rafmacalaba/datause-encoder-data huggingface dataset:
starting with the specification in column 4 of Table 3, we controlled for the level of formal and informal competition faced the stores in our sample.<sup>13</sup> Second, we controlled for the remaining two finance related variables reported in the survey which are dummy specifications for whether or not a store has a line of credit and overdraft facility. Third, we controlled for the number of days of inventory maintained by the store, a dummy variable equal to 1 if a store has a female principal owner and 0 otherwise, a dummy variable equal to 1 if a store is part of a larger chain and 0 otherwise, a dummy variable equal to 1 if a store reported experiencing theft during the year 2005-06 and 0 otherwise, and the proportion of a store’s annual sales in 2005-06 that were never paid for. Fourth, we controlled for the percentage of stores’ senior management’s time that is spent in dealing with business regulations. Fifth, we added a dummy variable equal to 1 if a store is located in a metropolitan city (Mumbai, Delhi, Kolkatta, Bnagalore, Chennai and Hyderabad) and 0 otherwise, a set of two dummy variables indicating whether a store is located in a leading or a lagging state (omitted category is the set of middle-income states)<sup>14</sup> , and square of _Size_ ( _Size_<sup>2</sup> ) to capture any possible non-linear effect of store-size on employment. With all these controls added, the estimated coefficient of the labor regulation index equaled -6.08 significant at less than 5% level (p-value of .039).

> 13 In one survey question stores were asked how important was the influence of formal and informal (people selling from pavements, etc) competitors over the prices of their products. Responses were recorded on a 1-4 scale, for each form of competition separately, with a higher value on the scale implying more competition. Our competition measures equal the average score at the city level for each form of competition. Although not important for our results, averaging was done to filter out store specific attributes from store-level responses.

> 14 The definition of leading, lagging and middle states is taken from Kochar et al (2006). The authors look at a number of state level development indicators including the quality of physical infrastructure, financial development, penetration of mass media, literacy rates and historical land tenure systems. Based on these indicators they group states into the three categories. Leading states are: Delhi, Gujarat, Karnataka, Kerala, Maharashtra and Punjab. Lagging states include Bihar, Jharkhand, Madhya Pradesh, Orissa and Uttar Pradesh. The remaining states belong to the intermediate middle group.

15
and quotas among affected parties may have a benefit that goes beyond improving the prospects of passing the necessary legislation.

_More information on emissions_ . A third option is to improve the availability of information on greenhouse-gas emissions. Most mechanisms that support cooperation in other social dilemmas depend on the availability of information about others’ players behavior: consider strategies that specify cooperating if and only if (sufficiently many) others cooperate; strategies that require separately punishing defectors; strategies that specify cooperating with people that have previously cooperated with others; and strategies that use cooperation in a public-good game to build a reputation valuable in other games. Public information on greenhouse-gas emissions is notably limited. The World Resources Institute’s website, which seems to be the best source of comprehensive national information, contains no information on emissions after 2004, and no comprehensive information is available after 2000.<sup>15</sup> In that year, emissions from sources not reported in later years made up 41 percent of the global total. For Indonesia and Brazil, which had the third and fourth highest levels of estimated emissions in 2000, they made up 90 and 85 percent, respectively, of the national totals.

By contrast, information on inflation, gross-domestic product, and other economic phenomena is often available on a quarterly basis, with a delay of only a few months. Transparency International promptly publishes annual indicators of corruption. The World Bank promptly publishes annual indicators of the ease of doing business. All these indicators attract attention, create controversy, and influence policy. Climate change needs the equivalent. Moreover, the best information would not only be national. It would also cover finer groupings, such as provinces, cities, and perhaps large firms, as well as facilitating estimates of emissions for representative individuals.

> 15 See http://cait.wri.org (accessed August 2008).

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_Table 3_ **: Baseline Econometric analysis for Arrivals Determinants:** 2000-2016

|**VARIABLES**|**(1)**|**(2)**|
|---|---|---|
|**Dep. Var.**|**LN_ARRt**|**LN_EAt **|
|**LN_GDPt-1**|0.550***|-0.731*|
||(0.158)|(0.377)|
|**LN_POPt-1**|0.139|0.463|
||(0.206)|(0.331)|
|**LN_Xt-1**|0.242**|0.618***|
||(0.0969)|(0.181)|
|**LN_SEATSt-1**|0.129**|-0.166|
||(0.0649)|(0.150)|
|**RER_EUROt-1**|0.604***|-0.598**|
||(0.178)|(0.261)|
|**LN_GDPEUROt-1**|-0.507***|0.655***|
||(0.158)|(0.199)|
|**LN_LAND**|-0.226*|-0.260|
||(0.132)|(0.174)|
|**Observations**|542|534|
|**Number of id**|36|35|

Source and notes: Author ́s calculations. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All specifications include yearly dummies, not reported. Random-effects model.

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**Table 3. Regression Results: The Magnitude of Devaluations, FDI and Exports among Developing Countries**

|VARIABLES|(1)<br>FDI|(2)<br>FDI|(3)<br>Exports|(4)<br>Exports|(5)<br>Ex-post<br>FDI|(6)<br>Ex-post<br>FDI|(7)<br>Ex-post<br>Exports|(8)<br>Ex-post<br>Exports|
|---|---|---|---|---|---|---|---|---|
|Lagged FDI||0.217***|0.0819***|0.0295||-0.0485|0.0638**|0.0493*|
|||(0.0451)|(0.0208)|(0.0183)||(0.0673)|(0.0305)|(0.0298)|
|Lagged REER|-0.0280|-0.0251|-0.00235|-0.0261|-0.100*|-0.105*|-0.0102|-0.0162|
||(0.0413)|(0.0406)|(0.0190)|(0.0164)|(0.0595)|(0.0599)|(0.0282)|(0.0273)|
|Lagged Exports|0.274***|0.196*||0.540***|0.210|0.211||0.248***|
||(0.105)|(0.105)||(0.0397)|(0.148)|(0.148)||(0.0593)|
|Observations|576|572|579|579|306|306|313|313|
|R-squared|0.048|0.092|0.153|0.375|0.048|0.050|0.188|0.239|
|Number of|||||||||
|countries|38|38|38|38|38|38|38|38|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 All estimations include period fixed effects and country fixed effects (demeaning approach). Source: Author’s calculations based on data from the IMF. See text for details.

15
27 25 to 64). However, the proportion of wage earners among young employed people is very close to that of adults (respectively 28 percent and 31 percent).

**Figure 12. Main Labor Market Indicators by Education Level for Youth (in %)**

<!-- Start of picture text --> 20.8<br>Labor Force 17.9<br>31.4 47.9<br>Participation<br>13.7<br>Employment-to- 9 Higher<br>22.5<br>Population 31.6 Secondary<br>Primary<br>34.1 49.7<br>None<br>Unemployment 28.4<br>34<br>0 10 20 30 40 50<br>(in %)<br><!-- End of picture text --> Source: EPCV 2004.

_Types of employment vary across living areas_ . Self-employment among youth is indeed more widespread in urban centers than it is in rural areas (Figure 13). The contrast is even more striking for unpaid family labor and apprenticeship: almost 40 percent of all young men residing in rural areas against 14.5 percent for those in urban areas. For young women, these figures stand at 43.6 percent and 11.4 percent, respectively. Hence, young people employed in urban areas are mostly wage earners whereas those employed in rural areas are mainly unpaid workers or self-employed.

_Youth’s participation in the labor market features substantial gender gaps, as does the overall population._ As evidenced by Table 17, young women’s labor force participation and employment rates are less than half of that of men of the same age. Although 41.5 percent of women aged 15 to 19 are not taking part in the labor force because they are in school, 31.4 percent of them report family reasons as a main motive for non-participation (compared to only 5.8 percent of males of the same age, Table 16). The differences are even more striking when considering the 20-to-24 age group: 55.7 percent of these women are out of the labor force for family reasons, whereas only 19.4 percent of them are in school (these figures stand respectively at 7.4 percent and 45.4 percent for men aged 20 to 24). Furthermore, females engaged in the labor market experience an unemployment rate of 40.4 percent, i.e. 10 percentage points above that displayed by males.
Figure 9: Distance to main cities Notes: Distance to main cities is defined as the population weighted (linear) distance to the 10 largest cities of South Africa. Each dot represents population-weighted community averages for 1 km bins. Negative distances correspond to communities inside homelands. Linear fit and 95% CI represented by line and shaded area, respectively.

Figure 10: Distance to main roads Notes: Distance to main roads is defined as the (linear) distance to a main road in 2001 as defined by Statistics South Africa. Each dot represents population-weighted community averages for 1 km bins. Negative distances correspond to communities inside homelands. Linear fit and 95% CI represented by line and shaded area, respectively.
10 below poverty line, in a population of n. Then the headcount ratio is: H = q/n = the proportion of the population deemed to be poor. For example if there are 10 poor people out of 100, the ratio H = 10/100 or 0.1.

Suppose now that a = 1 so that the "aversion to poverty" is increased, then This multiplies the "head-count ratio" or the proportion of the population below the poverty line, i.e. H, by the income or expenditure gap between the average poor person and the poverty line. To continue the example, if each of the 10 people above had an income of 40 percent of the poverty line, then the P2 measure would be 10/100 * 0.4 which equals 0.04. To better understand this, suppose q people are poor, i.e,.

PI = q/n * (Z-Y)/Z = HI, where H = q/n and I = (Z-Y)/Z.

Suppose now that a = 2. This weights the poverty of the poorest individual more heavily than those just slightly below the poverty line. This is done by squaring the gap between their incomes and the poverty line in order to increase its weight in the overall poverty measure.

The _Pa_ measure has the further advantage of being decomposable. For example, the national level measure of poverty can be expressed as the sum of regional measures weighted by the population share of each region: _P,,, = Y- kj Pa J_ where j=l .... m regions kj= population share of region j.

This in turn enables us to calculate the contribution cj of each region to national poverty.

kjPc,J Pct_ This measure has direct relevance for policy. This enables us to understand the impact of various policy measures in different groups and regions of the country. Also the knowledge about the share of each region or group in total poverty is essential for targeted interventions.

Having discussed the framework of our analysis we now have to outline the implementation of the framework and issues ensuing from it. The data set for this study was obtained from the National Integrated Survey of Households, undertaken by the Federal Office of Statistics (FOS). We have already noted the coverage of the data set and its main characteristics and now turn to a more practical look at the data, given the proposed framework for poverty analysis.
morbidity is under-reported; hence, the differentials in actual treatment per episode are even larger than reported.

Similarly, the anthropometric estimates do not indicate gender disparities. This could be for a variety of reasons, but one is that the severe malnutrition indicators, such as the fraction of the population that is three (or even two) standard deviations below reference group 13 norms, were very unreliably estimated .

# B) Educational enrollment

For the households interviewed in the more recent surveys, the DHS reports whether or not each child is "attending" school. Beyond the questions of national (or area) representativeness, the data are potentially better than national statistics both because they are based on reported attendance, not official enrollment, and because they compare children of similar ages rather than by grades. The data are broken down into two age groups, ages 6-10 and 11-14 (with some variation in the cutoff ages across countries). We focus mainly on children in the 11-14 age range, as they are still young enough to be part of the "basic" education cycle of most countries but are reaching the ages when any gender-based discrimination in education may worsen. In contrast, the 6-10 age range is potentially more problematic an indicator as ,so many countries have achieved 100 percent enrollment for both sexes. The correlation between the gender ratios of enrollment for the two age groups is quite high at .84.

'3 This lack of association among alternative indicators was found also in the other studies discussed below.

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# 4 The results

Given the initial conditions, the system evolves under the influence of demographic developments and (disembodied) technical progress, which are treated as exogenous. Recalling Section 2.4, these are the sequences of fertility, {Ni,t<sup>c}</sup> t<sup>t</sup> =0<sup>=T, age-specific mortal-</sup> ity, {qi,t}<sup>t</sup> t<sup>=</sup> =0<sup>T,andtotalfactorproductivity,{Ai,t}t</sup> t<sup>=</sup> =1<sup>T.Inordertoestablishtheeffects</sup> of the epidemic, we need these sequences for two disease environments: first, there is the counter-factual (denoted by D = 0), in which the epidemic never appears; second, there is its actual and projected course (denoted by D = 1).

We begin with demography. Although there are projections for the overall Ethiopian population by age group till 2050 (UN, 2004), no such projections exist for the two sectors separately. To produce them, we assume that there will be a complete demographic transition to stationary fertility levels in five periods. To be precise, Ni,t<sup>cis</sup> assumed to decline linearly from its current value (t = 1) to 2.041 at the start of period 5 (see Table 1). The series for premature mortality among adults, q1<sup>a</sup> ,t<sup>(D=1),was</sup> estimated on the following basis. (i) Drawing on the experience from the epidemic elsewhere in Africa, Loewenson and Whiteside (1997) conclude that: the spread of HIV becomes exponential when about 2% of the sexually active population are infected; the ensuing period until the peak is reached is comparatively short, probably 10 years or less; and the epidemic may then become endemic, with perhaps about 10% of the sexually active population being HIV-positive at any one time. (ii) The (estimated) upswing in prevalence rates in Ethiopia’s rural sector (see Figure 1) will probably gain momentum;<sup>19</sup> for urbanization is commonly associated with return flows, including those associated with the agricultural peak periods, all of which intensify the spread of HIV/AIDS (Degefe and Nega, 2000: 74). A notable feature of D = 1 is that mortality reaches its peak before 2020. The counterfactual series for qi,t<sup>a(D=0)hasbeen</sup>

> 19This is consistent with Hladik et al. (2006). Their analysis suggests an ‘expanding HIV epidemic in rural and all Ethiopia, but a possible decline in some urban areas’.

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Policy Research Working Paper 7920

# **Abstract**

Although the measurement and determinants of poverty have been widely studied, vulnerability, or the threat of future poverty, has been more difficult to investigate due to data paucity. This paper combines nationally representative household data with objective drought and price information to quantify the causes of vulnerability to poverty in Ethiopia. Previous estimates have relied on self-reported shocks and variation in outcomes within a survey, which is inadequate for shocks such as weather and prices that vary more across time than space. Historical distributions of climate and price shocks in each district were used to simulate the probable distribution of future consumption for individual households; these were then used to quantify vulnerability to poverty. The analysis shows that many Ethiopians are unable to protect their consumption against lack of rainfall and sudden increases in food prices. A moderate drought causes a 9 percent reduction in consumption for many rural households, and high inflation causes a 14 percent reduction in the consumption of uneducated households in urban areas. Vulnerability of rural households is considerably higher than that of urban households, despite realized poverty rates being fairly similar. This finding reflects that the household survey in 2011 was conducted during a year of good rainfall but rapid food price inflation. The results highlight the need for caution in using a snapshot of poverty to target programs, as underlying rates of vulnerability can be quite different from the poverty rate captured at one point in time. The results also suggest that significant welfare gains can be made from risk management in both rural and urban areas.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at rhill@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(provided the consequences for absolute wellbeing are sufficiently strong to ‘offset’ the negative consequences of social comparisons). However, these are again early results and more detailed and rigorous analysis is needed to better understand the social mechanisms and interactions involved.

# **VI. Insider – outsider politics**

In the light of the preceding discussion a central question for the jobs-social cohesion link is how societies create the environments for positive interactions and spill overs that improve the wellbeing of all groups in society. Economic and labor market institutions clearly matter in this context. Economic theory suggests that societies with more consensual institutions are generally better able to absorb conflicts between winners and losers of economic and political transformations (Fernandez and Rodrik 1991, Alesina and Drazan 1991). This is supported by several empirical studies, which document that societies with more consensual institutions experience fewer and shorter economic crises (Rodrik 1999, see also Easterly et al. 2006), or that crises are at least not more severe than in otherwise comparable countries (Forteza and Rama 2006).<sup>22</sup> These more stable conditions have obvious advantages for all groups of society who will benefit from the absence of economic insecurity and volatility.

The flip side is that many of the positive consequences of work-based interactions can deepen social differences if they interact with existing economic divides and segregation processes. Political scientists and economists have long been concerned that collective action problems and resulting lower levels of political organization among the unemployed facilitate the emergence of policies that favor the interests of labor market ‘insiders’ over those of ‘outsiders’ (see for instance Lindbeck and Snower 2002, Olson 1982, Rueda 2005, 2006).<sup>23</sup>

> 22 The recent review of literature on labor regulations carried out for the WDR 2013 concludes that, outside of extreme scenarios of excessive regulation or de-regulation, efficiency enhancing and undermining effects of labor market institutions can be found side by side (World Bank 2013:26, see also, Freeman 2008, 2009, and OECD 2011).

> 23 Reviews of labor market policies provide some indications that this is indeed the case. In most countries labor market regulations disproportionally benefit young males to the detriment of other more vulnerable groups (OECD 2011, WDR 2013, Lindbeck and Snower 2002). Similar evidence is found by Heckman and Pages (2000) for Latin America where job security provisions are comparatively costly (OECD 2011: 162) and by Botero et al. (2004) at the cross-national level.

22
Proposition 1 establishes that when using a _household-level_ Paasche index, one inclusive of all items (food and nonfood), then using a single or multiple index approach leads to the same real consumption h h aggregate: xSI = xMI. In other words, the choice of the method is irrelevant for the estimation of poverty and inequality indexes, as well as for any other moment of the empirical distribution function. The situation described by Proposition 1 corresponds to the conditions assumed by Deaton and Zaidi (2002).

Does such a Paasche index exist in practical work? Arguably, it is unlikely for two main reasons. First, household survey data that are typically used for welfare measurement do not include information on prices faced by each household. Rather, households within a defined geographical area are associated with same set of prices, either because expenditure data collected at household level are matched with prices from a common market in the area or because prices are proxied by unit values.<sup>3</sup> In either case, the lack of household level price data leads to r≠H, and therefore xSI ≠xMI. Second, the ideal scenario of full coverage of prices for all items included in the consumption aggregate (i.e. k= K ) is rarely realized, either because not all items are sold in the market of reference (in case household questionnaires are complemented by market level questionnaires) or because is often impossible to collect information on quantities consumed for non-food items and hence is not possible to proxy prices with unit values.

By relaxing the assumptions of Proposition 1, we obtain a new theoretical result, that clarifies how important is, in practice, the choice between the use of one deflator (SI) versus two deflators (MI).

# **Proposition 2 (Distributional bias).**

Assume (r, k, kf, knf, α, αf, αnf) = (r, k, kf, knf, α, α, α ), and k< K or r< H or both, then h h f nf h h xSI ≠xMI. Moreover, if Ii > (<)Ii , then xSI −xMI > 0 if and only if:

> 3 h As discussed in DZ, the most common approach when using unit values is to replace the individual pk by their medians over households in the same PSU or locality in order to minimize noise and address potential problems related to outliers (DZ, p. 42).

8