rafmacalaba/datause-encoder-data huggingface dataset:
with ∑ = 1 , is the subsistence need of good _,_ and is the share of residual income (i.e., KK ) devoted to consuming good after the minimum expenditure is incurred. kk=1 kk kk kk ββ γγ kk ββ KK kk=1 kk kk kk kk xx−∑ pp γγ kk pp γγ Based on equations (2)-(4), the equivalent income function for the household , facing the pandemic, is given by: ii 0 1 ) = ∑ + −∑k <u>1</u> 1, (5) KK xx∏ii1 ~~�~~ KKk=1,kpp� kkγγkk ii ii1 kk=1 kk kk KK pp ββkk,ii ΓΓ (pp , pp , xx pp γγ kk=1 pp0,kk The equivalent income function given in (5) has a clear interpretation in terms of real income. Indeed, if ∑ represents the subsistence expenditures, only the residual income −∑ is KK KK 0 = available for the discretionary allowance, which is deflated by a household-specific price index, kk=1 1,kk kk ii1 kk=1 1,kk kk pp γγ xx pp γγ ∏KK �pp1,kk ~~�~~ ββkk,ii . If one then adds the initial cost of subsistence needs to the actual residual income, we ππ1ii obtain the equivalent income. kk=1 pp0,kk One can further simplify and assume that subsistence needs are low (converging to 0), in which case , becomes the budget share devoted to good , and the equivalent income function is reduced to that generated by Cobb-Douglas preferences. kk,ii ββ kk <u>i</u> 0 1 ) = 0 xx i1 Based on this exercise, post-pandemic consumption is generated and the effects of the crisis on poverty ΓΓii(pp , pp , xxii1 ππ1ii Based on this exercise, post-pandemic consumption is generated and the effects of the crisis on poverty and inequality are estimated.<sup>10</sup>

# **4. Results**

Based on the recent World Bank projections in Tunisia, the sectoral breakdown of the GDP contraction due to COVID-19 can be estimated to be such that the six hardest hit sectors in terms of a decline in employment are Tourism or Hotels, Cafes and Restaurants (expected to contract by 29 percent under Scenario 1 and 47.4 percent under Scenario 2), Textiles (expected to contract by 21 percent under Scenario 1 and 28 percent under Scenario 2), Mechanical and Electric industry (expected to contract by 17 percent under Scenario 1 and 23 percent under Scenario 2), Transport (expected to contract by 19 percent under Scenario 1 and 32 percent under Scenario 2), Construction (expected to contract by 22 percent under Scenario 1 and 26 percent under Scenario 2), and Commerce (by 5 percent under Scenario 1 and 8 percent under Scenario 2).<sup>11</sup> Combining these projected estimates along with employment growth elasticities, we estimate the projected employment losses in these sectors as shown in Figure 7. The biggest employment losses are concentrated in Mechanical and Electric Industry and Tourism or Hotels, Cafes and Restaurants.

> 10 While our methodology uses historical elasticities, an important caveat of this approach is that the impact of the growth shock due to COVID19 could affect unemployment trends differently from past shocks.

> 11 These are calculated based on recent World Bank projections estimated by the Macro, Trade and Investment team at the World Bank Group using the MFMod model and information from government and other sources to inform the forecast.

13
|||**ELMPS**|**ELMPS**|**Economic**|**Establishm**|
|---|---|---|---|---|---|
||**Pooled**|**2012 in est.**|**2018 in est.**|**Census**|**ent Census**|
|Lower Egypt|1.331***|0.948|0.937|1.956***|1.438***|
||(0.081)|(0.187)|(0.233)|(0.123)|(0.009)|
|Upper Egypt|1.431***|0.932|1.022|2.738***|1.414***|
||(0.096)|(0.202)|(0.263)|(0.282)|(0.011)|
|**N**|766840|1200|1155|52287|712198|
|**Pseudo R-squared**|.113|.0821|.0953|.119|.0989|

Source: Authors’ calculations based on ELMPS 2012, ELMPS 2018, EcC 2012/13, EsC 2017 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now move to the results from Model 2a estimated in the EcC data and shown in Table 3. The results on size are in the same direction as before, but are somewhat attenuated because of the inclusion of other regressors associated with firm size. The results on firm age, industry, and region are little changed when compared to the results of Model 1 on the EcC data. With regard to the new variables added, we see that collective ownership as opposed to sole proprietorship slightly and significantly raises the odds of formality. Higher labor productivity is associated with substantially higher odds of formality, as is higher capital intensity. As expected, having unpaid family workers reduces the odds of formality, but having wage workers increases it. The proportion of female workers in the firm’s workforce is not associated with the odds of formality.

24
65

# TABLE 6: Estkmates of Area of the Ejido Sector

# DATE/ SOURCE

|

<!-- Start of picture text --> 1970 1970 1981 1982 198S<br>Census SRA Census SRA SRA<br>'~~000<br>Hectares<br>(a) CROPLAND 12,763 12,511 10,012 14,230 14,380<br>- rainfed 10,993 10,831 8,339 12,360 12,471<br>-<br>irrigated 1,760 1,680 1,673 1,870 1,909<br>(b) TOTAL AREA<br>I (1) 89,724 82,148 na 101,319 102,877<br>(2) na na 16,235 na na<br><!-- End of picture text --> Key to (b) (1) - Whole ejido sector; (2) ' Sum of parcels in individual ejidos; ndividual ejldos; na - Not available.

# SRA - Secretarla de Reforma Agraria

# Note:

The census data for "Total area" are not comparable owing to the different way in which ejido land was measured In 1970 and 1981; the degree of comparability is greater In the case of 'cropland' (see Appendix A). wTotal area" covers cropland, forest, natural grazing and unproductive land. "Cropiand" includes sown pasture.

SOURCE: Agricultural Census, 1970 & 1981; INEGI, 1986.
# **Leveraging Growth Regressions for Country Analysis**<sup>*****</sup>

**_Konstantin M. Wacker_** _, University of Groningen,_ <u>k.m.wacker@rug.nl</u>

**_Robert C.M. Beyer,_** _International Monetary Fund,_ <u>RBeyer@imf.org</u>

**_Lars Christian Moller,_** _World Bank,_ <u>lmoller@worldbank.org</u>

## **<u>JEL Classifications</u>** : O47, C5

**<u>Keywords</u>** : economic growth, growth regressions, growth econometrics, comparative

country analysis, economic policy This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author(s) may be contacted at k.m.wacker@rug.nl, RBeyer@imf.org, lmoller@worldbank.org.

> * Corresponding author: K.M. Wacker. The authors would like to thank Alice Brooks, Fisea Haile, Aart Kraay, Lazar Milivojevic, and Frederico Gil Sander for helpful comments on earlier versions of this paper. Eline Koopman provided excellent research assistance.
|Second Lag|-0.03**|||
|---|---|---|---|
|Std. Error|(0.01)|||
|Third Lag|0.01|||
|Std. Error|(0.01)|||
|Fourth Lag|0.06***|||
|Std. Error|(0.01)|||
|Cumulative Effect|-15.47|||
|Observations|1639|||
|**Blue Line Second Extension (Nov 11, 2006)**|0.07|-8.65*|-0.28|
|Std. Error|(5.47)|(4.96)|(9.30)|
|First Lag|0.42***|0.02***|0.39***|
|Std. Error|(0.02)|(0.002|0.05|
|Second Lag|-0.06**|-0.003*|-0.12***|
|Std. Error|(0.02)|(0.002)|(0.03)|
|Third Lag|0.01|0.001|0.08**|
|Std. Error|(0.03)|(0.001)|(0.04)|
|Fourth Lag|0.07***|0.003***|0.04|
|Std. Error|(0.02)|(0.001)|(0.03)|
|Cumulative Effect|0.12|-8.82|-0.45|
|Observations|1605|1605|1268|
|Source: Authors' analysis based on data describ|ed in the text.|||
|Estimates for NO2 and CO pertain to a nine we|ek window, an|d for PM 2|.5 to a|
|seven week window. Std. errors are clustered a|t one week. *|indicates si|gnificantly|
|different from zero at 10 percent level, ** at 5|percent level,|and *** at 1|percent|
|level.||||

61
# **Acknowledgments**

This paper is based on case studies that were commissioned by the _World Development Report 2005_ team and the Foreign Investment Advisory Service, a multi-donor facility managed by The World Bank Group. We would like to thank Gokhan Akinci, Andy Bennett (Georgetown University), Penelope Brook, Jackie Coolidge, Simeon Djankov, Pierre Guislain, Arvind Gupta, Kris Hallberg, Benjamin Herzberg, Omer Karasapan, Phil Keefer, Gregory Kisunko, Peter Ladegaard, Syed Mahmood, Xiaofang Shen, and Andy Stone for their helpful comments at different stages of the drafting process. We are especially indebted to Warrick Smith for inspiring this work and providing guidance and valuable insights.
implies that with lower childcare costs provided by the presence of grandparents in extended families but also greater child costs, parental labor supply is not reduced as a consequence of an increase in family size, suggesting that as an informal childcare provision mechanism, extended families play an important mitigating role for the time cost of children.

However, as family structure in developing countries becomes more nuclear with economic development, the time cost of children may increase for parents which may lead to further fertility decline and fewer opportunities for non-parental household members to help with childcare or household chores. In view of this decline in intergenerational co-residence over time and also the inadequate provision of public childcare in the context of Albania, one possible public policy intervention should be the implementation of childcare subsidies including direct provision of day-care nurseries, public kindergartens and pre-schools in order to promote labor supply and sustainable economic development.

# **References**

- **Aaronson, Daniel, Rajeev H Dehejia, Andrew Jordan, Cristian Pop-Eleches, Cyrus Samii, and Karl Schulze.** 2017. “The Effect of Fertility on Mothers’ Labor Supply over the Last Two Centuries.” Mimeo.

- **Ag ̈uero, Jorge M, and Mindy S Marks.** 2008. “Motherhood and female labor force participation: evidence from infertility shocks.” _The American Economic Review_ , 98(2): 500–504.

- **Ag ̈uero, Jorge M, and Mindy S Marks.** 2011. “Motherhood and female labor supply in the developing world evidence from infertility shocks.” _Journal of Human Resources_ , 46(4): 800– 826.

- **Angrist, J.D., and W.N. Evans.** 1998. “Children and their parents’ labor supply: Evidence from exogenous variation in family size.” _American Economic Review_ , 80(3): 450–477.

- **Angrist, Joshua D, and William N Evans.** 1996. “Children and their parents’ labor supply: Evidence from exogenous variation in family size.” National Bureau of Economic Research.

- **Arnold, Fred, and Liu Zhaoxiang.** 1986. “Sex preference, fertility, and family planning in China.” _Population and Development Review_ , 12: 221–246.

- **Bailey, Martha J.** 2013. “Fifty years of family planning: new evidence on the long-run effects of increasing access to contraception.” National Bureau of Economic Research.

- **Becker, Gary S, and H Gregg Lewis.** 1973. “On the Interaction between the Quantity and Quality of Children.” _Journal of Political Economy_ , 81(2): 279–288.

- **Becker, Gary S, and Nigel Tomes.** 1976. “Child endowments and the quantity and quality of children.” _Journal of Political Economy_ , 84(4): 143–162.

- **Bisbee, James, Rajeev Dehejia, Cristian Pop-Eleches, and Cyrus Samii.** 2015. “Local instruments, global extrapolation: external validity of the labor supply-fertility local average treatment effect.” National Bureau of Economic Research.

29
14 futnctioning capital markets) current expenditures are a better measure of permanent income than is current income. Second, extensive experience with household surveys has shown that it is tremendously difficult (if not impossible) to measure the incomes of the agrarian self-employed who constitute the bulk of our sample (Deaton, 1997).

In order to estimate the impact of social capital on incomes we first adopt a specification of the determinants of per person household expenditures that includes both individual (variables Z1j) and village (Xj) level variables'<sup>2</sup> : _Hj=P *Social Capital + a * Z, +_ y _* X +e_ A number of household characteristics are included: the average years of schooling of male and female adults (over 20) in the household'<sup>3</sup> , a dummy variable if the head of the household was 'self-employed in agriculture," a dunmmy variable for female headed households and an index of non-land, non-agricultural, physical assets".

12 This specification was previously developed and used in an examination of rural poverty in Tanzania (World Bank, 1995).

**_'3_** The average of both males and females adults is used for simplicity, although it is worth noting that when the genders are included separately the average education of adult females in the household had a much larger estimated impact on incomes than male schooling.

" The index was created by assigning the following weights to ownership of the following assets if they are in working condition (in the SCPS); sewing machine, bicycle, car, motorcycle, van or truck, 16, radio 8, table 6, clock, watch or bed 4, chair 3, lamps 2. The HRDS has a less complete list of assets; bicycles 16, radios or cameras 8, watches 4. Originally a weight of 400 was assigned to bicycle, car, motorcycle, van or truck, but the very few observations with ownership of those assets dominated the variation, so it was (regrettably and arbitrarily) reassigned a weight of 16.
# **References:**

Atkinson A. B. (1987). "On the Measurement of Poverty." _Econometrica._ Behrman Jere and James C. Knowles (1999). "Household Income and Child Schooling in Vietnam." _World Bank Economic Review._ Vol. 13 (2). pp. 211-256.

Birdsall Nancy and Londono Juan Luis (1997). "Assets Inequality Matters: An Assessment of the World Bank Approach to Poverty Reduction." _The American Economic Review,_ Vol. 87 No. 2.

Birdsall Nancy, Ross David and Sabot Richard (1995). "Inequality and Growth Reconsidered: Lessons from East Asia." _World Bank Economic Review._ Vol. 9 (3), pp. 477-508.

Deaton Angus and Magaret Grosh. (2000). "Consumption." In Margareth Grosh and Paul Glewwe, eds., _Designing Household Survey Questionnaires: Lessons from Ten Years of LSMS Experience for Developing Countries._ Washington D.C.: World Bank.

Deaton Angus and John Muellbauer. (1980). _Economics of Consumer Behavior._ Cambridge, U.K.: Cambridge University Press.

Foster, James E., Joel Greer, and Erik Thorbecke. (1984). "A Class of Decomposable Poverty Measures." _Econometrica_ 52(3): 173-177.

Fofack Hippolyte. (2000). "Combining Light Monitoring Surveys with Integrated Surveys to Improve Targeting for Poverty Reduction: The Case of Ghana." _The World Bank Economic Review_ 14(1): 1-25.

Fofack Hippolyte and Albert Zeufack. (1999). "Dynamics of Income Inequality in Thailand: Evidence from Household Pseudo-Panels. _World Bank Economists Forum,_ World Bank.

Gelb Alan (1999). _"A Note on Growth and Poverty Alleviation in Africa "._ The World Bank.

Gibbons Jean Dickinson. (1985). "Nonparametric Methods for Quantitative Analysis." American Sciences Press. Series in Mathematical and Management Sciences.

Institut National de la Statistique et de la Demographie (INSD). (1996). Burkina Faso, Ministere de l'Economie et des Finances.

Institut National de la Statistique et de la Demographie (INSD). (1998). Burkina Faso, Ministere de l'Economie et des Finances.

Kakwani Nanak. (1980). "Income Inequality and Poverty: Methods of Estimation and Policy Applications." The World Bank, Oxford University Press.

Kakwani Nanak (1990). "Poverty and Economic Growth with Application to C6te d'lvoire." ." _LSMS Working Papers_ No. 63.

Leibbrandt Murray, V., Christopher Woolard, and ingrid Woolard. (1996). "The Contribution of Income Components to Income Inequality in South Africa. A Decomposable Gini Analysis." _LSMS Working Papers_ No. 125.

Londono Juan Luis. (1996). "Poverty, Inequality and Human Capital Development in Latin America 1950-2025." World Bank Latin America and Caribbean Studies, June 1996..

Marchant T. and Grootaert C. (1991). "The Social Dimensions of Adjustment Priority Survey: An Instrument for the Rapid Identification and Monitoring of Policy Target Groups." _SDA Working Paper No. 12,_ The World Bank Washington, DC.

Osberg Lars. (1991). "Economic Inequality and Poverty: International Perspectives." Sharpe.

Ravallion Martin. (1992)."Poverty Comparisons: A Guide to Concepts and Methods." _LSMS Working Papers_ No. 88.

31
**Information Disclosure and Demand Elasticity of Financial Products: Evidence from a Multi-Country Study**<sup>∗</sup>

Xavier Giné Development Economics Research Group, World Bank Cristina Martínez Cuellar Citibank Rafael Keenan Mazer CGAP, World Bank Keywords: financial information, disclosure, lab experiment.

JEL codes: C91, D14, D83, G18, G21, O16.

>  Giné: xgine@worldbank.org; Martínez: cristinam@chicagobooth.edu Mazer: rmazer@worldbank.org. We thank Leyla Castillo and Marianna Camino from the World Bank, CONDUSEF in Mexico and the Superintendencia de Banca, Seguros y AFP (SBS) in Peru and in particular, Luís Daniel Allain Cañote, Daniel Bouroncle, Jesús Chavez Ugalde, Isis Marín, María Fernanda Saldivar Cortes, and Mariela Zaldivar Chauca for their constant encouragement and support. We also thank the team at Berumen and IPA for data collection and to Ildrim Valley and Bernardo Ribeiro for superb research assistance. In addition, Luís Daniel Allain Cañote and Jesús Chavez Ugalde provided valuable help with the calculations of key terms. We gratefully acknowledge financial support from SECO and KCP. The views expressed in this paper are those of the authors and should not be attributed to the World Bank, its executive directors, or the countries they represent.
**Procedure no. 8** : Transferring a land plot from one category into another, or changing the designated use of a land plot.

**Procedure no. 9** : Privatization of a real estate object (building or premise) which is currently municipal property.

For the purposes of this paper we have used only procedures guiding land transactions, i.e. Procedures nn. 1, 2A, 2B, 3A, 3B, and 8. Table 2 shows the number of relevant responses collected in each of the 15 surveyed regions.

**Table 2.  Number of respondents that carried out locating procedures**

||Land and<br>proc<br>|Real estate<br>edures<br>|Land p<br>|rocedures<br>|
|---|---|---|---|---|
||BIS|ARCS|BIS|ARCS|
|Kaliningrad oblast|58|100|53|93|
|Saint-Petersburg|17|44|7|3|
|Leningrad oblast|12|29|11|21|
|Moscow|47|60|25|24|
|Moscow oblast|24|18|19|20|
|Sverdlovsk oblast|26|99|24|29|
|Novgorod oblast|14|100|13|70|
|Tomsk oblast|37|100|53|41|
|Khabarovsk Krai|42|70|41|58|
|Irkutsk oblast|51|100|47|92|
|Rostov oblast|18|100|43|48|
|Perm oblast|73|100|53|76|
|Novosibirsk oblast|49|100|53|42|
|Nizhny Novgorod oblast|37|100|43|76|
|Sakhalinoblast|12|68|9|60|
|Total|517|1188|494|722|

# **Nature of the analysis**

This paper is the first attempt to apply econometric tools to the analysis of the survey dataset. We purposely limited ourselves to basic models both due to the limited number of observations in some of the models we have tested for this paper and to avoid, where possible, overly complex models that might complicate interpretation of the results. This paper can be seen as a “teaser” and introduction to the wealth of information collected via the Survey of Land and Real Estate Transactions in the Russian Federation.

Three of six hypotheses outlined above (see page 8) are dealing with regional policy environment (e.g. in hypotheses 1, 2, and 4), the others are aimed at testing how specific administrative procedures affect individual land transactions in the surveyed regions (e.g. hypotheses 3, 5, and 6). The variety of models, hypotheses and data used is a result of the exploratory (“teaser”) nature of this paper. The future further and deeper analysis will, probably, concentrate on onetwo hypothesis related to one of specific issues outlined above.

11
measure of exposure to robots. We employ a principal component analysis to identify the relative importance of these factors to predict automation in Indonesia. Specifically, we focus on the 2006 values of the following variables: i) firm (employment) size; ii) a dummy for having more than fifty percent foreign capital; iii) the sum of revenue shares of import and export, and iv) the share of secondary education workers.<sup>15</sup> Column (2) of Table 3 presents the results of replacing the share of secondary education workers with this variable.<sup>16</sup> The positive correlation of this alternative exposure measure with machinery investments is consistent with the existing evidence for advanced economies and adds to the thin evidence on the drivers of technology adoption in developing countries (Verhoogen, 2020). In light of this result, we can use this measure in the next section to check the robustness of the plant level results obtained with our preferred exposure measure.<sup>17</sup> Table 3: Correlations between plant level exposure to robots and plant level investment.

||(1)<br>Investment|(2)<br>Investment|
|---|---|---|
|ETR _×_ secondary<br>ETR _×_ PC (secondary, size, foreign capital, openness)|0.089***<br>(0.011)|0.074**<br>(0.021)|
|Observations|53,447|53,447|
|R-squared|0.658|0.658|
|Plant FE|yes|yes|
|Year FE|yes|yes|
|Industry-year FE|yes|yes|
|Other technologies|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots and investment. The dependent variable is the log of plant level investment in machinery and equipment. Secondary is the 2006 share of plants’ employment with secondary education, normalised to have zero mean and unitary standard deviation. The PC term is the first principal component of the 2006 values of the following variables: i) firm (employment) size; ii) a dummy for having more than fifty percent foreign capital; iii) the sum of revenue shares of import and export, and iv) the share of secondary education workers. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

> 15The principal component analysis reveals that only the first component has an eigenvalue larger than 1, equal to 1.69. The first component explains 42 percent of the variation in the four variables.

> 16As for the share of secondary education workers, we normalize this variable such that it has zero mean and unitary standard deviation in the sample.

> 17Including the share of secondary education workers in the principal component analysis increases substantially the predictive power of the component, which in turn provides additional support for our preferred specification.

16