douglas type production function approach. Consider the following production function:

> 12 The following two-step process was carried out to use the Aghion et al definition for delicensed industries. First, since the manufacturing industries listed by them are expressed in terms of the Indian National Industrial Classification (NIC) 1987 industrial codes, we map the listed industries in terms of their NIC 1970 classification. This step is essential given that state level information on three digit manufacturing industries between 1986 and 1988 is available from the Annual Survey of Industry (ASI) in terms of NIC 1970 only.  Second, we follow Aghion et al in dropping all three digit industries which are either included in any given state for less than 10 years or are active in less than five states.  This step was carried out in order to maximize the comparability of states’ experience with delicensing.  We thank Rana Hasan and Jewel Cain of the Asian Development Bank for helping us sort out this important data issue.

14
fertility that existed between these two treatment arms immediately after the program disappeared two years later, and (b) UCT beneficiaries, on average, have a higher age at marriage and a lower level of overall empowerment than CCT beneficiaries by Round 4.

In Appendix Table S12, we present _q-values_ controlling for FDR, as described in Anderson (2008). We use Anderson’s Stata code to calculate FDR-adjusted _q-values_ , which uses a simple method proposed by Benjamini and Hochberg (1995) to calculate the smallest _q_ at which each hypothesis would be rejected.<sup>18</sup> The _q-values_ for the 14 primary outcomes in this study, presented alongside the original _p-values_ of the impact estimates for each treatment arm, confirm the robustness of our findings to multiple hypothesis testing adjustments: every statistically significant impact for the CCT arm among _baseline dropouts_ has a _q-value_ below 0.099, while every _q-value_ is greater than 0.289 among _baseline schoolgirls_ .

Our analysis so far point to two main findings: first, among the more vulnerable group of baseline dropouts, CCTs improved school attainment and decreased marriage and fertility rates, which were sustained over time. Second, the large effects of UCTs among _baseline schoolgirls_ during the program have all but disappeared within two years. In this sub-section, we find that these two main findings are robust to attrition and multiple hypothesis testing.<sup>19</sup>

# **4.6 Husband Characteristics**

The program impacts on empowerment presented above, particularly the negative effects apparent in the UCT group,<sup>20</sup> motivate the examination of marriage market outcomes. As described earlier, two years after the end of the transfer program, CCT beneficiaries among

> 18 The Stata code and the paper that describes the method can be found here: https://are.berkeley.edu/~mlanderson/ARE_Website/Research.html.

> 19 Reinforcing the idea that our findings are robust to attrition in Round 4, findings of baseline balance and impact estimates from earlier publications, such as Baird, McIntosh and Özler (2011), replicate in the Round 4 sample.

> 20 Note that the negative empowerment result among married women remains robust to adjustments for IPW, Lee bounds, and multiple-hypothesis testing.

24
# **The Distributional Impact of Taxes and Social S endin in Romania** **<u>p g</u>**

Gabriela Inchauste<sup>†</sup> and Eva Militaru<sup>♦1</sup> JEL classification: H22, I38, D31 Keywords: fiscal policy, fiscal incidence, social spending, inequality, poverty, taxes, Romania

> † The World Bank

> ♦ National Research Institute for Labour and Social Protection, Bucharest, Romania

> 1 We are deeply indebted to Tatiana Proskuryakova, Luis-Felipe Lopez Calva, Catalin Pauna, Vlad Grigoras, Maria Davalos, Emilia Skrok, Briana Wilson, Radu Comsa, Suzy H. Yoon, Alina Sava, Andrei Dospinescu, and Huihui Wang for helpful comments, inputs and advice. All remaining errors are our own.
experienced would increase children’s odds of developing to potential—but COVID-19 setbacks have increased the number and severity of challenges families face.

Figure 1. The likelihood of being developmentally on track declines with the number of risk factors, children aged 3 and 4 in Sindh <!-- Start of picture text --> Percent of children 3-4-years old Percent of children developmentally on track<br>25.00 100<br>90<br>83.02<br>20.00 80<br>69.71<br>70<br>57.77<br>15.00 44.76 60<br>43.23<br>37.43<br>50<br>10.00 40<br>26.46<br>30<br>5.00 20<br>10<br>0.00 0<br>0-1 2 3 4 5 6 7+<br>Number of risk factors<br>(stunting, less than primary maternal education, no ece, poorest wealth quintile, rural area,<br>disability, stimulation (engaged in <4 stimulating activities with a caregiver, harsh<br>punishment)<br>Percent of children 3-4-years old<br>Percent of children developmentally on track<br><!-- End of picture text --> Source: Authors’ calculation using data from the Multiple Indicator Cluster Survey 2018–19.

Risk factors included being in the lowest income quintile, stunted, living in a rural area, having a functional disability, mother with less than a primary school education, low early stimulation at home (less than four activities in the previous 24 hours), exposed to harsh parenting, and not enrolled in early childhood education.

COVID-19 has introduced loss and hardships for families worldwide, including through reduced household resources and increased caregiver distress, which pose direct threats to young children’s wellbeing. In Pakistan, the pandemic has severely contracted economic activity, with GDP growth expected to slow to 4.0 percent in FY23 (World Bank, 2022), half of the working population experiencing job or income losses, and an increase in the incidence of poverty from 4.4 to 5.4 percent (World Bank, 2021a). More than 40 percent of Pakistani households experienced moderate-to-severe food insecurity in 2020 (World Bank, 2021a), which is associated with serious psychological distress among parents (Tseng et al., 2017) and obvious 4
<!-- Start of picture text --> : ' 3 ¢<br>a = xa ‘ , a a es<br>=| 4 - oo | t 4 4 Pos a By<br>oS ~ I valuesaebelowequal theor greater 25th percentilethan 25th and less than 50th percentile a —. a<br>[BB values equal or greater than 50th and less than 75th percentile<br>BB values above the 75th percentile<br><!-- End of picture text -->

|Lending-depositspread(%)||Number of<br>countries||Average|| Median ||Standard<br> deviation|||Weighted<br>average®|
|---|---|---|---|---|---|---|---|---|
||World|129|(et|6.3|6.4|0.1|41.5|6.9|
|Bydeveloped/developingeconomies|||||||||
||Developed economies|28|3.8|3.5|2.0|0.2|8.1|2.2|
||Developing economies|101|8.8|6.9|6.7|0.1|41.5|7.3|
|Byincomelevel|||||||||
||High income|28|3.8|3.5|2.0|0.2|8.1|2.2|
||Uppermiddleincome|43|6.7|6.2|5.3|0.1|34.0|6.5|
||<br>Lower middle income|39|8.8|8.0|4.7|2.4|24.8|6.0|
||Low income|19|13.7|10.2|10.1|3.3|41.5|13.0|
|Byregion|||||||||
||High income: OECD|14|2.6|2.7|1.2|0.2|4.7|1.9|
||High income: non-OECD|13|5.1|4.9|1.9|1.8|8.1|5.1|
||East Asia & Pacific|17|a3|5.5|4.7|2.4|20.2|3.6|
||Europe & Central Asia|17|tad|6.2|5.2|0.4|20.8|6.7|
||LatinAmerica&Caribbean|27|9.6|7.2|6.8|4.1|34.0|16.9|
||<br>Middle East & North Africa|10|4.6|4.9|2.6|0.1|9.5|46|
||South Asia|5|5.9|5.9|0.5|5.2|6.4|6.0|
||Sub-SaharanAfrica|26|137|8.8|8.9|3.3|41.5|12.8|

Source: Global Financial Development Database, 2008-10 data.

Note: OECD = Organisation for Economic Co-operation and Development. a. Weighted average by total population.
10 The analysis below is relevant for, but does not directly focus on, other ways in which politicians use transfers to voters before the election. Nichter (2008) emphasizes the importance of turnout-buying – when voting behavior is unobservable, politicians pay voters who are likely to support them simply to vote. Cox and Kousser (1981) analyze the opposite phenomenon: a key development after the introduction of the secret ballot in the United States was to pay opponents’ voters to stay home (“deflationary” vote-buying).<sup>6</sup> The analysis here is entirely consistent with either turnout-buying or deflationary vote-buying, since both entail pre-electoral transfers that politicians make instead of relying on pre-electoral promises of post-electoral benefits.

# **The model**

Keefer and Vlaicu (2008) conclude that clientelist public policies are more likely to emerge the smaller is the fraction of voters to whom political competitors can make credible commitments. Under these circumstances, politicians are less likely to promise broadly beneficial public goods to voters and more likely to promise narrow transfers, targeted to those few voters who believe their promises. However, their static model does not inform the inherently dynamic question of when politicians will make narrow transfers prior to the election (vote-buying) or promise transfers after the election. We construct a dynamic model that allows such tradeoffs to be directly analyzed.

Elements of the basic set-up are standard. We assume a probabilistic voting framework with heterogeneous groups, as in Dixit and Londregan (1996). The electorate consists of a continuum of groups of measure _N_ , each group of measure one. All citizens have the same income, normalized to one. Each group is indexed by the variable m ε [0, N]. Two political parties, _A_ and _B_ , compete for power. Voter _i_ in group _m_ has a partisan bias given by σi(m). Positive values of σi signal that voter _i_

> 6 They rely on newspaper reports to quantify the prevalence of deflationary vote-buying and “inflationary” (or “turnout”) vote-buying.  Before the secret ballot, deflationary vote-buying was rare (fewer than 3 percent of newspaper articles on corruption discussed it).  Even after the secret ballot was introduced, though, inflationary vote-buying was significantly more common.  Deflationary vote-buying accounted for 23.7 percent of articles, compared to 43.4 percent accounted for by inflationary vote-buying (32.9 percent of articles on corruption were of an uncertain modality) (Cox and Kousser p. 659).
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the previous one if it were to reveal a superiority in the performance of divested firms in comparison with their counterparts that remained public. If the comparison alternatively revealed insignificant differences in performance, as happened in the Bishop and Kay study of the U.K., several competing explanations are possible: the economy was booming so that all firms were doing well, performance improvements of undivested firms were the result of the threat of divestiture, or a combination of the two.

To sort out the effect of the change of ownership from other exogenous concurrent factors, such as the effect of a booming economy, we can make a third comparison between the performance of the divested firm and an explicit counter-factual (i.e., the hypothetical performance of the divested firm had it continued public). The counter-factual can be constructed on the basis of knowledge about the operation of the enterprise before its divestiture, the actual operation of the enterprise once divested, and additional independent knowledge (e.g., the state of economic activity, the income elasticity of demand).

The fourth comparison is between the performance of divested firms in competitive and non-competitive markets in the same country (e.g., Chile). By permitting sectoral variations (e.g., textile and electricity), this comparison should uncover the extent to which market structures, regulatory arrangements (if warranted), and technologies made a difference.

The fifth and final comparison is between the performance of divested firms in the same industry (e.g., textile) but across countries (e.g., the
achievers also go on to occupy a high earnings rank (for example, our average sample member is at the 93<sup>rd</sup> percentile of the New Zealand earnings distribution), highlighting the fiscal consequences of their mobility for countries with progressive income taxation. Moreover, it is a subgroup whose composition is likely to be much less affected by desire to emigrate than studies focusing on a specific occupation.

Our focus on the best and brightest is also justified by the stress in the literature that it is likely to be the migration of the most skilled and talented individuals for which any negative effects are greatest. Kapur and McHale (2005, p. 97) write that “clearly people of exceptional talent have a highly nonlinear impact.”. However, little empirical evidence is available on the migration of the “best and brightest”. The only study which exists is a simple descriptive exercise which examines the emigration rates of graduate students of IIT Mumbai, one of India’s most prestigious tertiary institutions, in the 1970s, finding 31 percent settled abroad, compared to an estimated migration rate of 7.3 percent for engineers in the country as a whole (Sukhatme, 1994).

# **2.2 Country Choice**

Brain drain rates, as measured by the share of tertiary-educated individuals born in a given country who are living abroad, are highest in small states and a few sub-Saharan African countries (Beine et al. 2007, 2008), and it is in such places that concerns about the possible negative consequences of brain drain are most common. We therefore chose to focus our survey efforts in such countries. We began by choosing three developing countries in the Pacific – the Federated States of Micronesia, Papua New Guinea and Tonga, since the Pacific Islands are the region with the highest brain drain rate in the world (Docquier and Marfouk, 2005). We then also chose Ghana, which has one of the highest brain drain rates in sub-Saharan Africa and which has been one of the countries most involved in discussions about medical brain drain. Finally, we chose New Zealand, which is the OECD country with the highest tertiary brain drain rate. The population, GNI per capita in current US dollars<sup>6</sup> , and brain drain rate in the year 2000 for those who entered the destination country after age 18 (Beine et al, 2007) are:

- Ghana: 23.4 million population, $US670 GNI per capita, 44.9% brain drain rate

> 6 Population and GNI per capita are World Bank 2008 estimates.

6
<!-- Start of picture text --> Policy Rosewohl<br><!-- End of picture text --> <!-- Start of picture text --> Country Operatlons<br><!-- End of picture text --> WPS 1168 This paper -a product of the Office of the Chief Economist, Africa Regional Offlce -is part of a larger effort to assess the progress of adjusting countries in the Africa region with macroeconomic and structural reforms. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 23433. Please contact Nandita Ttnnan, room J5-101, extension 34581 (August 1993, 109 pages).

Policy framework paper (PFPs) have become Important documents because they provide a framework for the economic policies that a country will pursue and for donor assistance. The projections included in these documents reflect the policy targets and the expected outcomes of policy reforms.

The quality and realism of PFP projections are likely to Improve, says Faruqee, if certain steps are taken in making projections:

* All PFPs after the first one should contain a review of outcomes in relation to projections in previous PFPs.

Faruqee focuses on the qaality and relevance of these projections, comparing actual outcomes with the projected or targeted outcomes for selected variables. The Idea is that a retrospective survey such as this will eventually Improve projections.

Faruqee recommends further country-bycountry analysis of PFP projections and actual outcomes to identify how much of the divergence between the two is due to external iactors (such as weath.T and terms of trade) and how much to lack of progress in policy reform. Delayed progress could be due to unforeseen circumstances (such as political changes or internal strife) or to unrealistic targets.

* Whenever the chances of failing short of projec'ou or targeted outcomes are high, the PFP should say so.

* Using references such as this review, the projections could consider benchmark figures based on experience with successful cases of adjustment.

* From time to time, each country department could carry out a review to assess the realism of departmental PFP projections, and the Chief Economist's Office could review projections to assess their quality.

The PoHicyResearchWorking PaperSeriesdisseminates the findings of workunder way in the Banlc An objectiveof the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
18 _Foreign trade._ Imports are produced with foreign exchange. Imports and domestic goods are transformed into domestic supply with CES functions. Exports are produced with value added and intermediate goods, as in equation (4). The allocation of exportables among exports, domestic consumption and government revenue through export taxes is: where _Qx_ is output of exportables gross of export taxes, _Xi_ is net exports, _Gxh_ is domestic consumption of exportables, _Gx_ is the use of exportables as intermediate inputs, and zx is the export tax rate.

_Government._ The government's income is indirect taxes and foreign savings (the trade surplus and aid). Its expenditure is consumption of goods and services and foreign debt service. Any excess of income over expenditure is rebated to households so that the government's budget constraint is where R indicates revenue, A is aid, D is foreign debt service (principal and interest), _Cg_ is government consumption, and E _Vh_ is the sum of govemment transfers to households. The variables R and Cg must be 2 0, while _A, D,_ and X _Vh_ 0; in the _h h_ base data set, _A_ > 0, _D >_ 0 and _Y Vh_ > 0 (Annex 1). Revenue is _h_ _(10) R = E(X,x + G,mn + E Ga,-,a)_ The tax rate on cocoa in equation (10) has two parts. The first is the explicit export tax (the _DUS)._ The second is the sum of the stabilization margin and allocations to CAISTAB reserves. This second part has two putative uses: ex-post producer price stabilization, and for marketing and other service costs. Because ex-post price stabilization is not paid and because CAISTAB's costs are neither competitively determined nor directly related to real services, the stabilization margin amounts to a second export tax"<sup>9</sup> . The 1997/98 DUS was 18.1 percent of FOB and the implicit export tax rate was 8.6 percent, giving a total taxation of 26.7 percent with respect to FOB<sup>2 0</sup> .

> **19** It may be objected that this revenue is in fact used to fund other government services, but this does not change the incidence of the cost with respect to coffee and cocoa producers; they are still taxed.

> 20 The 18.1 percent is that shown in Table 1; the 8.6 percent includes the 7 percent for the stabilization margin
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Pettis, M. 2014. The Great Rebalancing: Trade, Conflict, and the Perilous Road Ahead for the World Economy. Princeton: Princeton University Press.

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