rafmacalaba/datause-encoder-data huggingface dataset:
lower than previously estimated. The main reason.is that exchange rate and price data used in this paper on the former Soviet Union is from 1989, rather than 1992, to avoid the problem of high inflation and associated exchange rate uncertainty in recent years. In any case total subsidies in the former Soviet Union are in the range of US $145-172 billion. Estimates of total subsidies are for some countries significantly different than estimated in Larsen and Shah (1992c) primarily due to exchange rate fluctuations, domestic price changes and a more detailed data set used in this paper. Estimates in this study of subsidies on petroleum products in Brazil and Mexico and on coal in Czechoslovakia are significantly lower than the previous estimate. Subsidies in Argentina are now virtually non-existent due to the recent introduction of a new currency. Subsidies on petroleum products are estimated to be significantly higher in Indonesia, Saudi Arabia and Egypt, and on coal and petroleum products in India relative to the previous estimates. Estimates for China, Poland and South Africa are almost identical to previous estimates. Finally, estimates for South Korea, Iran, Romania and Bulgaria are included in this study.

# III. EmRirical framework:

The approach taken here is in partial equilibrium assuming factor prices, other than energy prices, and level of aggregate output in the economy are constant. This may be an acceptable approximation for small changes in energy prices. For large changes in energy prices however, as is evaluated in this paper, a country specific general equilibrium analysis is of course preferable but beyond the scope of this study given that the analysis extends to 16 countries. At best, estimated emission reductions from fossil fuel subsidy elimination alert to 13
# **L. Introduction**

> While it is widely accepted that financial crises have adverse consequences for the economy as a whole, relatively little empirical work investigates the mechanisms by which financial crises generate problems in the real sector. In this paper, we analyze data from developed and developing countries that have experienced financial crises during the last 30 years to investigate the impact of cri1scs **onI** irLU1sUIal **roLWU** L i I oL&ULUIes. Understar **dIingU UUZi** i111pact **is C.ucial for ruILIaUinLg11** policies to mitigate the costs of a crisis in the financial sector to the economy as a whole and contributes to the literature on the mechanisms linking financial shocks and real economic activity.

Much theoretical work has been done on how financial intermediaries and financial markets facilitate investment by firms and, hence, promote economic growth (see Levine (1997) and Rajan and Zingales (1998) for comprehensive overviews). Financial intermediaries and financial markets **are grvaially UIVLou=L Lt reUuc ieiLiv** al aZatu **aiu** adverGVe **sIVle6LIVU jJIVLUeUI LIW.L** c **U Lar** mak raising extemal funds difficult and expensive for firms. Well-functioning and well-developed financial intermediaries and markets thus should disproportionately benefit firms that are most dependent on extemal funds to finance their growth. Conversely, crises in the financial sector should have a disproportionately negative impact on firms that rely heavily on external sources of finance. Specifically, we investigate whether the impact of a financial crisis on sectors dependent on extemal sources of finanLcing varies **witiL u1v** lvevl W. **UoVf** UPelII.etL o **UIV, MiBArLidI syLVItLI. Lv evaiuatL ui.** empirical relevance of this theoretical mechanism, our empirical work focuses on the differential impact of financial crises on sectoral growth.

To preview our results, we find that in well developed and deep financial systems, sectors highly dependent on extemal finance tend to experience a greater contraction of value added during a I
||At-risk-ofpov|ertyrate (60% of|mediandisposabl|eincome)|
|---|---|---|---|---|
|Overall|34.2%|17.1%|25.1%|-9.2%|
|Single adult|25.8%|9.4%|14.1%|-11.7%|
|Single parent|33.6%|25.1%|35.7%|2.1%|
|Couple without children|30.5%|11.9%|18.1%|-12.5%|
|Couple with 1 child|10.0%|10.6%|16.1%|6.1%|
|Couple with 2 children|10.8%|14.7%|22.6%|11.8%|
|Couple with 3+ children|26.6%|28.0%|41.0%|14.3%|
|Single retiree|96.3%|11.1%|20.0%|-76.3%|
|Couple of retirees|95.5%|2.5%|5.9%|-89.5%|
|Mixed|33.6%|20.8%|29.5%|-4.0%|

<mark>Source: Own estimates based on HBS 2014.</mark>

# **V. Progressivity, Marginal Contributions, and Pro-poorness of Taxes and Transfers**

How did each of the fiscal interventions contribute to the observed changes in poverty and inequality? Figure 4 presents the distributional impact of different components of the tax and benefit system as a share of market income (including pensions). Most components of the system are progressive, with the bottom 50 percent of the distribution being net receivers of social benefits. However in cash terms, households beginning in the second decile were net payers to the treasury in 2014, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population. This coincides with the results presented above, particularly once indirect taxes are included.

Since the influence of specific interventions may be different from that of the overall system, a fundamental question in the policy discussion is whether a particular fiscal instrument (or a particular combination of them) is equalizing. If there were a single fiscal intervention, using the typical indicators such as the Kakwani index<sup>14</sup> to determine whether a particular intervention is progressive or regressive would be sufficient to unambiguously determine whether that intervention was equalizing. Given there is more than one fiscal intervention, this one-to-one relationship between the progressivity of a particular intervention and its effect on inequality breaks down. As Lambert (2001) demonstrates, depending on certain characteristics of the fiscal system, a regressive tax—for example—can exert an equalizing force over and above that which would prevail in the absence of that regressive tax. This is because each fiscal intervention interacts with all the others. For instance, the proceeds of a regressive indirect tax could be used very

> 14 The Kakwani index for taxes is defined as the difference between the concentration coefficient of the tax and the Gini for market income. For transfers, it is defined as the difference between the Gini for market income and the concentration coefficient of the transfer. See, for example, Kakwani (1977).

19
**Figure 3: Financial Use vs. Depth**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>SSA<br>70%<br>Countries<br>60%<br>50% North<br>African<br>40%<br>Countries<br>30%<br>20%<br>10%<br>0%<br>0 50 100 150 200 250<br>Domestic Credit to Private Sector (% of GDP)<br>Percent of Adults with an Account<br><!-- End of picture text --> _Source_ : Demirguc-Kunt and Klapper, 2012 and World Bank, 2012.

# **_3.2 Barriers to Formal Account Ownership_**

Both in Sub-Saharan Africa and North Africa, the most frequently cited reason for not having a formal account is lack of enough money to use one. This is the response given by more than 80% of adults without a formal account, with 30% citing it as the only reason in Sub-Saharan Africa and more than 60% of respondents citing it as the only reason in North Africa. Cost, distance, and documentation are also cited by more than 30% of non-account-holders in Sub-Saharan Africa. In North Africa, cost is the second most frequently cited reason.

Insufficient documentation is a commonly cited reason for younger adults in Sub-Saharan Africa and distance from a bank is an important barrier for adults living in rural areas. Fixed fees and high costs of opening and maintaining accounts seem to be particularly important in Eastern and Southern Africa. For example, in Uganda maintaining a checking account costs the equivalent of 25% of GDP per capita annually and 54% of non account holders cite cost as a reason for not having an account.

Bringing financial services to rural clients is a major challenge on the financial inclusion agenda. Often the main barrier to financial inclusion in rural areas is the great distances that rural 7
# **Food Standards and Exports: Evidence from China**

Axel Mangelsdorf<sup>1</sup> Alberto Portugal-Perez<sup>2</sup> John S. Wilson<sup>2</sup> _JEL: F14, L15, Q17, Q18_ _Key Words: Food Standards, Trade Barriers, SPS, TBT, Harmonization_ _Sector Board Poverty Reduction (POV)_

> 2 World Bank

1 BAM Federal Institute for Materials Research and Testing, Germany, and Technical University Berlin, Chair of Innovation Economics 1
de Luca, Loretta, Hélène Sahy, Saba Joshi, and Mayra Cortés. 2013. _Learning from Catalysts of Rural Transformation_ . International Labour Organization, Geneva.

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar and Jake Hess. 2017. _The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution._ World Bank.

Desiere, Sam, and Valentina Costa. 2019. “Employment data in household surveys: taking stock, looking ahead.” Working Paper, Development Data Group, World Bank.

Donald, Aletheia; Gayatri Koolwal, Jeannie Annan, Kathryn Falb, and Markus Goldstein. 2017. “Measuring Women's Agency.” World Bank Policy Research Working Paper 8148.

Doss, Cheryl. 2018. “Women and agricultural productivity: Reframing the Issues.” _Development Policy Review_ 36:35-50.

Dutta, Puja, Rinku Murgai, Martin Ravallion, and Dominique van de Walle. 2014. _Right to Work? Assessing India's Employment Guarantee Scheme in Bihar._ Equity and Development; Washington, DC: World Bank.

Fletcher, Erin K., Rohini Pande and Charity Troyer Moore. 2018. “Women and Work in India: Descriptive Evidence and a Review of Potential Policies.” Faculty Research Working Paper, RWP18-004, Harvard Kennedy School.

Folbre, Nancy. 2018. _Developing care: Recent research on the care economy and economic development_ . International Development Research Centre.

FAO. 2011. _The State of Food in Agriculture 2010-11: Women in Agriculture: Closing the Gender Gap for Development._ Rome.

Fox, Louise, and Obert Pimhidzai. 2013. “Different Dreams, Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa: the Case of Uganda.” World Bank Policy Research Working Paper 6436.

Gaddis, Isis, Gbemisola Oseni, Amparo Palacios-Lopez, and Janneke Pieters. 2019. “Measuring Farm Labor: Survey Experimental Evidence from Ghana.” World Bank Policy Research working paper no. WPS 8717.

Gaddis, Isis, and Amparo Palacios-Lopez. 2018. “Counting Women’s Work and Employment: Recent Methodological Advances and Tracing the Links Between Data and Policy.” Presentations made at the 20<sup>th</sup> ICLS, October 2018, Geneva; and at the workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

Giné, Xavier and Ghazala Mansuri. 2016. “Money or Management? A Field Experiment on Constraints to Entrepreneurship in Rural Pakistan”, Mimeo. World Bank.

Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. “Intra-household decisionmaking in agriculture: The value added of asking who makes the decisions around specific agricultural activities.” Working Paper No. 18. GSARS Working Paper: Rome.

25
contrast, in villages that were affected by droughts, the school enrollment of boys fell by 14 percentage points, while that of girls fell by 11 percentage points. The implied negative difference-in-difference effects of the drought on school enrollment are quite large, on the order of 20 percentage points.<sup>18</sup> Similar negative effects of weather shocks on child schooling are reported elsewhere in Africa. In Malawi in 1994-95, a rainfall shock of 10 percent below the long-run average was associated with an increase of 23 percent in the fraction of students who missed two or more consecutive weeks of instruction in the last 12 months, with the largest effects concentrated among children in the poorest households (World Bank, 2007).

How does the evidence just reviewed compare with the testable predictions of the framework in Section 2? All else being equal, the theory would lead us to expect investments in schooling to behave counter-cyclically in richer countries, and in those with better developed credit systems, but pro-cyclically in poorer countries, and in those where access to credit is severely limited. In addition, deeper, more prolonged crises are more likely to have negative effects on schooling than shorter ones. To summarize the pattern of effects across countries, Table 4 reports per capita GDP levels at the time of the crisis, in PPP 2005 US dollars, the magnitude of the shock, and the effect it had on school enrollment.<sup>19</sup> (No comparable data are available on the development of the financial sector in these countries, although it is likely to be highly correlated with per capita income levels.) The table shows that Mexico and Brazil had the highest income levels at the time their economies went into recession: consistent with the framework in Section 2, schooling outcomes in these countries were counter-cyclical—they behaved in a fashion similar to those in the United States. In terms of Figure 1, this is best represented as a movement from point A to points B or D: insofar as there was an income effect of the recession on schooling, it was dominated by the substitution effect. On the other hand, Malawi and Cote d’Ivoire were the poorest countries in the table. Here, the income effect appears to have dominated the substitution effect, with school enrollment behaving in a pro-cyclical manner. In terms of Figure 1, this involves a movement

> 18 Although the results in Jensen (2000) are suggestive, the large, positive change in school enrollment rates in a single year in villages that were unaffected by the drought could be a source of concern. Moreover, because data are available for only two periods, Jensen cannot test whether the two groups of villages had different trends in outcomes, including enrollment, prior to the period he analyzes.

> 19 Note that the studies for Nicaragua (Maluccio, 2005) and Cote d’Ivoire (Jensen, 2000) refer to rural areas, where incomes are likely to be much lower than in urban areas, so the GDP values in the table for these two countries give a somewhat misleading picture of the welfare of households affected by the crises described in these papers.

16
Policy Research Working Paper 6706

# **Abstract**

Based on a comprehensive worldwide firm survey, this paper looks at how the business environment and economic agglomeration affect job creation, holding constant conventional determinants of firm growth, such as firm ownership, size, and age. The analysis finds that economic agglomeration is most important, especially modern telecommunications, access to export markets, concentration of economic activity in large cities, and capacity agglomeration (the concentration of large firms in a city). Although the business environment affects job growth less than agglomeration does, some elements of the business environment matter, such as labor flexibility, unionization, and local skill levels. There is strong heterogeneity in job creation across firm size and age.

This paper is a product of the Finance and Private Sector Development 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 lxu1@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
examination of the criteria and methods of their underlying sources, to better understand what they are measuring, and to determine (roughly) their degree of interdependence. Depending on one’s purposes, customized indexes based on a subset of the TI or WBI components might be more appropriate. Also, the weights used by TI and WBI are essentially arbitrary, particularly when it is acknowledged that many of the data sources are highly interdependent. If the underlying data were made more accessible, data users could choose the weights they deem appropriate for their purposes, in customizing an index. They could also compare two countries, or two time periods within a country, using only data sources common to both. All users of the composite indexes and their perceptions-based components should follow TI’s example and acknowledge that these are measures of corruption _perceptions_ , not of corruption.

In comparison to broad expert assessments, a virtue of BEEPS (and WEF) is “unbundling” corruption in a large set of survey questions. Firm-level analyses, e.g. on firm characteristics associated with different forms of corruption, can also be conducting using BEEPS, although not with WEF. An important limitation of firm surveys such as BEEPS however is that it “provides a very incomplete measurement of corruption” (Gray, Hellman, and Ryterman, 2000: 54) by measuring only interactions between firms and public officials.

To improve on the existing set of country-level corruption indicators, more data collection is needed on several margins. First, the BEEPS should be replicated for other regions. The World Bank, in partnership with some of the regional development banks, is already working towards this goal. Second, firm surveys should be complemented by more systematic household surveys measuring experiences with corruption and other 47
Table 5 Olley and Pakes Regressions with Contemporaneous Spillover Variables and Tariff controls: all firms, foreign-invested, and domestic firms with zero foreign investment (sector-level FDI are calculated based on sources of FDI)

||All firms|Foreign-invested<br>firms|Domestic firms (0<br>foreign investment)|
|---|---|---|---|
|Foreign<br>share<br>|-0.0028|0.00312||
|(HK-Taiwan-Macau)|(0.003)|(0.0047)||
|Foreign share (by other countries)|0.0059*<br>(0.0032)|0.013***<br>(0.0048)||
|Horizontal_HK|-0.0198|0.0143|-0.0647|
||(0.099)|(0.106)|(0.105)|
|Backward_HK|0.570|0.339|0.697|
||(0.580)|(0.581)|(0.659)|
|Forward_HK|-0.227|-0.241|-0.162|
||(0.177)|(0.154)|(0.217)|
|Horizontal_FR|0.284**|0.183|0.350***|
||(0.132)|(0.149)|(0.131)|
|Backward_FR|0.872*|1.110**|0.764*|
||(0.445)|(0.522)|(0.437)|
|Forward_FR|0.332**|0.457***|0.214|
||(0.148)|(0.116)|(0.179)|
|lnTariff|-0.0263*|-0.00987|-0.0313*|
||(0.0154)|(0.0180)|(0.0165)|
|lnTariff_backward|-0.0379|-0.0265|-0.0413*|
||(0.0231)|(0.0259)|(0.0245)|
|lnTariff_forward|-0.0037|-0.0153|-0.0008|
||(0.0078)|(0.0118)|(0.0071)|
|Constant|1.715***|1.997***|2.046***|
||(0.0638)|(0.0703)|(0.0440)|
|Observations|1,545,626|348,029|1,197,597|
|R-squared|0.180|0.205|0.167|
|_F_-stat (HHK=HFR)|3.01|0.80|5.42|
|Prob>_F_|0.088|0.374|0.023|
|_F_-stat (BHK=BFR)|0.11|0.61|0|
|Prob>_F_|0.736|0.438|0.944|
|_F_-stat (FHK=FFR)|3.61|10.63|1.02|
|Prob>_F_|0.062|0.002|0.32|
|_F_-stat (three conditions jointly)|4.64|6.99|4.16|
|Prob>_F_|0.006|0.004|0.009|

41
hiring of temporary employees, employers may have discarded hiring and screening practices which would have ensured a good job match. The job search process may have become noisier, particularly for the younger generations who, as described in the results of section 4.1, have seen a decrease in average job tenure even when holding a permanent employment contract.

Another possible driver of the decrease in tenure could be trade openness. Evidence from the United States and Europe shows that globalization has had strong effects on labor markets (see, for example, Acemoglu and Autor 2010; Autor, Dorn, and Hanson 2013). We hypothesize that trade openness has a negative impact on tenure, as it increases competition for firms, leads to the adoption of more efficient ways of production, and facilitates the allocation of jobs, increasing job turnover.

The top panel of Table 5 presents the estimates of θ of a specification of equation (5) in which, instead of the EPL index, we include the change in trade openness, measured as the change in the sum of exports and imports as a percentage of GDP, as an additional regressor. The results show that increases in trade openness increased the probability of holding a short-term job. There appears to be no correlation with changes in the probability of holding a medium- or long-term job.

The growing evidence on the effects of ICT–related technological change on the labor market indicates that innovations and new technologies affect different groups of workers differently. This skill bias has been accelerating over the last decades (see, for example, <u>Acemoglu 2002).</u> Technological progress affects the organization of the labor market, including labor market policies and production processes; through these channels, it is expected to affect the structure of employment and job tenure.

The bottom panel of <u>Table 5</u> shows the estimation of the impact of ICT-related technological change on the probability of having jobs of different duration (as in equation 5). We use the growth rate in the per capita ICT capital stock, derived from the KLEMS database (Jorgenson 2012), as a proxy for the penetration of technological innovations and digitalization in the economy. The estimations demonstrate an overall positive association between a higher ICT capital stock and the share of short- and medium-tenure jobs. The ICT-related technological change appears to increase these shares more among female, better-educated, and more senior workers. Shorter job tenure among better-educated workers may reflect increased job mobility and voluntary movement to better job matches; the larger share of short-term jobs among senior workers could indicate higher dismissal 22
**Table 5: long term impacts of the 4P program on education and labor market**

||Completed<br>schooling|Years of<br>education|Enrolled in<br>non-<br>formal<br>school|Employment<br>Women|Labor force<br>participation|Paid<br>employment|Under-<br>employment|
|---|---|---|---|---|---|---|---|
|ITT|-0.006|0.266|0.051|-0.050|-0.031|-0.007|-0.068|
||(0.87)|(0.40)|(0.16)|(0.32)|(0.51)|(0.89)|(0.42)|
|Observations|437|436|437|437|437|148|148|
|Adj. R2|0.33|0.39|0.02|0.00|0.01|0.07|-0.02|
|Control mean|0.645|10.714|0.132|0.364|0.418|0.910|0.308|
|||||Men||||
|ITT|0.021|0.238|-0.007|-0.040|-0.029|0.018|-0.073|
||(0.65)|(0.59)|(0.89)|(0.28)|(0.39)|(0.56)|(0.15)|
|Observations|435|435|435|435|435|317|317|
|Adj. R2|0.32|0.47|0.04|0.00|0.01|0.07|0.06|
|Control mean|0.523|9.464|0.241|0.764|0.864|0.889|0.389|
|P-values inpare|ntheses: *p<0.|1 **p<0.05 **|*p<0.01|||||

28