rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.  The Returns to Education in Russia and Poland**

||**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|**_FPT_**|
|---|---|---|---|---|---|---|---|---|---|
|**POLAND**|**_1991_**|**_1992_**|**_1993_**|**_1994_**|**_1995_**|**_1996_**|**_1998_**|**_2001_**|**_2002_**|
|Years of education*|0.060|0.071|0.081|0.080|0.079|0.070|0.081|0.092|0.106|
|Dataset|ISSP|ISSP|ISSP|ISSP|ISSP|ISSP|ISSP|ISSP|ISSP|
||**_FPT_**|**_FPT_**|**_FPT_**|**_S&G_**|**_S&G_**|**_S&G_**|**_S&G_**|**_S&G_**|**_S&G_**|
|**RUSSIA**|**_1991_**|**_1997_**|**_2001_**|**_1985_**|**_1990_**|**_1996_**|**_1998_**|**_2000_**|**_2002_**|
|Years of education|0.028|||||||||
|*||0.072|0.084|0.028|0.039|0.079|0.091|0.094|0.097|
|Dataset|ISSP|ISSP|ISSP|RLMS|RLMS|RLMS|RLMS|RLMS|RLMS|

Sources: Poland: FPT Flabbi et al. (2005); S& G) Sabrianova, K; Gorodnichenko, Y. “Returns to Education in Russia and Ukraine: A Semi-Parametric Approach...* Controls include: gender, location, age, family status.

_Arrears_ as reported by Lehmann and Wadsworth (2001) were responsible for up to a third of the “excess” inequality in wages in Russia. At the peak of wage arrears in November 1998, 64 percent of workers were owned back wages and the Gini index for wages actually paid was as high as 0.58. By 2004 the share of workers who were owed wages fell to 15 percent<sup>6</sup> and the Gini index for paid wages fell to around 0.44, i.e. by just less than a third. But, even at this level, wage inequality was considerably higher than in Poland or other countries in Central and Eastern Europe. Therefore this factor, while providing a partial explanation for the inverse U-shape of the evolution of wage inequality in Russia, does not fully account for excess inequality in the distribution of earnings.

Another explanation is provided by distinct differences in _minimum wages_ which were set at around 40 percent of the average wage in Central and Eastern Europe, as opposed to at 10 percent of the average wage in Russia. [see World Bank (2005c)]. This allowed Russian firms to maintain low-paid jobs that otherwise would have been economically unviable, so that low minimum wages were a very important policy induced factor contributing to higher wage dispersion.

As opposed to relatively stable sectoral and inter-industry wage differentials, _regional variation_ in real wages, relative to the national average, almost tripled in Russia between 1995 and 2003. Segmentation of labor markets is a common feature of many transition economies, but in Russia this dispersion takes particularly extreme forms due to institutional, infrastructure and geographical realities.

Increasing wage inequality in the transition countries of Eastern Europe and the former Soviet Union reflects a rising education premium, minimum wage policies and increased divergence of wages across sectors, regions and occupations. But wages, although important, were not the only determinant of inequality outcomes and the following factors played a role as well.

> 6 The data are taken from RLMS.

13
to both the level and composition of protective measures. In particular, our policy simulations vary the composition of security interventions to determine the marginal rate of substitution between navy patrols and private maritime security contractors. Using cost estimates, we discuss the optimal allocation of limited funds between these two crime protection instruments. Given the deterrence and displacement externalities generated by private security, we contrast the optimal protective regimes obtained when the objective is to minimize overall crime incidence only versus when the distributional impacts of private protection are considered. In doing so, we emphasize the role of navy assets deployed on the Indian Ocean in addressing the implications of crime displacement.

Our paper directly relates to the few economic analyses of piracy, and notably to Ambrus, Chaney and Salitskiy (2018). The authors analyze data from Renaissance-era piracy activity in the Mediterranean sea to estimate a model of delay in bargaining. While our paper adopts a similar approach to link ransoms and durations of captivity, we also model other decisions such as entry and vessel choice. The main objective of our paper is to capture enough features of the piracy business to be able to discuss the role and interaction of security measures.

Our contribution, therefore, is an addition to the literature on the economics of crime pioneered by Becker (1968) and Ehrlich (1973), and recently reviewed by Draca and Machin (2015). One outcome of our analysis is the estimation of the crime-policing elasticity, where policing here is achieved by a mix of navy patrols and private maritime security contractors. We thus complement the findings of Levitt (1997), Corman and Mocan (2000), Di Tella and Schargrodsky (2004), or Draca, Machin and Witt (2011). A noteworthy difference is that the identification of the impact of protection on crime in these and most other studies relies on arguably exogenous spatial heterogeneity in the level of policing. Such quasi-experimental settings generate a “natural” counterfactual, which allows identifying crime-policing elasticities. These studies, however, focus on a single protection instrument and thus cannot speak to the central question of this paper, which is the tension between public and private policing. In our setting, there is no such natural counterfactual, since all pirates are subject to the same policing environment. We instead estimate a structural model of the piracy business. By putting structure on agents’ behavior, our model 4
|**Table 9. Empowerment outcomes**|**(ITT), 2010, 2011**<br>Poo<br>|**and 2012 co**<br>led 2010‐20<br> <br>|**horts**<br>12 Cohorts<br>||
|---|---|---|---|---|
||Baseline mean<br>[Std Dev]|<br>OLS<br>(1)|IPSW<br>(2)|NN<br>(3)|
|Any money of your own (1=Yes)|0.681|0.055**|0.052**|0.058***|
||[0.466]|(0.022)|(0.024)|(0.021)|
|Control over Earnings (1=Yes), if|0.664|0.041|0.047|0.046|
|respondent has earnings|[0.472]|(0.029)|(0.033)|(0.030)|
|Control Over Savings (1=Yes), if|0.571|0.041|0.035|0.043|
|respondent has savings|[0.495]|(0.030)|(0.033)|(0.033)|
|Control over household spending|3.364|0.221**|0.249**|0.246**|
|(# categories out of 8)|[3.304]|(0.109)|(0.113)|(0.114)|
|Mobility (# places visited in last|4.713|0.035|0.015|0.040|
|month out of 9)|[1.674]|(0.081)|(0.082)|(0.076)|
|Any Mentor Related to Work|0.602|0.067***|0.068***|0.073***|
|(1=Yes)|[0.490]|(0.024)|(0.024)|(0.023)|
|Entrepreneurship Score (0‐32)|14.938|0.751***|0.535*|0.758***|
||[6.029]|(0.279)|(0.275)|(0.265)|
|Financial Literacy (1=Yes)|0.616|0.020|0.021|0.020|
||[0.486]|(0.025)|(0.027)|(0.023)|
|Self‐Regulation Score (0‐16)|10.403|0.330**|0.296*|0.285*|
||[2.355]|(0.162)|(0.159)|(0.164)|
|Self‐Confidence Score (0‐27)|19.014|0.346***|0.375***|0.383***|
||[3.312]|(0.117)|(0.114)|(0.110)|
|Clustered Standard Errors||Yes|Yes|No|

Notes: All columns report difference‐in‐difference estimates. "ITT" indicates that everyone whose score qualified them for a given training event is included in the "treatment" group. Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

32
||||(0.012)|||(0.069)|
|---|---|---|---|---|---|---|
|Constant|2.101***|6.704***|6.835***|1.655***|1.861***|7.207***|
||(0.369)|(0.469)|(0.524)|(0.390)|(0.422)|(0.924)|
|Year FE|No|Yes|Yes||||
|Province FE|Yes|Yes|Yes||||
|Observations|567|567|565|567|567|565|
|R2/ Chi2|0.0767|0.420|0.289|2298|3926|4210|
|Number of provinces|63|63|63|63|63|63|
|Arellano-Bond test for AR(1)||||-7.365|-7.437|-5.325|
|Arellano-Bond test for AR(2)||||2.939|2.837|1.197|
|Sargan test||||311.1|310.8|62.81|

Note: i) Authors’ estimation from VHLSSs using province FE model (columns 1 to 3) and GMM model (columns 4 to 6), ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01 ** p<0.05, * p<0.1.

42
|**6 digit HS**|**Description of the product line**|**MFN tariff**|
|---|---|---|
|**code**||**rate**|
|20319|Fresh or chilled :-- Other|42|
|630240|Table linen, knitted or crocheted|42|
|20900|Pig fat, free of lean meat, and poultry fat, not rendered or otherwise extracted|42|
|180610|<br>Cocoa powder, containing added sugar or other sweetening matter|42|
|20714|Of fowls of the species Gallus domesticus :-- Cuts and offal, frozen|41|
|160249|Of swine :-- Other, including mixtures|41|
|640510|With uppers of leather or composition leather|41|
|610719|Underpants and briefs :-- Of other textile materials|40|
|670290|Of other materials|40|
|620113|Overcoats, raincoats, car-coats, capes, cloaks and similar articles :-- Of man-m|40|
|481410|Ingrain paper|40|
|20322|Frozen :-- Hams, shoulders and cuts thereof, with bone in|39|
|20727|Of turkeys :-- Cuts and offal, frozen|39|
|60310|Fresh|39|
|620530|Of man-made fibres|39|
|80810|Apples|38|
|420310|Articles of apparel|38|
|611790|Parts|38|
|630140|Blankets (other than electric blankets) and travelling rugs, of synthetic fibres|38|
|401110|Of a kind used on motor cars (including station wagons and racing cars)|38|
|611212|Track suits :-- Of synthetic fibres|38|
|640220|Footwear with upper straps or thongs assembled to the sole by means of plugs|38|
|620192|Other :-- Of cotton|38|
|610590|Of other textile materials|38|
|610791|Other :-- Of cotton|38|
|20110|Carcasses and half-carcasses|38|
|<br>640192|<br>Other footwear :-- Covering the ankle but not covering the knee|<br>37|
|640420|Footwear with outer soles of leather or composition leather|37|
|160100|Sausages and similar products, of meat, meat offal or blood; food preparations b|37|
|630190|Other blankets and travelling rugs|37|
|620791|Other :-- Of cotton|37|
|610329|Suits :-- Of other textile materials|37|
|481490|Other|37|
|170290|Other, including invert sugar|36|
|610333|Jackets and blazers :-- Of synthetic fibres|36|
|160510<br>|Crab<br>|36<br>|
|640399|Other footwear :-- Other|36|
|620990|Of other textile materials|36|
|620442|Dresses :-- Of cotton|36|
|620413|Suits :-- Of synthetic fibres|35|
|570259|Other, not of pile construction, not made up :-- Of other textile materials|35|
|611519|Panty hose and tights :-- Of other textile materials|35|
|640330|Footwear made on a base or platform of wood, not having an inner sole or a<br>rote|35|
|610711|p<br>Underpants and briefs :-- Of cotton|35|
|850710|Lead-acid, of a kind used for starting piston engines|35|
|420299|<br>Other|35|
|610811|Slips and petticoats :-- Of man-made fibres|35|
|940190|Parts|34|
|220290|Other|34|
|620219|Overcoats, raincoats, car-coats, capes, cloaks and similar articles :-- Of other|34|
|610721|Nightshirts and pyjamas :-- Of cotton|34|
|940490|Other|34|

40
- 30 -

outer fence excluded, the same models are estimated.<sup>29</sup> The price elasticity of energy demand is estimated at –0.252 with a standard error of 0.032 in the SUR specification. This appears slightly lower than the previous estimate with outliers included but remains statistically significant and within the conventional range in the literature.

One may also be concerned that pooling data from Albania, in which the quality of electricity services was extremely bad in the sample period, would create significant statistical noise in our case. Recall that the country experienced massive power outages around the sample year. With 118 observations from Albania discarded, the price elasticity is estimated under the same framework; the result has been found mostly unchanged, regardless of whether Albania’s data are included or not (Table 5).<sup>30</sup> The energy elasticity may be slightly lower than before. This implies that a large variation in the public energy supply conditions in Albania might play a certain role in exaggerating the measured demand response in other countries.

For the same reason, one may think that different countries would have different economic structures and thus respond to a possible change in energy prices differently. One empirical approach to address this problem is to estimate the model separately for each country and evaluate the price elasticities within its own country data. A great advantage of this separate strategy is that the cost structure is no longer assumed the same among countries.<sup>31</sup> Moreover, the estimates are independent of any country-specific fixed unobservables, which could potentially generate the omitted variable bias in the pooled model. On the other hand, one of the significant disadvantages of the separate estimation approach is obviously the relatively small sizes of country subsamples. In particular in our specification, there are a

> 29 The upper outer fences are estimated at 19.75 for _WL_ , 1.06 for _WE_ , and 15.14 for _WK_ , respectively. In total, 195 observations are excluded from the sample, leaving 797 observations as non-outliers.

> 30 The estimated cost function is presented in Appendix Table A1.

> 31 An alternative may be to evaluate the pooled model at each country’s sample means. However, it mans that the cost structure is still assumed to be the same across countries. It may be a relatively strong assumption to impose the same cost structure on all countries.
Ravallion, M. (2004). “Pro-Poor Growth: A Primer”. _World Bank Policy Research Working Paper_ 3242. Washington DC: The World Bank.

Ravallion, M. (2012). “Why Don’t we See Poverty Convergence”. _American Economic Review_ 102(1): 504-523.

Ravallion, M. and S. Chen. (1997). “What Can New Survey Data Tell Us About Recent Changes In Distribution And Poverty?” The World Bank Economic Review, 11, 357–82.

Ravallion, M. Datt, G. and Van De Walle, D. (1991). “Quantifying absolute poverty in the developing world.” _Review of Income and Wealth_ 37(4): 345-361.

Sundberg, R., and Melander, E. (2013), “Introducing the UCDP Georeferenced Event Dataset”, _Journal of Peace Research_ , vol.50, no.4, 523-532 Shimeles, A., and Nabassaga, T. (2018). Why is inequality high in Africa? Journal of African Economies, 27 (1), pp. 108-126 Thorbecke, E. (2023). The Interrelationships Among Growth, Inequality and Poverty in Africa _. Journal of African Economies_ 32, ii81–ii86.

Thorbecke, E. and Ouyang, Y. (2022). “Towards A Virtuous Spiral Between Poverty Reduction And Growth:Comparing Sub Saharan Africa With The Developing World”. World Development 152: 1- 18.

World Bank. (2018). “ _Poverty and Shared Prosperity Report: Piecing together the poverty puzzle_ ”. Washington DC: The World Bank.

World Bank. (2022a). “ _Poverty and Shared Prosperity 2022: Correcting Course_ ”. Washington DC: The World Bank.

World Bank. (2022b). “ _Poverty and Inequality Platform (version 20220909_2017_01_02_PROD)._ World Bank Group. www.pip.worldbank.org. Accessed Septermber 2022.

World Bank. (2022c). “ _Poverty and Inequality Platform Methodology Handbook_ ”. Washington DC: The World Bank.

17
# **_1.2 Learning matters_**

These learning gaps matter, because learning and skills drive many development outcomes. As the World Development Report (WDR) 2018 argues, Intuitively, many of education’s benefits depend on the skills that students develop in school. As workers, people need a range of skills—cognitive, socioemotional, technical—to be productive and innovative. As parents, they need literacy to read to their children or to interpret medication labels, and they need numeracy to budget for their futures. As citizens, people need literacy and numeracy, as well as higher-order reasoning abilities, to evaluate politicians’ promises. As community members, they need the sense of agency that comes from developing mastery. None of these capabilities flows automatically from simply attending school; all depend on learning while in school. (World Bank 2018, pp. 45-46) Although the empirical literature on impacts of education has focused much more on schooling than on learning, mounting evidence supports this intuition. Even after controlling for schooling, empirical studies find that levels of learning and skills in the adult population affect outcomes:

- _Earnings of individuals:_ “Across 23 OECD countries, as well as in a number of other countries, simple measures of foundational skills such as numeracy and reading proficiency explain hourly earnings over and above the effect of years of schooling completed” (WDR 2018, citing Hanushek and others 2015 and Valerio and others 2016).

- _Health:_ Across 48 developing countries, “[e]ach additional year of female primary schooling is associated with roughly six fewer deaths per 1,000 live births, but the effect is about two-thirds larger in the countries where schooling delivers the most learning (compared with the least)” (WDR 2018, citing Oye, Pritchett, and Sandefur 2016).

- _Financial behavior:_ **“** Across 10 low- and middle-income countries, schooling improved measures of financial behavior only when it was associated with increased reading ability” (WDR 2018, citing Kaffenberger and Pritchett 2017).

- _Social mobility:_ In the United States, “the test scores of the community in which a child lives (adjusted for the income of that community) are among the strongest predictors of social mobility later in life **” (** WDR 2018, citing Chetty and others 2014), indicating that education quality has an impact beyond the number of school years completed.

4
consistent with the definitions in use) for a product with a higher _PRODY_<sup>_11_</sup> as a continuous measure of product sophistication (Cirera, Marin and Markwald 2012). An alternative definition (to support the robustness of the concept) is for a contract with a higher _average selling price_ (adjusted for weight) of the product in the subsequent FPD. These two definitions were chosen over others, such as the technological content sophistication index from the OECD, because they are continuous and therefore allow for a finer level of analysis.

The expectation is that some of the experience variables will support upgrading over simple terminations (while still helping to reduce the hazard rate of death). For example, having already had experience with a destination or being very familiar with it would help sustain an export overall, and in the event of termination, make an upgrade more likely. This could be because the trust levels between traders have already been raised by a repeated interaction and the reliability of the Lao exporter was rewarded with the subsequent contract. Moreover, having a large number of peers could act as a “trap”, whereby the network would help a firm’s contracts survive but prevent it from upgrading through peer pressure that drags it back into the norm. Still, other variables could prove irrelevant for the death vs. upgrade choice model, and in such cases the coefficients would be indistinguishable (i.e. the two effects would be symmetrical).

# 4.3 **Estimation Strategy**

The use of duration models is well suited for a multivariate analysis of survival and its determinants. As discussed above, the analysis is run on an arrangement of the data into export units, which consist of monthly aggregates of firm-product-destination triplets (i.e. FPDs). This arrangement has already been used in the literature (Cadot, et al. 2010, Tovar and Martinez 2011), and allows for a simpler and more detailed analysis of various firm-, product- and destination-specific characteristics than using the firm as the export unit (akin to a micro versus macro perspective). The policy focus of this kind of arrangement is on the survival of “contracts” rather than exporting firms.

It has been common to use continuous-time duration models, and in particular the Cox proportional hazards model, to evaluate determinants of export survival. As shown theoretically and empirically (Hess and Persson 2011a) this is not appropriate for three

> 11 PRODY is the weighted per capita income of the countries that export a specific product (Hausmann, Hwang and Rodrik 2005). C _eteris paribus,_ “an economy is better off producing goods that richer countries export”. Since a core determinant of income levels across countries is the relative productivity of workers, rich countries should, on average, export products with higher value addition. Hence the measure of PRODY is a useful proxy, albeit with limitations, for the value gained from producing and exporting different goods. The values used are World Bank averages for 2000-2004. For example, raw cotton has a PRODY of 530 while electronic microcircuits have 11,907.

17
is crucial. Collecting data on time spent in childcare in standard surveys can be complex, as it is often a secondary activity conducted with other domestic or paid work, and as a result underreported. The new Women’s Empowerment in Agriculture Index (WEAI) survey module includes more detail in the interviewer-guided introduction about time spent with children to lower the risk of being masked by other activities (Seymour, 2018); other approaches such as activity tracking devices or phone-based apps (Kilic, 2018; Daum, 2018) could help in recording which activities are conducted together/simultaneously, with less recall error.

Figure 6. Uganda National Panel Survey, 2013-14: hours spent across paid and unpaid non-market activities in the last week <!-- Start of picture text --> Rural Urban<br><!-- End of picture text -->

# Summary and ways forward

By narrowing the definition of employment as well as highlighting the need to collect data on individuals’ total work burdens, the 19<sup>th</sup> ICLS motivates the need for additional data on labor underutilization, as well as ways that data on unpaid work could be better incorporated in socioeconomic surveys. This has important implications for rural women. Box 4 summarizes areas for future work.

## Box 4.  Summary and ways forward: Labor underutilization and time use

- Labor underutilization: Including questions on desire to work, along with reasons for wanting/not wanting to work, is important, to accompany questions on working hours, availability, and search.

- Unpaid work burdens: in developing a refined set of unpaid work categories for socioeconomic surveys, key areas to cover for rural women include market activity in agriculture (significant even among own-use producers), as well as care work. Exploring newer, more cost-effective and direct approaches (including using mobile and other technology) to collect these data could possibly shed greater insight on their working day and secondary activities as well.

- Proxy reporting also has a significant downward effect on reporting of hours worked in paid and unpaid work, particularly for women proxies.  Reasons for this pattern need further investigation, and have implications for respondent choice going forward.

14
Table 2: Factor Analysis - Bonding and Bridging (Step 3)

|**Factor**|**Eigenvalue**|**Difference**|**Proportion**|**Cumulative**||
|---|---|---|---|---|---|
|Factor1|0.87608|0.61096|1.3965|1.3965||
|Factor2|0.26512|0.14275|0.4226|1.8192||
|Factor3|0.12237|0.11105|0.1951|2.0142||
|Factor4|0.01132|0.05488|0.018|2.0323||
|Factor5|-0.04355|0.08164|-0.0694|1.9628||
|Factor6|-0.1252|0.01579|-0.1996|1.7633||
|Factor7|-0.14099|0.01337|-0.2247|1.5385||
|Factor8|-0.15435|0.02913|-0.2461|1.2925||
|Factor9|-0.18348|.|-0.2925|1||
|**Variable**|**Factor1**|**Factor2**|**Factor3**|**Factor4**|**Uniqueness**|
|importance of family|0.2327|0.2274|-0.0894|0.0413|0.8844|
|importance of religion|0.483|0.136|0.0282|-0.013|0.7473|
|importance of work|0.3344|0.2072|-0.1157|0.0114|0.8317|
|child obedience|0.3375|-0.1833|0.0527|0.0459|0.8476|
|trust in institutions|0.2145|0.1159|0.2269|-0.0076|0.889|
|importance of child independence|-0.2882|0.2334|-0.0378|-0.0441|0.8591|
|importance of child imagination|-0.235|0.1675|0.0311|0.0189|0.9154|
|justified homosexuality|-0.3776|0.0456|-0.0389|0.0653|0.8496|
|trust inpeople|-0.1897|0.1427|0.2049|0.0242|0.9011|

Observations: 251718; Correlation method: Principal factors rotation; Number of parameters: 30 27