rafmacalaba/data-use-mentions-extended huggingface dataset:
# Surviving the Global Financial Crisis: Foreign Ownership and Establishment Performance<sup>�</sup>

Laura Alfaro<sup>y</sup> Maggie Xiaoyang Chen<sup>z</sup> Abstract This paper examines how di¤erent establishments performed during the recent global ...nancial crisis, focusing on the role of foreign ownership. The paper investigates how foreign ownership a¤ected establishments’ responses to negative economic shocks, using a cross-country panel dataset with detailed information on operation, location and industry for more than 12 million establishments from 2005-2008. The evidence shows that multinational subsidiaries on average fared better than local counterfactuals with similar economic characteristics. Among multinational subsidiaries, establishments with stronger production and ...nancial linkages with parent companies showed greater resilience. Finally, in contrast to the crisis period, the impact of foreign ownership and linkages on an establishment’s performance was insigni...cant in non-crisis years.

JEL codes: F2, F1 Key words: global ...nancial crisis, establishment response, foreign ownership, production linkage, ...nancial linkage Sector board: EPOL

> �We are grateful to two anonymous referees, James Harrigan, James Markusen, Ariell Reshef, and session and seminar participants at the American Economic Association Meeting, the Midwest International Economics Group Meeting, the LACEA Trade, Integration and Growth Network Meeting, University of Virginia, IMF, and the Kiel International Economics Meeting for valuable comments and suggestions. We also thank Dun & Bradstreet and Dennis Jacques for helping with the D&B dataset and HBS and GW CIBER for ...nancial support.

> yHarvard Business School and NBER.

> zGeorge Washington University and World Bank.

1
an active participant but also to cases in which the decision-maker is not directly involved, as it was the case in Cappelen et al. (2020).

# **7. Aversion to Disadvantageous Inequality Predicts Field Behavior**

List et al. (2021) argue that one reason for studying children’s decision-making is that “children are active participants in their human capital production process”; their preferences and decisionmaking abilities may influence investments in their human capital with long term-consequences. In this section, we investigate this hypothesis by combining data from the incentivized experiments in Rio with administrative data on test scores. In particular, we study the relationship between the economic preference affected by daycare attendance – namely, aversion to disadvantageous 24
effects (column 6). This finding aligns with our previous results, which show that there are no program effects on household income and poverty levels.

# **5.4. Heterogenous impact**

Due to variations in individual characteristics, the impact of the 30A Program may differ among people. To investigate this issue, we interact the program treatment variable ( _Program*Post-program period_ ) and several individual characteristics. Table 9 focuses on the heterogeneity in the program impact on nonfarm employment. The table shows that the program has more pronounced effects on males (column 1) and individuals with higher levels of education (column 3) compared to females and those with lower education levels. Moreover, the program impact appears to be lower for ethnic minorities compared to the Kinh group (column 2). We observe a positive and statistically significant interaction between _Program*Post-program period_ and the dummy variable indicating VHLSSs conducted since 2006 (column 4). This suggests that the program impact on nonfarm employment tends to be higher in the longer term. Lastly, our analysis does not identify any heterogeneous program impact across communes with different levels of infrastructure (columns 5 to 8).

# **6. Conclusions**

Poverty in Viet Nam exhibits a strong correlation with geographic areas, with a higher concentration of poverty observed in mountainous and midland regions where ethnic minorities are more prevalent. In an effort to alleviate poverty, the Government of Viet Nam has initiated the 30A Program since 2009, targeting the poorest districts with a poverty rate exceeding 50% in 2006. This cutoff point allows us to use a sharp Regression Discontinuity 26
for all States Rs. 132,588 million in revenues, or 6.2 percent of total tax revenue of the States, behind only State sales taxes (43.3 percent) and State excise duties (9.4 percent) in relative importance.<sup>27</sup> This section examines the revenue performance of stamp duties. This performance has several dimensions. First, what are the levels of stamp duty collections in the States, and how have stamp duty revenues varied over time for the various States? Second, what are the estimated revenue losses due to the widespread undervaluation of transactions? Third, how are stamp duties linked to other taxes in the Indian public finance system? In particular, how does undervaluation in stamp duties affect revenues from other taxes that are linked either directly or indirectly to the duties? Fourth, how are stamp duty revenues, as well as revenues of the other linked taxes, likely to be affected by a change in stamp duty rates?

# **A. Stamp Duty Collections and Trends**

Table 7 gives revenues from non-judicial stamp duties for selected States for 2000-2001, and also shows the total revenues from stamp duties and registration fees for all States for the same year.<sup>28</sup> Tables 8 and 9 give some additional information on the tax structures of selected

> 26  For a detailed discussion of these distortions, with a special focus on Tamil Nadu, see David Dowall (2003), “Tamil Nadu Urban Land Management and Urban Planning Assessment”.

> 27  See Reserve Bank of India (2003), _State Finances – A Study of Budgets 2002-03_ (New Delhi, India).

> 28  These data come from different sources, and so are not strictly comparable.  Researchers at the National Institute of Public Finance and Policy collected the information on non-judicial stamp duties; the information on stamp duties

17
gradient height (kg/m3); ��� is the radius of maximum winds (in meters); � is the radius of any location to the center of the storm (in meters); �� is the hurricane translation speed (in meters per second); � is the Coriolis parameter (in 1/seconds); φ is the angle between the North and the storm heading.

Holland’s shape parameter (B) incorporates some characteristics of the terrain. Roughness, for example, may affect angular momentum due to surface friction. These variables have been calibrated for the Central American region using different sources: terrain roughness from the Global Land Cover Dataset 2000, topography data from the Shuttle Radar Topography Mission data base, speed‐up occurring in escarpments and ridges using the methodology proposed by the American Society of Civil Engineers in 1994, and wind gusts factors using Vickery and Skerlj’s (2005) estimation method.

To assess the model’s accuracy and applicability, the estimates were evaluated against the tracks and wind fields of historical events. Predicted results by the model were compared to the values reported by the United States’ National Oceanic and Atmospheric Administration (NOAA) aircraft for Hurricanes Mitch

> 6 This research has been funded by the World Bank through a Global Facility for Disaster Reduction and Recovery (GFDRR) grant (TF014499) from the Government of Australia (AusAid) under the CAPRA Probabilistic Risk Assessment program (P144982).

4
Costs of attendance play a role in hindering uptake. Private preschools are funded by charging fees. As an alternative, parents enroll their children earlier than the recommended age of 7 in the free-of-charge public primary schools (Jung and Hasan 2014). Appendix Table 10 tabulates the average annual cost of attending private and public preschools in 2000. Means and standard deviations are significantly higher in private compared to public preschools, which might signify both higher use and higher variability of resources among private preschools.

Recognizing the importance of ECED, the Government of Indonesia adopted it into the national education system in 2003. The medium-term plan between 2004 and 2009 envisioned an increase in ECED enrollment. The Ministry of Education and Culture offer a block-grant subsidy to encourage the private provision of ECED services: funds are directly channeled to the providers from the central government. Public expansions are harder to achieve. Following decentralization reform in 2001, districts are responsible for financing, implementing, and supervising ECED services, while the central government is responsible for developing quality assurance mechanisms (Chang et al. 2006).

Other ECED services, such as daycares and nurseries (PAUD<sup>3</sup> ), may also affect mothers’ work participation. We focus on preschools due to limited data availability. Figures 1 and 2 illustrate the geographical variation of public and private preschool access across the country. Figure 3 shows the upward trend in both public and private preschool access over the years, notably since the passing of the National Education System Act (NSEA) in 2003. In the next section, we elaborate how we exploit spatial and temporal variations in public and private preschool access to estimate the elasticity of maternal employment to preschool access.

# **3. Data**

The Indonesia Family Life Survey (IFLS) is a longitudinal household survey, first conducted in 1993, with subsequent tracking of the original and split households in 1997, 2000, 2007, and 2014. It was first fielded in 13 (of 27<sup>4</
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C15.3. SOSD: Social Sustainability Index C15.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.92*** 0.97***<br>H<br>Kendall tau-b 0.83*** 0.78*** 0.89***<br>Spearman 0.92*** 0.85*** 0.78**<br>A<br>Kendall tau-b 0.78*** 0.72*** 0.67**<br>Spearman 0.98*** 0.92*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br
direct contradiction of the Second Principle as it denies equal access to all.

The perceptions of inequality of opportunity are quite widespread in the region. For instance, almost half of the adults in the region believe that it is very important or essential to have connections to get a good government job, in Croatia and in FYR Macedonia the share exceeds three quarters, and in Serbia it is greater than 80 percent. On the other hand, in the United Kingdom and in Sweden no more than 20 percent of adults share this view. In some of the transition countries the need for connections to get a good public sector job is also perceived to be low, such as in Estonia, Kyrgyzstan, Moldova, Tajikistan and Uzbekistan where less than one-third of adults believe that connections are very important or essential for public sector jobs. The need for connections is generally perceived to be lower in other domains of life, although the share of adults who believe that connections are necessary in these other areas remains non-trivial in most countries (Table 2).<sup>5</sup> The first part of Rawls’s Second Principle of Justice allows for inequalities to exist to the extent that they are to the greatest benefit of the least advantaged. This is not the case in practice. In the LiTS survey the respondents are further asked if they knew of anyone (friends, relatives, classmates, local boss, etc.) who could influence decisions with respect to getting a good government job, or getting into university etc., should such influence be needed. Overall in the LiTS survey 32 percent of the adult population reported that they do not know any such person, but this share is generally higher for individuals who place themselves at the bottom of the welfare ladder, or those who (or whose parents) had low levels of education. It is perhaps not surprising that disadvantaged groups are least likely to thrive in the presence of – inequality of opportunity. These two characteristics the importance of connections – to get ahead
Nature and Source of Agglomeration Economies. _Handbook of Regional and Urban Economics_ , 4: 2119 – 2171. Rothenberg, A.D., Gaduh, A., Burger, N.E., Chazali, C., Tjandraningsih, I., Radikun, R., Sutera, C., Weilant, S. (2016). Rethinking Indonesia’s informal sector. _World Development_ , 80: 96–113. <mark>Rozo, S., and Winkler, H. J. (2019). Is Informality Good for Business? The Impacts of IDP Inflows on Formal Firms.</mark> _<mark>Journal of Human Resources</mark>_ <mark>, forthcoming.</mark> Sabarwal, S., & Terrell, K. (2008). _Does gender matter for firm performance? Evidence from Eastern Europe and Central Asia_ : The World Bank.

Syverson, C. (2011). What determines productivity? _Journal of Economic literature, 49_ (2), 326365.

<mark>Tokman, Victor E., (1978), Competition between the informal and formal sectors in retailing: The case of Santiago,</mark> _<mark>World Development</mark>_ <mark>, 6, issue 9-10, p. 1187-1198.</mark> Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm‐level data. _World Economy, 30_ (1), 60-82.

33
.9|5.5|6.1|4.6|
|**Consumption - private**|65,095|4.2|5.7|6.3|4.9|
|**Consumption - government**|13,307|1.5|1.5|1.5|1.5|
|**Fixed investment - private**|19,224|4.5|6.8|7.7|5.6|
|**Fixed investment - government**|1,969|4.1|6.1|7.4|4.8|
|**Exports**|35,280|4.4|7.1|10.7|5.7|
|**Imports**|63,874|3.8|6.7|9.1|4.5|
|**GDP at factor cost**|59,315|4.3|5.6|5.7|5.9|
|**Total factor employment (index)**|n.a.|2.4|2.9|3.1|2.6|
|**Total factor productivity (index)**|n.a.|1.9|2.6|2.6|3.3|
|**Real exchange rate (index)**|n.a.|0.2|-2.9|-4.2|-0.2|
|**Headcountpoverty rate(%)**|21.4|9.5|5.7|5.0|8.0|

Note: Columns other than 2012 show results for 2020. Figures in first column are in million lei. Poverty rate figures in simulation columns show final-year rates.

23
Policy Research Working Paper 5679

# **Abstract**

This paper uses detailed data on bound and applied tariffs to assess the consequences of the World Trade Organization’s December 2008 Modalities for tariffs levied and faced by developing countries, and the welfare implications of these reforms. The authors find that the tiered formula for agriculture would halve tariffs in industrial countries and lower them more modestly in developing countries. In non-agriculture, the formulas would reduce the tariff peaks facing developing countries and cut average industrial country tariffs by more than a third. The authors use a political-economy framework to assess the implications of flexibilities for the size of the tariff cuts and find they are likely to substantially reduce the outcome. However, despite the flexibilities, there are likely to be worthwhile gains, with applied tariffs facing developing countries cut by about 20 percent in agriculture and 27 percent in non-agriculture, and sizeable cuts in tariffs facing industrial countries. The welfare impacts of reform are evaluated using a new approach to aggregation that improves on the traditional, flawed approach of weighted-average tariffs. This substantially increases the estimated benefits of an agreement along the lines of these modalities, with estimated global income gains of up to $160 billion per year from market access reform.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group; and Development Prospects 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 wmartin1@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
0<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>Informality Engel Curve Slope Informality Engel Curve Slope<br>-20 -20<br>-25 -25<br><!-- End of picture text --> This figure plots informality levels and the slopes of the informality Engel curves for households located in rural regions (graphs a and c) and urban regions (graphs b and d). It only contains 29 countries instead of 31, since the expenditure surveys in Chile and Senegal concern urban population only.

58
each year. The data covers around 270,000 trainees per year.

To compute GFC-induced foreign demand shocks for firms, we rely on customs data covering the universe of firm-level export and import transactions collected by the Brazilian Secretariat of Foreign Trade (SECEX). We merge the customs data to the worker panel database based on a common unique firm identifier. We derive a firm panel database from the starting RAIS worker panel database where, for each firm, labor market outcome variables are constructed by aggregating across all its workers in a given year. This firm panel database includes all firms that export at least once during the 2004-2017 period.<sup>12</sup> In addition, we use the Annual Industrial Survey (Pesquisa Industrial Anual (PIA)) collected by the Brazilian Institute of Geography and Statistics (IBGE) for the 2003-2014 period to augment the set of firm-level outcomes considered. This is a longitudinal manufacturing census database with information on firm financial characteristics that we use to construct measures of productivity, profits and non-labor inputs.<sup>13</sup> We link this firm panel database with the RAIS firm panel database and the customs data based on a common unique firm Finally, we supplement the worker panel database with data from the Brazilian Census in 2000 to measure characteristics of the worker’s municipality. We construct a measure of informality as the ratio between the sum of informal salaried and self-employed workers and the total number of workers (formal, informal and self-employed) in a municipality. We follow Dix-Carneiro & Kovak (2019) in defining informal workers as those without a signed work card based on information in RAIS.

Table 1 shows the sample sizes as well summary statistics for the main worker variables (Panel A) and firm variables (Panel B). Our worker analysis relies on more than 342,000 workers (about 3 million worker-year observations). On average workers are employed 10 months per year. This lower than full-year (12 months) work average can be rationalized by the
the best combination of quality determinants in Qjc. In other words, LASSO tries combinations of quality determinants (which are therefore “floating” variables) conditional on the “fixed” variables. The selected subset of determinants for category _d_ , Q∗djc , is an input for the second estimation stage.

## **Associations between quality determinants and outcomes**

LASSO’s ability to work as a covariate-selection method makes it a nonstandard estimator and prevents the estimation of standard errors. Therefore, we implement a second stage that predicts a given outcome as a function of its selected determinants through the following OLS regression: The estimated parameters of interest are in the βd1 vectors, reflecting the association between the selected quality determinants and the outcome. Standard errors are clustered at the HEI level.

# **4.2 Country-Specific Estimations Using Individual-Level Data**

Program-level data does not allow us to satisfactorily address student self-selection into programs. To address this limitation, we use the individual-level administrative data for Brazil and Ecuador and follow Dinarte et al. (2022), Melguizo et al. (2016), and Smith and Stange (2016) to estimate the following model for a given outcome and country (Brazil or Ecuador): where yij is an outcome for student _i_ in program _j;_ Ri is an index of individual characteristics (e.g., gender, age, socioeconomic status, parental education) and previous labor market experience; and Zij is an index of peer characteristics and previous labor market experience. These indexes were created (via PCA) to reduce the dimensionality of the corresponding variables (see Appendix I).

23
rate for politicians. In particular, when many voters have cars, a large amount of gasoline subsidy may be required to sway voters in their desired direction.

Drazen and Eslava (2009) argue that political budget cycles change the composition rather than the level of government expenditures. They test their claims on data of local public finances of municipalities in Colombia and find evidence in favor of electoral manipulation of the budget by shifting public spending towards the goods voters as a whole prefer in the attempt to seduce voters to vote for the incumbent. They also conclude that voters penalize the incumbent party for running large government deficits before elections and that voters reward the incumbent party for increasing the amount of targeted spending that is attractive to the voter. Another interesting observation of Drazen and Eslava (2009) is that spending of visible development projects like physical-infrastructure spending (roads, power- and water plants) expand significantly before elections while less visible spending category – interest payments, transfers to retirees, payments to temporary workers – contract.

# _2.4 Economic factors behind fossil-fuel pricing_

Many traditional economic arguments also lie behind energy subsidies or taxation. A key factor is whether the country is a net exporter or importer of the type of energy that can be subsidized or taxed; or energy more generally. Fossil fuel exporting country governments may see less of a reason to resist subsidizing fuels, for fiscal or energy security reasons, in particular when such revenues are large; and they may be subject to more pressure to provide energy subsidies.<sup>7</sup> Similar explanations may follow from either a country’s debt or energy security position. A high level of debt, or an insecure energy supply, may lead the country to reduce its fuel consumption by eliminating subsidies to fuels, or taxing it. There are also good reasons for many countries to tax its motor fuels when the (national average) level of negative externalities from motor vehicle traffic is high. Thus we might find lower than otherwise subsidy levels (or higher tax