rafmacalaba/datause-encoder-data huggingface dataset:
period of 14 years). To the best of our knowledge, there is no rigorous analysis of the evolution of intergenerational educational and occupational mobility in rural China in the post reform period in the existing economics literature.<sup>7</sup> We use two rounds of Chinese Household Income Project (CHIP) survey data for the years 1988 and 2002 for the analysis of intergenerational persistence in educational attainment and occupational choices over a span of almost a decade and a half. The data in both 1988 and 2002 are based on almost identical questionnaires (2002 round has some added information), and are comparable. This allows us to trace out the _changes_ in intergenerational occupational and educational persistence from 1988 to 2002.

The economics literature on the intergenerational economic mobility in developed countries focuses on income mobility, especially between father and son.<sup>8</sup> There is also a relatively small literature on occupational mobility in labor economics, again mostly in the context of developed countries.<sup>9</sup> Similar to income mobility, the existing literature on occupational mobility focuses primarily on the father‐son linkage (see Lentz and Laband (1983), and Dunn and Holtz‐Eakin (2000) on U.S, Sjogren (2000) on Sweden, and Behrman et. al. (2001) on Latin America). It is, however, difficult to focus on income or consumption mobility across generations in the context of most of the developing countries because of the unavailability of appropriate data. It is extremely difficult, if not impossible, to find reliable data on income and consumption of the parental generation. As emphasized in recent literature, to understand the intergenerational persistence in income, it is important to have data over many periods (years) so that the problem of measurement error due

> 7 The only paper we are aware of is Sato and Li (2007) which educational mobility among three generations of men in rural China using 2002 CHIP data set. In contrast, we focus on the _changes_ , if any, in the _average_ economic mobility in rural China from 1988 to 2002. Among the recent papers, Gong et al. (2012) and Deng et al. (2013) analyze income mobility in _urban_ China; Yuan and Chen (2013) focus on deriving intergenerational income elasticity, while we focus on causal effect.

> 8 The handful of studies focusing on the intergenerational correlations for daughters include Chadwick and Solon (2002) for USA and Dearden, Machin and Reed (1997) for UK.

> 9 The intergenerational occupational mobility has, however, been the focus of a large, mostly descriptive, literature in sociology.

4
Table 2: Women employment rates in Bangladesh by cohort and age group

||||||||||Cohorts<br>|<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Age|**Statistics**|_1940-_|_1945-_|_1950-_|_1955-_|_1960-_|_1965-_|_1970-_|_1975-_|_1980-_|_1985-_|_1990-_|_1995-_|_2000-_|**_Wgt_**|
|group||_44_|_49_|_54_|_59_|_64_|_69_|_74_|_79_|_84_|_89_|_94_|_99_|_04_|**_Ave_**|
|**15-19**|Share(%)|||||||11%|15%|16%|13%|8%|17%|22%|15%|
||N|||||||116|1772|1819|2098|1837|3243|683||
|**20-24**|Share(%)||||||12%|20%|23%|19%|18%|25%|35%||23%|
||N||||||150|2633|2589|3218|3378|5510|2411|||
|**25-29**|Share(%)|||||19%|24%|29%|27%|22%|31%|47%|||30%|
||N|||||116|2810|2844|3020|3193|5853|2490||||
|**30-34**|Share(%)||||30%|28%|34%|30%|26%|36%|54%||||35%|
||N||||107|2052|2289|2673|2591|5221|2470|||||
|**35-39**|Share(%)|||25%|28%|34%|34%|23%|37%|57%|||||36%|
||N|||93|1684|1800|2253|2304|4313|2138||||||
|**40-44**|Share(%)||9%|26%|34%|30%|23%|36%|58%||||||34%|
||N||57|1234|1490|1874|1886|3924|1682|||||||
|**45-49**|Share(%)|7%|19%|25%|25%|22%|37%|56%|||||||32%|
||N|75|949|1336|1867|1820|3486|1662||||||||
|Weighte|d average|7%|19%|25%|29%|28%|31%|31%|31%|30%|29%|27%|25%|||

Source: DHS surveys for 1993, 1996/97, 1999/20, 2004,2007, 2011, 2014, and 2017. Note: For each age group and birth cohort, i.e. for each ‘cell’, this table shows the number of (unweighted) observations (N) and the share (as percentages) of women who are employed in Bangladesh. Reading the table along the column provides an approximation to the age effect, i.e., what happens to the employment rate of women from a specific birth cohort as they age. Reading along the rows, the table provides an approximation of the cohort effect, i.e., the differences in the employment rate among cohorts observed at the same age. The last row, “weighted average”, represents the employment rate for a specific cohort average along its whole life cycle. The weights from the different surveys as well as the size of the cell are used in the calculation.

34
- 28 -

Table 6

|||SPEARHA<br>(|N CORRELA<br>75-country|TION COEF<br> <br>sample)|FICIENTS||
|---|---|---|---|---|---|---|
||YGR<br>................|CAR<br>....................|IR<br>................|HON|GOV|DINF|
|YGR|1.0000|00.11963|0. i,3852|0.23829|-0.24052|0.15397|
||0.0000|0.3066|0.0001|0.0395|0.0390|0.1872|
|CAR|0.11963<br>|1.00000<br>|-0.29869<br>|-0.02088<br>|-0.30053<br>|0.29690<br>|
||0.3066|0.0000|0.0092|0.8589|0.0093|0.0097|
|IR|0.4385<br>|2 -0.2986<br>|9 1.0000<br>|0 0.3386<br>|1 0.1163<br>|6 -0.06842<br>|
||0.0001|0.0092|0.0000|0.0030|0.3235|0.5597|
|NON|0.2382|9 -0.0208|8 0.3386|1 1.0000|0 0.2556|2 -0.14882|
||0.039|5 0.858|9 0.003|0 0.000|0 0.027|9 0.2026|
|_.Gy_|0.2405|2 -0.3005|3 0.1163|6 0.2556|2 1.0000|0<br>-0.26285|
||0.039|0 0.009|3 0.323|5 0.027|9 0.000|0 0.0237|
|DINF|.0.153|97<br>0.2969|0 -0.0684|2<br>-0.1488|2<br>-0.2628|5 1.00000|
||0.187|2 0.009|7 0.559|7 0.202|6 0.023|7 0.0000|

The first statistic in each pair is the Spearman rank correlation coefficient. The second is the probability that the absolute value of the observed correlation will occur for null hypothesis of zero correlation. There are 75 observations for each correlation with the exception of GOV; those have 74 observations.
operational for our purposes. For the counterfactual analysis we use as baseline the latest available year in our data set, which is 2011 and was determined by the availability of capital stock data. In order to perform the counterfactual analysis, we need to set values for the parameters. Some parameters are borrowed from the literature: i) the elasticity of substitution is set equal to _σ_ = 6 , which is standard in the trade literature, ii) the consumer discount factor is set equal to _β_ = 0 _._ 98 (Yao et al., 2012), and iii) the country-specic capital shares of production _αj_ and the country-specic adjustment costs of capital _δj_ are calculated using the Penn World Tables and reported in columns (3) and (4) of Table 5, respectively.

We calibrate other parameters in order to match the observed data. The share of technology capital of a country to all destinations as a share from total world technology capital ( _ηi_ ) is calculated using _FDIij_<sup>_value_</sup> : _φj_ is calculated using the relationship between inward FDI ( _FDIj_<sup>_in_</sup> =<sup>�</sup> _i_<sup>_FDI_</sup> _ij_<sup>_value_</sup> ) and physical capital in the production function along with FDI and physical capital data and data on the capital shares: The exact values for _η_ and _φ_ are given in columns (5) and (6) of Table 5.

Table 5: Calibrated Parameters

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|ISO3|Country|_α_|_δ_|_η_|_φ_|
|AGO|Angola|0.47|0.0528|0.00078|0.056|
|ARG|Argentina|0.57|0.0394|0.00792|0.024|
|AUS|Australia|0.44|0.0375|0.01375|0.052|
|AUT|Austria|0.43|0.0442|0.00521|0.058|
|AZE|Azerbaijan|0.79|0.0725|0.00058|0.041|
|BEL|Belgium|0.38|0.0452|0.00677|0.235|
|BGD|Bangladesh|0.47|0.0407|0.00322|0.003|
|BGR|Bulgaria|0.51|0.0565|0.00093|0.091|
|BLR|Belarus|0.48|0.0506|0.00152|0.031|
|BRA|Brazil|0.44|0.0475|0.02653|0.042|
|CAN|Canada|0.39|0.0371|0.01658|0.057|

Continued on next page 32
**Table 8: Difference in means for schooling outcomes between widow and non-widow’s children**

||School enrollmen<br>dependen|ts of co-habiting<br>t children|School enrollment<br>habiting dependent|gender gaps for co-<br>children (boy–girl)|
|---|---|---|---|---|
||Ever wi|dowed|Ever w|idowed|
|Child‘s age|Rural|Urban|Rural|Urban|
|6|0.01|-0.02|0.03|0.14|
||(0.44)|(-0.41)|(7.47)|(7.27)|
|8|-0.01|-0.06|0.02|0.11|
||(-0.45)|(-0.74)|(3.61)|(6.76)|
|10|-0.04|-0.09|0.01|0.08|
||(-1.25)|(-1.06)|(2.47)|(4.35)|
|12|-0.07|-0.13|0.02|0.04|
||(-2.13)|(-1.56)|(4.82)|(2.05)|
|14|-0.07|-0.16|0.03|0.09|
||(-0.64)|(-0.61)|(6.45)|(4.61)|
|Obs.|11267|4228|11286|4222|

Note: The table gives the mean for widows less that for non-widows. Ever-widowed includes currently widowed as well as previously widowed, currently married women. t-stats in parentheses. Source: 2006 Mali DHS.

**Table 9: Percent of women who reported obtaining permission and lack of money as impediments to seeking health care for themselves when sick, by marital status**

||**Permi**<br>|**ssion**<br>|**Mo**<br>|**ney**<br>|
|---|---|---|---|---|
||**Rural**|**Urban**|**Rural**|**Urban**|
|**Married once**|20.2|13.1|59.0|39.1|
||(40.2)|(33.8)|(49.2)|(48.8)|
|**Married, previously widowed**|25.2|29.4|65.5|60.7|
||(43.5)|(45.8)|(47.6)|(49.1)|
|**Married, previously divorced**|17.4|10.5|54.4|35.5|
||(38.0)|(30.6)|(49.8)|(47.9)|
|**Widowed**|18.4|10.1|80.5|62.7|
||(38.9)|(30.3)|(39.8)|(48.6)|
|**Divorced**|18.9|10.1|70.3|43.6|
||(39.4)|(30.2)|(45.9)|(49.7)|

Note: Figures are population weighted. Standard deviations are given in parentheses. Source: 2006 Mali DHS.

38
# **_State Control of Economic Activity_**

49. Despite the decline in state control since 2002 (a trend that started in 1999), Romania’s performance still lags behind that of the OECD average and the EU 15.

50. The gradual reduction of the state’s presence in the economy was a crucial element of the reform package associated with Romania’s EU pre-accession commitments. As a result, between 1999 and 2006, most of the commercial companies held in the portfolio of the privatization agency AVAS and in the banking sector were privatized. The energy sector was unbundled and important privatizations took place in electricity and gas distribution. The national oil company was also privatized, although the government retains a golden share in Petrom. In parallel, price liberalization in many sectors and the adjustment of energy tariffs reduced direct state involvement in services and improved the efficiency of resources allocation. Nevertheless, public enterprises still represent a substantial part of the economy and important energy generation companies, whose governance requires further improvement, continue to be managed by the state. Some energy tariffs have not yet reached import price parity.

51. The two measures of state control in the PMR system are public ownership and state involvement in business operations (through price controls and coercive as opposed to incentive-based regulation). A look at Figure 11 reveals that Romania does not favorably compare with OECD and EU averages and is closer to the more restrictive preaccession CEE with respect to both indicators.

**Figure 11. Public Ownership and State Involvement in Business Operation a. Public ownership b. State involvement in business operations**

<!-- Start of picture text --> 4.5 4.1 4.2 4.5<br>4.0 3.8 4.0<br>3.5<br>3.5 3.5<br>3.02.52.0 1.9 2.4 2.5 2.6 3.0 3.02.52.0 1.7 1.9 1.9 2.2 2.2 2.6 2.8 2.8<br>1.5 1.5<br>1.0 1.0 0.8<br>0.5 0.5<br>0.0 0.0<br>OECD MICs EU15Czech Rep.BulgariaHungaryRomania Poland OECD EU15Czech Rep.Romania MICsHungaryBulgaria Poland<br>Slovak Republic Slovak Republic<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

# _Public Ownership_

52. The aggregate indicator for public ownership covers size and scope of public enterprise sector, as well as direct control over business enterprises. Given that there is much more variation among EU15 or OECD countries for these indicators, we show the full set of comparators (Figure 12). We find that while the scope of the public sector is 18
tricts in our data set account for approximately 95% of the Indian population and 89% of total bank credit.<sup>7</sup> The dataset contains first, a measure of the intensity of cross-sectional exposure to the Adwdrs bailout, measured as the share of agricultural credit eligible to be written off in each district as a result of the Adwdrs program; second, detailed data on credit market outcomes for each district, including the number of loans, lending volume and loan performance; and third, credit market data merged with district-level information on real economic outcomes, including agricultural productivity, rural wages and per capita consumption. In this section, we describe each set of variables in turn.

# **3.1 Measuring Program Exposure**

To measure a district’s exposure to the Adwdrs bailout, we collected data on the amount of debt relief granted under the program from each state’s State Level Bankers’ Committee (Slbc), the administrative body responsible for maintaining data on publicly supported credit market programs within each state. We were able to obtain this information for 23 of India’s 28 states, including all major states of mainland India.<sup>8</sup> For each district in the data, we observe the amount of credit eligible for the bailout, consisting of overdue principal and accumulated interest, and the amount of debt relief actually disbursed.

Using this dataset, we construct our measure of program exposure. Because settlement was voluntary for borrowers above the two hectare collateral cutoff, our preferred measure of program exposure uses the share of credit _eligible_ to be forgiven under the Adwdrs program. Letting _crediti_ denote the total amount of outstanding agricultural credit in district _i_ at the

> 7Bank lending in India is typically concentrated within a district, and very little lending takes place across district boundaries. This is the result of India’s long-standing branch banking regulations, under which branch licenses and lending targets are typically assigned at the sub-district or “block” level. We exclude branches located in India’s largest metropolitan areas, Ahmedabad, Bangalore, Chennai, Jaipur, Hyderabad, Kolkata, Mumbai, and New Delhi, where the geographical mapping between bank and borrower location is less likely to hold.

> 8Data were not available for the Northeastern states Arunachal Pradesh, Assam, Meghalaya, Nagaland and Mizoram. In the base year, these states account for less than 10% of total credit outstanding.

9
16

**Table 8: World Commercial Service Exports and Shares by Income Group and Region**

||19|80<br> <br>|199|0<br> <br>|200|0<br>|201|0<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|Exports<br>($bill)|Share<br>(in %)|Exports<br>($bill)|Share<br>(in %)|Exports<br>($bill)|Share<br>(in %)|Exports<br>($bill)|Share<br>(in %)|
|**BY INCOME LEVEL**|||||||||
|Developed economies|290|75.0|669|80.6|1,182|77.1|2,650|70.0|
|Developing economies|63|16.2|141|17.0|303|19.7|1,000|26.4|
|High income developing economies|23|5.9|63|7.6|118|7.7|360|9.5|
|Upper middle income economies|25|6.5|48|5.8|126|8.2|390|10.3|
|Lower middle income economies|13|3.2|27|3.2|54|3.5|234|6.2|
|Low income economies|2|0.5|3|0.4|5|0.4|16|0.4|
|Of which: Least developed countries|2|0.5|3|0.4|6|0.4|17|0.4|
|**Memo:**|||||||||
|Developing econ, excl. China & India|57|14.9|131|15.8|256|16.7|707|18.7|
|Upper middle income, excl. China|23|5.9|42|5.1|96|6.2|220|5.8|
|Lower middle income,excl. India|10|2.5|22|2.7|37|2.4|111|2.9|
|**BY REGION**|||||||||
|World|387|100.0|830|100.0|1,535|100.0|3,784|100.0|
|Developing economies|63|16.2|141|17.0|303|19.7|1,000|26.4|
|Asia|26|6.6|87|10.5|196|12.8|742|19.6|
|Latin America & Caribbean|16|4.3|28|3.4|56|3.6|115|3.0|
|Europe, Middle East & North Africa|13|3.4|16|2.0|36|2.3|105|2.8|
|Sub-Saharan Africa|8|1.9|10|1.2|15|1.0|38|1.0|
|**Memo:**|||||||||
|BRICS 5 countries|9|2.4|26|3.1|70|4.5|382|10.1|
|OPEC 12 members|10|2.5|8|0.9|15|1.0|30|0.8|

Source: Based on World Bank World Development Indicator database.
rate in the energy eciency. We set the energy eciency in 2004 equal to one, and obtain the growth rate in the energy eciency from World Energy Council (2008).

# C.1.9 Forestry Sector

We set the number of forest tree vintages to 100 and assume that average densities of managed forest land corresponding to dierent tree ages are uniformly distributed. Following the literature on the economic analysis of managed forests (Sohngen and Mendelsohn 2007, Sohngen et al. 2009b) we assume that the merchantable timber yield function is given by the following equation: In equation (C.13),the parameters _ψ_ 1 and _ψ_ 2 are growth parameters determining the support and the slope of the timber yield function, and _<u>v</u>_ is a minimum age for merchantable timber. The yield function (C.13) parameters, the minimum age for merchantable timber, and the average planting and harvesting costs come from GTAP Global Forestry Data Base (Sohngen et al. 2009b). Similar to the agricultural sector, we assume that the merchantable timber yield per hectare of forest land with the average tree age _v_ grows linearly across time, adding a constant amount of technology gain per annum: where the parameters _θv,_<sup>_w_</sup> 0<sup>and</sup><sup>_κw_</sup> _v_<sup>correspondtotheinitiallevelsandtech-</sup> nology gains to the merchantable timber yield of vintage _v_ . We obtain the data for yield growth in the commercial forestry sector by annualizing the dierence in the average yields from global forest studies of Sedjo (1983) and Cubbage et al. (2010).

Forest harvesting costs are given by 20
# **1. Introduction**

A financial revolution is in progress. It is not happening under the skyscrapers of New York or on the streets of London. It is not taking place in Beijing or Mumbai but in the slums of Nairobi and in the markets of Kisumu (Mas, 2010). It is not the micro-lending with which developing and emerging markets are associated but something at the other end of the financial spectrum in the traditionally least exciting part of the financial system - payments. Notwithstanding this, it has fundamental implications for financial development and financial inclusion, for our understanding of financial systems, and for their regulation and supervision.

The revolution is mobile banking – the use of mobile phones to make financial transactions. Mobile money or branchless banking schemes are sprouting across the world. According to the deployment tracker of the GSM Association, one scheme was launched in 2001. By 2006, there were just 10 globally but the success of M-PESA in Kenya, which was launched in 2007, appears to have provided added impetus. 25 schemes started in 2009 and 38 in 2010. 2011 is on course for over 50 deployments. By the end of 2011 over 140 mobile money ventures will be operating globally, up from 95 currently. The current boom is focused on Africa with 45 schemes so far, followed by Asia and the Pacific with 25 in operation and Latin America with 12.

The verdict on the viability of the schemes is still out. One success currently stands out: M- PESA in Kenya signed up over 50 percent of all adults in the nation in less than 4 years to a mobile phone-based retail payment system. Brazil established “correspondent banking” around 2000. Over 95,000 shops across the country provided basic facilities for customers to make payments using a Point-of-Sale (POS) device, not a mobile phone. While Brazil is, next to Kenya, the country with the most far-reaching retail payment scheme, the financial viability of the approach remains fragile<sup>1</sup> .

2
4 important implications for the policy agenda of Russia and other former Soviet states.

The evidence for these two arguments is sumnarized below in the remaining two parts of the introduction. Section B below explains the 3ource and organization of the international and Soviet data used in the cross-country **analysis of** the role of services. Section C then reports cn che statistical **analysis of the** role of services and provides estimates of the gap in the actual and expected levels of service activity in Russia and other former Soviet states. Section D discusses the possible contribution of services to growth in Russia and **other former** Soviet states based on simulations of expected levels of service activity. Implications are then drawn for the governments' policy agenda. **The Gap in Service Levels** It is well known among Soviet specialists that the structure of the USSR economy, and to a lesser extent other socialist countries, was ciistorted compared to other countries--with a large industrial base and a small service sector. This unique economic structure developed for several reasons. First, central planning replaced the decentralized trading function with a centrally directed distribution system. Second, private production -- which might have otherwise fostered local, decentralized service providers -- was banned. And third, a Marxian doctrinal bias against both "non-productive" services and consumption led to a double bias against household consumption of services. The service deficit economy has been identified and discussed in such studies as Ofer 1973 and 1987 and others.

In 1985, onJy 40 percent of the GDP at factor cost of the USSR was produced by service industries - - including both publicly and privately provided services -- and only 39 percent of the USSR labor force was employed by the service
Figure 1: Relative Resource Endowments <!-- Start of picture text --> a Lan\<br>L.a moacco<br>Gr aap<br>EflA SI<br>La,DS~~~~~~~<br>-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>Labor 0.1 0.5 1.0 3.Z lO 32 CauftaJ.<br><!-- End of picture text --> Souzce: world Bank (1992), FAD (L99Z). UrODP (1993)