rafmacalaba/datause-encoder-data huggingface dataset:
Przeworski, A., M.E. Alvarez, J.A. Cheibub, and F. Limongi. 2000. _Democracy and Development: Political Institutions and Well-Being in the World, 1950–1990_ . Cambridge: Cambridge University Press.

Putnam, R.D. 1993. _Making Democracy Work: Civic Traditions in Modern Italy._ Princeton: Princeton University Press.

Robinson, A.L. 2014. “National Versus Ethnic Identification in Africa: Modernization, Colonial Legacy, and the Origins of territorial Nationalism. _World Politics_ , 66 (4): 709-746.

Rodden, J.A. 2006. _Hamilton’s Paradox: The Promise and Peril of Fiscal Federalism_ .

Cambridge: Cambridge University Press.

Rodden, J.A., G.S. Eskeland and J. Litvack. 2003. _Fiscal Decentralization and the Challenge of Hard Budget Constraints_ . Cambridge, MA: MIT Press.

Rovny, J. 2014. “Communism, Federalism, and Ethnic Minorities: Explaining Party Competition Patterns in Eastern Europe.” _World Politics_ , 66 (4): 669-708.

Sachs, J. 2005. _The End of Poverty: Economic Possibilities for Our Time._ London: Penguin.

Sen, A. 1999. _Development as Freedom_ . Oxford: Oxford University Press.

Singh, P. 2015. “Subnationalism and Social Development: A Comparative Analysis of Indian States.” _World Politics_ , 67 (3): 506-562.

Smoke, P. 2001. “Fiscal Decentralisation in Developing Countries: A Review of Current Concepts and Practice.” _Democracy, Governance, and Human Rights Programme_ Paper No. 2, United Nations Research Institute for Social Development, Geneva.

- Stiglitz, J.E. 2012. _The Price of Inequality: How Today’s Divided Society Endangers Our Future_ . New York: W.W. Norton.

- Stiglitz, J.E. 2015. _The Great Divide: Unequal Societies and What We Can Do About Them_ . New York: W.W. Norton.

Vaughan, S. 2012. _Understanding Ethiopia’s Transformation: Vision and Strategies for Development_ . World Bank, Addis Ababa.

- Wade, R.H. 2011. “Income inequality: Should we worry about global trends?” _European Journal of Development Research_ , 23 (4): 513-520.

- Wade, R.H. 2014a. “Growth, inequality, and poverty: arguments, evidence, and economists,” in Ravenhill , J. (ed.) _Global political economy_ . Oxford: Oxford University Press, 305-343.

28
Table 8: Deep trade agreements and GVC integration in services ‐sectoral estimations, PPML

||(1)|(2)<br>DVA|(3)|(4)|(5)<br>FVA|(6)|
|---|---|---|---|---|---|---|
|No. of provisions|0.00288*|‐0.0140*|‐0.00155|0.00249|‐0.00635|‐0.000882|
||(0.00170)|(0.00760)|(0.00216)|(0.00167)|(0.00726)|(0.00218)|
|No. of provisions* VA share||0.0320**|||0.0178||
|||(0.0143)|||(0.0139)||
|No. of provisions*High VA share {0,1}|||0.00730***|||0.00734***|
||||(0.00282)|||(0.00282)|
|BIT|0.00389|0.00397|0.00348|‐0.0252|‐0.0253|‐0.0263|
||(0.0608)|(0.0608)|(0.0609)|(0.0531)|(0.0532)|(0.0532)|
|PTA (in force or inactive)|‐0.0442|‐0.0385|‐0.0455|0.0563|0.0647|0.0589|
||(0.0657)|(0.0667)|(0.0662)|(0.0602)|(0.0619)|(0.0618)|
|Observations|295,381|295,381|295,381|295,076|295,076|295,076|
|R‐squared|0.922|0.922|0.922|0.950|0.950|0.950|
|Country‐pair‐industry FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer‐industry‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter‐industry‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|p‐value F‐test No. ofprovisions||0.0117|0.0095||0.1017|0.0020|

Clustered standard errors at the country pair level in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

27
# **Table 6 The Migration Decision and Land Reallocation Risk: Ordinary Least Square and First-Differenced Models**

||**OLS**|**FD**|
|---|---|---|
|(Land Reallocation in the Village)t+1|-0.016***|-0.005*|
||(0.006)|(0.003)|
|Male�(Time Trend)|0.010***|0.013***|
||(0.001)|(0.002)|
|Age�(Time Trend)|0.002***|-0.009***|
||(0.000)|(0.001)|
|Age-sq�(Time Trend)|-0.000***|0.000***|
||(0.000)|(0.000)|
|Years of Schooling|0.012***||
||(0.003)||
|Father Alive|0.021**|0.001|
||(0.010)|(0.006)|
|Mother Alive|-0.008|-0.001|
||(0.010)|(0.007)|
|Number of Siblings|-0.008***|-0.002|
||(0.002)|(0.004)|
|Land per Capita (log)|0.003|0.001|
||(0.008)|(0.003)|
|Consumption per Capita (log)|-0.001|0.002|
||(0.008)|(0.003)|
|Working-Age Females (age 16-60) as Share of|0.055***|-0.016*|
|Household Size|(0.013)|(0.008)|
|Working-Age Males (age 16-60) as Share of|0.044***|-0.022***|
|Household Size|(0.015)|(0.007)|
|Number of Young Women (19-24)|0.006|0.001|
||(0.005)|(0.002)|
|Number of Young Men (21-26)|0.002|0.001|
||(0.006)|(0.002)|
|Village Population (log)|-0.056|0.047**|
||(0.049)|(0.020)|
|Village Per Capita Income (log)|0.001|-0.005|
||(0.009)|(0.005)|
|Village Land Per Capita (log)|0.062**|-0.004|
||(0.029)|(0.008)|
|Number of Village Cadres|0.001|0.001|
||(0.002)|(0.001)|

_Land Tenure Insecurity and Migration, Page 48_
- Palmer, Michael, Sophie Mitra, Daniel Mont, and Nora Groce. “The Impact of Health

- Insurance for Children under Age 6 in Vietnam: A Regression Discontinuity Approach.” _Social Science & Medicine._ 145 (2015): 217-226.

- Polimeni, Rachel and David Levine. “Adverse Selection Based On Observable and Unobservable Factors in Health Insurance”. Unpublished. (2011).

- Robinson, Christine “Younger Siblings Can Be Good for Your Health: An Examination of Spillover Benefits from the Supplemental Nutrition Program for Women, Infants, and Children (WIC)” _Journal of Family and Economic Issues_ , _34_ No. 2 (2013): 172184.

- <mark>Sauerborn, Rainer, Adrien Nougtara, and Eric Latimer. "The Elasticity of Demand for Health Care in Burkina Faso: Differences Across Age and Income Groups."</mark> _<mark>Health Policy and Planning</mark>_ <mark>9.2 (1994): 185-192.</mark>

- Sheth, Ketki. “Assessing Adverse Selection and Health Care Demand in Micro Health Insurance: Evidence from a Community-Based Microfinance Insurance Model in India”. Unpublished. (2014).

- Sommers, Benjamin. “Insuring Children or Insuring Families: Do Parental and sibling coverage lead to improved retention of children in medicaid and CHIP?” _Journal of Health Economics_ 25 no. 6 (2006): 154–1169.

- Steel, Knight, Paul M. Gertman, Caroline Crescenzi, and Jennifer Anderson. “Iatrogenic illness on a general medical service at a university hospital.” _Quality and Safety in Health Care_ 13, no. 1 (2004): 76-80.  First published in _New England Journal of Medicine,_ 1981, Volume 304, pages 638-42.

23
11 only water access indicator available for a reasonably large sample of countries. JMP defines access as follows: “[t]he availability of at least 20 liters of improved water per person per day from a source within one kilometer of the user's dwelling. Improved water supply sources are: household connection, public standpipe, borehole, protected dug well, protected spring, rainwater collection. Unimproved sources include unprotected well, unprotected spring, vendor-provided water, bottled water (based on concerns about the quantity of supplied water, not concerns over the water quality), and tanker truck-provided water.” Our choice was based solely on data availability and not on the quality of the indicator. Other indicators, although better, have serious coverage issues. For example, Clarke, Kosec, and Wallsten (2004) were able to gather household data on the number of connections to piped water, but only for some Latin American countries. They noted how difficult it is to find cross-country data in the water sector. In theory, access to improved water sources as defined by JMP is a good indicator of access, because it is based on household data and is available for multiple countries, but it is available only for two points in time. Furthermore, the JMP estimates access values by running a regression with data from multiple household surveys and using the estimated coefficients to calculate access in 1990 and 2002. The access figures for these two time periods are constantly reestimated as more surveys are carried out. Thus, it is difficult to track the marginal improvements in access to water over time.

# **4.2 Basic data analysis**

On average, access has improved in all sectors between 1990 and 2002 (Table 3). The greatest improvement is observed in the telecoms sector (an increase of 330 percent), followed by electricity (22 percent), and water (7 percent). This difference reflects both technological advances and changes in the sectors. The fact that there has been very little technological progress in energy and water, however, suggests that the other factors may be relatively more important—in particular, the potential contribution of reform policies. Technology is no doubt the driving force in the telecoms sector.

**<u>Table 3 Evolution of average access between 1990 and 2002</u>**

||**Average**|**Standard**<br>**deviation**|**Number of**<br>**observations**|
|---|---|---|---|
|**Energy use (kg of oil equivalent per capita)**||||
|1990|1098|1071|76|
|2002|1335|1255|95|
|**Telephone subscribersper 1,000people**||||
|1990|57|70|150|
|2002|245|269|151|
|**Access to improved water sources (percentage of**<br>**population)**||||
|1990|72|21|102|
|2002|77|19|138|

Source: Authors’ calculations using data from WDI, JMP, WHO, and UNICEF.
**<u>Table 12: Vulnerability decomposition</u>**

||**_National_**|**Bamako**|**Urban**|**Rural**|
|---|---|---|---|---|
|**Poverty rate**|_43.6%_|9.6%|30.7%|50.6%|
|**Mean vulnerability**|_43.2%_|12.2%|26.7%|50.1%|
|**Vulnerability rate**|_39.2%_|1.4%|15.7%|48.1%|
|**Poverty induced vulnerability**|_28.7%_|0.5%|11.5%|35.3%|
|**Risk induced vulnerability**|_10.5%_|0.9%|4.3%|12.8%|
|**Poverty/risk**|_2.7_|0.6|2.7|2.8|
|**Idiosyncratic vulnerability**|_43.1%_|10.8%|25.0%|50.5%|
|**Covariate vulnerability**|_40.7%_|3.3%|19.6%|49.2%|
|**Idiosyncratic/covariate**<br>Source: ELIM 2010 and authors' calculations.|_1.1_|3.3|1.3|1.0|

The other dimension along which we can differentiate the sources of vulnerability is whether vulnerability is induced mostly by community level (covariate) or household level (idiosyncratic) variations in consumption. The study finds that community level and household level are about equally important drivers of vulnerability in Mali, both in rural and urban areas. Bamako is the exception to this finding: idiosyncratic risk is the dominant source of vulnerability in the capital.

23
force participation in a sample of (only) women, estimating the OLS regression equation: where _Yicr_ is the outcome of interest for woman _i_ in country _c_ who was interviewed in Afrobarometer Round _r_ , _Gendericr_ is an indicator for having a gender language as one’s mother tongue, _νcr_ is a vector of country-round fixed effects, _Zicr_ is a vector of controls (age, age<sup>2</sup> , and a set of religion dummies), and _εicr_ is a mean-zero error term. As in our cross-country analysis, we wish to avoid confounding the impact of grammatical gender on women’s labor force participation (and education) with other cultural factors that might impact both men’s and women’s labor force attachment. To do this, we also report pooled OLS regressions that include data on both men and women. These take the form: where _Gender × Femaleicr_ is an interaction between a female dummy and the indicator for being a native speaker of a gender language. In these specifications, we also include interactions between the _Femaleicr_ dummy and our age and religion controls. Throughout our analysis, we cluster standard errors by language.

# **5.2 Labor Force Participation**

We report the results of our regressions of individual-level labor force participation on the indicator for being a native speaker of a gender language in Table 8. Here, the sample is restricted to women. We find a robust negative association between grammatical gender and women’s labor force participation. After controlling for country-round fixed effects, age, and religion, coefficient estimates suggest that women who speak gender languages as their native languages are 18 percentage points less likely to be in the labor force (pvalue 1 _._ 85 _×_ 10<sup>_−_5</sup> ). Speaking a gender native language is also associated with lower female labor force participation relative to men from the same ethnolinguistic group (Table 9).

28
**_Energy demand models for policy_** **_<u>formulation</u>_**

energy use, LBNL Reprot 52698, Lawrence Berkeley National Laboratory, California (See http://repositories.cdlib.org/lbnl/LBNL-52698/).

Zhou, N., 2007, What do India’s transport energy data tell us? A bottom-up assessment of energy demand in India’s transportation sector. Presentation at 8th ECEEE Summer Study, (see http://der.lbl.gov/new_site/seminars/NZhou_ECEEEIndiaTransport.pdf).

Ziramba, E., 2008, The demand for residential electricity in South Africa, Energy Policy, 36, pp. 3460-66.

122
# 4. **Ghama Disel** md Kerosene **Price** Increase 198388

|Intrduction<br>....................................<br>|............<br>53<br>|
|---|---|
|MmEconomy<br>....<br>**...................................**<br> <br>|53|
|TIMEnergy<br>Sector...<br>...............................|..........<br>54|
|Structue of Energy Demand.........................<br>|........ 54|
|EnergyPolicyandPrices...........<br>...........<br> <br>|...........<br>55|
|Effec ofEnergy<br>Price<br>Chanps<br>**...........**<br>**.. .. . .. . .. .. .**<br>........|....<br>55|
|<br>DiselPrice<br>cemascs 1983-88: TheIndustrial Setor....<br>_S5_|_5_|
|KerosenePriceChagsl1987-88:TheHouseholdSector........|.......<br>S8|
|MacroemouincImpacts<br>........................|...........<br>60|
|_DataSources_.......................................|.........<br>62|

# S. Zlmbobwe Elecicty Pice Iwnr 198-84

|Introduction...................................|63|
|---|---|
|TheEconomY....................................|63|
|The EnergySector<br>...............................|<br>64|
|Structu of EneqyyDenand............................|..... 64|
|EnergyPolicyandPrices..................................|.<br>65|
|EffectsofEnergyPrice Changes<br>.<br>.<br>.65||
|Electriity Price Incrases 1983-84:<br>duesIndustrial<br>Sector ...|65|
|Electricity Price Increases 1983-84: The Household Sector.........|.....68|
|MacroeconomicImpacts......... ..................|........<br>69|
|DataSourcs<br>...............................................<br>**ombIc**Elecricity**Nice Inmases**1985-38|71|
|<br>Inrduction<br>.<br>...............................................|72|
|TheEcnomy...............................|'<br>72|
|TheEoergySector......................................|......<br>73|
|StructamofEnergyDemand............................|.....73|
|ElctricityPolicyandPrices................................|......73|
|EffectsofEey<br>PreChanges .<br>................<br>..........|........<br>74|
|Ecriity<br>PriceIncraes 1985-88:TheIndustrialSector......<br>.|.......<br>74|
|Eletricity Price Increases 1985-88: The Household Sector<br>.|.<br>78|
|Macroeconomic Impacts<br>..<br>........|.....79|
|Anex 6.1 Colombia: Evolution of the Foreign Debt of the Power Sector....|....<br>.3<br>1|
|Data Sources .<br>......................................|..........<br>82|

# **6. ColombIc** Elecricity **Nice Inmases** 1985-38

# 7. Turkey: ORl md Ectrrkity Prke Increases 1986-88

|Intrduction<br>...........................|83|
|---|---|
|lTiEconomy<br>............................|83|
|Tbe Energy Sector<br>..........................|_84_|
|Stuure of Energy Demand<br>..|_84_|
|Energy Policy andPrices..................................|.......<br>84|
|Effects of Energy Price Changes..............................|..... 85|
|Dieseland EktricityPrice Changes1986-88: TheIndutrial Sector|85.|
|Electricity Price Change 1983: The Household Sector<br>.<br>.|97|
|Macroeconomic Impactss...............................|....90|
|Da Souces ................................................|91|
25 young reflect false reporting of being not in the labour force. To demonstrate Schmidt's proposition and to check its validity with LFS data a simple arithmetic exercise will be undertaken. Let's take the example of young males in May 1992, where the unemployment rate of this group is 23.1% and the participation rate 46.9%. Let's have the (unrealistic) assumption that in all age groups no person among the identified unemployed works in the shadow economy. Assume then that the "true" participation rate among young males is 65%, the rates one finds in e.g. Sweden and Germany for this age group. The difference between 46.9% and 65% is then due to unreported employment of the young in the shadow economy. Given these assumptions, the imputed "corrected" unemployment rate for young males is 16.6% instead of 23.1%. Under similar assumptions a participation rate of 55% for women implies, again for May 1992, a "corrected" unemployment rate of 20.6% instead of 30%. When a percentage of those employed in the shadow economy is falsely reported as unemployed and this percentage is constant across age groups, the gap between the unemployment rates of the young and of other age groups narrows further.

This arithmetic exercise is not meant to find the "true" unemployment rates for young males and females. It is meant to point to the dangers of readily identifying "problem groups" and targeting labour market policies at them. In the Polish case, the participation rates of the young, very low in international comparison, need to be closely investigated. Alternatively to employment in the shadow economy such low rates could also imply that the young see opportunities from education which are greater in an economy of transition than in mature Western economies. The validity of this explanation of low participation rates would mean that the unemployment rates of the young were indeed as high as shown by the LFS data.

Whatever the reasons for the low participation rates one should point out, however, that even after the "correction" of the unemployment rates, the LFS data still produce a higher incidence of unemployment for young males and females than for any other age group in Poland. So these groups should be targeted anyway even if the amount of targeting depends crucially on which of the two scenarios is valid.

Men and women over 55 years but before retirement age have a relatively weak labour market attachment as their relatively low participation rates show. Also, if we ignore the groups above 65/60 years of age, their job accession rates are by far the lowest among the age groups while their labour force exit rates are at least twice as high. The relative values of both types of rates might hint at a discouraged workers effect which might be quite strong in transition economies and which artificially
cars; their parts, pieces and accessories’ and ‘Bodyworks (including cabs) for motor vehicles; trailer cars; their parts, pieces and accessories’, respectively.

Finally, we study the import content of domestic production at the product level. For the 195 products we have data on total imports, imported intermediates, total supply, (domestic) production value and exports. Using this information we calculate, for product _j_ , the following indicators:

- (5) IM _j_ = imported intermediates/(production value + total imports), (6) TM _j_ = total imports/(production value + total imports), ∙

- (7) VS _j_ = (imported intermediates/production value) exports.

Note, as before, that for each product _j_ , the VS share of total exports as calculated in (2) can be obtained from (7), and reduces to the imported intermediates’ share of domestic production (here called production value). Particularly, computing this indicator for some products gives values above 100 percent. Although at first sight this might seem odd, values above 100 percent might arise if, for some products, domestic production is not enough to meet intermediate requirements for the production of other goods. Naturally, this is indicative of the specialization pattern of the Argentine economy. Instead, IM _j_ will always give values between 0 and 100 percent.

Table 4 shows the results of computing (5), (6) and the VS share of exports for each product. As expected, many of the products with a high value for IM correspond to products that could be considered capital goods (i.e. engines, valves, electrical components). Also, some minerals (especially iron) and chemical products show a high share of intermediate imports.

# **4 Conclusion**

Understanding the extent of vertical specialization in developing countries can be helpful for understanding the capabilities and limitations of export promotion and exporting booms in these countries. In this paper we have gone one step in this direction in providing an estimate of the extent of vertical specialization in Argentina in 1997.

Our results indicate that vertically specialized trade as indicated by the imported content of exports represented 14.5 percent of total exports in 1997. This figure is lower than similar estimates for developed economies in 1990 and 1995 that can be found in the related literature. We have found that manufacturing accounts for the largest share of imported content of exports, which accords with patterns observed in other economies. Moreover, for this broad aggregate, we find that imports are concentrated in a few product categories and are consistent with a high degree of intra-industry trade. At a more disaggregated level, imported intermediates as a share of production and imports are considerably high in product categories that can be associated with capital goods and the mining sector.

Before concluding, we offer a warning against incorrect interpretation of the results. The data used in this paper comes from the 1997 input-output tables for the Argentine economy. Therefore, our estimates should not be taken as representing the 5
1,

**Table** 7: Educational Qualifications held by Zambian Employees, 1965 and 1983 a)

|1|965|1|983|
|---|---|---|---|
|Number||Number|<br>%|
|Less than Form II<br>226681|<br>96.0|189514|<br>54.4|
|Forms II/III<br>7282|3.1|85123|24.4|
|;0' Level(b)<br>1516|<br>0.6|45308|13.0|
|A' Level(b)/Diploma 517|0.2|17566|<br>5.0|
|Degree<br>150|0.1|10759|<br>3.1|
|TOTAL<br>236146|<br>100.0|348270|<br>100.0|

- Note: a - Data for 1965 refer to Africans, rather than Zambians. Those for 1983 are based upon a sample survey of employees along the line of rail, covering approximately 70 per cent of total formal employment. The percentage distribution revealed by that survey has been applied to separate estimates for total Zambian employment, published by the CSO.

- b - '0' level and 'A' level are examinations typically taken after 5 years and 7 years of secondary schooling, respectively.

- Sources: GRZ 1966, Table II.3, GRZ 1983, Table 8.0, and GRZ 1985, Table 6.