rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1987 1999 2011 1987 1999 2011<br>1933-38 1939-44 1945-50<br>Age 49-54 Age 43-48<br>Age 37-42 Age 31-36 1951-56 1957-62 1963-68<br>Age 25-30 1969-74 1975-80 1981-86<br><!-- End of picture text --> Note: Estimated marginal effects of graduate education, see appendix table A1.6.

These results are in line with the selection channel driving the effect of graduate education: the marginal effect is lower for younger age groups than for older age groups in a given year. Furthermore, for each age group, the effect declines by about one third over time in each of the two 12-year periods. Looking at the same results presented by birth-year cohort in panel (b), we see some decline in the effect of graduate education over time in those cohorts we can trace, but the changes are much smaller.<sup>27</sup> All in all, this is further supporting evidence that declining positive selection is a plausible explanation why graduate education has become less positively associated with women’s labor force participation in urban India.

> 27 For secondary education, the pattern is similar but somewhat less clear than for graduate education (see Table S1.5).

36
(Kroodsma et al., 2018). This measure likely underestimates the number of subsidy days, but by much less than if we applied the MOA’s minimum VMS ping rate to the AIS data (Figure A1). AIS transponders transmit similar data to terrestrial receivers and satellites as VMS transponders, but they have worse reception than VMS. We calculate that the fuel subsidy for the median vessel-year is $223,380 (2022 USD) using this measure of subsidy days, but because our measure is an underestimate, the median vessel-year annual fuel subsidy is likely greater. This subsidy amounts to about 10% of annual variable costs for the median vessel-year in our data (Sala et al., 2018). This underestimate of subsidy days affects our elasticity of fishing with respect to fuel subsidy received, but it does not affect our estimate of the effect of the fuel subsidy policy on fishing.

# **3 Data**

By combining publicly-available data from international organizations and Chinese agencies, we identify and collect the characteristics of subsidized Chinese distant water fishing vessels. Then we match these characteristics with fishing activity data. The resulting panel data are at the level of vessel-month-year.

We begin by collecting data from the seven Regional Fishery Management Organizations (RFMOs) that include vessel registrations from mainland China (Appendix B). For each RFMO-vessel, we recorded the vessel’s registry name and vessel characteristics, including gear, length, gross tonnage, engine power, and the year the vessel was built. Gross tonnage is a non-linear measure of a vessel’s internal volume. When vessels have the same name in Chinese pinyin, but the Chinese characters name and other characteristics are different, we regard them as different vessels. In most RFMOs, vessels reported additional identification information such as previous name, Maritime Mobile Service Identity (MMSI), call sign, and International Maritime Organization (IMO) number. We also gathered data from RFMO websites on the species vessels target when they fish, vessels’ authorized fishing area, freezer type (if the vessel has a freezing system), the firm the vessel belongs to, and the RFMOs the vessel is registered in. We combine these variables into a single dataset at the vessel-RFMO level.

9
<!-- Start of picture text --> 50 46 a7 av<br>39<br>40 36<br>a 3a a2<br>—<br>= 25<br>—_<br>o<br>Be 20<br>a<br>e<br>oa<br>2 10<br>a<br>a<br>a<br>Micro Small <=l0 years >10 years SSA LAC Yes No<br>Size Age Location Like to register<br><!-- End of picture text -->
effect that Rodrik (2016b) defines as “premature deindustrialization.” In traditional structural transformation, the growth of industry, especially manufacturing, played a big role. Several developing countries did not follow the development pattern of already industrialized countries and basically skipped the transition from agriculture to manufacturing, going straight to service sector development. The modern service sector in the developing world, however, is highly polarized, with few very well-paid jobs and a vast majority of low-paid jobs in retail and construction (Molini and Paci 2015; World bank 2019).

# 4 Our Proposal to Reduce the Inequality Underestimation

## 4.1 Introduction

The previously provided empirical estimations clearly demonstrate that the choice of the consumption or income variable is far from neutral. We propose a solution to overcoming the emerging potential mismesaurement. In the past decades several methods have been suggested to include the welfare of hard-to-survey populations, in particular, the extremely rich (e.g. Hlasny and Verme 2021; and references therein). The most famous method is to compare top incomes in household surveys with tax records (see, for example, Atkinson et al. 2011). In developing countries, where generally it is difficult to obtain this type of information from tax authorities, analysis on top incomes started later than in developed countries but has recently gained momentum (see, for example, Leigh and Van der Eng 2009; Alvaredo 2010; and Sanhueza and Mayer 2011).

More recently, Clementi et al. (2020) depart from the top-income literature and present recalibration exercises in order to involve a much bigger portion of the distribution. The principal novelty of this approach consists in the fact that, different than in other works, the reestimation of the top incomes tail is based on parameters coming from the same sample. In what follows, we first outline the methodology proposed by Clementi et al. (2020) for the purposes of analyzing inequality in SSA and then present the results of its applications to the sample of African countries considered in this study.

4.2 Multiple-Imputation Approach to Inequality Estimation and Inference Schematically, the approach to inequality measurement proposed in this paper goes through the following steps.<sup>12</sup>

> 12 The approach introduced by Clementi et al. (2020) is adapted from earlier work by Jenkins et al. (2011), who proposed a parametric multiple-imputation method to measure income inequality with right-censored (top-coded) data. This method has 13
- Bazzi, Samuel, Sudarno Sumarto, and Asep Suryahadi. 2012. “Evaluating Indonesia’s Unconditional Cash Transfer Programme, 2005‒2006.”

- Benhassine, Najy, Florencia Devoto, Esther Duflo, Pascaline Dupas, and Victor Pouliquen. 2013. “Turning a Shove into a Nudge? A "Labeled Cash Transfer”. Cambridge, MA.

Bhowmik, Sharit K., Indira Gartenberg, and Kanchan Sarker. 2013. “Food or Cash: Assisting the Urban Poor in India.” In _The Food Crisis: Implications for Labor_ , edited by Cristoph Scherrer and Debdulal Saha. ICDD.

- Biron, Aurélie. 2012. “Adapting to Urban Displacement: The Use of Cash Transfers in Uraban Areas”. Sciences Po Grenoble.

- Bobonis, Gustavo J. 2009. “Is the Allocation of Resources within the Household Efficient? New Evidence from a Randomized Experiment.” _Journal of Political Economy_ (November): 453–503.

- Braido, Luis H. B., Pedro Olinto, and Helena Perrone. 2012. “Gender Bias in Intrahousehold Allocation: Evidence from an Unintentional Experiment.” _Review of Economics and Statistics_ .

- Brewin, Mike. 2008. “Evaluation of Concern Kenya’s Kerio Valley Cash Transfer Pilot (KVCTP), April - June 2008.”

- Bukuluki, Paul, and Carol Watson. 2012. “Transforming Cash Transfers: Beneficiary and Community Perspectives on the Senior Citizen Grant (SCG) in Uganda”. London, UK, and Kampala, Uganda.

- Case, Anne, and Angus Deaton. 1998. “Large Cash Transfers to the Elderly in South Africa.” _The Economic Journal_ : 1330–1361.

- Creti, Pantaleo, and Susanne Jaspars. 2006. _Cash-Transfer Programming in Emergencies_ . Oxford: Oxfam GB.

- Cunha, Jesse M. 2012. “Testing Paternalism: Cash versus In-Kind Transfers.” _Unpublished Mimeo_ . Naval Postgraduate School.

- Dasso, Rosamaría, and Fernandez Fernando. 2013. “Temptation Goods and Conditional Cash Transfers in Peru”. Washington, DC.

- Davis, Christopher G., Jennifer Thake, and Natalie Vilhena. 2010. “Social Desirability Biases in SelfReported Alcohol Consumption and Harms.” _Addictive Behaviors_ : 302–311.

- Decker, Sandra L., and Amy Ellen Schwartz. 2000. “Cigarettes and Alcohol: Substitutes or Complements?”

- Devereux, S, and M Mhlanga. 2008. “Cash Transfers in Lesotho: An Evaluation of World Vision’s Cash and Food Transfers Pilot Project”. Brighton, UK, and Maseru, Lesotho.

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TABLE 2: POLICE AND CRIME AGAINST FIRMS (LOSSES DUE TO CRIME/SALES)

||% of Losses Due<br>to Crime Over<br>Sales|% of Losses Due<br>to Crime Over<br>Sales|% of Losses Due<br>to Crime Over<br>Sales|% of Losses Due<br>to Crime Over<br>Sales|
|---|---|---|---|---|
||1|2|3|4|
|Police per 100,000 persons|-0.0008|-0.0060**|0.0065**|-0.0019***|
||[0.0005]|[0.0026]|[0.0025]|[0.0004]|
|Police x firm with female owner and|-0.0028***||||
|manager|[0.0006]||||
|Firm with female owner and manager|1.0427***||||
||[02078]||||
|Police x Quality of Government|.|0.0083*<br>[0.0043]|||
|Quality of Government||-3.4696**<br>[1.5425]|||
|Police x Voter Turnout|||-0.0104***<br>[0.0033]||
|Voter Turnout|||2.5419**<br>[1.0928]||
|Police x Religious Fractionalization||||0.0025*<br>|
|Religious Fractionalization||||[0.0014]<br>-0.6244<br>[0.4729]|
|GDP per Capita Growth|-0.0856***|-0.0970***|-0.0829***|-0.0762***|
||[0.0237]|[0.0211]|[0.0199]|[0.0221]|
|Firm with Female Owner||0.1202*|0.1168*|0.1180*|
|||[0.0661]|[0.0626]|[0.0625]|
|Real GNI per capita|-0.0056***|-0.0041***|-0.0054***|-0.0052***|
||[0.0011]|[0.0013]|[0.0013]|[0.0013]|
|GINI|-0.0093|-0.0181|-0.0098|-0.0024|
||[0.0128]|[0.0114]|[0.0113]|[0.0119]|
|Population in Millions, Total|0.0029*|0.0028*|0.0049***|0.0041**|
||[0.0016]|[0.0016]|[0.0016]|[0.0015]|
|Small firms|0.3513***|0.4069***|0.4033***|0.4020***|
||[0.0990]|[0.1032]|[0.0952]|[0.0943]|
|Medium firms|0.1328*|0.1755**|0.1683**|0.1653**|
||[0.0754]|[0.0674]|[0.0664]|[0.0657]|
|Large or Capital city|0.0579|0.0354|0.0567|0.0516|
||[0.0901]|[0.1018]|[0.0983]|[0.0957]|
|Manufacturing|-0.2275**|-0.2251**|-0.2172**|-0.2224**|

25
humanitarian organizations such as the UNHCR should work together with development organizations such as the World Bank from the very beginning of a forced displacement crisis to devise solutions that are sustainable for the livelihoods of the forcibly displaced and their hosts (Verme and Macleod, 2016; Devictor, 2016).

The growth of the forced displacement phenomenon and the new consensus on the humanitarian‐ development nexus encouraged development organizations to start looking at refugees, IDPs and their hosts. These populations were largely excluded from the development discourse until a few years ago. For example, the global count of poverty excluded for the most part refugees and IDPs because national household budget surveys would normally exclude these populations. Humanitarian organizations that conducted surveys on these populations did so mostly for humanitarian protection purposes and did not have special expertise on the measurement of welfare and poverty. Data remained scarce and economists did not pay much attention to forced displacement. This changed when development organizations started to engage with refugees, IDPs and their hosts, with this phenomenon contributing to raise economists’ interest.

Until these recent events, the economics profession approached the question of displacement from the perspective of internal (within nation) or external (across nations) migration. The economics of migration finds its theoretical and empirical roots in labor and development economics and is now an established discipline on its own with thousands of contributions in international journals. More recently, some contributions emerged on forced migration (Ruiz and Vargas‐Silva, 2013) but these contributions remained empirical in nature and scarce in number.

A search by keywords of the major economic and social science research repositories shows a vast difference in the number of hits between the terms “migration” and “migrants” on the one hand and the terms “refugees” and “internally displaced” on the other hand (Figure 4). For example, a research in titles and keywords among published journal articles in the main economics repository ( _Econpapers_ ) shows 6,350 hits for the word migration, 1,586 for the word migrants, 383 for the word refugees and only 18 for the word internally displaced. Similar outcomes in relative terms are visible if we do a search by title, keywords and abstracts of the research papers contained in the _Social Sciences Research Network_ (SSRN), the main repository of social sciences articles, or google scholar. It is also extremely rare for studies on forced displacement to be published in top economics journals. Despite mass press coverage, studies on refugees and IDPs remain very scarce in number and quality.

6
them as possible both at the country level and the region level. We need to make a judgment call to decide the relevant cleavage along which groups are identified. In most cases, language forms the most politically relevant cleavage, but in other cases religion or other another cleavage dimension is more politically salient so we focus on those.

Our preferred way of coding regional distinctiveness is the following: for each region, identify the dominant group (A) as the largest group in the region that is not the national majority and collect information on what share of the country-wide population of group A lives in that region as opposed to other regions of the country; as well as information on other (non-A) national minority groups in the region. A region is coded as sufficiently ―different‖ if (i) most its inhabitants belong to group A (a _national_ minority) and (ii) most of A’s members live in the region. The intuition behind this ―double-requirement‖ approach is clear: if almost all people belonging to an ethnic group live in one region, and they represent a strong majority there, such a region will be very distinct from the rest of the country. Ideally the data would fill a matrix with one dimension giving ranges of the share of group A inside the region and the other dimension giving ranges of A’s share of the regional population. The intensity of demands for sovereignty should be proportional to the degree of regional difference.

Data to fill such a matrix are available for a few countries using census data, other government documents, and secondary sources. Switzerland, for example, has good data. It also has several regions that are dominated by an ethnic minority: Fribourg, Geneva, Jura, Neuchatel, Valais (Scion), and Vaud (Lausanne) are all dominated by French-speakers, with regional population percentages in the 60-88% range. However, French speakers are not concentrated in any one of those regions, so the degree of ―regional difference‖ from the rest of the country is not large. Because such high-quality data from censuses do not exist for most countries, we computed the index of ethnic difference using, where possible, actual census numbers and shares of group populations, but in most countries data we were reduced to coding a binary variable denoting whether or not a region is sufficiently ethnically different from the rest of the country.

24
Yugoslavia. Secession was a divisive issue and, at the time of our trust study in 2012, divisions in society remained influenced by the referendum on independence. Despite important gains in income per capita and poverty reduction in the period 2002-2012, high unemployment remains a problem and broader economic development had not advanced in step with people’s expectations; in addition, the economy was struggling to resume robust growth in the aftermath of the global financial crisis (World Bank, 2012). Problems relating to the viability of former socialist industry, employment, and public finance – the same types of problems that other post-socialist societies had experienced some two decades earlier – were present.<sup>1</sup>

# _1.3 Conclusions on trust_

Our study finds levels of trust in Montenegro comparable to high-income countries. Indeed, for participants from one group in our study, who we associate with private-sector activity, the results show extremely high levels of trust. Our study, therefore, suggests that impediments to growth and development in Montenegro are not to be sought in lack of trust in the private sector. Our experiments provide no data on trust in institutions – because interactions were between private individuals. We, however, asked participants in the experiments about their trust in government and in institutions and included the responses in empirical estimates of determinants of observed personal behavior.

# _1.4 Implications_

The trust revealed in Montenegro is consistent with per capita income considerably higher than formally reported in official statistics. Informal activity

> 1 The problems in Montenegro, for example, paralleled the problems of transition in Bulgaria two decades earlier described in Bogetić and Hillman (1995). See also the studies in Campos and Fidrmuc (2003).

4
10

-

_

# VII. ESTIMATION OF UNIT VALUE INDICES

The results of our estimation of the unit value indices of CPEs' exports to highly competitive world markets are sho-,n in Table 2. The relative prices of CPEs' exports to the EEC markets were estimated in two ways: as CPEs' export prices relative to export prices of developed countries (here approximated by EEC countries) and as CPEs' export prices relative to export prices of developlug countries in the same markets. The results have been aggregated into three broad commodity groups--agriculture, raw materials and manufactures--with each group showing the relative price performance in more. disaggregated subgroups. The results are presented in the form of indices; indices equal to one indicate that relative prices of CPEs were identical to those of their competitors.
check, this adjustment may also shed light on how the reforms affected the traditional measure of outcomes. When we used log(per capita GVO) as the dependent variable, we also controlled for log(deflated costs of material inputs) in the right hand side of the production function, and kept other variables intact.

**Table 4. Alternative Estimates of the Production Function**

||Dep. Var. = GVO<br>Dep. Var. = Value Added|
|---|---|
|The marginal retention rate|0.049**<br>0.109**|
|AUTONOMY|0.023**<br>0.060**|
|Year* AUTONOMY|0.013***<br>0.029***|
|S_PLANQUAN|-0.079***<br>-0.167***|
|WDISCRETION|0.021<br>0.076**|
|Year*WDISCRETION|0.015***<br>0.034***|
|CRS|0.024<br>0.018|
|Year*CRS|_-0.0l5**_<br>-0.022|
|W_ELASTICITY|0.022<br>0.131***|
|Appointing new managers for firms<br>governed by all levels of gov't|insignificant<br>insignificant except a significantly negative<br>level effect for county-governed firms|
|Year*appointing a new manager for<br>centrally-governed firms|0.006<br>_-0.005_|
|Year*appointing a new manager for<br>provincially-governed firms|0.014*<br>0.064***|
|Year*appointing a new manager for<br>prefecture-governed firms|0.005*<br>0.022***|
|Year*appointing a new manager for<br>county-governed firms|-.007<br>0.066***|

Note. *, **, and * represent significance levels of 1%, 5%, and 10%; no asterisk implies insignificance.

The estimates of this production function show that the signs of the reforms effects are quite robust, and the magnitudes are smaller. The effects of reforms (in the 23
undergraduate and for postgraduate education, allowing us to investigate the educationselectivity of return migrants.<sup>11</sup> Table 13 shows that the three survey methods do give different pictures of how the return migrants compare to non-migrants. The stratified survey shows little selectivity, with an undergraduate education being the only variable significant at the 10 percent level. The intercept survey also shows very little selectivity, except in one respect. Living in a household with strong usage of Japanese media is strongly and positively associated with being a return migrant in the intercept point survey, but not in either of the other two survey methods. This is consistent with the intercept point oversampling both return migrants and those with high usage of Japanese media, perhaps leading to a spurious correlation between the two.

**Table 13: Do different sampling methods give different pictures of who return migrants are**

Marginal effects from Probit estimation of being a return migrant

||STRAT|IFIED|INTER|CEPT|SNOW|BALL|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Age|-0.0000349|-0.000394|-0.00132|-0.00295|-0.00656|-0.00654|
||(0.0036)|(0.0038)|(0.0032)|(0.0033)|(0.0054)|(0.0055)|
|Female|-0.0835|-0.0800|-0.0381|-0.00814|0.130**|0.130**|
||(0.060)|(0.060)|(0.064)|(0.063)|(0.066)|(0.064)|
|Married|0.0779|0.100|0.0260|-0.00341|0.0395|0.0389|
||(0.11)|(0.11)|(0.082)|(0.074)|(0.15)|(0.14)|
|Undergraduate Education|-0.136*|-0.131*|-0.0156|0.0388|0.257***|0.256***|
||(0.072)|(0.076)|(0.071)|(0.076)|(0.078)|(0.079)|
|Postgraduate Education|0.333|0.373|-0.0579|-0.0306|-0.311***|-0.311***|
||(0.30)|(0.31)|(0.12)|(0.12)|(0.090)|(0.089)|
|Issei|0.139|0.132|0.419*|0.257|0.487*|0.490*|
||(0.23)|(0.22)|(0.23)|(0.26)|(0.29)|(0.27)|
|Nissei|-0.0984|-0.106|0.151|0.144|-0.149|-0.146|
||(0.13)|(0.13)|(0.17)|(0.18)|(0.25)|(0.24)|
|Sansei|0.00809|-0.00644|0.0946|0.0735|-0.380**|-0.378**|
||(0.15)|(0.16)|(0.16)|(0.16)|(0.17)|(0.17)|
|Japanese media use index||0.0156||0.116***||-0.00179|
|||(0.017)||(0.021)||(0.036)|
|Sample Size|195|195|362|359|140|140|

Notes: Sample restricted to 18-59 year olds. Sample weights used, and observations are clustered at the household level to account for multiple observations per household in stratified and snowball surveys. Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 11 Of course some of these characteristics may have changed after returning from Japan. The longer stratified survey and snowball survey contain questions on where education was obtained, but the shorter intercept survey does not. Less than five percent of highest qualifications were completed in Japan.

34

-  -
20

**Table 5. Estimated Effects of Population, Per Capita Income,** and **Population** Density **on the Vehicle-Road Ratio**

|||_C_|_oefficient estimat_|_es_a|
|---|---|---|---|---|
|_Dependent variable_|_Functional_<br>_specification_|_Population_|_Per capita_<br>_income_|_Population_<br>_density_|
|_At the national level_|||||
|Vehicles/total road km|Cross section|0.0|0.4*|0.2*|
||20-year first differences|0.3|0.4|n.a.|
|Vehicles/paved road km|Cross section|0.0|0.0|-0.1*****|
||20-year first differences|-0.9|0.2|n.a.|
|_At the urban level_|||||
|Vehicles/road km|Cross section|0.1|variesb|0.6**_*_**|
||First differences|0.5|0.9|0.5|

n.a. not applicable

***** Statistically significant at the 0.05 level.

a. All variables are in natural logarithm, and elasticities are jointly estimated.

b. The elasticity first rises and then falls with income.

_Source:_ estimated in Ingram and Liu (1998).

**Figure 5. Per Capita** Income and **Vehicle-to-Road Ratio in 50 countries and 35 Cities**

<!-- Start of picture text --> 1000<br>Seoul<br>Manila Bangkok HKAi<br>JakdAng A Santiago London Osaka<br>100 Guangzhou Singapore<br>0 ~~~~~~~~~~~~Malaysia .<br>ABeujing * Korea * Japan<br>------- .... ..-... A * - Switzerland<br>;< ~~~~~~Thailand* l<br>s 10 Indonesia * Ireland * Australia<br>Chirna s sBrazil Nationaltotalroad<br>A Urban road<br>Philippines Gabo a Natonaltotalroad eq.<br>Malawi India ------- Nationalpavedroadeq.<br>Rwanda + * Ca pteroon L Urban road eq.<br>100 1,000 10,000 100,000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text --> Note: Data points for paved roads are not plotted for the sake of clarity. _Source:_ Ingram and Liu, "Vehicles, Roads, and Road Use."