rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5984

# **Abstract**

Evidence from a range of different sources suggests that Chinese workers lost 20–36 million jobs because of the global financial crisis. Most of these layoffs affected migrant workers, who have typically lacked employment protection, tend to be concentrated in export-oriented sectors, and were among the easiest to dismiss when the crisis hit.

Although it was severe, the employment shock was short-lived. By mid-2009, the macroeconomic stimulus and other interventions had succeeded in boosting demand for migrant labor. By early 2010, abundant evidence pointed to scarcity in China’s labor market, as labor demand was once again leading to brisk growth in wages.

The paper reviews different available sources of evidence for the effects of the crisis, and notes the biases associated with alternative ex post efforts to measure the employment effects of the crisis. In particular, the paper highlights the usefulness of household surveys with employment histories relative to surveys based on sampling through firms.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jgiles@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 6a: Per Capita Annual GDP (PCGDP) in ’000** **<mark>local currencies converted to Indian Rupees unde</mark>** <mark>r</mark> **2011 ICP Consumption PPPs and Counterfactual PPPs of Table 5 (Base Country: India)**

|||**PCGDP e**<br>|**valuated at**<br>|||**PCGDP ev**<br>|**aluated at**<br>||**PCGDP**<br>|**evaluated at**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Country**|**2011 ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|**Region**|**Country**|**2011 ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|**Region**|**Country**<br>**2011**<br>**ICP**<br>**PPP**|**Counter**<br>**factual**<br>**PPP**|
||Benin|25.54|31.98||South Africa|169.82|101.19||Albania<br>133.19|<br>394.24|
||Burkina Faso|19.72|35.11||Swaziland|90.49|146.00||Bosnia and Herzegovina<br>126.93|<br>17.52|
||Cameroon|41.06|50.44||Tanzania|21.10|37.22||Bulgaria<br>218.56|<br>35.57|
||Chad|30.11|29.59||Togo|18.88|27.73||Latvia<br>274.49|<br>54.54|
||Congo, Rep.|47.99|53.27|**AFRICA**|Uganda|21.48|27.34||Lithuania<br>314.48|<br>71.87|
||Congo, Dem. Rep.|17.33|25.62||Zambia|45.05|78.60||Macedonia(FYR)<br>160.43|<br>32.82|
||Cote d'Ivoire|38.82|30.54||Bangladesh|39.81|36.30|**EUROSTAT-**<br>**OECD**|<br>228.81|<br>111.60|
||Djibouti|33.70|19.94||Bhutan|108.43|63.68||Mexico||
||Egypt|150.21|68.24||Cambodia|37.85|30.31||Montenegro<br>187.72|<br>29.74|
||Ethiopia|16.96|15.05||China|141.35|84.93||Romania<br>216.86|<br>56.95|
||Gabon|219.64|88.68||Fiji|98.58|56.35||Russia<br>368.47|<br>70.99|
||Gambia(The)|21.48|25.13||**India**|71.54|71.54||Serbia<br>157.75|<br>31.65|
||Ghana|46.93|46.32||Indonesia|115.61|98.76||Turkey<br>244.05|<br>299.56|
|**AFRICA**|Guinea|19.59|21.80||Lao PDR|55.91|45.58||Bolivia<br>81.86|<br>40.04|
||Kenya|30.99|33.86|**ASIA AND**|Maldives|1.61|0.87||Brazil<br>202.89|<br>93.42|
||Lesotho|32.05|39.92|<br>**THE**|Mongolia|126.98|78.23||Colombia<br>161.30|<br>76.07|
||Liberia|7.50|0.14|**PACIFIC**|Nepal|32.22|25.88|**LATIN**<br>|El Salvador<br>103.63|<br>102.10|
||Madagascar|20.54|50.46||Pakistan|64.74|50.45|**AMERICA**|Guatemala<br>96.84|<br>43.00|
||Mali|21.97|29.99||Philippines|81.74|66.41||Honduras<br>61.09|<br>56.19|
||Mauritania|50.00|34.05||Sri Lanka|116.59|45.88||Nicaragua<br>59.84|<br>118.22|
||Mauritius|209.38|87.45||Thailand|191.63|49.90||Peru<br>160.15|<br>62.99|
||Morocco|90.26|41.10||Vietnam|66.06|55.57|**WESTERN**|Iraq<br>160.21|<br>137.09|
||Mozambique|14.51|27.63||Armenia|115.15|56.44|**ASIA**|Jordan<br>155.73|<br>35.15|
||Namibia|114.08|137.15||Azerbaijan|285.56|86.62||Yemen<br>52.97|<br>19.65|
||Nigeria|45.14|52.19|**COMMON**<br>|Belarus|285.86|75.61|**CARIBBEA**|Jamaica<br>109.00|<br>49.74|
||Republic of Cabo|94.07|33.29|**WEALTH**|Kazakhstan|330.60|93.97||||
||<br>Verde|||**OF**<br>**INDEPEND**|Kyrgyz<br>Republic|52.44|29.35||**MEAN 104.66**|**59.77**|
||Senegal|32.55|35.44|**ENT**<br>|Moldova|70.45|27.54||**MEDIAN 81.80**|**46.10**|
|||18.63|14.53|**STATES**|Tajikistan|36.82|18.11||||
||Sierra Leone||||Ukraine|139.91|35.11||||

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revealing a recurrent practice in empirical models of conflicts. For example, in a study on weather shocks and peasant revolts in historical China, Jia (2014) interacts indicators of droughts and floods with sweet potatoes as food production (known to be more resilient to bad weather than rice or wheat production) as a ”risk absorber” moderator. The inclusion of interaction terms can help researchers identify the mechanisms at play by highlighting heterogeneous effects depending on the characteristics of the geographical areas studied. These moderator variables are model-specific and rarely similar across studies. Because interaction models can help identify or mitigate the action of specific mechanisms, result searching bias is likely to be captured, if existent, in the choice of interacted variables.

<!-- Start of picture text --> Indirect (+)<br>Risk Enhancer (258)<br>Risk Absorber (99)<br>Undetermined (364)<br>(878) Higher risk of conflict<br>Direct (+) (1599)<br>SHOCK<br>Direct (-) Lower risk of conflict<br>(400) (744)<br>Indirect (-)<br>Risk Enhancer (159)<br>Risk Absorber (60)<br>Undetermined (125)<br>Stressfull<br>Theoretically Mechanisms<br>Pacifying<br>Theoretically Mechanisms<br><!-- End of picture text --> Notes: Expected signs of direct and indirect (interaction) estimates. The number of observations concerned is given in parenthesis. 121 observations (not shown) refer to mechanisms with uncertain sign (including 113 direct estimates, 5 indirect estimates with a risk enhancer, 3 indirect estimates with a risk absorber). _Source: Authors compilation from MRA database._ Figure 5: Expected Signs of Direct and Indirect Channels of Transmission To test if these methodological choices are a source of publication bias per se, we augment the baseline model with dummy variables indicating the type of mechanism or the type of interaction that is tested. When the multilevel random effect model includes covariates (or moderators) accounting for heterogeneity between studies, the model becomes best described as a ”multilevel mixed-effect model”. More specifically, Equations 2 and 3 become: 21
of the surveys do consider FAFH in some form. However, when looking more deeply into the way this information is collected we find a huge variation in quality. More detailed statistics reported by Smith et al. (2014) paint far from optimal picture in the collection of FAFH data. For example, a quarter of the surveys aim to capture all related household consumption from FAFH using just one question, while only one in five surveys considers multiple places of consumption. With respect to snacks, only 35 percent takes them explicitly into account, and close to half of the surveys do not include FAFH received in kind.<sup>4</sup> Recent analysis from India and Brazil provide new evidence on the implications that not accounting for FAFH can have on food security analysis. In the case of India, Smith (2013) argues that the great Indian calorie debate originated by an apparent increase in undernourishment at the time of falling poverty rates can be partly explained by inaccurate data on calorie intake due to the lack of measurement of FAFH. Similarly, work by Borlizzi and Cafiero (2014) in Brazil shows how the distribution of food consumption by income strata changes once food consumed at school is taken into account. In fact, they show that proper account for food received through a school feeding program targeted at the poorer strata of the population results in a more equal distribution of food consumption than previously thought, allowing for a long due revision of the FAO assessment of undernourishment in Brazil.

In this paper, we evaluate the impact of accounting FAFH on poverty and consumption inequality estimates in Peru.<sup>5</sup> To do this, we simulate a situation where we move from a world where FAFH is not accounted for to one where it is. In the process, we show that from a theoretical point of view, the direction of the effect on poverty or inequality of properly accounting for FAFH cannot be predicted ex-ante.

> 4 In addition to _what_ information to elicit, _how_ that information is elicited is also of great importance. In the case of FAFH, the informant plays a critical role. While most surveys apply the consumption or expenditure module to a household informant, this strategy is expected to result in much higher measurement error when it comes to reporting FAFH, because by definition most of this consumption takes place outside the house (and therefore out of sight from the informant). According to Smith et al. (2014), only 17 percent of the surveys collect FAFH at the individual level.

5 A few papers analyze the impact of different aspects of survey design on total expenditures, and poverty and inequality measures (Backiny-Yetna et al. 2014; Beegle et al. 2012; Deaton and Grosh, 2000; Gibson et al., 2003; Joliffe, 2001; Pradhan, 2001). The work by Backiny-Yetna et al. (2014) is the only one to look in particular at the impact of food consumption data collection methods on poverty and inequality.

4
the relevance of the ES is evident when one reflects upon the growing role of the corporate sector as far as income generation is concerned, over the previous three decades (Ellis and Smith, 2010).

Despite its importance, there have not been any studies that analyze the ES using firm-level data to identify what firm characteristics explain the ES observed in aggregate level studies. This paper fills that gap and contributes to the literature by analyzing the ES using firm-level annual accounting data for a sample of industrial firms in Germany, France, Italy, Japan and the UK in the period 1997-2011. First, we formally test for the existence of a trend in the ES and its components (gross savings and capital formation), and show how the three applications of the ES (debt reduction, acquisitions, and liquidity accumulation) evolved over time. Second, we identify the factors that could explain the growth trend of the ES in the last fifteen years. In particular, we seek to examine the role played by credit constraints, volatility in the business environment, and growth in operating activities in shaping the observed aggregate trend.

The analysis of firm-level data confirms the existence of an increasing trend of the ES for the total sample and for 9 of the 10 size deciles. This trend was accompanied by decline in capital formation, decrease in debt, and increase in the share of non-operating assets in total assets.

The econometric results show that: (i) the ES is related to credit rationing problems because financial-constrained firms increased their ES at a significantly higher rate than the rest of the firms; (ii) the ES growth rate was significantly higher among companies operating in a more volatile operating environment; (iii) the ES growth rate was higher among those high-growth firms that experienced the largest slowdown; and (iv) the increasing trend of the ES and the decreasing trend of Gross Capital Formation is robust to alternative specifications and sets of control variables.

The structure of the paper is as follows. Section II describes the database, defines the main variables of interest and shows some basic descriptive statistics. In Section III, we statistically test for the existence of a trend in the ES, and describe the evolution over time of the ES, its components (gross savings and capital formation), and its main applications. Section IV provides a brief literature review that allows us to frame the analysis and identify three main testable hypotheses regarding the factors driving the growth of the ES. Section V 3
- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “The Exceptional Persistence of India’s Unorganized Sector,” World Bank Policy Research Working Paper 6454 (2013a).

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “Input Usage and Productivity in Indian Manufacturing Plants,” World Bank Policy Research Working Paper 6656 (2013b).

- Ghani, Ejaz, William Kerr, and Stephen O’Connell, “Female Business Ownership and Informal Sector Persistence,” World Bank Policy Research Working Paper 6612 (2013a).

- Ghani, Ejaz, Stephen O’Connell, and Gunjan Sharma, “Friend or Family or Foe? A Tale of Formal and Informal Plants in India,” World Bank Policy Research Working Paper 6612 (2013).

- Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova, “Imported Intermediate Inputs and Domestic Product Growth: Evidence from India,” _Quarterly Journal of Economics_ 125:4 (2010), 1727-1767.

- Gonzalez, Anabel, “Helping Small Business To Be an Engine of Growth and Employment,” _The Trade Post (World Bank online blog),_ September 15 2014.

- Hsieh, Chang-Tai, and Peter Klenow, “Misallocation and Manufacturing TFP in China and India,” _Quarterly Journal of Economics_ 124:4 (2009), 1403–1448.

- Hsieh, Chang-Tai, and Peter Klenow, “The Life Cycle of Plants in India and Mexico,” _Quarterly Journal of Economics_ (2014), forthcoming.

- Hsieh, Chang-Tai, and Ben Olken, “The Missing Missing Middle ,” _Journal of Economic Perspectives_ (2014), forthcoming.

- Kothari, Siddharth, “The Size Distribution of Manufacturing Plants and Development”, Working Paper (2014).

- Kotwal, Ashok, Bharat Ramaswami, and Wilima Wadhwa, “Economic Liberalization and Indian Economic Growth: What’s the Evidence?” _Journal of Economic Literature_ 49:4 (2011), 11521199.

- Krueger, Anne, “The Missing Middle,” Chapter 9 in _Economic Reform in India: Challenges, Prospects, and Lessons_ edited by Nicholas C. Hope, Anjini Kochar, Roger Noll, and T.N. Srinivasan, Cambridge University Press (2013).

- La Porta, Rafael, and Andrei Shleifer, “Informality and Development”, _Journal of Economic Perspectives_ (2014), forthcoming.

- Lall, Somik, and Taye Mengistae, “The Impact of Business Environment and Economic Geography on Plant Level Productivity: An Analysis of Indian Industry,” World Bank Policy Research Working Paper No. 3664 (2005).

- Lall, Somik, Zmarak Shalizi, and Uwe Deichmann, “Agglomeration Economies and Productivity in Indian Industry,” _Journal of Development Economics_ 73:2 (2004), 643–673.

- McKinsey Global Institute, India’s Urban Awakening: Building Inclusive Cities, Sustaining Economic Growth, Report (2010).

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# **8 Appendix 2: Additional tables**

**Table 3: Global poverty rates in 2030**

|**Scenario**||**Poverty rate in 2**|**030 atpoverty lines**|**(USDper capitape **|**r day, 2005 PPPs)**|
|---|---|---|---|---|---|
|**Growth**|**m**|**1.25**|**1.45**|**2**|**2.5**|
|10 year historic|-2|9.0%|12.0%|20.2%|24.8%|
||-1|6.4%|8.6%|15.9%|22.6%|
||0|4.7%|6.3%|12.1%|18.4%|
||1|3.6%|4.7%|9.1%|14.6%|
||2|2.7%|3.5%|6.1%|10.4%|
|20 year historic|-2|11.8%|15.1%|23.2%|27.0%|
||-1|8.6%|11.5%|19.9%|25.3%|
||0|6.7%|8.6%|15.8%|22.8%|
||1|4.9%|6.9%|12.6%|18.2%|
||2|3.7%|5.1%|9.1%|14.1%|
|Zero growth|-2|24.6%|27.2%|36.6%|44.8%|
||-1|21.7%|25.2%|35.1%|43.5%|
||0|16.8%|22.0%|34.1%|42.3%|
||1|11.7%|17.3%|32.9%|41.2%|
||2|7.9%|11.3%|31.0%|41.0%|
|2011 Estimate||14.7%|19.5%|31.1%|38.9%|

**Table 4: Regional poverty rates in 2030**

||**Scenar**|**io**|||**Poverty r**<br>**(USDper**|**ate in 20**<br>**capitap **|**30 at pov**<br>**er day, 20**|**erty line**<br>**05 PPPs)**|**s**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Region**|**Growth**|**m**|**1.25**|**1.45**|**2**|**2.5**|**4**|**6**|**8**|**10**|
|East Asia & Pacific|10 year|-2|2.6%|4.6%|10.1%|14.2%|25.0%|39.6%|49.2%|51.9%|
|||-1|1.1%|2.2%|6.7%|10.9%|21.2%|35.3%|46.1%|52.0%|
|||0|0.5%|0.9%|3.9%|7.3%|18.3%|31.5%|41.6%|50.1%|
|||1|0.2%|0.4%|1.7%|4.2%|16.2%|27.6%|37.3%|45.3%|
|||2|0.1%|0.1%|0.6%|1.7%|13.6%|23.9%|33.5%|40.7%|
||20 year|-2|2.5%|4.2%|9.9%|14.6%|25.1%|40.1%|49.2%|52.0%|
|||-1|1.3%|2.3%|6.5%|10.6%|21.3%|35.8%|46.9%|52.2%|
|||0|0.5%|1.1%|3.5%|7.2%|18.3%|31.9%|42.4%|51.1%|
|||1|0.2%|0.4%|1.9%|3.9%|16.1%|28.1%|38.4%|46.0%|
|||2|0.1%|0.1%|0.6%|1.8%|9.6%|24.7%|34.0%|41.3%|
||Zero|-2|20.6%|24.8%|33.6%|41.2%|51.3%|69.5%|80.2%|86.7%|
|||-1|14.4%|19.2%|28.3%|36.9%|52.3%|70.4%|81.0%|87.4%|
|||0|9.4%|13.2%|23.7%|32.1%|53.3%|71.5%|81.9%|88.2%|
|||1|5.1%|8.1%|17.0%|27.6%|54.7%|73.0%|82.8%|89.2%|
|||2|2.4%|3.9%|10.1%|23.7%|41.1%|75.1%|84.4%|90.6%|
|Europe & Central Asia|10 year|-2|0.2%|0.3%|0.8%|1.7%|7.0%|15.8%|24.0%|29.5%|
|||-1|0.1%|0.1%|0.4%|0.9%|4.1%|11.4%|18.7%|26.8%|
|||0|0.0%|0.1%|0.2%|0.5%|2.4%|7.6%|14.3%|21.8%|
|||1|0.0%|0.0%|0.1%|0.2%|1.2%|4.4%|10.3%|17.1%|
|||2|0.0%|0.0%|0.0%|0.1%|0.5%|2.1%|5.2%|13.4%|
||20 year|-2|0.9%|1.5%|3.8%|6.1%|12.7%|27.1%|36.1%|40.2%|
|||-1|0.5%|0.8%|2.2%|4.1%|9.7%|20.9%|32.0%|38.6%|

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14 where . Since both contributions in this decomposition are positive, it is natural to interpret as the amount of inequality due to circumstances, and the amount of inequality due to effort. I therefore propose, as a measure of the _degree_ of opportunity equalization, the index: We may want to think of as an upper bound on the fraction of inequality due to effort because surely some circumstances have not been taken into account, whose effect is measured, residually, as ‘effort.’ My suggestion is that we measure economic development by the ordered pair . Note that neither component of _d_ is a consequentialist measure. One cannot recover either or η from knowledge of the distribution of income (more generally, the objective) alone. One must know, as well, the circumstances of individuals, which capture the concept of responsibility salient for the society in question.

# 4.  Country calculations of the <u>level and degree of development</u>

In this section, I calculate the value of _d_ for a set of OECD countries. The data upon which these calculations are based are taken from EU-SILC 2005. The sample consists of male workers, who are partitioned into three types, based upon the maximum of the worker’s parents’ educational levels: <u>Type 1: the worker’s more educated parent had at most lower secondary</u> education
registration programs (e.g. land demarcation, titling or certification) that promote joint registration of both spouses can improve land ownership and tenure security among women and reduce gender gaps when coupled with a conducive legislative framework (O'Sullivan 2017; Ali et al 2014; Goldstein et al 2018). This paper also complements the growing body of literature that documents a relationship between gender-equitable laws and women’s economic opportunities (e.g. Htun et al 2019; Islam et al 2019; Hyland et al forthcoming).

However, the effectiveness of legislative changes is conditioned by sticky social norms in an environment where women are in general seen as less valuable than men (Deere et al 2013). These may even lead to perverse effects as found by Bhalotra et al (2020b) in India, where the inheritance reforms increased female feticide. Moreover, in areas with traditional social norms and large gender gaps in property ownership, men may underestimate women’s preference for owning immovable property, thus leading to information asymmetries (Najjar et al 2020). This might open another channel for improving women’s property ownership – correcting or updating husbands’ beliefs about the preferences of their wives (e.g. using an intervention that is similar in spirit to Bursztyn et al forthcoming). Our results also provide an argument for deepening legislative reforms by enacting new anti-discriminatory laws (e.g. moving away from a separation of property marital regime, eliminating employment-related discriminations), enhancing the ambit of current laws (e.g. extending employment laws to the formal and informal sector), and working towards more effective implementation of existing laws.

There are some limitations to the analysis presented here due to the DHS data, which only provide information on the incidence of property ownership. Information on how property assets are acquired by both men and women would help in understanding where the gaps originate from and why they persist. Data on various dimensions of ownership – reported, legal and rights-based – could unpack what asset ownership means for the individual. Individuals may not be legal owners of property, but still enjoy decision-making power over how the property is used or to whom it is bequeathed. Data on the value of assets owned are useful to assess differences between men and women in property wealth. In addition, panel data of individual property ownership, or at least repeated cross-sections, would allow researchers to measure changes in women’s and men’s property ownership over time and support the identification of causal relationships. Emerging bestpractice standards (e.g. Doss et al 2013, 2020) for the collection of individual-level data on ownership, use and control of assets are an important milestone towards advancing this data collection and analytic agenda.

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