rafmacalaba/data-use-mentions-extended huggingface dataset:
# PLANNING AND LABOR MARKET LINKAGES IN CHINESE SECONDARY VOCATIONAL AND TECHNICAL EDUCATION

# IJ INTRODUCTION

China has embarked on a series of reforms designed to improve the efficiency of productive enterprises through the introduction of elements of a competitive market economy. Vocational and technical education and training (VTE) is to be expanded and improved to meet the skilled labor requirements of a changing economy.

The efficiency of the VTE system in meeting changing requirements for skilled labor depends in large part on effective planning and linkages with employment. This study analyzes VTE planning and labor market linkages in the context of the economic reforms, and in comparison with the vocational education and training systems of other countries.* The discussion is constrained by several lim. Firsts the focus of the larger study of which this forms a part han been on<sup>1</sup> training and education for employment in industry and the tertiary sector only, plus some training for self-employment. Hence training for agriculture and rural employment has not been addressed. As 61 percent of China's population lives in the rural areas of the country, the special focus of the larger study constitutes an important limitation.

*This analysis has been undertaken as part of a larger World Bank study of VTE in Cnina, and is based on interviews with Chinese officials at various levels, a survey of schools in three provinces, observation of VTE institutions and enterprises in two of the three provinces, and review of relevant literature.
because directly asking about income was gauged to be both a sensitive question and potentially difficult to report in the context of irregular income streams.

# **Statistical Analysis**

All analyses were conducted in Stata Version 17.0. Survey probability weights were applied. For all regression analyses, we used Stata’s ‘svy’ prefix to account for the complex survey design, employing linearized standard errors. Analyses were conducted separately by child age group, as child development outcomes were measured differently for the two age groups (i.e., CREDI for children ages 0-35 months and MELQO-MODEL for children ages 36-72 months). Additionally, examining these age groups separately has a conceptual justification, in that prior qualitative evidence from rural communities in Pakistan has found that children’s experiences of individual, family and household, community, social and cultural factors can vary as the child ages and becomes increasingly mobile and independent (Jeong et al., 2018). This includes how mothers and fathers differently engage with their children (Jeong et al., 2018). Disaggregating by child age allowed us to more precisely explore the relationship between child gender and key risk and protective factors across the early childhood period.

Risk and protective factors were selected a priori informed by literature indicating associations of variables of interest with child development outcomes in Pakistan and globally (Hentschel et al., 2023; Rathore et al., 2023; Rahman et al., 2003; Tomlinson et al., 2023). Province, child age, caregiver gender and monthly expenditure on mobile phone (a proxy for income) were also identified a priori as covariates given existing evidence in the Pakistan context (Hentschel et al., 2023). We additionally controlled for sociodemographic variables including maternal and paternal education, rural residence, and household size. Descriptive statistics and bivariate associations of child development outcomes, risk and protective factors, and demographics with child gender were first summarized within each age group in the non-imputed data. Bivariate relationships were explored using Pearson chi
of the civil war. The HFPS sample is drawn from a nationally representative face-to-face survey fielded in 2019 (the 4<sup>th</sup> round of the Ethiopian LSMSISA). These combined data offer several advantages and a unique opportunity to link households’ welfare outcomes to exposure to conflict events. First, the spatiotemporal coverage of the HFPS data permits the construction of aggregate (affected versus unaffected regions) and disaggregated (household-level) measures of exposure to conflict. Importantly, because the HFPS surveys are georeferenced, we were able to merge the household data with granular conflict events data from the Armed Conflict Location and Event Data (ACLED) project. Second, the HFPS data also allow us to go beyond standard welfare measures that are typically assessed in conflict studies. In

> 7 The HFPS data were collected by the World Bank in partnership with the Central Statistical Agency of Ethiopia and were designed to monitor the local impacts of the COVID-19 pandemic.

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for surveys other than detailed agricultural surveys, the approach of aggregated land is likely a more practical and affordable option. Similarly, survey practitioners may lean towards the proxy respondent approach, given the apparent ease of implementation relative to the self-respondent approach.

However, the ease of implementation of a given module must be weighed against the data quality implications associated with that choice. Moore (1988), in a literature review on survey response quality of self and proxy respondent approaches, highlighted the lack of studies isolating the impact of proxy response, calling on researchers to further explore this theory through studies carefully designed for this purpose. Since then, research on proxy respondent bias through methodological validation studies has been conducted across multiple sectors. Todorov and Kirchner (2000) found that the use of proxy respondents in representative surveys on disability resulted in systematically biased estimates of national disability rates, with proxy reports estimating 8.8% of people to be experiencing functional limitations, as compared to 16.9% indicated by self-reports (although the direction and magnitude of the bias differed across age group and disability type). In a study on the measurement of income using data from Malawi, Fisher, Reimer, and Carr (2010) found that in 66% of households, the husband underestimated his wife’s income by an average of 47%. This resulted in total household income being underestimated by 26% on average across the full sample when the husband reported income for himself and his wife, as compared to total household income measured through husband and wife self-reports. Kilic et al. (2020) explored the implications of proxy versus self-responding on measuring employment in Malawi, finding that allowing proxy respondents led to underestimates of employment in multiple sectors and with genderdifferentiated magnitudes relative to private self-respondent interviews.

The effect of proxy respondents on the measurement of assets in low-income contexts has recently been explored by the EDGE initiative and the Living Standards Measurement Study-Plus (LSMS+) project.<sup>3</sup> The United Nations Guidelines for Producing
The current global financial crisis has underlined the need for further and deeper reforms, while at the same time potentially undermining progress made so far. The drastic reduction in international capital flows forces most countries in the region to rely more on domestic resources and increase domestic intermediation efficiency. Beyond macroeconomic policies and institutional reforms, issues of market structure will become important in the coming years. Theory and international evidence are ambiguous on the relationship between bank concentration and stability. The experience in Nigeria is too fresh to draw inferences from, but has raised awareness of the issue across the region. As we will discuss below, there is no clear mapping from market structure to competition; attempts to consolidate the sector might increase efficiency and stability, with ambiguous implications for outreach, but the ultimate effect will depend on the future ownership structure of the Kenyan banking system and the underlying financial infrastructure, including the contractual framework and possible credit registry.

The remainder of this chapter is structured as follows. Section 2 discusses Kenya’s financial safety net, including recent changes in the regulatory framework and the deposit insurance scheme. Section 3 addresses the stability of banks, in the context of the current market structure. Section 4 presents trends in interest rates spreads and its components over the past seven years. Section 5 discusses the outreach of Kenya’s banking system, based on recent household surveys. Section 6 discusses the case for regulation-induced consolidation in the sector, based on the findings of the previous sections and international experience, and section 7 concludes.

# **2. The Regulatory Framework for Banking in Kenya**

Given the critical role of banks for a modern market economy, the opacity of banks’ balance sheets, the dispersion of banks’ creditors – typically many small depositors – and the maturity transformation banks perform converting short-term deposits into medium- to long-term assets there are limitations to market discipline and additional sources of fragility, compared to nonfinancial corporations. Banking has therefore historically been one of the most regulated sectors, with regulation ranging from licensing requirements
Before the commencement of the warrantage program, we conducted a census and a baseline survey in September and November 2014, respectively. These surveys provided extensive information on socio-demographic characteristics, consumption, food security and diversity, agricultural investments and production, access to credit, and savings. Concurrently, COPSA-C implemented several sensitization activities with CISV support.

Immediately after harvest in December 2014 and early January 2015, we implemented public lotteries that operationalized the randomization. Specifically, we used a two-stage randomization process, with the first stage conducted at the village level and the second at the household level. Villages were randomly allocated to either the high exposure or the low exposure treatment condition, in which the probability of access to warrantage was 67 percent or 33 percent, respectively, for interested households. Village-level randomization was stratified on village storehouses (i.e. multiple villages store in the same nearby village storehouse), and randomization was implemented in Stata. Thereafter, we implemented the household-level randomization. In each village, we conducted a public lottery in which each interested household drew a ball from a bag to determine their treatment status.<sup>6</sup> In high exposure villages, the bag contained 67 percent of winning (green) balls, while low exposure villages had only 33 percent of winning balls.

Crops were stored after the lottery until May 2015, when bags were redistributed to the farmers. At the time of both storage and redistribution, we collected administrative data documenting the types of crops stored, as well as the number of bags owned by each farmer. Finally, we conducted an endline survey in August 2015, several months into the subsequent agricultural growing season.

# **3. Data and Empirical Strategy**

_3.1 Data Sources and Description_ 9
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reconstruction years after World War II), was the so-called "golden age" of the 1950. and 1960s. During the two decades to the watershed set by the first oil shock, output growth was close to 5 per cent per annum, cyclical fluctuations were extremely mild by the standards of 0arlier periods and unemployment virtually disappeared in most OECD countries. A further notable feature of these years was the sharp rise in capital accumulation. Pre-1950 investment data are poor, but the available estimates suggest that the non-residential investment to GNP ratio was some 75 per cent higher in Europe in the years 1950-70 than in the interwar period [Maddison, 1976], whose ratios were, in turn, equal. to, or higher than, those of the years to 1913 lKuznets, 19661. The same is true for Japan [Ohkawa and Shinohara, 1979] (but net for the United States for which both growth rates and investment ratios were highest in the 19th century).

While a number of authors have looked at the reasons for the relatively pronounced differences in performance auross countries in this period [Kaldor, 1966; Denison, 1967; Kindleberger, 19671, few have studied the more general issue of overall acceleration. Some of the explanations that have been put forward stress the favourable impact, at least for Europe and Japan, of cheap labour and cheap technology [Maddison, 1964; Kindleberger, 1967; Cornwall, 1977]. The abundance of underemployed labour resources in agriculture or in countries of emigration, on the one hand, and the presence of a large technological gap vis-a-vis the United States, on the other, did, in this view, concur in boosting profit expectations, profits themselves and ultimately investment.

Such an explanation would seem eminently plausible. It is well known that the 1950s and 1960s witnessed a massive influx of labour into indus:ry from agriculture and, in North-Western Europe, also from other
either 1960 or 200 are removed. With subregional shares, the differences in the log ratios are small, but the correlation coefficients are not as high as in other cases because Chile‘s immigrant profile is bimodal. Chile has a small number of large immigrant stocks and a large number of very small stocks. Although the predictions for the size of the stocks are reasonable, the relative rankings are not as accurate.

The results indicate that interpolation is the most effective method of allocation, although the allocations based on the propensity measures and on the subregional shares fair reasonably well. This is heartening, since around a quarter of the observations and 14 percent of the world migrant stock is allocated for 2000 using interpolation. Filling a missing country-year of data using propensities is less effective. Even so, the correlations remain high and the resulting data are not sufficiently inaccurate to warrant throwing them away. It is important to remember, however, that simulation 1 represents a worst case. This extreme measure is resorted to only for a few countries for which data are missing. In almost every case, aggregate categories are much narrower in the raw data. Nevertheless, even with this constrained method with extreme assumptions (missing all data for a country in a region with very few comparable countries), the results seem reasonable. And even when the results are skewed, this is generally due to the over- or underpredicting of a handful of key migrant corridors.

Finally, the aggregate figures obtained are compared with those from the Trends in International Migrant Stock database (United Nations 2006, 2009) to highlight key differences. The database provides data by destination only, not for each bilateral 17
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species from georeferenced data provided by the Global Biodiversity Information Facility (GBIF). In this paper, we have combined those maps with extinction threat factors to produce composite global indicators of extinction threat that provide new insights into the spatial domain for conservation planning. The paper develops threat factors for terrestrial species that include their species occurrence region sizes, their sensitivity to human encroachment, the density of neighboring populations, and the degree of formal protection. We also develop threat factors for marine species that include species occurrence region size, degree of formal protection, degree of coverage by EEZs (Exclusive Economic Zones), the impact of coastal population, and two measures of commercial fishing activity. The paper explores three approaches to composite threat indicator construction. The first comes from econometric estimates of the relationship between our threat indicators and expert-based Red List ratings from IUCN. Our approach extends numerous pilot exercises in the published database. The model uses our threat indicators to estimate the probability that a species is assigned a threatened status (NT, VU, EN, CR) by IUCN. We find a close statistical association between the threat probabilities predicted by our model and IUCN threat status assignments. We also find a close spatial association between the two variables. We use our econometric results to predict threat probabilities for mapped GBIF species that have not been rated by IUCN. We add these to the comparative mapping exercise and find that radical expansion of the species We construct a second composite indicator that assigns higher conservation priority to species based threat indicator, but modifies it to accommodate grouping by habitat scale. A spatial are more widely distributed than threatened species generally. By implication, assignment of warrant formal protection.

|medians and means of the four factors that have significant effects on IUCN threat ratings for<br>second-order differences at global scale.<br>We compare their spatial pattern with the pattern<br>concentration of high-threat areas in the latter case, although the overall spatial pattern of<br>indicators.<br>Thistrendcontinuesforthemodel-based<br>
suggest that these two targets—one for absolute poverty and one incorporating social inclusion— are reasonably consistent.

Alternatively, one can ask what reduction in the new poverty measure is implied by the expected 5% (per capita) growth rate with no change in inequality under a seemingly reasonable assumption about the elasticity. Based on the aforementioned country data for the longest available spells between surveys, and controlling for changes in inequality, the empirical elasticity of the combined poverty measure to the mean has been 0.44.<sup>37</sup> This would also get us to a poverty rate of 33% in 2022. The empirical elasticity will tend to fall over time with growth, so some extra redistributive effort or higher growth would probably be needed. Nonetheless, a target of two-thirds of the population deemed to be social included appears to be reasonably consistent with the scenario underlying the absolute target of 3% by 2022.

Reaching this ambitious target would still leave 2.2 billion socially excluded people 10 years from now—though 0.5 billion less than current projections. But remember that half of these 2.2 billion people can be accounted for by those who are absolutely poor this year. So this goal would actually entail a substantial reduction in the number of people who are currently relatively, but not absolutely, poor. That number is 1.7 billion in 2012,<sup>38</sup> but (under this target for relative poverty) it would fall to 1.1 billion by 2022.

# **Conclusions**

To set goals that can motivate extra effort, they must represent real progress but not be impossible! For absolute poverty, the proposed optimistic benchmark for the $1.25 poverty rate in 10 years time is 9%. This would clearly be a very good performance—maintaining an impressive trajectory of poverty reduction going back to the early 1980s when the corresponding poverty rate was over 50%. In the 20 years from 1990 to 2010 the developing
avoid the downward bias associated with the second de fi nition in the presence of prohibitive tariff barriers.

As before, the slope of the import demand functions were obtained from the estimation of import demand elasticities in Kee et al. (2004). Using the notation in Kee et al. (2004) it is easy to show that dmp,n,c/dpp,n = −annGDPp − m<sup>2</sup> p,n,c<sup>/GDPp−mp,n,c.If the formula in (22) were to be applied</sup> as such, it could be severely bias. Indeed, it would include in the MA-OTRI of country c levels of protection on products that c does not export at all. Thus in order to correct for this, the formula in (22) is calculated conditional on the particular product representing more than 0.1 percent of country c exports to the world. Obviously MA-OTRI can be calculated bilaterally to obtain the level of trade restrictiveness that country p imposes on exports of country c. Again the formula after the fi rst equality in (22) can be used to answer this question. But instead of summing over n and p one would obviously only sum over p to obtain MA-OTRIc,p. By de fi nition MA-OTRIc,p = OTRIp,c. To be economically meaningful, both would need to be estimated conditional on country c’s export bundle to the world.

# 4 Data

Tariff data comes from different sources. The main sources are the WTO’s IDB and UNCTAD’s Trains. Because these sources rarely provide ad-valorem equivalents of speci fi c tariffs, we use recent computations available through the MAcMap database, developed jointly by ITC (UNCTAD-WTO, Geneva) and CEPII (Paris).<sup>13</sup> The tariff data is for the most recent year for which there is data available between 2000 and 2004. For more than half the countries the
each country comprise of the following: **_a) Type:_** This variable identifies the form of the deposit insurance – explicit or implicit – present in each country. The variable takes the value of one for countries with EDIS, and zero otherwise (Table A.1.1). **_b) Date Enacted /_**

> 6 The data is available as an Excel workbook consisting of three worksheets. The first worksheet includes the main cross-country dataset, the second worksheet provides historical levels of coverage limits and coinsurance, and finally the third worksheet provides the coverage ratios (coverage limits as a share of GDP per capita).

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8 assesses 15-year-old students in grade seven or higher every three years.<sup>7</sup> Four of the five top places in the 2010 report in both reading and mathematics went to Asian countries: China (Shanghai and Hong Kong SAR, China), S Korea, and Singapore. By contrast, the US ranked 11<sup>th</sup> on reading and 26<sup>th</sup> on mathematics. Discussions of these results, such as OECD (2010), typically focus simply on scores, without factoring in the amounts that countries spend on education. It is perfectly possible in principle that the countries achieving the best outcomes are those that spend the most, and that those who do less well spend relatively little. The more interesting—but rarely asked—question from a policy perspective is how countries vary in their success at translating resources into learning outcomes.<sup>8</sup> We explore this issue using data on knowledge and skills in mathematics, reading and science from the PISA exercise, and secondary education spending data from the World Bank‟s World Development Indicators (WDI). We have an unbalanced panel comprising 118 sets of test scores and secondary education expenditure per pupil for 49 countries for some or all the years 2000, 2003 and 2006.<sup>9</sup> Because information about expenditure per student is missing in the WDI for some countries in some years, 25 of the 118 observations are interpolated or extrapolated. Although in each assessment of PISA, one of the three areas (science, reading and mathematics) is selected as the major domain and is given greater emphasis, while the two minor domains are assessed less thoroughly, differences in test scores across countries and over time are comparable because the tests are linked assessments and the scores are scaled.<sup>10</sup>

# **International health systems—OECD**

It is well known that countries vary considerably in their per capita