rafmacalaba/datause-encoder-data huggingface dataset:
- Figueiredo, Octávio, Paulo Guimaraes, and Douglas Woodward, “Localization Economies and Establishment Size: Was Marshall Right after All?”, Journal of Economic Geography 9 (2009), 853-868.

- Foster, Lucia, John Haltiwanger, and Chad Syverson, “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?”, American Economic Review 98:1 (2008), 394-425.

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “Spatial Determinants of Entrepreneurship in India”, National Bureau of Economic Research Working Paper 17514 (2011). Forthcoming in Regional Studies.

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “Local Industrial Structures and Female Entrepreneurship in India”, National Bureau of Economic Research Working Paper 17596. Forthcoming in Journal of Economic Geography.

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “The Exceptional Persistence of India’s Unorganized Sector”, World Bank Policy Working Paper 6454 (2013a).

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “Female Business Ownership and Informal Sector Persistence”, World Bank Policy Working Paper 6612 (2013b).

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Is India's Manufacturing Sector Moving Away From Cities?”, National Bureau of Economic Research Working Paper 17992 (2012a).

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing”, National Bureau of Economic Research Working Paper 18524 (2012b).

- Glaeser, Edward, Heidi Kallal, José Scheinkman, and Andrei Shleifer. “Growth in Cities”, Journal of Political Economy 100:6 (1992), 1126-1152.

- Glaeser, Edward, and William Kerr, “Local Industrial Conditions and Entrepreneurship: How Much of the Spatial Distribution Can We Explain?”, Journal of Economics and Management Strategy 18:3 (2009), 623-663.

- Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova, “Imported Intermediate Inputs and Domestic Product Growth: Evidence from India”, Quarterly Journal of Economics 125:4 (2010a), 1727-1767.

-

- Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova, “Multi product Firms and Product Turnover in the Developing World: Evidence from India,” Review of Economics and Statistics 92:4 (2010b), 1042-1049.

- Greenstone, Michael, Richard Hornbeck and Enrico Moretti, “Identifying Agglomeration Spillovers: Evidence from Winners and Losers of Large Plant Openings”, Journal of Political Economy 118 (2010), 536-598.

- Grossman, Gene, and Elhanan Helpman, Innovation and Growth in the Global Economy (Cambridge: MIT Press, 1991).

Halpen, Lazlo, Miklos Koren, and Adam Szeidl, “Imports and Productivity”, Working Paper (2006).

23
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Table 7: **Characteristics of Low Income Households--By Gender of the Head of the Household**

|Low<br>|Income Households'<br>||
|---|---|---|
|National Average|Male-Headed|Female-Headed|
|Proportion (% of total)<br>100.0|69.0|31.0|
|Household Size (ave.)<br>3.0|3.5|1.9<br>|
|(o/w children)<br>0.8|1.0|0.5|
|(o/w spouses)<br>0.8|1.0|0.1|
|(o/w other)<br>0.4|0.5|0.3|
|Average Age of|||
|Household Head<br>57.6|53.6|62.4|
|Age distribution of Household Head**(%):**|||
|Below 40<br>15.0|17.3|10.3|
|40 - 49 years<br>17.0|19.8|9.2|
|50 - 59 years<br>15.8|15.0|19.6|
|60 and over<br>51.2|47.9|60.9|
|**Economic Activity of**Household Head:|||
|Economically Active<br>26.8|30.6|20.7|
|Unemployed<br>7.0|8.2|3.4|
|Economically Inactive<br>63.3|57.8|75.9|
|o/w pensioners<br>(62.8)|(57.2)|(75.9)|
|Others<br>2.9|3.4|0.0|

" Lowest quintile of the population.

Source: World Bank staff estimates from 1992 Individual Budget of Households. NSI.

# V. Who Benefits from the Social Safety Net?

Bulgaria began the transition in 1989 with a well developed pension and shortterm benefit system, very little social assistance and no unemployment benefits. Since 1989, I
The first is that the income earned by overseas and domestic migrants from Kerala may be similar, in light of the educational background of most of the members of these two groups. While the bulk of overseas migrants tend to have a secondary-school education, a majority of domestic migrants have gone on to pursue post-secondary studies. In Figure 4, I show the education distribution of Kerala’s migrants, drawing from the 2014 KMS, which collected data on the income of Keralite households. For overseas migrants, there is a clear peak at 10 years of education—the middle of the distribution. In contrast, for domestic migrants, the mass of the distribution is around 15 years of education, or negatively skewed.

The concentration around 10 years of schooling for overseas migrants is not coincidental. India’s Emigration Act of 1983 established a requirement that, in order to work overseas, individuals without a secondary school leaving certificate (SSLC)—which one can only obtain by passing a public examination after completing class 10—must apply to the office of the Protector-General of Emigration (PGE) for emigration clearance (Sasikumar and Hussain, 2008). To avoid this bureaucratic process, households that plan to send family members abroad are more likely to do so after those individuals fulfill the SSLC requirement.

With respect to monthly income, Figure 5 shows that overseas migrants with around 10 years of schooling earn amounts similar to those of peers with post-secondary schooling who migrate within India. As most domestic migrants have earnings at least equal to what most overseas migrants make, the gains experienced by migrant households in Kerala, all else being equal, would be similar, on average, across both migration pathways.

A second possible explanation is that migrant households could be allocating similar amounts of overseas and domestic remittances to the types of asset purchases examined in this paper, irrespective of how much money is actually being sent home in either case. A tendency for households—particularly those with domestic or international migrants—to compare themselves socially with other households may lead them to make housing investments and purchases of durable 23
||EU27|||Rest of<br>|Rest of|**Append**|**ix Table 3.**|**Emissio**|**ns Tax in**|<br>**dollarspe**|Rest of<br>**r ton carb**|<br>**on**|Middle<br>|Sub-||High|Low and<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||with<br>EFTA|United<br>States|Japan<br>|high<br>income<br>Annex 1|high<br>income|Brazil|China|India|Russia<br>|Rest of<br>East Asia|South<br>Asia|Rest of<br>ECA|East and<br>North<br>Africa|Saharan<br>Africa|Rest of<br>LAC|<br>income<br>countries|middle<br>income<br>countries|World<br>total|
|NBTA17|332.8|248.7|246.6|218.1|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|241.1|n.a.|66.6|
|BTAFU|318.1|245.0|238.9|215.7|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|234.8|n.a.|66.0|
|BTADU|330.0|247.2|247.7|218.4|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|239.9|n.a.|66.6|
|BTADE|339.7|257.1|256.9|237.9|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|249.3|n.a.|69.3|
|BTAFR|345.4|252.7|257.0|225.0|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|248.1|n.a.|69.5|
|BTADR|337.4|250.8|249.7|221.5|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|244.0|n.a.|67.7|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 40
The percent of respondents perceiving this to be case vary little with their counterparts (84 percent) in State B which does not possess a counseling policy.

Parallel systems are driven in part by physician preferences for posts near urban areas (88 and 82 percent of respondents in States A and B respectively) and in locations with good potential for private practice (54 and 63 percent of respondents in States A and B respectively). Some locations are apparently so undesirable that the majority of respondents agreed that MOs prefer to secure medical leave to avoid such a posting (79 and 69 percent of State A and B respondents respectively).<sup>13</sup>

**Table 3. Posting Preferences and Processes in States A and B Perception Survey Results, 2011** **<u>(in percentages)</u>**<sup>**a**</sup>

||**% Ag**<br>|**ree**<sup>**b**</sup><br>|
|---|---|---|
|**Summary Statement**|**State A**|**State B**|
||N=273|N=266|
|**Preferences**|||
|Many candidates opt for (initial) postings in rural / tribal PHCs to secure<br>quicker eligibility for residency requirements|84.6|86.3|
|MOs generally choose locations which have good potential for private<br>practice|59.9|59.1|
|MOs generally choose locations that are nearer to urban areas|87.8|82.4|
|Some MOs go on medical leave to avoid undesired postings|78.9|69.2|
|**Processes**|||
|The vacancies that are suppressed during counseling are generally in and<br>around urban areas|85.8|N/A<sup>c</sup>|
|The reason for suppressing [desirable] vacancies is to fill the periphery first|53.6<sup>d</sup>|62.6|
|The counseling process has improved transparency of the posting process|70.8|N/A<sup>c</sup>|
|**Parallel systems**|||
|The reason for suppressing [desirable] vacancies is to facilitate some<br>prefixed unofficial arrangements motivated by political influence and|80.7<sup>d</sup>|82.8|
|financial dealings<br>Political connections are used toget desiredplaces ofposting.|86.3|84.1<sup>e</sup>|

aPercentage based on valid responses (exclude missing data). bCombines responses for “agree” and “completely agree.” cCounseling process not used in State B. dState A statements refer to the counseling process. e Survey statements for State B refer to approaching local politicians to secure a desired place of posting.

10
for sectoral FDI presence and imports. In a second step, we estimate the regressions using an instrumental variables two-stage least squares (IV 2SLS) approach.

# **3.2 Data and Instruments**

The World Bank Enterprise Analysis Unit recently published the Enterprise Surveys Indicator Database.<sup>9</sup> This publication covers 215 enterprise surveys for 126 countries over the period 2006 to 2010. Enterprise surveys represent a comprehensive source of firm-level data in emerging markets and developing economies. One major advantage of the enterprise surveys is that the survey questions are the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of firms using three levels of stratification: sector, firm size, and region. Sectors are based on the ISIC Rev. 3.1 classification, but in some cases are further aggregated.

The Enterprise Surveys Indicator Database covers a wide range of indicators on firm characteristics, the business environment, innovation and technology, and workforce and skills among others. We merged this dataset with data on firm-level output, value added, and capital stock obtained from the Enterprise Analysis Unit of the World Bank.<sup>10</sup> All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2000). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys for each country; (ii) We include only countries that cover foreign firms in the surveys<sup>11</sup> ; (iii) We drop high-income countries to cover only emerging or developing countries<sup>12</sup> ; and (iv) We drop countries for which we cannot calculate foreign output share due to unavailable output data. We focus only on the effects of productivity spillovers on domestic manufacturing firms, since productivity measures were unavailable for services firms.

The procedure above results in more than 25,000 domestic firms and 3,400 foreign firms in 78 countries covering eleven manufacturing sectors. The list of countries, year of most recent survey and number of domestic and foreign manufacturing firms by country can be found in Appendix 2.

> 9 See <u>http://www.enterprisesurveys.org/~/media/FPDKM/EnterpriseSurveys/Documents/Misc/IndicatorDescriptions.pdf</u> for a description of the indicators. Our analysis is based on the October 2011 release of the Enterprise Surveys Indicator Database.

> 10 We thank Federica Saliola and Murat Seker for making these data available to us.

> 11 Only Kosovo did not fulfill this criterion.

> 12 We drop these, as the database only included eleven high-income countries which were not representative of high-income countries (some OECD countries, some non-OECD countries, and one oil exporter).

14
combined effect of these forces. All else equal, larger revelations and higher values of _τ_ (higher uncertainty) should lead to larger increases in spreads.

**Table 7:** Spread response to hidden debt revelations

||Model|Output|Data|base|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Revelation size|1.29***|1.25***|0.22***|0.22***|
||(0.04)|(0.04)|(0.08)|(0.08)|
|Years since last revel. (_τ_)||0.03***||0.01|
|||(0.00)||(0.07)|
|Growth|-1.22***|-1.20***|-1.45***|-1.45***|
||(0.02)|(0.02)|(0.34)|(0.34)|
|Debt/GDP|1.76***|1.74***|0.79*|0.79*|
||(0.02)|(0.02)|(0.43)|(0.43)|
|Disclosed borrowing|4.32***|4.38***|-0.25|-0.25|
||(0.04)|(0.04)|(0.24)|(0.24)|
|Constant|4.49***|4.26***|4.73***|4.70***|
||(0.02)|(0.03)|(0.12)|(0.26)|
|Observations|594|594|594|594|
|Country fxed efects|||✓|✓|

_Notes:_ This table shows the results of OLS regressions of next-period sovereign spreads (measured in percentage points) on the size of hidden debt revelations, years since the last revelation ( _τ_ ), and additional control variables. Columns (1) and (2) use the data output from our calibrated benchmark model. We run 500 regressions in samples of 594 randomly drawn observations and report the mean coefficients across runs and their standard errors in parentheses. Columns (3) and (4) use our hidden debt database and spread data from JP Morgan’s EMBI+. We include country fixed effects to focus on within-country time variation. Robust standard errors are reported in parentheses.

We validate these spread response predictions in both the simulated data from our model and our new hidden debt database by using a fixed effect regression model.<sup>44</sup> In Table 7, we show that larger revelations are indeed associated with larger increases in spreads after controlling for growth, the initial debt stock and new (disclosed) borrowing. The effects in the benchmark model (column (1)) are much larger (129 bps increase in spread for a revelation of 1 SD) than those in the database (22 bps increase), which is likely explained by the low frequency of our hidden debt database (annual) and by the fact that the availability of data on spreads is limited to countries with typically higher statistical capacity.<sup>45</sup> We then introduce _τ_ into the regression framework to capture the duration of the hidden debt build-up.<sup>46</sup> As expected, in both the benchmark model (column (2)) and real-world data (column (4)), higher uncertainty as measured by higher values of _τ_ is associated with higher

> 44To make the two datasets comparable, we run regressions in 500 randomly drawn samples of identical length as in the real-world data, and we then report the mean coefficients and standard errors.

> 45The spread data is from the EMBI+ compiled by JP Morgan which calculates the average spread for sovereigns based on USD-denominated bond instruments that meet certain minimum liquidity, size, and time-to-maturity criteria.

> 46In our database, we do not measure _τ_ directly but can nonetheless approximate it as the time passed since the country experienced an above-mean revelation (measured in years).

37
the calibrated “frictionless norm” already includes frictions that may distort the real exchange rate. Further, the “norm” is calibrated as a typical value, which is not necessarily normative or frictionless. It is therefore difficult to interpret these measures as misalignment of the real exchange rate.

We propose an alternative methodology for assessing real exchange rate misalignment following the seminal work of Hsieh and Klenow [2009]. We propose to use firm-level or industry-level data to assess whether there are systematic dispersions in the marginal productivities of producers of tradable and and non-tradable goods. Such a dispersion would be indicative of a real exchange rate misalignment, as in the frictionless competitive equilibrium, marginal productivities are equated across all firms. We illustrate how this methodology can be useful not only in identifying and quantifying real exchange rate misalignment, but also in assessing its causes.

# **2 The real exchange rate, the terms of trade and the saving rate**

The starting point of the discussion on real exchange rate misalignments is that a devalued real exchange rate is tied to cheap exports and excessive saving. However, it turns out that the conditions under which these equivalences hold are quite subtle. Broadly, reducing domestic demand will result in a lower real exchange rate, and cheaper exports; however, a decline in domestic demand need not coincide with an increase in domestic savings. Supply shifters (such as subsidizing exports) may or may not result in a devalued real exchange rate, depending on the substitutability of inputs from the tradable to the non-tradable sector. When permanent, they will have no effect on savings.

To illustrate these interactions, consider an economy with three goods: an imported good (denoted with superscript _∗_ ), a domestically produced tradable good (denoted with superscript _T_ ), and a domestically produced non-tradable good (denoted with superscript _NT_ ). Consumers spend a constant fraction of their expenditures on each good; the expenditure shares are denoted _α_<sup>_∗_</sup> , _α_<sup>_T_</sup> and _α_<sup>_NT_</sup> respectively. The prices of the goods are denoted _p_<sup>_∗_</sup> , _p_<sup>_T_</sup> and _p_<sup>_NT_</sup> respectively.

4
patients with known or anticipated adherence problems show medium effects (Amico et al., 2006). A more recently completed systematic review, which compares the adherence benefits of directly observed versus self-administered ARV treatment moreover finds that directly observed therapy does not outperform self-administered treatment on viral suppression and a range of other adherence measures (Ford et al., 2009), thus bearing out Liechty and Bangsberg’s (2003) pessimism regarding the benefits of directly observed therapy programs for ARV treatment. Yet, another systematic review, which expands study selection criteria to include nonrandomized study designs, claims that directly observed therapy does outperform self-administered treatment on adherence, immunologic and virologic outcomes (Hart et al., 2010). Two recently published studies not included in the former syntheses in turn do report statistically significant, positive impacts on adherence of directly observed therapy interventions (Babudieri et al., 2011; Berg et al., 2011), the latter of which employs a randomized study design. To date, therefore, there appears to be no consensus that community-based adherence support provided through the likes of lay workers impacts positively on adherence and clinical response to ARV treatment.

This paper employs longitudinal, panel data from the Effective Aids Treatment and Support in the Free State (FEATS) study to determine how a randomized peer-adherence and nutritional support intervention impact on various subjective and more objective measures of ARV treatment adherence. Section 2 describes the study design and data, while section 3 summarizes the methods employed in the statistical and econometric analyses. Section 4 presents the results, while section 5 concludes.

# **2. Methodology**

The Effective Aids Treatment and Support in the Free State (FEATS) study, a prospective cohort study and experimental study with a combined group time-series, quasi- or field experiment and Zelen-type double randomized consent design,<sup>1</sup> was approved by the Ethics committee of the Faculty of Health Sciences (UFS) [ETOVS 145/07]. The experimental component of the FEATS

> 1 In the Zelen design subjects are only offered and provided with information regarding the treatment that they are assigned. The Zelen design is deemed appropriate given the fact that: blinding is not practicable or possible; the use of classical randomization and informed consent procedures significantly threatens internal validity; the interventions are highly attractive; the control group receives standard care; the study focuses on a clinically relevant objective(s) and offers important new insights (Kaptchuk, 2001; MacLehose _et al_ , 2001; Rains & Penzien, 2005).

3
-7-

**Table 1: Average Household Income Per Capita, 1992 (Leva)**

||National|Urban|Rural|
|---|---|---|---|
|Average Income Per Capita (Leva)|16,803|15,085|19,722|
|Standard Deviation (Leva)|9,137|7,477|10,813|
|Minimum Income|3,324|3,324|3,526|
|Maximum Income|156,006|78,055|156,006|
|Coefficient of Variation|54|50|_55_|

Memo Item: Leva/US$ (1992 average) 23.7 Source: **World** Bank estimates from 1992 Individual Budget of Households Survey, NSI.

The Composition of Income. In 1992, nearly half (40 percent) of average household income in Bulgaria is derived from wages and salaries, excluding the wage component in self-employment income and royalties (see Table 2). Self-employment, including 'incomes from sales of farm produce' and 'in-kind income', accounts for more than 30 percent of household income. As is common in many transitional economies, social insurance is an extremely large component of household income, accounting for 24 percent. Of this, pensions alone account for about 21 percent; this is the result of Bulgaria's large