rafmacalaba/datause-encoder-data huggingface dataset:
# **1.  Introduction**

Between 2000 and 2005 Egypt experienced a large nominal depreciation of the Egyptian pound, much of it concentrated around a sharp devaluation in early 2003. The objective of this paper is to assess the welfare implications of the large changes in consumer prices that accompanied this movement in the exchange rate. To address this issue I first need to isolate the component of observed price changes during this period that are due to the depreciation. I do this by estimating disaggregated exchange rate pass-through regressions, using monthly consumer price index (CPI) data over the period July 2000 through June 2005, for 8 regions in Egypt, disaggregated into 20 different goods and services.<sup>1</sup> The fitted values from these regressions provide estimates of the effect of the depreciation on 160 different price indices. Disaggregation of exchange rate pass-through to this level is important, as there is considerable heterogeneity across commodities in the response of domestic consumer prices to the exchange rate. In particular, I find that on average, exchange rate pass-through was greater for food items than for non-food items, and even within food items varied considerably. Regional variation in pass-through is also present, but is not as large as across consumption items.

I then bring the estimated price changes due the depreciation for each of these 160 different price indices to the household survey for Egypt, to investigate their welfare effects. I empirically construct estimates of the compensating variation associated with these price changes for each household. In particular, I estimate how much higher (or lower) each household's total expenditure would have to be in order to attain the predepreciation level of utility at post-depreciation prices.<sup>2</sup> This compensating variation consists of two parts. The first is the change in the cost of households' initial 1 The regions are dictated by the disaggregation available in the CPI data, and are Cairo, Alexandria, Canal, Border, Upper and Lower Urban, and Upper and Lower Rural. The commodity disaggregation is dictated by overlap between expenditure categories in the household survey and the CPI data.

2 See Friedman and Levinsohn (2002) for a similar exercise investigating the welfare effects of relative price changes following in Indonesia during the East Asian crisis of 1997. The main difference with this paper is that they do not estimate exchange rate pass-through to consumer prices, but, reasonably enough in the case of the enormous depreciation of the rupiah, assume that all of observed price changes were due to the depreciation. Ferreira et. al. (2004) study the distributional consequences of a large depreciation in Brazil, using a sectorally-disaggregated macro model to quantify the effects of the depreciation on wages and prices, and then linking this to a household survey.

1
where empl share _rj_ 2015 is industry _j_ share of CZ _r_ employment in 2015, and tariff exposure _jt_ is defined in the following sections. Pre-sample employment shares (2015) are used for exogeneity.

The CBP excludes farm employment, consisting of Crop Production (3-digit NAICS 111) and Animal Production and Aquaculture (3-digit NAICS 112), but includes Support Activities for Agriculture and Forestry (3-digit NAICS 115). Following Blanchard et al., we therefore allocate farm-related tariffs to employment in the associated support activities. Specifically, we calculate the average tariff across 3-digit NAICS sector 111 and allocate this to total employment in 4-digit NAICS sector 1151. We then calculate the average tariff across 3-digit NAICS sector 112 and allocate this to total employment in 4-digit NAICS sector 1152.<sup>11</sup>

# **3.4 Sectoral tariff exposure**

The following sections describe the construction of the sectoral tariff exposure measures used to generate local labor market exposure in equation 1: output exposure, input exposure, and export exposure. The three measures focus on tariff barriers specifically and do not take into account any changes in quotas, in the form of import quotas or voluntary export restraints, and hence could be seen as a lower bound estimate of the exposure to new trade barriers.

## **3.4.1 Exposure to reduced competition through import tariffs**

One channel through which imposing or increasing import tariffs could affect US businesses is by increasing the price of imports that compete with US products, hence rendering US firms more competitive in the domestic market. As in Blanchard et al. (2019), the sectoral exposure used in this paper creates an average dollar value of exposure to the tariff per worker in that sector, based on US imports in 2016:

> 11We do not remove sectors 111 and 112 from the BGT data for two reasons. First, the measure described in the text still accounts for the tariffs in these sectors, even if they are allocated to slightly different NAICS codes when conducting the employment weightings. Second, the coverage and accuracy of the sector classifications in the BGT data is much weaker than the county classification and hence it’s unclear that we would be able to convincingly isolate and remove postings within these particular sectors. The classification issue comes from the challenge of assigning a sector to the data scraped from a job posting (i.e. job postings will almost never provide NAICS codes inside the associated text).

16
**Plc, Planning, and Research**

**Population, Health, and Nutrition**

Rclatively little attention has been paid to the problem of premature adult mortality in developing countries, despite high levels of mortality - in many countries and despite the potentially severe social and economic consequences of adult deaths.

This inattention is reflected in a dearlh of information about the causes of adult deaths in these populations. Such information is available from vital registration data in certain Latin American and Middle Eastern countries, but elsewhere - especially in Sub-Saharan Africa and Southem Asia - the sparse information available comes mainly from survey and hospital data and is generally of limited usefulness.

Circulatory diseases and extemal causes (injuries) appear to be major causes of adult deaths in most countries. The relative contribu- tion of other important causes including tuberculosis, cancer, liver disease, respiratory disease, and maternity-related complicationsvaries between countries.

After reviewin- methods used in previous studies to diagnose the causes of death in children and adults, the authors recommend developing and validating diagnostic algorithms to determine the causes of adult deaths, for use in single-round surveys, using lay interviewers to conduct retrospective interviews of relatives of the deceased.

Techniques for determining **i** se-specific adult mortality require thorough field testing and validation. The authors discuss several possible approaches, and categorize selected major causes of death according to whether they are likely to be diagnosed or excluded, on the basis of symptoms reported by relatives. They consider methods for classifying and presenting data on cause of death and conclude with recommendations for further methodological research.

This paper is a product of the Population, Health, and Nutrition Division, Population and Human Resources DeDartment. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sonia Ainsworth, room S6-065, extension 31091 (40 pages with charts and tables).

The PPR Working Paper Serics disseminates the findings of work under way in the Bank's Policy, Planning, and Rcscaich Coniplcx. An objective of the serics is to get these findings out quickly, even if prcsentations are less than fully polished. The findings, interpretations, and cor.clusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Centcr
China, Indonesia and Korea used for basic descriptive statistics presented above. Summary statistics for important variables used in the analysis can be found in Table 1. Below, we provide some additional discussion of the analysis sample used for each country:

**_China_** . The China Health and Retirement Longitudinal Study (CHARLS), directed by Yaohui Zhao at the National School of Development at Peking University, was developed and implemented by an accomplished team of collaborators from China and abroad. The sample used in this analysis is from the pilot survey conducted in 2008 in Gansu and Zhejiang provinces, and is representative of adults over 45 years of age in these provinces. Gansu, in the Northwest is a relatively poor province and Zhejiang, on the coast south of Shanghai, is relatively affluent. CHARLS follows the model of the HRS, ELSA and KLoSA in the US, UK and Korea, and enumerates information on respondent and spouse socio-economic status, health conditions, work and employment, incomes and pensions, and also includes household and community level information. In the pilot sample used in the analysis below, there are 1570 households and 2685 eligible respondents.<sup>21</sup>

**_Indonesia_** . The Indonesian Family Life Survey (IFLS) is an ongoing project conducted by RAND Corporation and universities in both Indonesia and the US. To date, four waves have been conducted in 1993, 1997/98, 2000, and 2007/08. The survey covers individuals, households, communities, and the community-level health and educational facilities that respondent households access in their daily life. While it is not a formal member of the “HRS family” of aging surveys, the data are comprehensive and include information on respondent health status, employment and labor allocation, pension participation and household and family characteristics, all of which are crucial for study of retirement and labor supply decisions of the elderly. The original survey was representative of 83% of the Indonesian population and 13 of 27 provinces. As it tracks the movers and split-offs from the original sample, the IFLS was a pioneer among household surveys in the developing world. Tracking is important when working with the 2007/8 round of the IFLS, because it allows us to avoid using a selected sample of

> 21The CHARLS project has funding for nationally representative surveys in 2011 and 2013, and fieldwork on the first national wave will begin in July 2011. Public use data should be available for the research community by June 2012.

13
**_Table 1.  Average Proportion of Survey Respondents' Land Rights in 2004, %_**

||Ownership|Lease|No Land|
|---|---|---|---|
|SMEs (General)<sup>/1</sup>|6|34|57|
|Firmsinvolvedin land/realestate transactions <sup>/2</sup>|18|45|43|

_Sources: 1/ CEFIR, 2/ FIAS (note figures sum > 100% because some respondents have more than one parcel of land and may have ownership rights on some and lease rights on others)_

# **Research task**

Using the results of the “Survey of Land and Real Estate Transactions in the Russian Federation”<sup>13</sup> , FIAS outlined a number of hypotheses regarding the influence of the policies and administrative barriers in the area of privatization and leasing of land plots as follows:

1. Demand for land is influenced by the pricing policy implemented by sub-national authorities, and also by non-market factors, such as administrative barriers, e.g. duration and cost of completing the relevant procedures.

2. Russian businesses often apply to lease land because the procedure for land privatization (purchase of land from municipalities and other public sector bodies) is often accompanied by higher administrative costs, delays, and by frequent refusals by bureaucrats.

3. Procedures where companies have already incurred significant sunk costs are characterized by longer duration and higher reported frequency of unofficial payments.

4. Regions where more land tenders and auctions are held will have a higher rate of land privatization transactions, and land transactions that are conducted through tenders will be less prone to corruption and bureaucratic delays.

5. Excessively complicated, longer, and costly procedures are associated with more frequent unofficial payments.

6. Having “good connections” with government officials reduces costs and delays for carrying out procedures.

In this paper we assemble simple models reflecting the above hypothesis and test them econometrically using OLS regression analysis.

# **Previous Research**

We were unable to find other examples of survey data on land transactions, so our research in this area appears to be quite unique. However, we did draw upon several other studies about businesses’ access to land and real estate, including various sections of the World Bank’s _Land Policies for Growth and Poverty Reduction_ ,<sup>14</sup> and _Doing Business 2007_ (Chapter on “Registering Property”)<sup>15</sup> , and Land Markets: Promoting the Private Sector by Improving Access to Land<sup>16</sup>

> 13 FIAS, op.cite.

> 14 World Bank Land Policies for Growth and Poverty Reduction, 2003, Oxford University Press.

> 15  http://www.doingbusiness.org.

> 16 Muir and Shen, available at http://rru.worldbank.org/PublicPolicyJournal/Summary.aspx?id=300

8
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I-TIP Services is a joint initiative of the World Trade Organization and the World Bank. It is a set of linked databases that provides information on Members’ commitments under the WTO's General Agreement on Trade in Services (GATS), services commitments in regional trade agreements (RTA), applied measures in services, and services statistics.

GATS. Regional Trade Agreements Services Trade Policy | Database tall statistics This module contains information The RTA module allows to access The Applied Services Trade Policy The Services Statistics module on Member's market access and search for information on module provides information jointly | accompanies the GATS, RTA and commitments and exemptions to Members’ commitments in regional compiled by the World Bank and Applied regimes modules by providing the Most favoured Nation (MFN) trade agreements notified under the WTO on services regulatory relevant trade in services and related obligation. It also includes the Article V of the GATS. Currently, frameworks. Currently, the statistics. preferential market access the database covers a good database covers 23 service sectors commitments for least-developed proportion of services RTAs (see list in 68 Members. The information for countries granted by WTO Members _ attached). 25 of those Members was collected under the LDC Services Waiver (see in 2016-2017 through a survey list of notifying Members). The conducted by the World Bank and latter feature of I-TIP was the WTO. The information for the developed in cooperation with the remaining 43 Members was mainly International Trade Centre (ITC). sourced from the OECD STRI regulatory database (2016 version), thanks to the co-operation of the OECD, which is gratefully acknowledged. Links to other useful sources of information on applied services policies are also provided (e.g. WTO monitoring reports, WTO trade policy reviews).
# PROXTRA = RNEEDi * SUPBUD

In the report’s simulation, SUPBUD is assumed to be 22 percent of total federal transfers for primary education.

# **III.- Simulations and Results**

The first simulation (Table 1 ) asks the question: What would be the pattern of allocation of Federal transfers for lower secondary education among states, if Formula I (uniform per-student or capitation method) were used, and how would it compare with the actual allocation? To answer this question, the states were classified into three categories, depending on the size of their net gain per student. Category I consists of states with large losses, while Category III is composed of the big gainers. The middle category is a group of relatively small negative and positive gainers. A similar exercise was undertaken, using Formula II (productivity adjusted formula). Enrollment elasticities with respect to per child transfers were computed for each state, using probit regression equations (see table 1 in the appendix),

**<mark>TABLE 1   Net Gainers from Formula 1 (Per Child)</mark>**

||**Average Net Gain**<br>|**Per Capita State**<br>|
|---|---|---|
|**Category**|**(1993 Pesos)**|**GDP(1993 Pesos)**|
|High Net Gainers|353|1,041|
|Medium Net Gainers/Losers|18|1,081|
|High Net Losers|-474|2,062|

The results indicate that contrary to the usual concern previously mentioned, application of a uniform per-student formula in Mexico would tend to redistribute Federal transfers from high to low income states. This outcome is even more pronounced in Formula II as shown below.

**<mark>TABLE 2  Net Gainers from Formula 2 (Per Child)</mark>**

||**Average**|**Net Gain**|**Per Capita State**|
|---|---|---|---|
|**Category**|**(1993**|**Pesos)**|**GDP(1993 Pesos)**|
|High Net Gainers|499||871|
|Medium Net Gainers/Losers|-32||1,150|
|High Net Losers|-729||2,559|

7
# **4 Results**

Before presenting the impact results of the training, we first analyze characteristics of the women that took up training and examine the relationship between psychology and economic success.

# **4.1 WHO TAKES UP TRAINING?**

The initial self-reported interest in business training was high (over 90% said they were interested in attending entrepreneurship training during the baseline surveys) but actual take-up was lower: 52% for DOT training in Mekelle and 41% for PI/BSED trainings in Addis Ababa. The take-up rates correspond to rates found for other entrepreneurship trainings in comparable contexts (cf. McKenzie & Woodruff, 2014). The most common reason given for why a business owner did not take-up training was because they were “not able to find the time”.

We find that women who take-up training are systematically and significantly different from those who were offered training but did not participate. Table 3 presents the t-test statistics for the differences in means among these groups for both studies (see columns (4) and (8)). The take-up rate for the DOT training among the treatment group was 52%. Table 3 Column (4) shows that those who select into entrepreneurship training in Mekelle seem to be the women entrepreneurs who have smaller and less profitable businesses (we find statistically significant lower monthly profits and capital stock at business start). The women who attended the DOT training also differed on a number of characteristics: they are slightly older with lower educational attainment and digit-span scores (proxy for memory recall) than those who do not attend. These women also had lower household asset wealth and were less likely to save in a bank at baseline.

Similarly, the average take-up rate for the PI/BSED study in Addis Ababa was 41%. Table 3 Column (8) presents a similar pattern to the DOT sample where those who attend the training seem to have smaller and less profitable businesses at baseline (we find statistically significant lower profits, sales, employees and starting capital stock). From Table 3 we can also see that the sample of WEDP businesses in Addis Ababa,
that follows a loose hierarchy, in which household demographic characteristics are defined first, followed by labor supply decisions, labor and then non-labor income. This ordering is consistent with the literature on transitions into and out of poverty<sup>27</sup> , and is akin to a VAR structure in the macroeconometrics literature. Finally, as shown in step 2 in Table 2, this approach includes the contribution of changes in the consumption-to-income ratio as part of the decomposition, since the welfare measure is based on consumption rather than income.

|**Ta**<br>|**ble A2. Proposed Methodology**<br> <br> <br>||
|---|---|---|
|**1.**|<br> <br> <br> <br> <br> <br>|Initial poverty rate|
|**2.**|<br> <br> <br> <br> <br> <br> <br> <br>|Contribution of the consumption-to-income ratio is<br> <br>|
|**3.**|<br> <br> <br> <br> <br> <br> <br> <br> <br>|Contribution of share of household adults is <br> <br>|
|**4.**|<br> <br> <br> <br> <br> <br> <br> <br>|Contribution of the share of occupied adults is <br> <br>|
|**5.**|<br> <br> <br> <br> <br> <br> <br> <br>|Contribution of labor income is <br> <br>|
|**6.**|<br> <br> <br> <br> <br> <br> <br> <br> <br>|Contribution of non-labor income is <br>|
|**7.**|<br> <br> <br> <br> <br> <br> <br> <br> <br>|Final poverty rate. The unexplained portion <br> is<br>identified separately.|

> 27 See Bane and Ellwood (1986). For a recent review of that literature, see Jenkins (2011).

40
Other variables included in the regression are: a measure of political stability (to be discussed below), a dummy for a hard-peg exchange-rate regime (a currency union, currency board, or with no separate legal tender), and a time trend. The governments of less stable countries are more likely to focus on the short term and to ignore longer-term issues like fiscal sustainability, so one would expect them to have worse fiscal balances. A hard-peg regime constrains the government’s ability to raise seigniorage revenue, so the fiscal balance needs to compensate for this. Of course the exchange-rate regime is a policy choice, so there is an issue of endogeneity here: countries that have difficulty in closing their fiscal deficits might be more reluctant to adopt a hard peg. There are counterexamples to this argument, however. Argentina adopted a currency board system in 1991 out of despair over its persistent history of high inflation, and precisely to wean itself off repeated resort to seigniorage revenue. Ecuador adopted the U.S. dollar as its currency for similar reasons. Moreover the Sub-Saharan African countries that have adopted hard pegs have mostly done so by historical accident: they were formerly colonies of France rather than of some other European power. Thus a case can certainly be made for the exogeneity of the hard-peg dummy. A time trend is included to capture significant shifts in the relationship over time.

A country’s measure of political stability is the political risk rating supplied by the International Country Risk Guide (ICRG), which is measured on a scale of 0 (most risky) to 100 (least risky). It is based on 12 risk indicators, as judged by ICRG staff. Government stability, socioeconomic tensions, risks to investments, internal conflict, and external conflict count for a maximum of 12 points each. Less important factors are: corruption, involvement of the military in politics, religious tensions, law and order, ethnic tensions, democratic accountability (all six points maximum), and the quality of the bureaucracy (four points maximum). In this sample the political risk rating has a mean of 62, with a standard deviation of 10.3.

Hard pegs are identified using the annual exchange-rate regime classification of Bleaney and Tian (2014), mainly because the data run to 2012. It is well known that different exchange-rate classification schemes do not agree with one another, so in principle choosing a classification scheme might make a difference (Tavlas, Dellas, and Stockman 2008), but this seems unlikely in this case since the disagreements between classification schemes do not lie in the identification of hard pegs. As Bleaney and Tian (2014) show, this classification scheme, which is based on purely statistical criteria, is quite similar to the International Monetary Fund’s (IMF’s) de facto classifications.

Table 1 shows regressions for the general government fiscal balance, total revenues, and total expenditures, all as a percentage of gross domestic product (GDP). There are more than 1,360 5