rafmacalaba/datause-encoder-data huggingface dataset:
_Site Selection_ .<sup>3</sup> This journal, in a series of articles called the _The Million Dollar Plant_ , reports the final location of a new plant creation, along with the identity of the potential alternate or the _runner-up_ location(s). We refer to each such set of winning and losing counties as a _deal_ . Our final number of deals varies depending on the outcomes we are looking at (e.g. labor market or retail inflation) which determine our sample sizes.<sup>4</sup>

# **3.2 Retail Price Data: IRI Data**

Our primary retail price data set covers various grocery stores and drugstores from 2001 to 2012 and is provided by IRI Worldwide. The data set includes store-week-UPC sales and quantity information for products in 31 categories, representing roughly 15 percent of household spending in the Consumer Expenditure Survey. In total, the data cover around 7,200 stores in about 47 IRI markets, corresponding to 968 US counties. IRI markets are composed of U.S. counties, which can represent a single metropolitan area (e.g. Los Angeles) or various metropolitan statistical areas aggregated into one region (e.g. West Texas/New Mexico covers all FIPS counties of New Mexico but only some of Western Texas). There are a large number of papers in the literature that study regional inflation using the definition of market constructed by the IRI (Pandya and Venkatesan, 2016; Bronnenberg et al., 2008).<sup>5</sup> While the raw data are sampled weekly, we construct quarterly prices both at category and barcode level, similar to Stroebel and Vavra (2019), since this reduces high-frequency noise. We construct the prices at quarterly-IRI market-store-barcode level by summing the total value of products sold in a particular quarter in a particular IRI market in that store and then dividing that by the total quantity sold of that product for that barcode/UPC in that store. Table OA1 in the appendix shows that the average IRI market sold about 13,255 unique UPCs per quarter across 52 stores, and the average unit value of the UPC was about 5 USD.

> 3Also see Greenstone and Moretti (2003) for the first use of this identification methodology. Greenstone et al. (2010), and Kim (2020) follow the same methods.

> 4For example, we have number of deals ranging from 19-36 depending on the outcome under study. For the retail inflation, we have data on 19 deals. We had to drop some deals for which we have missing information on either the winning or losing county or for which we do not observe the IRI market in the IRI data. The IRI market is a collection of geographically close counties. So, for example, a mix of counties in Maryland, DC, and Virginia all fall under the “BALTIMORE/WASHNGTN” IRI market. Overall, it covers a total of 968 counties, aggregated at 47-48 IRI markets.

> 5IRI set its market definitions in 1987 to achieve a representative sample of U.S. consumers. The data is shared by IRI at this market level in order to protect store identity. These broader geographic units are also able to account for the fact that consumers would sometimes shop in nearby counties outside their own counties.

18
median regression) is $47,112 to $100,927. The amount for non-health spending is between

# $863,000 and $1 million.<sup>30</sup>

Table 5: Cost of averting a death derived from different specifications of health status regressions.

|Source of estimate of a<br>[coefficient on ln(share of<br>public health in GDP)]|Value of<br>a<br>(13)|Increasing<br>public<br>expenditures<br>on health|Transferring<br>expenditures<br>from non-<br>health to health|Increasing non-<br>health GDP|
|---|---|---|---|---|
|Two-stage least squares|-.1917<br>(-.4044)|<br>47,112|<br>49,381|<br>1,025,398|
|OLS|-.1354<br>(-.4756)|<br>66,680|<br>72,202|<br>871,894|
|Median regression|-.0895<br>(-.4805|)<br>100,850|<br>114,193|<br>863,107|

The highest fraction of deaths, and in particular of child deaths, in developing countries are due to infectious and parasitic diseases (such as diarrheal diseases, TB, or malaria). As reported in Table 6, about 28 percent _of all deaths_ in developing countries are due to infectious and parasitic diseases among children under-5. The two most prominent among those are children under-5 dying of diarrheal diseases and Acute Respiratory Infections (ARI) which each account for slightly over 10 percent of all deaths. An additional 8 percent of all deaths in

> 30 Our confidence in these method for producing reasonable magnitudes is bolstered by the fact that Vicusi (1993) reports the estimated individual "willingness to pay" to avert a statistical death for the United States. The range of estimates is very wide, between $2.5 and $7.3 million which is between 124 and 375 times the average workers' income. There are few studies like this for developing countries, but a hedonic wage regression for Taiwan, China, gives an estimate of $650,000 which is 54 times average worker income (assuming GDP per capita is half average worker income). If we view the "non-public health share" part of our estimates to be related to private expenditures on health, then our estimate from the two-stage least squares estimate is $1 million, or 265 times the average worker income of the countries under study (again assuming GDP per capita is half average worker income) which is lower than the US range but higher than the Taiwanese estimates, but in both cases within a small multiple.

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We might be tempted to counter the observation that only 50 percent of third-graders can compute 32 x 4 by pointing out that in Australia, New Zealand, and Norway, less than 45 percent of fourth-graders could compute 15 x 9. This is a near-sighted argument. The key difference between Pakistan and India and the low-performing OECD countries is the length of time that children stay in school. Household data suggest that 40 percent of all children in Pakistan and 42 percent in India will have completed their formal schooling history by third grade (Pakistan) and fourth grade (India). In contrast, nearly all children in the three OECD countries will complete the twelfth grade. And low achievement observed in fourth grade (largely due to a curriculum designed to enhance creative thinking at the lower grades) will be more than compensated by accelerated learning later on—by the time these children are in seventh grade, they score _above_ the international average. By comparison, close to one-half of the 3 million born in Pakistan will leave school unable to add, subtract, multiply or divide; unable to read and write simple sentences in Urdu; and unable to read a short word like “BALL” in English.

# **5. The Educational Gaps**

A second basic characterization of the data decomposes these averages into differences across population subgroups. Subgroup decompositions—say along income or ethnicity—have played a critical role in policy and educational legislation in the United States. For instance, the blackwhite test-score gap—17-year-old blacks score roughly one standard deviation lower on the NAEP math test than their white peers (Hanushek 2001)—has spurred a large body of research and widespread legislation on financial equalization across public schools. Equally, the gender gap in mathematics and science has been the focus of much recent discussion. There are two issues—one methodological and one technical—we address prior to a discussion of the test-score gaps.

On a methodological note, a key question is the choice of subgroups. Should we look at rich and poor families or gaps across girls and boys? Or should we concentrate on differences across the landed and landless? Following established norms and new literature on the equality of opportunity, we focus on two subgroups. Our first set of subgroup decompositions are for _immutable_ characteristics such as gender, and arguably parental literacy and income. On moral grounds, we may believe that differences across children in these attributes should not affect their opportunities in life.

- 13 -
<!-- Start of picture text --> 100<br>B Illness or death<br>wn 80 Other reasons<br>ao} @ Consumption prices<br>oS ler<br>7H 60 = nemployment<br>S 50 @ Low incomes<br>2 40 B Selling prices<br><!-- End of picture text --> <!-- Start of picture text --> ee<br>© 30 @ Production costs<br>x<br>©. 20 @ Land/water surface<br>10 @ Livestock epidemics<br>° Natural events<br>Escsssegeec§cat NM St YH = um EFE550G5SG2Fecg NO StH = a F558cat ana mM tH5SGR= ua§<br>2010 2012 2014<br><!-- End of picture text -->
- 16 -

higher **is** v, the greater is the importance attached to the bottom of the income distribution. / The preceding analysis based on the Gini can be replicated in terms of the extended Gini.

- (11) (v-l) f [DCCji (F) (l.F)v-2 df -

- -

- [bj (v) / Sj bi (v) / _Si))_ Gy (v)

wherg

- cov (Xi, [1 - F(y)] 1)

- (12) b **i** (v) -Cv(.1 ()v1 coy (y, [1 - F(y)] -)

is the estimator of the (extended) Gini marginal propensity to spend on commodity i. The difference in the bi (v) is attributable to the choice of weighting schemes. Each weighting scheme is increasing with income up to a certain quantile and then it declines. Table 1 presents the quantile with the highest weight for various values of v.

Equation (11) provides additional necessary conditions for welfare dominance. If commodity i dominates commidity J, a shift from taxing commodity j to commodity i would decrease the extended Gini inequality index for all v, including the standard Gini case where v - 2. These necessary conditions are useful in empirical investigation of welfare dominance because they are fairly easy to calculate and can be used to identify the pair of commodities for which welfare dominance is possible.

Not surprisingly, the notion of welfare dominance is linked to the conventional wisdom of imposing luxury excises on items of consumption

**Hse11#4/wilte Domiuiee** a **the** Desin **_of_ zxocis Tatlon** **_in_ the Cote** _D'Zvoire/WThrsk:&b/5118/68_
**Figure 9: Capital over Total Assets (%) – top 20% banks and bottom 80%**

**Panel A: Top 20% banks**

# **Panel B: Bottom 80% banks**

<!-- Start of picture text --> 21.8<br>19.8 19.8<br>19.1<br>18.4<br>17.5<br>15.6 16.0 16.2 16.1<br>13.9 14.4 13.6 14.713.7 14.1 14.0<br>13.0<br>12.1 12.2 12.0 12.0 11.9<br>11.0 11.310.6 10.5 10.6 10.5<br>8.7 9.4 9.6 9.7 9.1<br>7.8<br>5.6<br>EU CE & BC WB SC CA Russia Turkey EE RoW EU CE & BC WB SC CA Russia Turkey EE RoW<br>2007 2017 2007 2017<br>22 22<br>20 20<br>18 18<br>16 16<br>14 14<br>12 12<br>10 10<br>8 8<br>Capital over Total Assets (% 6 Capital over Total Assets (% 6<br>4 4<br>2 2<br>0 0<br><!-- End of picture text --> Source: Archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe. We report means weighted by bank size.

Regulatory capital could have increased because of increases in the level and quality of regulatory capital or because of decreases in the measures factored into calculating total risk exposure. As we have seen, Tier 1 stringency has declined. We examine if there has been a similar decline in risk-weighted assets. Figure 10 shows the development over time of RWA (as a % of total assets). In general, for both small and large banks, the RWA ratio has declined over the past 10 years. This is especially true for EU banks that have the lowest average value in 2017. This development could be explained in part by a portfolio composition effect with an increase in the share of government bonds, which carry lower risk weights, due to the introduction of the Basel III liquidity requirements (Bonner, 2016), and the monetary policy interventions of the European Central Bank (Krishnamurthy et al., 2018).

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enterprises recovered to pre-tsunami levels, the profits and capital stocks of the unaffected firms grew by 30 percent to 50 percent over this same period. Indeed, the affected firms remain smaller in both profits and investment more than three years after the tsunami.

In sum, the data paint a fairly consistent picture of the process of recovery of business assets. The magnitude of the total aid flow to the affected area was roughly comparable to what might be expected following a disaster in the United States. But the flow was of a very different character, with less insurance and more aid. The portion of the aid and insurance flowing directly to households and small scale enterprises appears to be modest compared to the size of the losses incurred. Moreover, while we find some correlation between the magnitude of the aid flows and the magnitude of the losses with respect to losses of household assets, we find no correlation between losses of business assets and aid flows. Consistent with this, the recovery of household assets is complete within three years for a majority of households, while the affected enterprises lag behind a comparable group of unaffected enterprises more than three years after the event.

# **Section 3: Profitability and the Incentive to Recover Assets**

Given the low levels of official aid firms report receiving, we find even the partial recovery of assets surprising. The households of microenterprise owners have low levels of wealth, and few liquid assets. The recovery of the majority of their assets at a time when competing demands for available funds must have been great suggests the microenterprise owners had strong incentives to replace their assets. Did extraordinary opportunities for profit drive enterprise recovery in the tsunami impact zone? We provide

> bottom 1 percent, and then plot an index of mean real profits over the period November 2004 to September 2007. Nominal profits were converted to real profits using the monthly Sri Lanka Consumer’s Price Index, available at <u>http://www.statistics.gov.lk/price/slcpi/slcpi_monthly.htm. Annual inflation was 4.0 percent</u> between March 2005 and March 2006, and 18.6 percent between March 2006 and March 2007. Inflation was an additional 10.3 percent in the six months from March 2007 to September 2007.

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# **1. Introduction**

Past thinking about the impacts of economic growth on poverty in developing countries has emphasized the role played by population urbanization.<sup>2</sup> Building on classic papers by Lewis (1954) and Kuznets (1955), a standard theoretical formulation of the migration process assumes that distributions are preserved within both the rural and urban sectors.<sup>3</sup> In essence, a representative slice of the rural distribution is transformed into a representative slice of the urban distribution in the process of people moving from rural to urban areas. The “Kuznets hypothesis” implied by this model is often invoked in development policy discussions. A common view is that the hypothesis justifies the expectation that growth will inevitably be inequality increasing in poor countries, but that inequality will eventually start to decline.<sup>4</sup> In theory, poverty measures will, however, fall steadily under the Kuznets process as long as rural poverty measures exceed urban measures (Anand and Kanbur, 1985).<sup>5</sup> And the economy as a whole will grow as rural residents take up the more lucrative urban jobs.

It is clear that within-sector distributional neutrality is a strong assumption. The urbanization process in practice may well be quite selective, entailing significant distributional shifts within one or both of the urban and rural sectors. For example, relatively less poor rural workers may migrate, with gains, but be relatively poor in the destination urban sector; there is evidence consistent with this pattern for the developing world (Ravallion et al., 2007). The implications for inequality within each sector cannot be predicted easily.<sup>6</sup> Even without population urbanization, within-sector growth processes are also relevant to the overall outcomes for poverty. Various strands of the development literature have examined these factors. One strand of the literature has questioned whether the agricultural growth processes have helped the rural poor, many of whom are landless, while others have argued that the benefits of rising farm productivity are passed on in due course through higher wage rates.<sup>7</sup>

> 2  Overviews of the literature can be found in Fields (1980) and Ravallion (2016).

> 3  As in Robinson (1976), Fields (1980) and Anand and Kanbur (1985, 1993).

> 4  The existence of such a turning point requires certain conditions to hold, which are specific to the measure of inequality used; Anand and Kanbur (1993) derive those conditions for various measures.

> 5  This holds for all population-weighted measures, such as all those characterized by Atkinson (1987).

> 6  This is known from research on the effects of selective compliance in household surveys on measures of inequality; see the analysis of this closely related issue in Korinek et al. (2006).

> 7  Contributions to this debate are cited in Ravallion and Datt (1996) and Datt and Ravallion (1998, 2011).

2
individuals’ labor force participation and employment probability, while controlling for timevarying individual characteristics. This allows accounting for unobserved time-invariant heterogeneity at the individual level through fixed effects estimation. Additionally, it accounts for geographical heterogeneity and for aggregate factors that likely affect the labor market and employment by controlling for region and year fixed effects.

<mark>The results of this paper suggest that Internet job search is associated with an increase in the probability of employment for unemployed men. Indeed, controlling for home Internet access, I find that unemployed men who reported that job search is one of the purposes of Internet use are significantly more likely to find a job following an unemployment spell, relatively to unemployed men who do not use the Internet or those who use the Internet but not for job search. As for women, Internet job search is found to be associated with an increase in their labor force participation but not in employment. As for employed job seekers—be they men or women—Internet job search does not have an impact on wage growth. To make sure confounding factors related to both Internet adoption and local labor market outcomes do not drive these results, I rely on two sets of placebo regressions.</mark> <mark>Firstly, I rely on the 2006 round of the ELMPS and follow the same individuals interviewed in 2012 and 2018 in the 2006 round. This allows me to examine the impact of future use of Internet job search on past labor market outcomes. I examine the effect of the change in Internet job search between 2018 and 2012 on changes in labor force participation and employment between 2012 and 2006. Indeed, I do not find any effect of the future use of Internet for job search on past employment, which suggests that pre-existing trends in labor market outcomes do not drive my results.</mark> <mark>Secondly, I examine the impact of the percentage of Internet job searchers at the sub-district level in 2018 on labor force participation, employment, and unemployment, by gender, in the years 1988, 1998, 2006, and 2012, controlling for districts’ demographics, as well as governorate fixed effects. These placebo regressions also show no significant effect of Internet use in 2018 on past sub-district level labor force participation, employment and unemployment rates. The first test invalidates the assumption that that pre-existing differences in labor market outcomes between individuals who rely on the Internet for job search and those who do not are driving the results, while the second test bolsters confidence that sub-districts with high versus low levels of Internet adoption are not following different pre-trends in terms of labor force participation, unemployment, and employment rates.</mark> The remainder of this paper is organized as follows: Section 2 describes the data and presents recent unemployment trends in Egypt. Section 3 describes the empirical strategy. Section 4 presents the results, as well as robustness checks. Section 5 draws some conclusions.

# **2. Data and descriptive statistics**

4
difference between GDP per capita and per capita household income growth -known as passthrough. The NDG method produces poverty nowcasts in moment 0 as follows: where ỹig0 is household _i_ ’s nowcasted income obtained from applying the NDG method, and P(ỹg0) the corresponding poverty rate. Unlike the PE method, the NDG method uses household-level microdata to nowcast poverty –i.e., subscript _i_ is considered in the equation (6)and it does not use information between periods -1 and -2.

The NDG method assumes that the total household per capita income growth rate is the same across households<sup>7</sup> and that the pass-through θ is known.<sup>8</sup> <mark>The more similar income growth across households is and the more knowledge we have about the pass-through, the more accurate the poverty nowcasting produced under the NDG method is. Nowcasting</mark> errors will occur whenever per capita income growth differs across households and whenever we use a pass-through value which differs from the actual one.

# _3.2.3 The Quantile Growth Contribution method_

Unlike the NDG, the QGC method does not assume that the household per capita income growth rate is the same across households. The QGC method captures the heterogeneity among households by assigning different growth rates along the income distribution. Since we do not have panel data for most countries, it is not feasible to determine the individual performance of each household between two periods. Assuming that people with comparable welfare levels in one period will perform similarly in the following one, the QGC method assigns individual growth rates in a twostage process.

In the first stage of the process we group households into income-sorted quantiles and calculate the contribution of each quantile to total growth between -2 and -1. Generally, the total growth of the economy between _t-1_ and _t_ may be seen as the sum of the growth of all quantiles q,

> 7 The method assumes that (yity−yit−1it−1) = (yjty−yjt−1jt−1) = θ∗rgt,t−1 for all households i≠j.

> 8 In this paper we use θ= 1 as the default option. Section 5 tests the sensitivity of the method to the value of the passthrough.

10