rafmacalaba/datause-encoder-data huggingface dataset:
# **A.2 Sampling of Providers for SP visits: Representative Sample**

We conducted the SP work in three out of the five districts in our sample because of logistic considerations. Although SPs were recruited from the local community, they needed plausible reasons for their presence in the village (which they were not from), and the typical narrative was that they were traveling and/or passing by the village. In order to minimize SP detection, we excluded 5 remote markets (as assessed by road access) from the possible 60 markets, where we thought a traveling excuse might not be plausible.

We sampled providers for the SP work from a smaller set of “eligible providers” than what we had mapped. All public nurses and midwives (ANMs), community health workers (ASHA), day-care center workers (Anganwadi), chemists and pharmacists as ineligible for SP work, as they provided primarily preventive care such as vaccinations. We also exclude mobile and itinerant providers from the sample. Finally, we exclude 55 providers with whom we could not complete the provider census (typically due to the unavailability of provider, we were able to conduct the census with 17 of these providers in subsequent rounds). These restrictions remove 7 markets from our study, primarily because there were no eligible providers in these markets. We also drop two other markets because they share a cluster with other sampled villages and do not have eligible providers inside the village. Our study in the representative sample therefore covers 46 markets in 3 districts of Madhya Pradesh (see Table A.1). Based on the eligibility criteria defined above, these 46 markets have 649 eligible providers (130 public and 519 private) from which we sample.

In each market we randomly sampled up to two eligible providers in each public clinic and up to six private providers in each market.<sup>30</sup> In the private sector, we sampled one provider per clinic. We sampled a total of 247 providers of which 45 are public providers and 202 are private providers (Appendix Table A.1). We also sampled all MBBS providers in both public and private sectors.

# **A.3 Completion of SPs: Representative Sample**

We sampled 247 providers in 235 clinics and SPs were completed with 224 providers in 215 clinics. Of these, 214 providers are those we sampled. Furthermore, for 27 SP interactions (corresponding to 10 providers) we saw a provider who had not been sampled. We knew the identity of the provider because he/she had been included in the census, but was practicing in a clinic different from their own. For 18 observations (corresponding to 8 public and 2 private clinics and 10 public and 2 private provider sampled) we do not know the identity

> 30One market in Gwalior district was an exception to this rule. In the cluster village of a particular market, we found 113 providers. In this market, we relaxed our sampling protocol and sampled 20 private providers.

42
increased. Net capital flows to emerging and developing economies was around US$65 billion during 1999-2002, rose to US$ 201 billion in 2004 and to US$ 617.5 billion in 2007.

<u>The commodity price “crisis”. The volatility and crisis of the latter half of the 2000s had</u> their roots in this prosperity and interconnectedness. Growth in real incomes around the world led to continuous price increases in commodities. A “crisis” of sorts took place during mid2007-mid-2008 when food and fuel prices, which had been increasing fast since 2005, both jumped up substantially.<sup>7</sup> The commodity price hikes in world markets are noteworthy for the distributional effects they had on world markets. As might be expected, oil exporters benefitted in the form of higher export revenues. In 2007, Kuwait had a current account surplus (CAS) of 44.7% of its GDP, Qatar of 30.4%, and Saudi Arabia, of 24.3% of its GDP while Russia’s CAS was 6% of its GDP. While commodity exporters in the Africa, Latin America and Middle East and North Africa regions saw external positions improving many oil-importers saw a significant widening of their trade deficits (e.g. Ukraine, Mongolia, Seychelles, Dem. Rep. of Congo, and Togo among others). In addition, fiscal positions of many governments were changing along with commodity prices for a number of reasons: (i) governments that taxed fuel (or owned it) saw revenues rise; (ii) there were reductions in taxes and/or increases in subsidies for those who protected oil/food production or consumption; and (iii) governments put in place new subsidies to protect the poor. For those that suffered negative effects from rising prices, the median fiscal cost of subsides, tax decreases, and other fiscal responses to rising commodity prices was 0.6% of GDP in 2006 and 0.7% of GDP in 2007. The total fiscal cost of responding to commodity price shocks has been estimated at 2% of China’s GDP, and over 3% of GDP for India.<sup>8</sup>

> 7 The food price spike is explained to a large extent by a confluence of factors, namely the advent of US bio-fuel subsidies and poor wheat harvests in some major exporters. The concomitant jump in fuel prices has been explained by a number of reasons, such as low inventories reflecting high demand and insufficient investment in new refining capacity, and the high cost of new exploration and development.GEP 2009.

> 8 IMF Update on Food and Fuel Prices dated September 19, 2008.

4
Table 12: Aid & bilateral TRADE – 2<sup>nd</sup> stage - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.072***|-0.070***|-0.074***|-0.070***|
||(0.016)|(0.016)|(0.016)|(0.017)|
|_Ln(Affected)_|0.006|0.007|0.001|-0.000|
||(0.006)|(0.006)|(0.007)|(0.007)|
|_Ln(GDP p.c.)_|-0.058|-0.070|-0.111*|-0.108*|
||(0.061)|(0.061)|(0.065)|(0.065)|
|_Trade with donor_|0.106***|0.135***|0.107***|0.153***|
||(0.021)|(0.024)|(0.021)|(0.030)|
|_Regulatory quality_|-0.057|-0.332**|||
||(0.086)|(0.134)|||
|_Trade with donor_×||0.069**|||
|_Regulatory quality_||(0.028)|||
|_Corruption control_|||0.234**|-0.148|
||||(0.117)|(0.216)|
|_Trade with donor_×||||0.081**|
|_Corruption control_||||(0.038)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.077**|-0.074*|-0.088**|-0.080*|
||(0.041)|(0.041)|(0.046)|(0.044)|
|_Ln(Affected)_|0.022|0.025|-0.013|-0.022|
||(0.015)|(0.016)|(0.016)|(0.016)|
|_Ln(GDP p.c.)_|-0.057|-0.062|-0.354*|-0.335*|
||(0.140)|(0.145)|(0.190)|(0.192)|
|_Trade with donor_|-0.022|0.027|-0.030|-0.113|
||(0.036)|(0.051)|(0.035)|(0.075)|
|_Regulatory quality_|0.432**|0.205|||
||(0.194)|(0.278)|||
|_Trade with donor_×||0.082|||
|_Regulatory quality_||(0.066)|||
|_Corruption control_|||1.053***<br>(0.355)|0.294<br>(0.464)|
|_Trade with donor_×||||0.196**|
|_Corruption control_||||(0.088)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

26
2 First, we present a simple framework emphasizing the main characteristics and determinants of the unofficial economy. We then briefly review some of the microsurvey evidence, focusing on Ukraine. Next, we develop a simple, common empirical methodology to derive comparative estimates of the evolution and size of the unofficial economies in sixteen countries in the region (including Ukraine). The approach is based in part on estimates of aggregate electric consumption. In this respect, we draw partly from the recent work of Dobozi and Pohl, relying on electricity consumption as a reasonable proxy for overall gross domestic product (GDP) in a country.<sup>4</sup> In addition, we rely on independent estimates available for unofficial economies from earlier studies to construct a baseline (at the beginning of the transition). We also go significantly further, by providing a methodological approach to measuring the unofficial economy, which is based on differential assumptions of energy elasticities across countries.

The "macroelectric" approach to estimating an unofficial economy and the various possible biases and drawbacks of this methodology are discussed. The case of Ukraine is tested empirically as a case study for the applicability of the overall approach. The results for all countries in our FSU/CEE sample are analyzed in a separate section. Various scenarios with different assumptions (and sensitivity analyses) are presented. After reviewing the unofficial economy dynamics between 1989 and 1994, we analyze the relationship between the evolution of the unofficial economy and how overall performance fared in the sixteen countries in the region.

Our framework of analysis and the empirical results have multiple implications for policy. We discuss these in some detail in the concluding section of the paper.

# A **Simple Framework of Analysis for the Unofficial Economy**

# _Main Characteristics of the Unofficial Economy_

From a meaningful economic standpoint, we define an unofficial activity as the unrecorded value added by any deliberate misreporting or evasion by a firm or individual. As such, it correlates with untaxed incomes and (unrecorded) capital flight. In contrast to conventional notions of shadow or informal economies in many other countries, particularly in Latin America and Asia, the unofficial economies in Ukraine and many other FSU/CEE countries exhibit some distinguishing features.

# 1. Coexistence of State and Nonstate Activities and Enterprises

The state sector is rather active in unofficial activities, state officials and enterprise managers are often linked to nonstate activities and firms in order to operate more flexibly and generate "private income" flows (from state assets). Further, nonstate activities operating
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<!-- Start of picture text --> in Table 4 are from OLS estimates of (1) including fixed effects, and from an<br>Arellano-Bond GMM estimator. We include all three tax rate variables in<br>our regressions: the personal income tax rate (PITR), the corporate income<br>tax rate (CITR) and the VAT rate (or sales tax rate for the US and Brazil).<br>The first two columns of Table 4 give estimates for the entire sample. We<br>are particularly interested in estimates for developing countries—as their in-<br>clusion is novel to this dataset; we therefore show estimates for the developing<br>countries within the sample in the third and fourth columns.<br>We find a negative relation between increases in the average marginal<br>personal income tax rate and GDP growth in the following period. For<br>the entire sample, a one-percentage-point cut in the average marginal tax<br>rate increases GDP growth in the following period by 0.16%. The effect is<br>larger for developing countries, where a tax cut of similar magnitude increases<br>output by close to 0.4%. This result is statistically significant at the 95%<br>confidence level for all specifications, and the 99% confidence level for almost<br>all specifications. It is possible that the tax codes in developing countries are<br>more distortionary than those in high-income countries. This is a possible<br>explanation for the higher effect of personal income tax changes in developing<br>countries.<br>Results for the corporate income tax rate and the VAT rate are less clear.<br>The effect of changes in the corporate income tax rate are never statistically<br>significant. The effect of VAT increases are statistically significant at the<br>95% confidence level only in the OLS regression for the entire sample, and of<br>an unexpected sign. A potential explanation for this last result is that there<br>is very little time-series variability in the VAT, which gives the regression<br>less power to identify the effects of VAT changes. Moreover, VATs have<br>frequently been introduced, and phased in, in the context of broader tax<br>reforms or other economic reforms. Thus the positive coefficient on the VAT<br>10<br><!-- End of picture text -->
Across all age groups, women consistently experience higher unemployment rates than men (Figure 2). Among the youth, female unemployment rates have been over 40 percent between 2001 and 2004.

Table 3 reports the labor market transition probabilities for working-age individuals in Bosnia and Herzegovina, between 2001 and 2002 and between 2001 and 2004. The columns represent the labor market outcomes in 2002 and 2004 for each of the labor market states in 2001. These outcomes persist in the first few years of youth experience in the labor market. Among those 15-24 who where unemployed in 2001, 77 percent were jobless one year later, and 58 percent were still jobless three years later. Even among youth employed in 2001, a third of them were jobless in 2002, and a quarter of them were still jobless in 2004.

Table 3. Panel Estimates of Transition Probabilities in Bosnia and Herzegovina, 2001-2004

||Empl|oyment status in|2002|Empl|oyment status in|2004|
|---|---|---|---|---|---|---|
||Inactive|Unemployed|Employed|Inactive|Unemployed|Employed|
|Employment Status in 2001<br>All|||||||
|Inactive|73.3|12.4|14.3|63.3|15.3|21.3|
|Unemployed|32.3|33.7|33.9|30.0|27.4|46.6|
|All employed|13.0|6.9|80.6|12.9|8.3|78.7|
|Ages 15-24|||||||
|Inactive|71.0|16.6|12.4|53.4|21.2|25.5|
|Unemployed|36.3|40.3|23.5|25.8|32.3|41.9|
|All employed|20.3|13.5|66.3|11.4|15.2|73.4|

_Source_ : LSMS and authors' calculations.

In general, this does not compare favorably with other economies in transition, where flows out of joblessness among young workers are much larger (see Appendix Table 1). For example, in Estonia in the mid-1990s, close to half of all unemployed workers found employment the following year. In Russia, over this same period, over 40 percent moved out of unemployment. The flow of young workers out of unemployment in Poland, however, is comparable to that of Bosnia and Herzegovina.

Because transitions patterns are also affected by other individual characteristics, like educational attainment, marital status and geographic location, it is important to examine to what extent the conclusions from Table 3 remain valid once we control for these factors. Figure 3, for example, illustrates how among males, the unemployment rate for both skilled and unskilled workers decreases with age. However, among the youngest workers, the average unemployment rate of skilled men is higher than that of less skilled men. As age increases, the average unemployment rate among of skilled workers falls more rapidly that that of the less skilled workers. At age 35-44, for example, the unemployment rate of skilled workers is substantially lower than that of the less skilled workers. Figure 4 shows a similar breakdown of unemployment rates by skill 10
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42 percent of those surveyed in Lima supported transferring SEDAPAL to the private sector; by 1997 this was down to 22 percent (Apoyo Opinion Reports).

_Table 7. Rate of Approval of Privatization by Socioeconomic Level in Lima_ (percentages) <!-- Start of picture text --> Level (Average Monthly Sept. Feb. Dec. Dec. Dec. Dec. Dec.<br>Household Income) 1991 1992 1993 1994 1995 1996 1997<br>Total 52 59 48 49 42 28<br>High (US$3,200-5,000') 87 83 84 80 77 65 67<br>High middle (US$750-1,1502) 65 81 73 64 66 46 42<br>Low middle (US$330-4703) 55 63 58 45 43 39 25<br>Low (US$200-2404) 37 42 49 36 42 40 19<br><!-- End of picture text -->

**'Approximately 4.5% of total** households in Lima. 'Approximately 19.5% **of** total households in Lima. 3Approximately 33% of total households in Lima.4Approximately 43% of total households in Lima. SOURCE: OPINION REPORTS - APOYO (Income ranges from July 1997.)

Furthermore, support for Fujimori was sharply down by late 1997 (Statistical Appendix Table 1) and it became even riskier to chance alienating his core supporters, especially when he was trying to change the constitution so he could run for President again in 2000.

Since 1997, although several privatization transactions were implemented, they severely lost public approval (63% of the population was against them in October of 1999) and partly government support, making even more difficult to complete the concession of SEDAPAL. However, the results of the privatization reform proved a success and the process continues. Among many other benefits from the process, more than US$8,000 millions were obtained from privatization transactions (1991-1999) and two thirds of foreign investment came from privatizations; the government received US6,391; privatized firms substantially increased internal production and exports; the Peruvian economy became more competitive **(4th** in Latin America) and the country-risk
# **(4) Data**

# **(4.1) Data for China: CFPS (2016, 2010)**

The data for estimating the intergenerational mobility of education in China come from the China Family Panel Study (CFPS)- 2016 wave. In order to match children-parents pairs irrespective of residency status at the time of the survey, we take advantage of the Family Member Module, which has an innovative T-Tables design feature. As discussed in Xie and Hu (2014), the T-Tables in CFPS consist of three tables - T1, T2, and T3. Table T1 (on family members living together) and Table T2 (on immediate relatives not living together) record the basic socio-demographic characteristics of every family member and her immediate relatives (parents, children and spouses) who are not living with them. Table T2 (on relations) identifies the relations of all the family members and the corresponding relations between T1 and T3 members. Therefore, three tables jointly present a complete family network, and most importantly, allow us to avoid truncation of the sample.

More specifically, in order to identify all the children-fathers pairs, we take the following steps. For each individual currently living in the family, own education and father’s education are directly available in the Family Member module irrespective of whether the father is coresident at the time of the survey. This subsumes two cases involving three generations. (i) If such an individual is the household head (or head’s spouse) who lives in the family, their parents are matched, irrespective of whether they are co-resident or not. (ii) If such an individual is household head’s (or head’s spouse’s) child, who lives in the family, the child’s parents are of course matched, i.e. they are household head and head’s spouse. However, step 1 misses the following case: if household head’s (or head’s spouse’s) child is currently living outside. An important advantage of the CFPS survey is that it collected data on such nonresident children. In the second step, we include these nonresident children in the sample. Our estimation sample thus includes both the nonresident parents of household head and spouse, and also their nonresident children.

The summary statistics for various estimation samples are reported in Table T1.U for the urban households and Table T1.R for the rural households; the upper panel in each table corresponds to China, and the bottom panel to India. For China, the main estimation 15
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