rafmacalaba/data-use-mentions-extended huggingface dataset:
0.008|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.6 Country Risk and Investment Policies**

|Variables|Country risk<br>score|
|---|---|
|Estimated risk of the portfolio|-0.408***|
|Allocation to nontraditional assets|-0.423***|
|Number of eligible currencies|-0.041|
|Number of eligible assets|-0.157|
|Investment horizon – Total tranched and untranched portfolio (months)|-0.404***|
|Duration – Total portfolio (months)|-0.384**|
|Duration – Liquidity tranche (months)|-0.521***|
|Duration – Investment tranche (months)|-0.296**|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.7  Independence of the Investment Committee and the Broader Governance Environment**

|Variables|Independence of<br>Central Bank|Corruption<br>perception index|Governance Pillar<br>Score|Government<br>effectiveness|
|---|---|---|---|---|
|Independent Investment Committee|
To construct the funding amount variables, we use data on the total amount of funding during the project period (2009-2013), which was collected only in the 2013 survey. For the expenditure variable, we use annual expenditure data on teacher salaries, which were collected separately in 2010 and 2013. Table 2 shows that during the project period, the largest financial support (beyond the approximately 77 million rupiah project funds per center) came from government sources (22.25 million rupiah). Project playgroups received modest financial support from parents (4.70 million rupiah) and little support from non-governmental sources (0.60 million rupiah) during the project period. In addition, the average project playgroup significantly increased the share of annual expenditure on teacher salaries from 15% in 2010 to 18% in 2013.<sup>15</sup> Our second predictor category is playgroup quality, which is captured using three variables: (i) observed quality in the playgroup, (ii) proportion of teachers with a post-secondary degree, and (iii) proportion of teachers with teaching experience in early childhood education.

Our measure of observed quality was collected only in 2013 and used the Early Childhood Environment Rating Scale – Revised (ECERS-R) (Harms, Clifford and Cryer 2005). The ECERSR uses classroom observations, playgroups in this case, to assess the various interactions that occur between and among teachers, students, and parents. Each playgroup was assessed by two raters on a 7-point Likert scale, ranging from 1 = inadequate, 3 = minimal, 5 = good, to 7 = excellent.

In an effort to align the ECERS-R data with the reality of the Indonesian context, we compared ECERS-R to Indonesia’s national standard and found that 28 out of 43 ECERS-R items were discussed in the national standard (see Table 4 in Brinkman et al. 2017). We, therefore, calculate playgroup quality using only the 28 items that were found to be common between ECERS-R and the national standard. The average playgroup
because of tax-and-transfer policy. (see Table 7).

# **V. Some international comparisons and policy implications**

The literature on international comparisons regularly indicates an equalizing effect of tax-andtransfer systems. In general, this effect is found large in developed countries when compared to developing countries (e.g. OECD vs Latin America) and mostly driven by social expenditures rather than by taxation.<sup>28</sup> This analysis of the Mongolia fiscal redistribution effect shows a similar pattern: direct monetary transfers like pensions, Child money and in-kind transfers in basic education have a marginal effect in reducing inequality of 3.4, 1.2 and 1.2 Gini points. In contrast, personal income tax reduces inequality by only 0.7 Gini points and Social Security contributions by only 0.4 Gini points (see Table 6).

Mongolia has features in its redistributive fiscal system like other developing countries. Using the same methodology, comparison with eight countries studied by (Inchauste & Lustig, 2017) shows that Mongolia has, in most cases the same direction of impacts. That is, direct taxes and transfers as well as in-kind transfers reduce inequality. The exception is indirect transfers in Mongolia which, in contrast to other countries, have an inequality-increasing effect. In terms of magnitudes, the redistributive effect of Mongolian taxes and transfers stands in the middle of the effects observed in the countries of the comparison. For instance, public health reduces inequality in

> 27 The volume of subsidies allocated by the Mongolian government to electricity and heating companies is not explicit in official publications like the Mongolian Statistical Yearbook or Mongolian Information System. This study estimates, using the methodology explained in section III, that nearly 400 billion MNT is the value of the difference between production costs and tariffs for electricity and heating. This would represent about 1.7 percent of GDP in 2016.

28 Recent studies are: (Hanni, Martner, & Podestá, 2016), (Goni, López, & Servén, 2011)
As noted above, a key benefit of incorporating sub-area level auxiliary data in a household model framework is increased efficiency. In Burkina Faso, the mean estimated mean-squared error for sampled areas was half as small when estimating a household model with sub-area predictors (Edochie et al, forthcoming). A similar 45 percent reduction in mean MSE was observed for in-sample municipalities in Mexico when incorporating sub-area level predictors (Newhouse et al, 2022). While these are only two contexts, they suggest a large efficiency gain when using a household model with sub-area level predictors, relative to an area-level model.

Another option is the sub-area level model given in equation (2), in which the unit of analysis is the subarea and the dependent variable is sub-area-level poverty rates, in the spirit of Torabi and Rao (2014). Unfortunately, there is virtually no empirical evidence to our knowledge on the relative accuracy of estimates produced by a sub-area vs. a household model. In Mexico, when using a single household survey sample, the R<sup>2</sup> was equal to 0.70 relative to the evaluation benchmark, less than the 0.74 value for the household-level model. However, this only pertains to one context, and further research is needed to rigorously evaluate these different types of models.

Many of the same studies also compare coverage rates across the models, which are useful for evaluating the accuracy of uncertainty estimates. If estimates of uncertainty are unbiased, coverage rates should be approximately equal to 95 percent. Table 4 lists estimated coverage rates from selected studies. When estimating the household model, confidence intervals are calculated based on mean squared error, under the assumption that the point estimates are unbiased. For estimating coverage rates, generally the area-level model fares better than the household model, providing coverage rates of 18
For instance, most studies explicitly recognize the multiproduct nature of the port business, although a few focus on aggregate output measures. The most common is to see a disaggregation between general, dry-bulk, liquid-bulk and containerized cargo. On the input side, labor and capital tend to be present in every study. Everyone seems to agree on the labor variable but there is no consistency in the way the capital variable is being measured. Only a few studies have accounted for environmental variables (i.e. Is it a mainland port or an island? Is some of the cargo delivered by pipeline? ...).

It may be useful here to point to some very pragmatic implementation issues in the estimation of efficiency in the sector. It is not unusual also to see changes in the definitions of some of the variables during the periods of assessment. In the case of labor for instance, reforms tend to lead to major outsourcing policies. In this context, the employment data may end up comparing apples and oranges over time if the researcher is not careful. Indeed, the post reform data would only pick up employees under the port authority payroll and cover the others under the costs of service contracts. The risk is however limited for studies focusing on port authorities since they tend to be quite narrowly focused and are associated with very little outsourcing. For studies attempting to have a wider coverage of port services, the issue is more problematic.

With these limitations in mind, the few papers available provide a number of useful lessons. These can be separated in two types. From a market structure viewpoint, the research provides useful insights to any policymaker in developing countries. First, with respect to size, Cullinane et al. (2002) show that the size is directly influencing efficiency, at least in container terminals. Second, container terminals located in hub ports tend to be more technically efficient than those in feeder ports as documented by 15
WPS3890

# **Migration in Towns in China, a Tale of Three Provinces: Evidence from Preliminary Tabulations of the 2000 Census**

Anqing Shi* Development Research Group The World Bank

# **Abstract**

There is a concern that the growth of towns in China has been stalled recently and with it, the creation of non-farm jobs in rural industries. This study uses the 2000 census tabulations to look at this issue by examining in-migration in towns in three provinces in China, Zhejiang, Henan, and Sichuan, their educational attainment, original place, and occupational composition. In addition to the diversified patterns of town in-migrants revealed in the three provinces, the paper finds that town in-migrants generally possess a higher level of educational attainment than the local population in towns, especially in the less-developed western and central regions. This inflow of human capital could foster development in towns. There is also evidence that as economic opportunity increases in towns, such as in the richer coastal province of Zhejiang, better educated people in rural areas are likely to shift their jobs from the farm to the non-farm sector in towns nearby, instead of leaving the countryside to migrate to other provinces. This could reduce migration pressure on big cities. Finally, the labor market in towns in the less-developed west and central regions is more flexible in accommodating in-migrants, whereas in the developed province of Zhejiang the labor market is segregated between migrants and the local population.

Key words: migration, town development, Chinese census, labor segregation World Bank Policy Research Working Paper 3890, April 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank,
to each country’s data set in the RIGA database. Specifically, as in Clementi et al. (2020), we consider here the following household budget surveys: the Ghana Living Standards Survey of 2005; the Kenya Integrated Household Budget Survey of 2005; the Nigeria Living Standards Survey of 2004; and the Uganda National Household Survey of 2005.

For a second group of SSA countries—Burkina Faso, Ethiopia, Malawi, Niger, Rwanda, and Tanzania— we rely on data from the Rural Livelihoods Information System (RuLIS), a major database to access cross-country comparable data and information on household income and expenditures at the microlevel.<sup>5</sup> Started as a joint research project among FAO, the World Bank, and the International Fund for Agricultural Development, the RuLIS database builds on the methodology developed and adopted by the RIGA project, whose procedures were deeply revised, integrated, and extended to RuLIS. In general, while the RIGA project aimed at constructing comparable income measures from household surveys in order to provide annualized benchmark aggregates, which—despite differences in the quality of information in each survey—would be suitable for cross-country analyses (Carletto et al. 2007), RuLIS includes a more standardized database that is meant to be a tool for analysis and wider dissemination. The RuLIS surveys<sup>6</sup> considered in this study cover a diverse set of SSA countries,<sup>7</sup> some of which have more waves of data and give us the opportunity to gauge the trends.

The main consumption variable that is used in the paper is the household total annual expenditure on food and nonfood items. As for income, the household income aggregates and their components included in the RIGA/RuLIS database closely follow the definition given by the International Labour Organization (ILO 2003), which considers as income receipts those that (1) recur regularly, (2) contribute to current economic well-being, and (3) do not arise from a
For the sample starting in January 2010 to January 2020, the average number of protests per month per country is 21. In 34 percent of country-month pairs (2,910 of 8,549), there is no occurrence of protest. Panel A of Appendix Figure A1 presents the histogram of the entire distribution of available occurrence of protests from ACLED (Appendix Table A2 for summary statistics of actual protests). The distribution supports the use of the Zero-Inflated Poisson estimator, which we discuss in the next section.

We construct an alternative measure of protests capturing the intensity of media chatter around protests. The rationale for the news-based measure of protests is first and foremost that it allows to significantly expand the scope of the data on protests over space and time. This measure accounts for the saliency of specific protests. For instance, a protest sparked by a popular figure is likely reported and discussed more extensively in the news media. The news data are from Dow Jones FACTIVA, which is a global repository of over one billion articles published across the world and reported in 28 different languages. For each language, the following words are translated: (i) “street” and (ii) “protests” or “protest”. The search for each language is constructed such that the word “street” and at least one relating to “protest” appear. For example, in English, the precise search query is: _[(“street”) AND (“protest” OR “protests”)]_ .

This search is performed for all countries and all languages in the Dow Jones database, over the period from January 1, 2000 to December 31, 2019. In addition, we gather the number of total articles in the Dow Jones FACTIVA for each country and each month. A measure of normalized article counts is then constructed by taking the ratio between the number of articles mentioning “street protests” and the total number of articles for each country-month pair multiplied by 100. Formally, for country _i_
inclusion, financing, and transparency. The evaluation and performance data used in this study, as well as by the PVP management team for GP presidents’ evaluations, were based on a subset of this monitoring data, with an additional layer of verification conducted by the research team. For this additional verification, an independent survey firm was deployed to collect data from VPRC records, GP office records, focus group discussions. Section 2.2.1 and Appendix B provide more details on the collection and verification of project monitoring data.

In addition, at the end of the evaluation period the survey team also collected hamlet-level data for the four types of public infrastructure investments most commonly delivered through GPs, and the IAY housing scheme. This data was similar to the baseline data, except they included information on new investments and IAY transfers that were implemented after the intervention was announced (during the evaluation period). As in the baseline, this data was also verified through a village focus group meeting, and on-site verification in three randomly selected hamlets per GP.

We supplement this survey and monitoring data with three rounds of the Indian Census, and GP election data from the Tamil Nadu State Election Commission that we compiled and digitized. These data sets allow us to identify reservation status of the contested seat for all GP elections, including the reservation status for the next GP election scheduled for 2016.

# **5.2 Key outcomes and multiple hypothesis testing**

We first examine GP presidents’ response to our incentives by estimating the average treatment effect (ATE) on their overall GP-level performance metrics. For this purpose, we use the evaluation score (and the letter grades) received by GP presidents, based on which their eligibility for rewards was determined, as the primary outcome of interest.

In addition, we also estimate ATEs on two sets of secondary outcomes, each related to one of the two key functions of a GP president: (1) expanding access to private transfer schemes for eligible beneficiaries in the GP;
from 7.5 percent in 2000 to 3 percent in 2050. There were 113 million workers in the state sector in 1995. This figure had decreased to about 95 million by 1999. It is assumed to decrease further by 5 million annually from 2000 to 2020, and to remain stable afterward.

Since life expectancy will increase with rising living standards as the economy grows, a gradually declining motility rates are calculated each year in the baseline according to exogenously specified life expectancy estimates from recent World Bank population projections (World Bank 1994). The total fertility rate for women of childbearing years is assumed to increase slightly between 2000 to 2050 because of China's relaxation of its 'one child' policy in the coming years.

As mentioned, the individual accounts in China's current pension system are largely notional, and thus essentially a PAYGO system. The government is responsible for both the pension expenditure in notional individual accounts and the contributions in the social pooling PAYGO system of pillar 1. Thus, we combine pillar 1 and pillar 2 in our baseline calibration and assume 60 percent replacement and 24 percent contribution rates. Detailed assumptions on macroeconomic closure, demographic parameters, and pension system characteristics used to calibrate the baseline are summarized in table 2. (The major results from the baseline calibration are presented in table 6.) 20
, the commodity under investigation here, can be produced (due to soil characteristics) in only the Southern, the Central, and the Eastern provinces. At the national level, poverty increased from 1991 to 1996 and then declined in 1998. Poverty trends in Lusaka,

> 6The Zambia Central Statistical Office, CSO, is in charge of conducting the surveys. The data from the 2001 LCMS was under preparation when this paper was written.

8
10 For more details, see annex IV, in Nuñez et, al. (2019).

> 11 For further information on the conditions and operation of subsidies and contributions, see annex IV, in Nuñez et, al. (2019).

> 12 The number refers to the deficit when only regular users who belong to interconnected areas are considered in electricity.

> 13 Data downloaded from the Utilities Information System on March 19, 2019. It is important to mention that the wide deficit in energy is partly due to the fact that decree 2860 of 2012 excluded industrial establishments from payment of contributions for this service.

> 14 There is a need to clarify that these amounts refer to central government spending and exclude the spending of local administrations. Meanwhile, the amounts found in Table I-2 include both central government’s spending as well as that of local administrations.

> 15 Firm Tax (CREE) created by Law 1607 of 2012 that taxes business profits at a 9 percent rate between 2013 and 2015 and 8 percent onwards.

> 16 For a description of the methodology employed, see annexes II, V and VI in Nuñez et, al. (2019).

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- **McLaren, John.** 2002. “A Theory of Insidious Regionalism.” _Quarterly Journal of Economics_ , 117(2): 571–608.

- **Prusa, Tomas.** 2010. “Comment.” In _China’s Growing Role in World Trade_ . , ed. R C Feenstra and S J Wei, 338–343. University of Chicago Press for the NBER.

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- **Sanderson, Eleanor, and Frank Windmeijer.** 2015. “A Weak Instrument F-test in Linear IV Models with Multiple Endogenous Variables.” UCL/CEMMAP Working Paper CWP31/15.

- **Timmer, Marcel.** 2012. “The World Input-Output Database (WIOD): Contents, Sources and Methods.” WIOD Working Paper Number 10.

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- **WTO.** 2014. _World Trade Report 2014: Trade and Development: Recent Trends and the Role of the WTO._ Geneva:World Trade Organization.

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# **b. Railways**

<!-- Start of picture text --> .5<br>0<br>-.5<br>-1<br>-1.5<br>AlbaniaArgentinaBulgariaCroatiaEthiopiaGeorgiaIndonesiaKenyaKosovoRomaniaSouth AfricaUkraineUruguay<br>Adjusted net income as percent of GDP<br><!-- End of picture text --> _Source_ : World Bank Infrastructure SOE Database.

_Note_ : Data are for 2009–18. Boxes show interquartile variation (25–75 percent) of subsidies to assets. Dark lines across the boxes show the median per year. Whiskers show the maximum and minimum of this variable per year, capped at 1.5 times the interquartile range. Dots represent outliers.

Figure 2 shows the percent of SOEs that lose money every year, net of fiscal transfers. In the power sector, a third of firms incur losses every year. In the airlines and airports sector, about 35–55 percent of firms incur losses each year, with losses lower after 2014 than before it. In the railways and roads sectors, more than 60 percent of firms failed to turn profits before subsidies every year, and the proportions remained more or less constant over the sample period. Firms in both sectors tend to be funded directly by the government to carry out construction and maintenance works.

The power sector has fewer losses than other sectors in the database. This difference may be related to the fact that most of the countries in the sample adopted sectoral reforms that improved competition and regulation, vertical disintegration, and cost recovery (Foster et al. 2017).

13
Figure 3a. Kernel density estimates of the log of crop output value <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>0<br>0 5 10 15<br>Male Female<br>Density<br><!-- End of picture text --> Figure 3b. Kernel density estimates of the log of land area <!-- Start of picture text --> .5<br>.4<br>.3<br>.2<br>.1<br>0<br>-5 0 5<br>Male Female<br>Density<br><!-- End of picture text --> Figure 3c. Kernel density estimates of the log of output per acre <!-- Start of picture text --> .5<br>.4<br>.3<br>.2<br>.1<br>0<br>0 5 10 15<br>Male Female<br>Desnity<br><!-- End of picture text --> Source: Authors’ estimations using HIES 2016. Note: Estimates are based on the monthly value of gross crop output.

7