rafmacalaba/datause-encoder-data huggingface dataset:
where ���� is the probability of household _j_ , in stratum _h,_ and PSU _i_ to be included in the sample; kh is the number of PSUs selected in stratum _h_ ; mhi is the number of households selected in PSU _hi;_ �� is the total number of households in stratum _h_ ; ��� is the number of households to be selected in PSU _i_ and stratum _h_ based on the sampling frame; and �′�� is the number of households in PSU _i_ and stratum _h_ interviewed, listed, or consistent with the official population statistics.

For non-response corrections, the ���/�′�� term corresponds to the number of households to be selected divided by the actual number of households interviewed in each PSU. For corrections to the PSU size based on the listing exercise, the ���/�′�� term corresponds to the number of households in the PSU based on the sample frame divided by the number of households in the PSU based on the listing exercise.<sup>29</sup> Lastly, for post-stratification adjustments to make sampling weights add up to official population statistics, the ���/�′�� term corresponds to the number of households based on the official population statistics divided by the number of households based on the sample frame.

The sampling probability weights for the HIES 2016/17 have been adjusted _ex-post_ for two things: (i) to match the official share of urban population reported in all official publications produced by BBS after the 2011 Census of Population and Housing, and (ii) to match the official 2016 population projections estimates produced at the distict ( _zila_ ) level (BBS 2015). More specifically, the 2011 Census of Population and Housing calculated the share of urban population in Bangladesh at 23 percent. A month after completion of the Census, the Bangladesh Institute of Development Studies (BIDS), the independent agency in charge of

> equal the reference population of interest at the time of the survey data collection; and/or weights will vary across PSUs or be constant at the level at which PSUs are allocated. 29 The listing exercise consists of the enumeration of households in a PSU before a survey is implemented. This list is used to select households randomly within a PSU, and it is only conducted in PSUs that were selected in the first stage of a multi-state clustered household survey.

34
million. <mark>Many emigrants returned home after a stay of about 10 years,</mark><sup>4</sup> <mark>but the</mark> possibility of getting into an occupation resulting in upward social mobility (mainly self-employment or in the public sector) was associated with a shorter stay in the Gulf states, while the prospect of fewer job opportunities back home post-return significantly increased the length of stay (Czaika and Varela 2015).

Within the GCC countries, the distribution of migrants from Kerala has changed over time, with the Gulf countries becoming more popular over the past two decades. Temporary migration from Kerala to Saudi Arabia remained steady, barely increasing from 510,895 migrants in 1998 to 522,282 migrants in 2013. Migration to other Gulf countries increased substantially. Between 1998 and 2013, migrant flows from Kerala to the UAE more than doubled from 421,959 to 898,962; for Kuwait, it increased from 68,163 to 183,239; and for Qatar, it increased from 62,969 to 106,107. The GCC countries remain a major destination for migrants from Kerala, more so than other parts of India. <mark>According to the National Sample Survey of 2007–08, only 0.9 million migrant workers from Kerala were in other parts of India, compared to more than 2 million Kerala migrants in the GCC countries. Also, while residents of Kerala barely migrated to other Indian states, 2.5 million workers from other parts of India were temporarily working in Kerala during this period.</mark> Previous literature has documented that Indian <mark>emigrants to GCC countries, and their remittances, significantly contributed to the state economy and improved the educational profile of the migrants (R</mark> ajan and Zacharia 2019) <mark>. However,</mark> studies on the transfer of norms and values by return migrants from the Gulf or the Middle East to Kerala, India, or other South Asian countries are virtually non-existent. Given the scale and duration of migration from Kerala to GCC countries, it is possible that these migrants may have been exposed to new norms and values during their temporary stay and brought some of these back home with them. Moreover, substantial differences across the GCC countries suggest that migrants from Kerala may have been exposed to different norms and values depending on their destination country, suggesting

> 4 There are more flights to the GCC countries from cities in Kerala than to other parts of India. <u>https://economictimes.indiatimes.com/industry/transportation/airlines-/-aviation/kerala-airports-handle-more-internationalpassengers-than-domestic/articleshow/59568350.cms</u>

4
# **Table 23: Piecemeal Sensitivity of impact on Armenia preferential services liberalization with the CIS region**

# **Results are estimated changes in welfare (Hicksian EV) as a percent of consumption**

||Pa|rameter value||R<br>Piecemea|esults for EV<br>l sensitivity a|nalysis|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj) – services sectors|2|3|4|0.22|0.10|0.07|
|σ(qi, qj) – goods sectors||See below||0.11|0.10|0.10|
|σ(va, bs)|0.625|1.25|1.875|0.09|0.10|0.12|
|σ(D, M)|2|4|6|0.10|0.10|0.10|
|σ(M, M)|4|8|12|0.10|0.10|0.10|
|σ(L, K)|0.5|1|1.5|0.10|0.10|0.10|
|σ(A1,...An)|NA|0|0.25|NA|0.10|0.10|
|σ(D, E)|2|4|6|0.10|0.10|0.10|
|εARM|Central va|lues of all 4 s|ets of ε|0.11|0.10|0.10|
|εEU|parameters|are listed in|table 6B.|0.11|0.10|0.10|
|εCIS|Lower and|upper value|s are 0.5|0.05|0.10|0.15|
|εROW|and 1.5 t|imes central|values.|0.11|0.10|0.10|
|θr|NA|0|1|NA|0.10|0.01|
|θr - CRTS model|NA|0|1|NA|0.06|-0.03|
|θm|0.025|0.05|0.075|0.10|0.10|0.10|
|σ(qi, qj) – goods sectors|||||||
|Mining & quarrying|8.5|17.00|25.5||||
|Manufacturing|4|8.00|12||||

## **Key:**

σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic production and imports σ(M, M): Elasticity of substitution between imported varieties σ(L, K): Elasticity of substitution between primary factors of production in value added σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods σ(D, E): Elasticity of transformation (domestic output versus exports) εARM: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εCIS: Elasticity of CIS service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports Source: Authors‘ estimates.

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<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative proportion of population (ordered by pre-tax income)<br>Relatively progressive<br>Pre-tax Income Lorenz curve. Neutral in relative terms if on this line<br>Regressive<br>45 degree line; population share. Upper bound of regressive<br>Cumulative proportion of taxes or income<br><!-- End of picture text --> _Source:_ Adapted from Lusting (2018).

One final note on the starting point of the methodology is worth making. Pre-fiscal income reflects income before any transfers (including public spending on health and education, and cash transfers) or taxes (including personal income taxes) have been added. In analyzing contributory pensions, we treat pension contributions as deferred income and pension income received as market income. Thus, the team’s pre-fiscal income includes both pensions and contributions (top box in Figure 2).

# **B. Data**

We use three main data sources: the _Encuesta Continua de Hogares_ (ECH) 2017, administrative data obtained from public programs, and other sources for aggregate data.

The ECH is Uruguay ́s household survey conducted by the country ́s National Institute for Statistics ( _Instituto Nacional de Estadistica_ - INE). Running since 1968, the survey is representative at national and departmental levels. In 2017, the ECH sample included over 118,200 individuals and 45,300 households. ECH reports information on several topics, including labor market participation, income, housing, health, education, and others. Crucially, the ECH collects detailed information on the characteristics of individuals’ occupation, including income after taxes and contributions (such as net wages, bonuses, tips, capital income, private and public transfers), whether workers contribute to a pension system or not and whether workers and pensioners are covered by FONASA.

11
# **1 Introduction**

_The agreement to lure Amazon to Long Island City, Queens, had stirred intense debate in New York about the use of public subsidies to entice wealthy companies and the rising cost of living (New York Times, February 14, 2019)_ After facing resistance from local politicians and the residents of New York, Amazon decided to cancel its planned construction in Queens, NY, and focus instead on the second site in Virginia. While some local NY politicians and activists argued that the $7 billion in tax incentives offered to the tech giant outweigh the benefits that would have accrued to the local economy, others have argued the opposite. The argument of rising costs of living, or local inflation, has featured prominently in public debates. However, while we have some evidence on the effects of Million Dollar Plants (MDP) on local productivity and rents (Greenstone et al., 2010), to date we do not know how MDPs affect the local cost of living, specifically prices of retail goods, which ultimately affect real wages, and hence worker welfare. The main challenge in addressing this question is estimating the cost of consumer goods at a micro-region and granular barcode level of goods that is not possible using aggregated consumer price indices.

In this paper, using detailed barcode level prices from the IRI data, we analyze how the entry of an MDP in a county affects retail prices and how that affects the real wages of high and low skilled workers. To understand what rising local inflation means for local within-region inequality, in an event study we compare the wages and hours worked of both high skilled and low-skilled workers in winning compared to runner up counties. To shed light on how an MDP entry changes local prices, we build a partial equilibrium model that introduces a new mechanism in the MDP literature: Changing household optimization decisions over labor supply, leisure, and shopping time choice. The model allows for the possibility that the higher income generated by the MDP makes high-skilled workers spend less time on shopping and looking for deals, and thus makes them less sensitive to prices. The falling price sensitivity of consumers is reflected in firms’ price setting behavior, and thus in local prices. We document new empirical evidence on household shopping time behavior in winning versus losing counties that support the theoretical mechanism we introduce.

We show that both the local barcode and product level prices of common products which 2
Before the pandemic hit, the labor market in Egypt faced persistent challenges. While Egypt’s GDP growth reached 5.6 percent in 2019, labor force participation continued to decline. This partly reflected challenges in absorbing the sustained growth in the country’s working-age population. Egypt's unemployment rate was high, particularly for women, youth, and the most highly educated, signaling a difficulty in transitioning from school to work and in integrating women into the labor market. Furthermore, informality was high and increasing, reflecting the need to improve the quality of jobs. Jobs with no social security rose to encompass 67 percent of all jobs in 2018, up from 57 percent in 2010. These trends reflected difficulties from the demand side of the labor market, where the most productive sectors were not the most important in creating jobs. Pre-COVID labor market trends pointed to the importance of accelerating private sector job creation. While about 78 percent of jobs in Egypt were in the private sector, this share remained well below the global average of 90 percent, and about 3 in 4 private sector workers in Egypt were engaged in informal, low-quality activities characterized by low productivity and low pay (Cortes et al. 2022).

The COVID-19 pandemic exerted its greatest impact on Egypt during the final quarter of FY20, with the declaration of a national public health emergency in mid-March 2020. That quarter overlapped with the main lockdown implemented by the GoE. Mobility trends show how activity fell during the last quarter of FY20 and subsequently in workplaces, transit stations, and areas related to retail and trade. Mobility and economic activity started to recover during FY21 (Figure 2).

Figure 2: Mobility trends in Egypt, relative to February 2020 (pre-COVID-19) <!-- Start of picture text --> 60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>retail & recreation transit stations workplaces<br><!-- End of picture text --> Source: Authors’ calculations based on Google COVID-19 Community Mobility Trends (as of March 2022).

Following its mid-March 2020 emergency declaration, the GoE undertook immediate precautionary measures aimed to contain the spread of the virus and prevent and mitigate crisis impacts. Measures implemented included nighttime curfews, restrictions on gatherings and domestic and international travel, and decrees facilitating home-based work for civil servants. Educational institutions, including schools and universities, were closed, as were religious establishments. Sports and recreational activities were also suspended, and restaurants were shut. Businesses and shops were required to close by 5 p.m., in line with the national curfew, and were fully shuttered on weekends. Some villages and cities with a high number of COVID-19 cases were completely isolated. A national campaign for 4
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# Table 12: RelationshIp of Sector Work To S&bsequent Investments

Studies Focused on Policy and Institutional **Development**

|**Region**|Total<br>t.ier<br>of<br>Countries|During<br>Investme<br> <br>**Program**|After<br>nt Last<br>Investmen|Later**Focus**<br>on Science<br>as..<br>t**Technology**|No Sector*irkor<br>Llttle<br>Policy Content|
|---|---|---|---|---|---|
|Asia|5|4||2|1|
|EMENA|<br>8|4|1|2|3|
|**LAC**|8|4|1|1|3|
|Africa|<br>13|4|3||6|

3.16 Table 12 addresses both the timing and the major impact of sector analysis in country investment programs. Studies during the investment program that contributed directly to policy and institutional development components in subsequent investments are identified, as are analyses which have come after the most recent investment. The incidence of recent studies which focus on science and technology development are noted. The last column shows the number of countries where sector work was either absent, or had relatively little policy or institutional development content.

3.17 Sector work was not a pre-requisite to institution-building investment. However, it was associated with strong and generally successful efforts in larger middle income countries (Korea, Indonesia, Malaysia, Mexico, Brazil, Jordan, Turkey. Morocco). In
-8-

consumption patterns observed in more recent years than tha CPIAL. This index has been made available only recently for individual states. In the present paper we decided to use the consumer price index for agricultural labourers because it is the nnly index available for the most recent years 1986-87 for which the poverty estimates are presented here. Also since majority of the poor in rural India are agricultLral labourers, their consumption bracket will be closer to that of the poor.-/ Minhas, et al (1987) have applied the index to compute the incidence of poverty in the entire rural and urban areas of India for 5 different periods ranging from 1970-71 to 1983. They performed the poverty computations on the All India expenditure distributions which are published by the NSS separately for rural and urban areas. These aggregate distributions are derived from the state distributions by means of population weighted averages. This procedure of obtaining the aggregate distributions is nor satisfactory because of widely observed differences in price levels in different states. The expenditures in current prices given for each state have to be adjusted for state-wise price differences before they can be aggregated. Since this procedure was not adopted in the Minhas, et al study, their All India estimates of rural poverty are inaccurate notwithstanding the advance made on the construction of a new price index.

To obtain comparable poverty levels across different states, we need 6/ Gaiha (1989) points out that since agricultural labourers are typically net buyers of food, the CPIAL can be expected to provide a close approximation to the prices confronting the net buyers of food among the rural poor.
Figure 20. Average hours worked by week, ages 25-59 Figure 21. CME at mean, children in the household ages 0-3 <!-- Start of picture text --> No children Children<br>51<br>50 50 49<br>48 48 48<br>47<br>47<br>45 45<br>43 44<br>43<br>41 41<br>40<br>39 39<br>35<br>2019 2021 2019 2021 2019 2021 2019 2021<br>No higher education Higher education No higher education Higher education<br>Men Women<br>Figure 22. AME, children in the household<br>ages 0-3, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household 0-3 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Figure 24. AME, children in the household<br>ages +3-6, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household +3-6 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Pr(labour force=1)<br>Pr(labour force=1)<br><!-- End of picture text --> <!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>0.0 Children in the household 0-3 years<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->

Figure 23. CME at mean, children in the household ages +3-6, ages 15-59 <!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household +3-6 years<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Figure 25. CME at mean, Household per capita<br>income, ages 15-59<br>1.0<br>0.9<br>0.8<br>0.7<br>160 350 500 1000 2000 3000<br>Household per capita income (thousands COP)<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Pr(labour force=1)<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 25. CME at mean, Household per capita income, ages 15-59
blue chip issuers whereas exchanges with lower standards attract lower quality companies.<sup>52</sup> By enhancing its listing rules an exchange can improve investor protection in the same way as cross listings do (individual companies adopt higher governance practices which provide greater protection to minority shareholders and this results in turn in greater liquidity, improved share price and lower cost of funds). Enhancing listing standards can also slow migration of top companies to overseas exchanges (e.g., the NYSE) where companies meeting the highest standards attract the attention of international investors.

The last decade has seen an emergence of new markets, which are trying to duplicate the success and viability of the Nasdaq, and which cater to new and highgrowth companies. The track record of these new exchanges has been mixed, with some resounding failures such as the Neuer Market in Germany, and some which have fared better, such as the AIM in the United Kingdom and Kosdaq in South Korea.<sup>53</sup> Although these markets have pursued broadly similar strategies, there are distinct differences in their focus and context which have affected the outcome of their efforts. One major determining factor has been timing – the failure of several markets occurred during the bursting of the dot-com bubble, and it is possible that they would have done better under different circumstances.

**_<u>Table 1: Summary of selected alternative markets</u>_**

|**Exchange**<br>|**Launch**<br>**date**<br>|**Focus and**<br>**target**<br>**companies**<br>|**Regulatory approach**<br>|**3-year index**<br>**performance, end**<br>**2002-end 2005**<br>|
|---|---|---|---|---|
|AIM (UK)|1995|Domestic and<br>foreign, small<br>growing<br>companies,<br>allsectors|Less stringent than LSE<br>official list|FTSE AIM index,<br>+73%|
|GEM<br>(Hong<br>Kong)|1999|Domestic and<br>mainland<br>China,<br>companies<br>with growth<br>potential,<br>informed<br>investors<br>only, mostly<br>high-tech|More stringent on<br>disclosure and<br>governance, less<br>stringent on track<br>record/profitability<br>issues|S&P/HKEx GEM<br>Index, +6%|
|Neuer<br>Market<br>(Germany)|1997|Domestic,<br>new and<br>innovative<br>companies,<br>high-tech<br>sector|More stringent<br>disclosure and<br>governance<br>requirements than main<br>board, lower track<br>recordrequirement|Closed in 2002|
|Novo|2000|Domestic,|More stringent thanthe|IGCX(Corporate|

> 52 Chemmanur and Fulghieri 2003

> 53 Appendix 1 sets forth statistical information about selected alternative markets.

19
FIGURE III.1 <!-- Start of picture text --> DOLLAR VALUE AND CODITY PRICES<br>(Quarly. 1975. 1-1)<br>1.61<br>1.51<br>0.9<br>1975.1 1977.1 1979.1 1991.1 1983.1 i9SU.1 1987.3<br>a Dollar Effc.ftt + Cowmodity Price<br><!-- End of picture text --> The U.S.A. is no exemption to the points made above. Although the dollar devaluations helped a lot in making tourism more expensive for americans,this being measured by the weighted sum of CPI's converted into dollars, it was not so much successful in increasing the dollar prices of U.S. traded goods. Figure 2 shows the two measures of international prices we<sup>have</sup> been discussing:the CPI's weighted basket(obtained from IMF Information Notice System) and the index of USA traded goods prices (constructed as a logarithmic index of USA unit exports and imports values with equal weights).

33
Table 10 Flow of Funds of Corporate Sector (billion yen) Sources of Funds

||Loans|(PFI)|(GFI)|Stocks|Domest<br>Bonds|Foreign<br>Bonds|Trade<br>Credit|
|---|---|---|---|---|---|---|---|
|1961/65|17126|15746|1380|3334|1056|--|8255|
|1966/70|33311|29882|3430|2491|1358|--|20476|
|1971/75|73956|66657|7299|5307|3650|407|37600|
|1976/80|62756|53832|8743|6178|3505|999|38406|
|1981/85|101864|93571|8293|8943|3458|6812|19362|
|1986/90|182406|163045|19361|24152|10496|26001|62325|
||||Uses|of Funds||||
||Cash|Demand|Time<br>Save|Secur|Trust|Trade<br>Credit||
|1961/65|121|3359|4437|1842|356|10574||
|1966/70|199|5011|7474|1448|655|27024||
|1971/75|574|14129|14974|4427|i832|46590||
|1976/80|477|6916|17856|7209|2840|48603||
|1981/85|521|9769|31454|10433|4933|30752||
|1986/90|3946|15281|58267|12334|30320|82789||

Note: Except for 1961/65, all figures are for fiscal years. Source: Bank of Japan, _Economic Statistics_ Annual, various issues.

41