rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 1: World Development Indicators:  Share of Agricultural Employment Out of Total Employment vs Log GDP for Select Countries**<sup>**2**</sup>

<!-- Start of picture text --> 100<br>90<br>80<br>Argentina<br>70<br>Bolivia<br>60<br>China<br>50<br>France<br>40<br>Japan<br>30<br>Netherlands<br>20<br>United States<br>10<br>0<br>Log GDP per Capita<br>Figure 2:Urban Population Share of Select Countries 3<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1500 1529 1560 1590 1621 1651 1682 1713 1743 1774 1804 1835 1866 1894 1925 1955 1986 2016<br>Year<br>Germany India Japan South Korea<br>Turkey United States Uzbekistan<br>Share of Agricultural Employment<br>Percent Urban<br><!-- End of picture text -->

**Figure 2:Urban Population Share of Select Countries**<sup>**3**</sup>

2 Notes: Courtesy of Our World in Data. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

> 3 Notes: World Development Indicators, courtesy of Our World
large farm or the nearest large farm growing the same crop and the total area cultivated by large farms or devoted to large farm cultivation of a specific crop within a certain radius.

Information on smallholders comes from 11 years (2004-14) from CSA’s smallholder survey which had been conducted annually since 1980 by resident enumerators on a sample of some 1,400 kebeles nationwide. Figure 1 illustrates the location of the kebeles included in the 2013/14 round as well as that of large farms above 50 ha. As sample kebeles were changed only in 2007/8, this provides us with a panel of kebeles in the 2003/4-2006/7 and the 2007/8-20013/14 period. Recovery of kebele codes, properly adjusting for splits, mergers, etc. was, however, possible only for about half the kebeles included in the earlier period, providing us with data from about 500 and 2,000 kebeles before and after 2006/7, respectively. Information on inputs is based on a random sample of 20-40 farmers per kebele, resulting in a coverage of 28,000 to 56,000 farmers per year. Data on yield is based on crop cuts of randomly selected fields in each EA, i.e. not those of the farmers interviewed, limiting the ability to for example estimate production functions.

We complement these surveys with two data sources. First, as the smallholder production survey lacks data on labor use, we use data on labor supply at individual level from the 2011/12 and 2013/14 rounds of the

> 6 Information on the year of establishment is used to reconstruct the inter-temporal evolution of large farms, following Ali _et al._ (2015). As nonoperational farms are not included in CSA’s sample, this implies that our results are valid for operational farms.

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poverty and distributional impact of the twin crises. In doing so, it examines how the population has been affected across the (income or consumption) distribution, assesses spatially differentiated impacts, estimates the effect of the crises through macro level shocks (operating through the macro economy) and the micro level shocks (primarily large scale internal displacement), predicts the poverty impact on the existing poor and those who have fallen into poverty (including internally displaced peoples (IDPs)), and compares the characteristics of these groups.

The paper is structured as follows: first, it presents the methodological approach, which differs from traditional ones –i.e. elasticity of poverty to output or GDP – and also from micro‐simulation methods used in the past.<sup>7</sup> Section II describes the macroeconomic inputs and assumptions behind each simulated scenario. Section III discusses the welfare and distributional impacts of the twin crisis in Iraq. Section IV presents some validation exercises using the Continuous IHSES collected in 2014. Section V concludes the study, summarizing key findings and discussing a few implications of the results for the country.

> 5 Last official estimates, Crude oil represents about 44% of total GDP in 2014.

> 6 80 percent of new jobs were created in the public sector, especially in the financial, insurance and professional services sector, with accompanying increases in wages and salaries between 2007 and 2012. Further details see World Bank (2015).

> 7 The findings of this paper represent ex‐ante estimates based on available data and assumptions made after discussions and agreements with counterparts from the Government of Iraq and Kurdistan region, and as such do not constitute official estimates and may be updated when more recent and relevant data becomes available.

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number of types and a short time-series. Therefore, we consider two separate regressions for the two data-sets we have in hand. In the next section, when we run counterfactual simulations, we demonstrate how we use results from both regressions in the same simulation.

# **Estimation with CPS**

Since we do not observe sectoral history, neither unobserved heterogeneity nor sectoral experience is included. This means that we have to impose two restrictions in order to make the model identifiable with the CPS data: _C_<sup>2</sup> = 0 and _C_<sup>3</sup> = 0. Henceforth, we call this version of the model the “constrained model.” The restrictions imply that workers’ sectoral experience and unobserved type have no affect on mobility costs. For the first step regression (the flow equation), we correct the mobility rates reported in CPS to annual rates. Then, we run Poisson pseudo maximum likelihood regression using (18). For the second step (the Bellman equation), we use time lagged variables as instruments and run IV regression using (20). We normalize the average wage in the economy to one. Since we can not identify the discount parameter, _β_ , we assume that _β_ = 0 _._ 95. The estimation procedure is explained in detail in Artuc (2012).

The regression results are reported in Table 3. We find that the moving cost parameter, _C_<sup>1</sup><sup>_,s_</sup> _/ν_ , is increasing with age and is between 2 _._ 62 and 4 _._ 5, significant at the 99% level for all types. The moving cost does not seem to vary much across education groups. The variance parameter, 1 _/ν_ , is about 1 _._ 67 and also significant at the 99% level. We find that the mean of the fixed utility parameter for the metal sector, _η_
- More concentrated power in the hands of government parties in parliament leads to

- more motor fuel subsidies or lower fuel prices. This effect is particularly strong for middle-income countries, and is stronger for diesel than for gasoline.

When distinguishing effects between high-, middle- and low-income groups of countries, we have found that the significant subsidy-stimulating effects operate most effectively in middleincome countries, and less so in both high- and low-income nations. It is also, generally, in middle-income countries that subsidy levels and rates are most significant.

Many of our conclusions are novel, but clearly tentative and in need of scrutiny in future work, perhaps on improved data sets. Most of our conclusions however seem to fit rather well with economic theoretical and political thinking on re-distributing welfare through either providing public goods or targeted redistribution of public resources; some of this literature was summarized in Section 2 above.

Even though we have applied the best data set of this type that may be available today, our data still have its limitations. Many country/year observations are missing; our gasoline data are for premium grade while we are to a large extent lacking data for regular gasoline (possibly, the more interesting fuel in terms of subsidies); and our fuel data set itself has limitations (only one data point per year; and fuel price observations are typically taken from the country’s capital the representativeness of which is unclear).

In future work, we seek to make improvements to this analysis, on several fronts, among which we here will mention two. First, the data set applied for this study still lacks key variables such as motor vehicle stocks and measures of optimal country-specific fuel pricing. We do however have access to data for motor vehicle stocks by country, for the period in question, but not for all these countries.<sup>22</sup> We intend, in the future, to pursue empirical work where such data are embedded. Secondly, we will seek to correct for possible problems of
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ratio between housing units and the number of households in cities, as shown in Figure 8. Note the correlation between periods of severe financial repression and a fall in the supply ratio (compare with Table 1).

53. As slready seen, tenants are required to provide large lumpsum payments which are about equal to one year of their salary. Most young renters without accumulated savings (or affluent parents) cannot afford to rent a full unit but must sublet part of a unit from others. Monthly rents are associated with low-income status until now. Korean surveys show that multiple renters are more frequent when income is lower because of limited chonsei savings. Crowding at the low-income end of the market is very severe. In the 1980 census, only 30 percent of urban households occupied an entire unit, while 30 percent shared it with one other family, 18 percent with two and 22 percent with three or more households in a single unit (see 1980 Census, Table 7).

54. Analyses of the censuses over time show that the low value of the "supply ratio" in cities graphed in Figure 8 is also linked to two other factors: the removal of a large number of small units or their enlargement. The enlargement of small units is again linked to the scarcity of financing which leaves room only for an incremental approach to housing investment. Apartment units do not lend themselves to such enlargement; this is another reason why the lack of mortgage finance increasingly distorts the supply of new housing in Korea.

# E. Rapidly Rising Real Rents

55. The lack of mortgage financing for new housing construction is reflected also in continuing rise of real rents with rental charges rising by 5.3 percent in 1986; this incryg7e is twice the CPI rise of - only 2.6 percent during the same period. Long-term series on real rent increases remain to be developed.

# VII. CONCLUSION

56. The findings for Korea confirm the hypothesis that the nature of economic planning and financial policies not only play
</u> by households and <u>subsidized by the</u> government. This typically restricts the range of products to a few items in any given country.

- The analysis is restricted to two prototype products that cover most typologies of subsidized - -

- products. The distinction is between <u>single priced</u> products and <u>multiple priced</u> products. By single priced-products we mean consumers‟ products for which the government fixes the unit price irrespective of quantities consumed. Examples are set prices for bread or sugar. By multiple-priced products we mean products for which the government fixes different prices according to different levels of consumption. Examples are set prices for electricity or water.

- The data requirement is restricted to only one household consumption survey. Users will be enabled to import into the analysis estimations made through other means such as input-output tables or computable general equilibrium models but the assumption is that the analyst has access to only one cross-section household survey. This is the most likely scenario faced by analysts when they first visit a country to carry out a subsidies analysis.

- The focus will be on the medium-term effects as defined further as opposed to short-term and long-term effects.

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8 In Egypt the definition of income is more complete, including income received in kind as well as in cash. In Egypt a money value was imputed to receipts in kind, household consumption of crops and crop by-products, and home-consumed livestock. Because of uncertainty about how to deduct imputed land rent from agricultural income, no values for imputed land rent were calculated. Similarly, because of the thin rental market for housing in rural Egypt,'<sup>3</sup> no values were imputed for the rent of owneroccupied housing. Finally, because of uncertainty about how to accurately calculate wage rates for family members, no values were imputed for family labor involved in crop and livestock production.

In Jordan the definition of income in the survey is more limited. Because the agricultural sector is much smaller in Jordan,'<sup>4</sup> the Jordan HIES did not collect data on either income earned from livestock or crop production. This is an important lacuna, which complicates efforts to compare the results of the Egypt and Jordan surveys. Given this omission, this study did not impute any values for the household consumption of crops, crop by-products or home-consumed livestock. While the Jordan HIES did collect data on income earned from land and house rent, to be consistent with the Egypt survey, no values were calculated for either imputed land rent or the rent of owner-occupied housing. Like Egypt, because of uncertainty about how to accurately calculate family wage rates for family members, no values were imputed for family labor involved in crop and livestock production in Jordan.

The study divided total income for each rural household into five sources (for Egypt) and four sources (for Jordan)'<sup>5</sup>
expenditure programs) that matters, not one particular component. As such, distributional concerns regarding the VAT do not need to be addressed directly through the VAT system itself.

The distributional impact of a VAT system, including who benefits from reduced VAT rates, will in practice depend on both the design of the VAT system and the consumption patterns of households (including their responses to price changes). As such, whether the VAT is regressive, whether reduced VAT rates are well targeted at poorer households, and whether alternative instruments can better target poorer households are all empirical questions that can be tested. A number of empirical studies have examined these questions, and are summarized briefly below. These studies typically draw on household expenditure survey microdata,<sup>24</sup> as this enables the fine distinctions present in many countries between expenditure categories subject to different VAT rates to be accurately modelled.

### 4.1.1 Regressivity of the VAT

Several studies (e.g., Leahy et al., 2011; Ruiz and Trannoy, 2008; O’Donoghue et al., 2004) examine the overall distributional impact of the VAT by measuring VAT burdens as a percentage of income across the income distribution for a single year. Following this approach, these studies find the VAT to be a highly regressive tax. However, as has been highlighted by various authors (e.g., IFS, 2011a; Creedy, 1998; Metcalf, 1994), a major problem with this approach is that it does not account for savings behavior. More specifically, it ignores the fact that income saved in the current year will incur VAT when it is eventually

> 24 Warren (2008) provides a summary of different approaches that can be taken to modelling the distributional effects of consumption taxes more broadly.

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.6|-|
|Uruguay|1994|895.7|39.1|2.0|0.8|0.0|57.7|23.9|
||1993|661.3|39.7|2.4|0.7|0.1|56.8|18.4|
||1990-92|565.6|45.0|3.3|0.9|0.1|50.4|6.9|
||1984-86|270.8|51.0|4.4|0.4|1.0|41.8|5.3|
||1979-81|312.2|49.5|1.7|1.8|--|46.4|9.1|
|MERCOSUR|1994|12,043.6|24.2|3.0|1.7|8.3|62.6|30.7|
||1993|<br>10,026.3|21.6|2.7|2.0|8.5|65.0|32.1|
||1990-92|<br>5,482.0|27.4|4.5|3.1|4.1|60.8|25.0|
||1984-86|2,290.9|30.7|4.9|5.2|6.4|52.5|16.4|
||1979-81|3,129.2|31.8|4.1|3.1|6.3|54.6|23.7|

Source: Computed from UN Comtrade records.
Figure 11a shows the results on our measure of workforce composition (the difference between the change in average log wages for stayers and the change in average log wages for all workers), again dividing industries into groups based on the magnitude of the shock to direct exports. Again the pre-shock trends across industry groups appear quite similar. Compared to industries that experienced only mild shocks, industries in the other two categories shed lowskilled workers at the end of 2008 and beginning of 2009, but reversed this trend at the end of 2009. Figure 11b shows similar results when grouping industries by the magnitude of the shock to related exports.

We now proceed to the econometric models that implement the continuous treatment design. Specifically, we estimated the following continuous-treatment effects models of the export and related-exports shocks on employment and wages: x (11) ∆Ei,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t, x (12) wi,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t, and x (13) diffi,t = βx,t ∙γt ∙∆xi,t∈crisis + βIx,t ∙γt ∙∆Ii,t∈crisis + ∆εi,t.

Equations 11-13 are estimated using the 99 industries for which both the trade and the labor market data exist for every observation in the sample, (1,089 observations).

In a nutshell, these models provide estimates of crisis-specific deviations from common period-specific shocks across industries. The parameters of interest vary over time. If the export shocks observed during the crisis period did not affect ongoing trends across all industries then βx,t = 0 and βIx,t = 0 across the whole sample period. If the crisis trade collapse induced interindustry dispersion in employment and wages that deviated from common trends, however
Deininger and Squire (1998) find that the coefficient on income inequality in growth regressions is not robust to the inclusion of regional dummies. Birdsall and Londono (1997) show that once land and human capital inequality are entered in a crosscountry growth regression, income inequality no longer has a significant effect on growth. Similarly, Castello and Domenech (2002) find a negative and robust growth effect of human capital inequality, but no robust income inequality effect. We construct a human capital Gini coefficient using census data and estimate the growth effect of inequality in human capital too. Our results indicate that it is human capital inequality rather than income inequality that affects growth; however, the effect we find for Uganda is positive.

On point (2), we present a study based on micro data for Uganda, which allows us to avoid data comparability problems that affect cross-country studies. While there is a small number of inequality-growth studies that use micro (meso) data, we believe this is the first such study for a relatively small country and the first for an African country. Our analysis has been made possible by recent advances in the field of small area welfare estimation (Hentschel et al., 2000). The data set thus consists partly of imputed variables or so-called small area welfare estimates. These are obtained by deriving expenditure estimates for a complete population, combining information from a census and a survey (Elbers et al., 2003).

The paper is organized as follows. Section 2 briefly reviews data problems encountered in empirical inequality-growth studies and introduces small area welfare estimates as an alternative data source. In Section 3 we present our growth model and descriptive statistics. Section 4 presents a discussion of econometric issues that need 3
ordination therefore points to interventions that could promote institutional reform, either by changing the perception of coordination failure as an offense, or by changing the rule of thumb to retaliate against such offenses.

The next section of the paper describes the design of our Stag Hunt experiment. Section 3 analyzes the results. Section 4 presents our survey on attitudes towards punishment. Section 5 develops a theoretical mechanism by which the culture of honor can impede efficient coordination. Section 6 addresses alternative hypotheses, and Section 7 concludes. Appendices contain the formal analysis of the model of Section 5 and supporting materials from the experiments.

# **2 The Stag Hunt Experiment**

## **2.1 Subjects**

In September 2005, we conducted a field experiment in the northern Indian state of Uttar Pradesh. We recruited 122 male subjects from 10 villages in Bakshi Ka Talab in the district of Lucknow. In each village, we recruited an equal number (generally 6) of high- and lowcaste subjects using systematic sampling that covered the entire village. This was done with the help of one to three village residents who were knowledgeable about the location of highand low-caste hamlets. As an example, if the village had only one low-caste hamlet and if thirty households resided there, then every fifth household was selected.

After explaining to the household that we were using a game to study how people behave, we asked if one man in the household would volunteer to take part in the game, in which he would earn money. In every selected household, a person volunteered. The caste composition of the high-caste subjects was: Brahmin (55 percent), Thakur (44), and Lala (1). Among the low-caste subjects, the composition was: Chamar (56), Rawat (38), Dhanuk (3), and Pasi (2). Two percent of the low-caste sample identified themselves by the generic term for low caste, _harijan_ .

## **2.2 Procedures**

We held only one
. This analysis detected which areas have lower rural accessibility and what would be the accessibility gains owing to the improvement of the rural road network. Accessibility was measured as the ability of people to reach hospitals in a given time frame. This analysis also includes schools as a critical point of interest for the broader human capital development interventions.

2. **Economic growth.** The analysis used agricultural production data (source: International Food Policy Research Institute [IFPRI]<sup>9</sup> ) and location of regional markets (source: OpenDevelopmentCambodia website) to assess the accessibility of rural farmers to regional markets, as a key indicator to foster economic recovery of rural areas post-crisis. This analysis detected which areas have lower rural accessibility and what would be the accessibility gains owing

> 9 <u>https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PRFF8V.</u>

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result of the gains in height and schooling. We also observe that the population with the highest levels of education earns the highest labor income, on average.

# **VIII. Conclusions**

This study focused on the analysis of the data collected as part of the Evaluation of Rural Households Survey 2017 (ENCEL 2017) and on the previous rounds of ENCEL and ENCASEH 1997. In particular, we use 1997-2000 as the baseline period in order to investigate the potential for intergenerational mobility, and the magnitude of the absolute intergenerational mobility of human capital (height as measurement of health and schooling as measurement of education), 33
). The last two columns of Table 4 test the robustness of these findings by comparing the change in primary completion rates at any given age between 10 and 18 years old, for districts with varying degrees of conflict intensification between the 2001 and 2006 DHS surveys (Equation 3). Columns (3) and (4) explore the long-term impact of conflict intensity experienced up to- and after 10 years old (Equation 2).

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