rafmacalaba/datause-encoder-data huggingface dataset:
13 from subsidies; total expenditures, including the share going on salaries, and expenditure per case; staff numbers, including the fraction of which are retirees; the number of items of equipment costing in excess of 10,000 RMB; inpatient discharges; the bed occupancy rate; length of stay; and the number of outpatient visits.

# V. **PROPENSITY SCORE ESTIMATION AND BALANCING**

Table 6 reports the results for the probit model used to obtain the propensity scores. Since the NCMS decision is a household one, the variables are all defined at the household level, or higher. To weight the data by household size, the model is estimated on individual data, the data coming from the 2003 survey.

The results suggest that larger households are more likely to enroll in NCMS, and that ethnic minority households are more likely to enroll. The age structure of the household makes no difference to its probability of enrolling. By contrast, its health status _does_ . Households with larger shares of chronically sick members and those with larger shares of members reporting their health as bad or fair (as compared to good or excellent) are more likely to enroll. NCMS seems, therefore to be suffering from adverse selection on observables. _Ceteris paribus_ , households whose head is a rural wage laborer are no more likely to enroll than those whose head is a professional worker, while households whose head is a farmer, retiree or employee are _less_ likely to enroll. Households whose head completed primary school only (as compared to no schooling) are less likely to enroll, but otherwise educational attainment does not predict enrollment, holding other variables constant. The gender of the household head makes no difference to the likelihood of it enrolling. Its income, by contrast, _does_ . The polynomial points to a highly nonlinear relationship between enrollment probability and income, with dips at the bottom and middle of the income distribution. Households living far away from facilities are less likely to enroll, but increasing distance reduces the probability only up to a point. A household officially designated as ‘poor’ ( _pinkun_ ) is less likely to enroll, holding other factors—including income. Receipt of a safety net payment in the previous year, by contrast, increases the likelihood of enrollment. Households with one or more members in the urban schemes (GIS or LIS) are less likely _ceteris paribus_ to enroll in NCMS. Households living in richer counties are
# **1. Introduction**

COVID-19 is adversely affecting the lifestyle, health, and livelihoods of billions of people around the world. Early forecasts and simulations show that, besides grave health consequences, the pandemic will contract global economic growth, increase global poverty and unemployment (ILO, 2020; Mahler et al., 2020; Vos et al., 2020; Sumner et al., 2020;). However, estimating the overall health and economic costs of COVID-19 is challenging for various reasons. First, the sheer magnitude of the pandemic in terms of confirmed cases and fatalities, the speed at which the virus has been spreading, and the number of economies it has impacted are unprecedented since the Spanish Flu pandemic in 1918. Second, while various government measures to contain the virus and limit economic damages are still evolving, there is limited understanding of how economic agents behave under such a large-scale pandemic, further complicating the mechanisms through which COVID-19 impacts economies across the world. Third, data on standard economic indicators for the real sectors, such as earnings, consumption, profits, and losses, are not yet available for much of the developing world to assess the impacts globally. Estimating the economic consequences of COVID-19, therefore, requires new sources of information and novel empirical approaches. Our paper investigates the impacts of COVID-19 on consumer demand using historical and near real-time Google search data. The Google search data have global coverage and are instantaneously available, permitting timely policy analyses.

As COVID-19 continues to wreak health, economic and social havoc across the world, governments and development financing agencies have rolled out rescue packages and large-scale measures to save jobs and livelihoods. For instance, about 193 countries around the globe have already implemented various policy measures and rescue packages, in addition to aid packages from the World Bank and International Monetary Fund (IMF).<sup>1</sup> The effectiveness of these measures in weathering the impacts of the pandemic depends on the ability to assess the effects on different sectors of the economy and target the most impacted ones. This is particularly imperative for governments with limited fiscal space and, which, thus, have been forced to allocate their limited resources among competing needs. Understanding the sectoral distribution of the impacts is also important to inform COVID-19 policy responses that have potential welfare and redistributive implications.

> 1 <u>https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19</u>

2
12

# **III.** AN **EFFICIENT FRONTIER SET FQR INTERNATIONAL ASSETS**

This section explores the optimal composition of the international portfolio and derives an efficient frontier for international assets. In the Markowitz mean-standard deviation portfolio model, a portfolio is efficient, "...if no portfolio with the same or higher expected return has lower standard deviation". (Fama and Miller, 1972, p.220).

The expected return on the portfolio, E(Rp), is simply the weighted average of the individual asset returns in the portfolio. Thus, E(Rp) = **IN** w; E(R;) where: w; = weight of asset i in the portfolio E(R;) = expected return of asset i The standard deviation of the portfolio (ap) is a weighted average of the standard deviation of the individual asset returns and the covariances between the asset returns. Thus, 2 = [NiI Wi o<sup>2</sup> + ENiI EN= W Wj Wja]ll **E[Ni** = **1** Nj=1 Wi Wj aij]<sup>2</sup> j•i The mean-standard deviation frontier set can be derived by minimizing the standard deviation of the portfolio at different given levels of return subject to certain constraints. The optimal
Demirci, M., & Güray Kırdar, M. (2021). _THE LABOR MARKET INTEGRATION OF SYRIAN_ _REFUGEES IN TURKEY_ (14973; IZA Discussion Paper Series, Issue December). Garrote Sanchez, D., Gomez Parra, N., Ozden, C., Rijkers, B., Viollaz, M., & Winkler, H.

(2021). Who on Earth Can Work from Home? _World Bank Research Observer_ , _36_ (1), 67–100. https://doi.org/10.1093/wbro/lkab002 Ginn, T. D. (2018). _Prison or Sanctuary? An Evaluation of Camps for Syrian Refugees_ . https://www.semanticscholar.org/paper/Prison-or-Sanctuary-An-Evaluation-of-Campsfor-Ginn/1d968936d17d45bb2dc5901000253ed37d6178fb Gordon, J. (2019). Refugees and decent work lessons learned from recent refugee jobs compacts. In _Employment Working Paper_ (Issue 256).

Gottlieb, C., Saltiel, F., Gottlieb, C., & Poschke, M. (2020). _Working from Home in Developing Countries Working from Home in Developing Countries_ (13737; Discussion Paper Series, Issue 13737).

Hoseini, M., & Dideh, M. J. (2022). _How do shared experiences of economic shocks impact refugees and host communities? Evidence from Afghan refugees in Iran 1_ (Issue Policy Research Working Paper 9915).

ILO. (2015). Access to work for Syrian refugees in Jordan: A discussion paper on labour and refugee laws and policies. In _Beirut: ILO Regional Office for Arab States_ .

Krafft, C., Assaad, R., Marouani, M. A., Cheung, R., & LaPlante, A. (2022). _Are labour markets in the Middle East and North Africa recovering from the COVID-19 pandemic?_ (ILO/ERF Working Paper).

31
**<u>Table2: Cumulative number and loan amount of registered mortgages by end of 2015 by region</u>**

||**Total**|**Kigali city**||**Pro**|**vince**||
|---|---|---|---|---|---|---|
||||**Southern**|**Western**|**Northern**|**Eastern**|
|**All mortgages**|||||||
|Amount (US$ bn)|2.604|1.66|0.173|0.307|0.164|0.3|
|No. of mortgages|49,694|19,285|7,371|6,682|6,888|9,468|
|Avg. amount|42,247|86,296|23,488|45,909|23,839|31,705|
|**Mortgages secured b**<br>|**y residenti**<br>|**al land & pro**<br>|**perty**<br>||||
|Amount (US$ bn)|1.4026|1.06|0.0899|0.0839|0.0578|0.111|
|No. of mortgages|31,989|16,281|3,889|3,605|2,792|5,422|
|Avg. amount|30,520|65,089|23,107|23,272|20,685|20,448|
|**Mortgages secured b**|**y agric. lan**|**d**|||||
|Amount (US$ bn)|0.4453|0.117|0.051|0.118|0.0533|0.106|
|No. of mortgages|14,843|2,231|3,210|2,363|3,585|3,454|
|Avg. amount|32,740|52,339|15,894|49,811|14,862|30,795|
|**Mortgages secured b**|**y commerc**|**ial land**|||||
|Amount (US$ bn)|0.7613|0.488|0.0322|0.105|0.0532|0.0829|
|No. of mortgages|2,862|773|272|714|511|592|
|Avg. amount|228,194|630,984|118,542|147,291|104,046|140,108|

Source: Rwanda Natural Resource Authority (RNRA), Land Administration Information System (LAIS) 17
given opportunity, that is, have a set of circumstances that make them unlikely to access the opportunity. In Paraguay, this prioritization exercise identified a set of four or five types of children as the groups that would benefit the most in terms of reducing inequitable differentials in access to or usage of secondary education and access to public hospital care, respectively.

A second application of the OIA develops an ex ante microsimulation focusing on opportunities rather than on outcomes, with the hope that the traditional short-term analysis of welfare outputs may be complemented with a discussion of longer-term effects addressing how to remove critical obstacles to an egalitarian society. For example, the illustrative ex ante microsimulation exercise conducted for Zambia identifies the winners and losers of alternative interventions in the size and composition of public education spending.

These results should contribute to the design of more effective policy decisions regarding fiscal resources to help policy makers better understand how circumstances act as serious constraints to equal opportunities. Ultimately, OIA provides more complementary information on opportunities, circumstances, and longer-term issues that complement information on outcomes, behavior, and shortterm immediate effects than existing incidence analyses provide.

# **References**

- Abras, A., and J. Cuesta. 2011. “Equality of Opportunities, Redistribution and Fiscal Policies: The Case of Liberia.” World Bank Policy Research Working Paper WPS 5801, Washington, DC.

- Abras, A., J. Cuesta, A. Hoyos, A. Narayan. 2012. “Equality of Opportunities and Fiscal Incidence in Côte d’Ivoire.” World Bank Policy Research Working Paper WPS 6048, Washington, DC.

- Abras, A., J. Cuesta, and S. Tiwari. 2013. “The State of Human Opportunities for Children in Tajikistan.” Mimeo, World Bank, Washington, DC.

- Addison, T., A. Chowdhury, and M. Murshed. 2004. “The Fiscal Dimensions of Conflict and Reconstruction.” In _Fiscal Policy for Development: Poverty, Growth, and Reconstruction_ , ed. Tony Addison and Alan Roe, 260–73. London: Palgrave.

- Arze, Javier, David Coady, and Robert Gillingham. 2010 _._ “The Unequal Benefits of Fuel Subsidies: A Review of Evidence for Developing Countries.” International Monetary Fund WP/10/202, Washington, DC.

- Arze, Javier, Sanjeev Gupta, and Alejandro Hajdenberg. 2010. “Is Social Spending Procyclical?” IMF Working Paper WP/10/234, Washington, DC.

- Bastagli, F., D. Coady, and S. Gupta. 2012. “Income Inequality and Fiscal Policy.” IMF Staff Discussion Note, SDN/12/08, Washington, DC.

- Breceda, K., J. Rigolini, and J. Saavedra. 2008. “Latin America and the Social Contract: Patterns of Spending and Taxation.” World Bank Policy Research Working Paper 4604, Washington, DC.

- Bourguignon, F., F. Ferreira, and M. Menendez. 2007. “Inequality of Opportunity in Brazil.” _Review of Income and Wealth_ 53 (4): 585–619.

33
<!-- Start of picture text --> (k) Italy: Standard Deviation of Real GDP (l) Japan: Standard Deviation of Real<br>Growth, % points GDP Growth, % points<br>1.6 3<br>Global Global<br>1.4 Crisis Crisis<br>2.5<br>1.2 Domestic<br>2 Financial<br>1 Crisis<br>0.8 1.5<br>0.6<br>1<br>0.4<br>0.5<br>0.2<br>0 0<br>(m) South Africa: Standard Deviation of Real (n) BRI: Standard Deviation of Real GDP<br>GDP Growth, % points Growth, Percentage Points<br>1.8 3.5 Russian Federation Global Crisis<br>1.6 Brazil<br>3<br>India<br>1.4<br>Global<br>2.5<br>1.2 Crisis<br>1 2<br>0.8<br>1.5<br>0.6<br>1<br>0.4<br>0.2 0.5<br>0<br>0<br>1983 Q1 1984 Q3 1986 Q1 1987 Q3 1989 Q1 1990 Q3 1992 Q1 1993 Q3 1995 Q1 1996 Q3 1998 Q1 1999 Q3 2001 Q1 2002 Q3 2004 Q1 2005 Q3 2007 Q1 2008 Q3 2010 Q1 1982 Q1 1983 Q4 1985 Q3 1987 Q2 1989 Q1 1990 Q4 1992 Q3 1994 Q2 1996 Q1 1997 Q4 1999 Q3 2001 Q2 2003 Q1 2004 Q4 2006 Q3 2008 Q2 2010 Q1<br>Q4-1972 Q4-1974 Q4-1976 Q4-1978 Q4-1980 Q4-1982 Q4-1984 Q4-1986 Q4-1988 Q4-1990 Q4-1992 Q4-1994 Q4-1996 Q4-1998 Q4-2000 Q4-2002 Q4-2004 Q4-2006 Q4-2008 Q4-2010<br>Q1-1998 Q4-1998 Q3-1999 Q2-2000 Q1-2001 Q4-2001 Q3-2002 Q2-2003 Q1-2004 Q4-2004 Q3-2005 Q2-2006 Q1-2007 Q4-2007 Q3-2008 Q2-2009 Q1-2010 Q4-2010 Q3-2011<br><!-- End of picture text --> Source: Author’s calculations based on data from OECD Statistics. Data from this common source are not available for period prior to early 1980s for a number of countries.

What did the international monetary system have to do with the 2008-2009 crisis? Economists have varied views. Most recently, for example, Lin and Treichel (2011) argue that the cause of the crisis was financial deregulation combined with lax monetary policy, and the latter was facilitated by the US dollar’s role as the dominant international reserve currency. In other words, the current structure of the international monetary system may be directly linked to the propagation of the worst financial crisis since the Great Depression. Others, for example, Caballero (2006) and Eichengreen (2011a) have also noted the roles played by the IMS and the dollar in the crisis; however, they approach the issue from a different analytic framework with different causality chains.

15
# encumbered transfer rights, respectively.

For the land transfer variable, the available indicators concern the degree and extent of restrictions on use rights transfers and restriction on land rental. Since village collectives formally own land, there cannot be individual land sales in China. However, in some instances, fanners are entitled to sell their contractual use rights to the land allocated to them under the household responsibility system (HRS). As shown in Table 2, land transfer rights have on average become less restricted since 1998, with the average factor score rising to 0.4 from -0.4.

There are three indicators variables that signal the degree of tenure security enjoyed by a household: Whether their land holding will be reduced if a household's population is reduced; Whether their land holding will be increased if a household's population is increased; and, The frequency of land adjustments (average number of adjustments per year) since the establishment of HRS in the village. The factor score for tenure security has actually diminished over the 1999 to 1993 time period, reflecting in part the greater number of land reallocations that had taken place by 1993 as compared to 1988. In addition, in 1993 more villages reported that their rules stipulated reallocations based on household demographic changes.

Of the other variables needed for the analysis, one of the most problematic is the land rental rate, rjt. Rental rate data were not recorded in the 1988 survey, and the analysis to follows thus relies on time and county dummy variables as well as a measure of village land scarcity (agricultural land per-capita) to control for differences in the rental rate. As can be seen in Table 2, land became slightly more scarce (relative to labor) over the period of the la See Liu _et al._ (1998) for an in-depth presentation of the underlying property rights indicator variables.

27
**<mark>Table 7: Impacts on specific criminal activities</mark>**

|Variable of interest|**Before**<br>(mean/sd)<br>(1)|**After**<br>(mean/sd)<br>(2)|**Difference**<br>(mean/se)<br>(3)||
|---|---|---|---|---|
|Involved in stealing last 6 months(treatments)|0.195|0.110|‐0.084|***|
||_0.397_|_0.314_|_0.023_||
|Involved in stealing last 6 months(controls)|0.240|0.188|‐0.052||
||_0.428_|_0.391_|_0.028_||
|Difference‐in‐Differences (DD)|||**_‐0.032_**<br>_0.045_||
|Involved in assault (physical or verbal) last 6 months|||||
|(treatments)|0.297|0.235|‐0.061|**|
||_0.457_|_0.425_|_0.029_||
|Involved in assault (physical or verbal) last 6 months|||||
|(controls)|0.400|0.336|‐0.064||
||_0.491_|_0.473_|_0.037_||
|Difference‐in‐Differences (DD)|||**_0.003_**||
||||_0.054_||
|Involved in alcohol‐related crime last 6 months|||||
|(treatments)|0.209|0.113|‐0.096|***|
||_0.407_|_0.318_|_0.027_||
|Involved in alcohol‐related crime last 6 months|||||
|(controls)|0.260|0.224|‐0.036||
||_0.440_|_0.418_|_0.033_||
|Difference‐in‐Differences (DD)|||**_‐0.060_**||
||||_0.046_||
|Involved in trespassing last 6 months(treatments)|0.116|0.052|‐0.064|***|
||_0.321_|_0.223_|_0.019_||
|Involved in trespassing last 6 months(controls)|0.188|0.084|‐0.104|***|
||_0.391_|_0.278_|_0.027_||
|Difference‐in‐Differences (DD)|||**_0.040_**||
||||_0.036_||

Notes: Table reports means and standard deviation (error) for two groups and its difference.

*** p<0.01; ** p<0.05; * p<0.1. The sample size is 338 for treatments and 249 for controls.

28
Policy ReseaRch WoRking PaPeR 4527

# **Abstract**

A newly created dataset including 239 decisions made by the Mexican Federal Competition Commission on horizontal mergers between 1997 and 2001 is used to estimate the different factors affecting the Commission’s resolution. The paper approximates the decision making process using two different discrete choice models. The results indicate that, contrary to the Commission’s objective, the presence of efficiency gains increases the probability of a case being issued. The findings also show that factors different from the ones explicitly mentioned by the Commission have a significant effect on the Commission’s final decision. In particular, the presence of a foreign company among the would-be merger firms significantly increases the likelihood of observing an allowed merger.

This paper—a product of the Development Prospects Group—is part of a larger effort in the department to evaluate competition policy in middle income countries. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at rdehoyos@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
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