rafmacalaba/datause-encoder-data huggingface dataset:
20 to 30 million per year for the next decade, counting the potential lay-offs of half of the people currently working in agriculture, SOEs, and TVEs. 20 Though different in estimates, these projections show that a considerable number of new jobs will have to be created – taking a medium estimation, about 100 million (mostly in urban areas) in the next ten years. The high technology and advanced manufacturing sectors will not be able to add more than 20–30 million new jobs. Therefore, the main sources of new job creation will continue to be labor-intensive industries, such as textiles (where China is projected to increase its global market share from 20 to 40% by 2010) and the service industries (including tourism, building and construction, etc.).

To dramatically increase employment in the service sector would be no easy job. Most of these new jobs will be in informal service sectors and basic infrastructure services (construction, transport, telecommunications), tourism, retailing, and commerce. Another important source for generating employment would be small private high-value business services (such as marketing, branding, consulting, management, financial, logistics, distribution) and other professional services. These are more intangible and knowledgeintensive sectors and traditionally underdeveloped in China, but they are the critical components for a knowledge-based economy. 21 If China wants to upgrade its industrial structure and greatly enhance its international competitiveness, these areas have to be well developed.

In sum, although the WTO accession will certainly help generate some additional employment by attracting more foreign direct investment (FDI), boosting certain industries, and expanding related services, considering the poor productivity in most industries and sectors in China (see the following analysis) and the fierce international competition after further opening up, the negative employment impact of the accession would be far greater than its positive effect, especially in the short run.

# **_II.3 Weak Productivities – An International Comparison_**

In China, the majority of the labor force is still in agriculture, traditional low and medium technology industries, and low skill services. These sectors are normally not performing very well, which can be seen from their poor productivity rates and a surprising decline in the share of services in overall GDP, despite rising prices.22 One of the reasons is the lack of new technologies and management efficiency. Thus, it is crucial to disseminate new technologies and advanced management practices throughout the economy.

## **Figure 1  Agriculture Productivity versus GDP/Capita, for the Period 1979–81**

> 20 See 1999 Chinese Academy of Social Science report by Feng Nanrui. Note also that the tenth 5 Year Plan estimates that 40 million rural laborers will have to find jobs outside of the agriculture sector.

> 21 Dahlman, C. and J. Aubert, _China & Knowledge Economy: Seizing the 21st Century_ , 2001, the World Bank, Washington, DC.

> 22 See Hwa (2000) for data and an analysis of the underdevelopment of the service sector in China.

9
Senegal and Tanzania. The data comes from the World Bank International Trade Department. In each country around 100 ...rms provided answers to the questions. The questions are designed such that each ...rm can give multiple answers to each of the questions. Therefore percentage do not add up to 100. The ...rst question investigates what are the main supply constraints to the expansion of the company exports. Among current exporters one answer largely dominates. 34% of current exporters cite the lack of ...nance as a main obstacle for the expansion of the company production to the larger scale. And 34% of current exporters cite the lack of ...nance as a main obstacle for improving the quality of the company’s products. Among past exporters–that at the time of the interview had ceased activities–47% cite the lack of ...nance as a main obstacle for surviving in the exporting activity. The second question further considers the costs relating to exporting as potential constraints to the company sales expansion abroad. Once more, the answer that dominated among companies operating in agriculture is the costs related to complying with SPS regulation (25% of the respondents). These results highlight the large dependency of ...rms on external ...nance to support both their working capital and longer term investment needs.

# 2.2 Standards, Cost of Compliance and Trade Duration

In recent years, developing countries’exports in high-value food products to developedcountry markets have expanded considerably in response to changing consumer tastes and with the support of advances in production, transport, and other supply chain technologies. Characterized by relatively high income elasticities of demand and lower price volatility than many traditional developing country export commodities, highvalue food products have emerged as a potentially major source of export growth for many African countries. Still, trade in such products is governed by a growing array of standards. Not only are food standards stringent, they are increasingly so. Standards relate to the products themselves and to the processes by which they are produced and handled.

To enter foreign markets African agri-food exports have to satisfy both stringent public and private standards. The EU being the main destination for Sub-Saharan African agricultural exports (see Appendix 8:A), the EU legislation remains of primary importance to these countries. The EU new comprehensive food safety policy (EC R 178/2002) involves the adoption of an integrated approach covering all aspects of the food chain. Traceability implies that EU food companies have to document from/to whom they are buying/selling produce such that products can be traced back to their origin in case of food safety problems. While, they are limited to a “one step forward, one step back”principle within the EU, with no obligations to keep records in third countries, in practise, however, EU buyers are going beyond the strict legal requirements. Complete African ...rms.

5
mortgage can help mitigate liquidity constraints and help realize large investments in housing earlier, mortgage debt holding itself may turn into a financial burden. If the income stream used to make repayment is subject to macroeconomic shocks, in the absence of a savings buffer, a household may fall into financial distress. These shocks can be channeled through income shocks (such as an economic slowdown) or through exchange rate (if the mortgage is in foreign currency) or interest rate (in case of variable-interest mortgages) shocks. Further, if the mortgage payments represent a large share of disposable income, rising debt burden may curb the household’s ability to respond to shocks to other expenditure categories, such as in case of rapidly rising food and energy prices, as observed in 2007 and 2008.

In this section, we assess whether mortgage holders are more subject to financial distress than non-mortgage holders, be they renters or owners without mortgage. Specifically, we use different financial burden and vulnerability measures and test for the marginal effect of being a mortgage holder on financial distress controlling for numerous other household characteristics. In the existing literature on household loan delinquency, the relevant threshold used to identify household borrowers in financial distress or at risk of default has been based on three primary approaches. First, some studies have used the concept of a “financial margin” (e.g., Johansson and Persson, 2006; Vatne, 2006; Żochowski and Zajączkowski, 2007 and 2008) or the disposable income net of debt service payments and average basic living costs. Second, some studies have employed a more arbitrary approach, using some indicative threshold debt service obligation as a share of disposable income, typically around 30 percent (e.g., Beer and Schurz, 2007). A third approach analyzes actual financial distress, as evidenced by mortgage payment arrears and other types of payment arrears.<sup>18</sup> The analysis in this section falls under this third approach. We control for the same variables that we used as explanatory variables in

> 18 Cash-flow difficulties have been analyzed, for example, by La Cava and Simon (2005) using Australian micro data and Whitley, Windram, and Cox (2004) using household survey data from the UK.

23
understanding the determinants of foreign direct investment. However, just as for services trade, they tend to be extremely difficult in terms of identification and impact assessment. It will therefore be important for future research on transparency to cover all of these dimensions.

Finally, a question as to the mechanics of reform underlies all of the above points. Although increased transparency and regulatory reform might be in the national interest, such moves might be opposed by vested interests and lobby groups. The political economy of reform is thus an important area for future research—including most importantly in relation to corruption and unofficial payments. Corruption does not exist in a vacuum, but is the outcome of a complex set of interactions among traders and officials, taking place against the background of national trade policy choices.<sup>21</sup> Moving forward on corruption therefore requires detailed analysis of its determinants, as well as on the design of incentive-compatible policy reforms.

# **References**

Acemoglu, Daron, Simon Johnson, and James A. Robinson, 2001, “The Colonial Origins of Comparative Development: An Empirical Investigation”, _American Economic Review_ , 91(5), 1369-1401.

Anderson, James E., and Douglas Marcouiller, 2002, “Insecurity and the Pattern of Trade: An Empirical Investigation”, _Review of Economics and Statistics_ , 84(2), 342-352.

Anderson, James E. and Eric Van Wincoop, 2003, “Gravity with Gravitas: A Solution to the Border Puzzle”, _The American Economic Review_ , 93(1), 170-192.

Anderson, James E. and Eric Van Wincoop, 2004, “Trade Costs”, _Journal of Economic Literature_ , 42(3), 691-751.

Arvis, Jean-François, and Monica Alina Mustra, 2007, “Measuring Global Connections: A New Set of Logistics Indicators”, Mimeo, The World Bank.

> 21 On this point, see: Fisman and Wei (2004), Gatti (1999, 2004), Javorcik and Narciso (2007), and Fisman and Gatti (2006).

31
scores (worse mental health) decreases the probability of being employed by 6.4 percentage points (or 14.1%, which equals 6.4/45.4); it also decreases weekly labor income by $192 Australian dollars (or 26.8%).<sup>11</sup> These effects are highly statistically significant at the 1% level, and the IV-Logit-FE and IV-FE estimates are between 1.4 and 3.8 times larger than the FE estimates.<sup>12</sup> While we offer the first estimates of the impacts of mental illness on refugees’ labor outcomes, it can be useful to compare our estimated effects with those on the general population in previous studies. For example, analyzing data from the HILDA, Frijters et al. (2014) show that a one standard deviation decrease in mental health leads to a 30 percentage point decrease in the probability of being employed.<sup>13</sup> Using data from the National Comorbidity Survey-Replication (NCS-R) in the United States, Chatterji et al. (2011) find that psychiatric disorder is associated with reductions of 13-14 percentage points in the likelihood of employment. Findings from our study add new and useful evidence for policies to support refugees, who are especially vulnerable to mental illness as discussed earlier. We return to further comparison in Section 4.3.

Table 2 shows that mental illness also has a negative impact on other labor market outcomes.<sup>14</sup> Several findings stand out from this table. First, refugees with higher mental health scores are 3.5 percentage points (or 8.4%) less likely to participate in the labor force (Column (1)). Second, refugees with worse mental health are less likely to have a permanent job, although the effect is statistically insignificant (Column 2). They are also 3.2 and 5.6

> 11 Our findings remain consistent when using the inverse hyperbolic sine transformation of the weekly labor income outcome (see Table A18, Appendix A).

> 12 Our results differ from Connor (2010), who employs an non-instrumented approach and finds little evidence for the correlation between self-reported sadness/depression and earning among refugees in the United States. But the bias of the non-instrumented FE estimates that we find may partially contribute to this difference. 13 The foreign-born population, however, is underrepresented in the HILDA survey (Watson and Wooden, 2010). Furthermore, it is not possible to identify refugees from HILDA as information on visa type is unavailable. 14 We use the same IV approach as in Table 1 to examine other labor market outcomes. This requires an assumption that the exclusion restriction remains valid, or our instrument affects labor market outcomes only through changes in mental health.

14
TABLE **5: Hourly Wage Differentials ARGENTINA (Peso, $1988=1.00)**

||**Sala**|**ried La**|**bor Market**|<br>|**Self-Em**|**ployed**|
|---|---|---|---|---|---|---|
||1988|1997|Difference|1988|1997|Difference|
|5Oth/lOth|2.16|2.29|0.12|3.00|3.40|0.40|
|50th/25th|1.45|1.52|0.07|1.50|2.08|0.58|
|75th/25th|2.45|2.29|-0.17|2.34|3.45|1.11|
|90th/50th|2.67|2.09|-0.58|3.23|3.00|-0.23|
|90th/75|1.58|1.39|-0.19|2.07|1.81|-0.26|
|90th/lOth|5.78|4.78|-0.99|9.69|10.20|0.51|
|95th/5th|10.50|8.48|-2.02|27.29|22.29|-4.99|
|Variance of Log Wage|_0.55_|0.44|-0.12|0.88|0.98|0.10|
|Coefficient of|0.94|0.73|-0.21|1.48|1.24|-0.24|
|Variation|||||||

**BRAZIL** (U.S. **Dollars, $1995=1.00)**

||**Sala**|**ried Lab**|**or Market**|**S**|**elf-Emp**|**loyed**|
|---|---|---|---|---|---|---|
||1989|1995|Difference|1989|1995|Difference|
|50th/lOth|2.26|2.38|0.12|3.63|2.50|-1.13|
|5Oth/25th|1.82|1.67|-0.15|1.78|1.67|-0.12|
|75thW25th|3.88|3.41|-0.47|3.47|3.33|-0.14|
|90th/50th|6.60|5.76|-0.84|7.80|6.00|-1.80|
|90th/75|2.06|1.92|-0.14|2.40|1.88|-0.52|
|90th/lOth|9.96|9.36|-0.60|16.95|9.38|-7.57|
|95th/5th|22.50|15.36|<br>-7.14|42.17|20.00|-22.17|
|Variance of Log Wage|0.97|0.78|-0.19|1.25|0.88|-0.37|
|Coefficientof|1.60|1.50|-0.10|3.69|1.96|-1.73|
|Variation|||||||

# COSTA **RICA (Colones, $1989=1.00)**

||**Sala**|**ried Lab**|**or Market**|**S**|**elf-Emp**|**loyed**|
|---|---|---|---|---|---|---|
||1989|1995|Difference|1989|1995|Difference|
|5Oth/1Oth|1.88|1.85|-0.02|3.25|2.75|_-0.50_|
|50/25th|1.48|1.44|-0.04|1.95|1.72|-0.23|
|75tWV25th|2.39|2.44|0.04|3.60|3.13|-0.47|
|90th/SOth|2.42|2.55|0.14|3.12|3.33|0.21|
|90th/75|1.49|1.51|0.02|1.69|1.83|0.14|
|90th/lOth|4.53|4.74|0.20|10.12|9.14|-0.98|
|95th/5th|7.87|8.11|0.24|18.04|17.25|-0.79|
|Variance of Log Wage|0.40|0.42|0.02|0.89|0.80|-0.09|
|Coefficient of|0.80|0.77|-0.03|1.57|1.49|-0.08|
|Variation|||||||
graduates.<sup>19</sup> The peak of this kernel density plot corresponds to a ratio of just 0.065 and in the majority of cases (123 out of 190), the ratio does not exceed 0.20. However the distribution is heavily right-skewed such that this ratio exceeds 0.50 in 32 countries, i.e. in not less than a sixth of the sample. The individual countries that comprise the thick right-hand tail of the distribution include nations of the Middle-East (that predominantly send emigrants to oil producing countries), Southern African nations (that principally send migrants to the Republic of South Africa) and ex-Soviet-block members, which are characterized by significant and voluminous migrations between one another.

Unsurprisingly, for many countries, a significant disparity exists when comparing highskilled emigration rates to all destinations as when compared to the OECD alone, which until now has been the focal group in the literature. These marked differences are illustrated in Figure 3.b, which plots, for each country the gross emigration rates of college graduates to OECD destinations on the x-axis, against those to all destinations on the y-axis. In doing so, the figure highlights the importance of our comprehensive global approach. Each bubble in Figure 3.b represents an origin country and the size of the bubble is proportional to the high-skilled emigration stock from that country. Overall there exists a strong correlation between our (OECD-) restricted and global measures, but in many cases, the inclusion of non-OECD destinations has a dramatic impact on the magnitude of our estimates of highskilled emigration rates, i.e. the gross brain drain, for many poorer developing countries. Examples of these differences include a 53 percentage points difference for the West Bank

> 19We use the gaussian kernel density estimator implemented in Stata.

35
power than larger firms. It therefore concludes that instead of giving quantity discounts, public monopolies could reallocate scarce resources by charging the larger firms more and the smaller firms less than they presently do. In Nigeria, the large firms would make intensive use of their idle generating capacity, while in Indonesia they would expand their facilities. In both countries, small users would realize savings by having to rely less on an expensive power generator.

Hallward-Driemeier and Stewart (2004) document patterns of access to infrastructure services by enterprises in developing countries and show that access varies by infrastructure service and firm size—with electricity often being the biggest problem, and larger firms expressing more concerns than smaller firms about all services. They report that the overwhelming majority of firms in poor countries is affected by electrical outages, leading to losses sometimes exceeding 10% of sales. In Bangladesh, China, Ethiopia, and Pakistan, improvements in the reliability of the power supply is found to increase garment manufacturers’ total factor productivity and the growth rates of their output and employment (Dollar, Hallward-Driemeier and Mengistae, 2005). Gulyani (1999) documents the impact of electricity hazards on an Indian car manufacturer and its upstream suppliers, which have devised an innovative generation and power-sharing system to solve their power problems. Gulyani argues that self-generation is economical and, combined with power sharing, can serve as a model that could be replicated to ameliorate the power problems plaguing large manufacturing firms in developing countries.

Closest to our paper are contributions by Reinikka and Svensson (2002) and, more recently, by Foster and Steinbucks (2009) and Steinbucks (2008). Reinikka and Svensson analyze a sample of 171 Ugandan firms, some of which responded to poor electricity supply by investing in generators. They show that this came at the cost of reducing overall investment and installing less productive capital. After providing very rich descriptive statistics on in-house electricity generation in 25 Sub-Saharan African countries, Foster and Steinbucks (2009) estimate that the weighted average costs of power own-generation for large firms remains relatively small and that the main victims are both existing informal firms and the formal ones that were not created as a results of the prevailing constraints. They also allude to the potential benefit of allowing firms with generation capacity to resell power into the national grid. Steinbucks (2008) uses firm-level data from Sub-Saharan African countries
We bring several different tools to enrich our analysis. Specifically, we explicitly discuss three major aspects of mobility. The first aspect is the welfare dynamics of the different income groups—both as relative positional changes along the income distribution and growth in income levels. We provide an explicit presentation of the analytical formulae to examine these positional changes, which are simple but do not appear to have been presented elsewhere. For the second aspect of mobility, we decompose it into a growth component and a distribution component. Since mobility can be driven by either economic growth or a redistribution of society’s resources (or often a mix of the two), understanding the relative contribution of each component can provide more insights into appropriate policies that ensure sustainable growth and more equality. The third aspect of mobility that we study is its linkage with inequality in the short term and in the long term. Notably, these different aspects of mobility have oftentimes been separately employed in the literature, but we combine them in an integrated manner to offer a more comprehensive picture of welfare mobility, especially for the lower-income groups.

Finally, we examine the different correlates of mobility, with a particular focus on individual employment characteristics. Almost all (i.e., 96 percent) Russians used to work for the public sector before the transformation in the 1990s thanks to the socialist ideology and economic model (Milanovic, 1998). As such, it is useful to understand whether subsequent changes in the type and sector of work are correlated with income mobility, especially since these labor transitions are amenable to policy.

Our paper is related to a few recent studies on income mobility and inequality in Russia, most notably those by Gorodnichenko, Peter, and Stolyarov (2010) and Lukiyanova and Oshchepkov Lanjouw, 2013), but these techniques currently focus on poverty mobility. See also Kopczuk _et al._ (2010) and Jappelli and Pistaferri (2009) for some recent examples of studies on long-term mobility in richer countries.

5