rafmacalaba/datause-encoder-data huggingface dataset:
# **Banking Crises and Exports:** Lessons from the Past<sup>_∗_</sup>

Leonardo Iacovone<sup>_†_</sup> Veronika Zavacka<sup>_‡_</sup> First Version: June 20, 2009 Current Version: August 18, 2009

## **Abstract**

This paper analyzes the impact of banking crises on manufacturing exports exploiting the fact that sectors differ in their needs for external financing. Relying on data from 23 banking crises episodes involving both developed and developing countries during the period 1980-2000 we separate the impact of banking crises on exports growth from that of other exogenous shocks (i.e. demand shocks). Our findings show that during a crisis the exports of sectors more dependent on external finance grow significantly less than other sectors. However, this result holds only for sectors depending more heavily on banking finance as opposed to inter-firm finance. Furthermore, sectors characterized by a higher degree of assets tangibility appear to be more resilient in the face of a banking crisis. The effect of the banking crises on exports is robust and additional to external demand shocks.

The effect of the latter is independent and additional to that of a banking shock, and is particularly significant for sectors producing durable goods.

> _∗_ The views contained in this paper are of the authors and not necessarily those of the World Bank. The authors are grateful to the World Bank Trade Brown Bag Seminar’s participants for suggestions and comments, Caroline Freund, Andrei Levchenko and Claudio Raddatz for sharing their data and useful comments. The financial support of the World Bank’s Research Support Budget is gratefully acknowledged.

> _†_ Development Research Group, World Bank, 1818 H Street, NW; Washington, DC, 20433, USA. Email: Liacovone@worldbank.org (Corresponding author).

> _‡_ Graduate Institute for International and Development Studies, Geneva. Email: veronika.zavacka@graduateinstitute.ch

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# **8. Conclusion and policy implications**

Understanding trends in firm dynamics, productivity growth and job creation is essential to design policy that facilitates the growth of the private sector. This paper explores firm-level dynamics in South Africa by providing a detailed set of descriptive data along with regression analysis of employment, wage, productivity, and other firm dynamics. A key contribution is the international firm-level data comparisons used to benchmark South African data. These comparisons provide valuable insight into the structure, performance and peculiarity of the South African economy in comparison to selected advanced and developing countries.

In the descriptive analysis, we find that the role of firm entry and exit in net job creation is negligible in South Africa. This is in contrast to other fast-growing developing countries such as Vietnam where firm entry constitutes the predominant form of job creation in other countries. Job creation is taking place predominantly by large and old firms and net job creation is mainly driven by the services sector, followed by the construction and commerce sectors.

The life cycle of surviving micro firms for which we observe entry shows that the average size of these firms increased from 4 employees to almost 17 over the 5-year period from 2010 to 2014. This growth trajectory compares favorably to other countries such as Vietnam, Kosovo and Côte d’Ivoire where the average size of surviving micro firms increased at a slower rate over a longer period of 9 to 12 years. This suggests that micro firms that are able to survive over the short to medium term experience relatively faster growth, or are able to expand more rapidly, than the growth experienced by surviving micro firms in countries such as Vietnam.

In our static decompositions, we find that the construction, business and finance and other services sectors did not allocate labor efficiently over the period. There is also a sharp decline in aggregate productivity in manufacturing, construction and commerce. When we decompose aggregate productivity growth, we find that firms that expand productivity are decreasing their employment shares (i.e. they are becoming more productive at the expense of shedding labor), which is consistent with evidence from other countries.

In our dynamic decompositions, there is a decline in productivity growth over time – this is in line with actual GDP growth outcomes over the period. Differences between our two measures of productivity (value added per worker growth and TFP growth) come mainly from the withinterm which is positive for TFP for all years (except a little negative in the first year). The withinfirm improvement in productivity comes mainly from manufacturing, commerce and other services.

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Policy Research Working Paper 7196

# **Abstract**

The 2014 Ebola Virus Disease outbreak in West Africa is the largest to date by far. Ebola Virus Disease causes disproportionate mortality among the working-age population, resulting in far more mortality for parents of young children than other health crises. This paper combines data on the age distribution of current and projected mortality from Ebola with the fertility distribution of adults in Guinea, Liberia, and Sierra Leone, to estimate the likely impact of the epidemic on the number of orphans in these three countries. Using the latest mortality estimates (from February 11, 2015), it is estimated that more than 9,600 children have lost one or both parents to Ebola Virus Disease. The absolute numbers of orphans created by the Ebola epidemic are significant, but represent a small fraction (1.4 percent) of the existing orphan burden in the affected countries. Ebola is unlikely to increase the numbers of orphans beyond extended family networks’ capacities to absorb them. Nonetheless, the pressures of caring for increased numbers of orphans may result in lower quality of care. These estimates should be used to guide policy to support family networks to improve the capacity to provide high quality care to orphans.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 authors may be contacted at devans2@worldbank.org and apopova@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
might affect performance through factors such as the educational attainment of agency officials, the capabilities of the firms engaged in international trade, and the quality of the infrastructure at the border, among other possibilities. We measure levels of development flexibly, including log per capita GDP and squared log per capita GDP on the right hand side. We also include a log GDP measure to control for variation in the overall size of the economy. The underlying data for these measures are taken from the 2011 World Development Indicators.<sup>20</sup> We include three measures of geography. Logged square kilometers is our measure of country size, and is also taken from the World Development Indicators. It may be that larger countries trade less and so have less developed customs agencies. We include dummy variables that indicate if a country is landlocked or if it is an island. These data are taken from the French research institute CEPII. The landlocked dummy is particularly interesting because the TAB indicators take into account the time and cost of border procedures in the landlocked country itself and at other border posts between the relevant port and the country of interest.<sup>21</sup> The landlocked dummy thus acts as a control for the fact that landlocked countries’ Doing Business measures may be influenced by the quality of customs in neighboring countries.

Finally we include dummy variables that indicate a country’s membership in either the OECD or the World Trade Organization (WTO). These data are taken from the respective organizations’ web sites. The OECD dummy acts as an additional indicator of development, but also attempts to control for any biases that might arise through the sequencing of the data collection efforts.<sup>22</sup> WTO membership might be thought to affect trade volumes in a manner that is independent of customs performance.

# **3.3.2 General measures of governance**

The policy indicators that are our primary interest are used to estimate the effect of border policy, specifically, on perceptions of the performance of customs. These perceptions might be driven, in part, by the overall quality of governance in the country. Since we are trying to isolate the effect of policy in customs, we also include measures of the quality of broad governance in each country as control variables. Our source for these data are the Worldwide Governance Indicators (WGI), as

> 20The 2012 data on per capita income are incomplete.

> 21The data are collected under this definition to get an estimate of the costs of _overseas_ trading. This treatment may mean that our estimates understate the average effect of policies on clearance times within the country of interest.

> 22Recall that the OECD policy indicators were first calculated for OECD member countries in 2010 and for the remaining countries in 2012.

12
evidence consistently shows that the poorer populations often carry the heaviest burden. For example, stunting in children after the floods that hit Bangladesh in 1998 was substantially higher among households in the bottom 40 percentile of the consumption distribution (Del Ninno and Lundberg 2005).

Seeking to contribute to that literature, this paper looks at the vulnerability of households to large rainfall shocks in a context where natural risks are prevalent and poverty is pervasive, with more than half of the population living in poverty. More specifically, employing a doubledifference (D-D) analysis that exploits spatial and time variation in extreme (excessive) rainfall, we investigate whether households in the proximity of Agatha –a major tropical storm that hit Guatemala in 2010 and dropped the largest rainfall in the country since 1963– saw a fall in their consumption and were likely to fall further into poverty as result of the event, and whether they engaged in sub-optimal strategies to confront the shock. The paper also examines the variability of the impacts across different groups of people and postulates hypotheses about some of the potential mechanisms at play. The data used for this study come from two crosssections of national representative household-level survey data collected before (2006) and almost one year after the storm occurred (2011) as well as administrative data with monthly rainfall and temperature data for the period 1963-2013 recorded by 73 meteorological stations scattered across Guatemala.

The study finds that household welfare, measured by per capita consumption, fell on average by 8.2% of the median consumption at the baseline among affected households (i.e. located in areas where the windstorm brought rainfall substantially above the historical levels) relative to households less affected or not affected by Agatha. Further inspection of the data shows, however, that the losses in consumption attributed to the shock arose mostly among households from urban areas, which experienced a decline of 12.6% of their median consumption at baseline. Point estimates from a specification that captures variation in the intensity of the shock with categorical levels of precipitation anomalies yield similar results, with the impact on per capita consumption ranging from 9% to 14%. Nearly half the drop in consumption is explained by a reduction in food expenditures of 10% that correspond to 43-108 fewer calories per person per day. Affected households also cut back expenditures on durables, including basic items such as a stoves or refrigerators.

3
Figure Vi1 Taxes Versus Standards--Standard Preferred <!-- Start of picture text --> $ \<br>MCI$<br>E MCI<br>t Mc<br>MB<br>za" a a Abatement<br><!-- End of picture text --> A pollution abatement standard is preferred to a pollution tax. The requlator, who must set either a tax, t, or a standard, a, in the face of uncertainty about the exact location of the marginal cost of abatement curve, will choose to equate the expected marginal cost, EfMC], with the marginal benefit, MB, of abatement. The optimal level of abatement will, in contrast, be given by the intersection of the true MC of abatement (either MC" or MC') with the MB of abatement. A tax, t, will induce abatement of only a" units from a firm with high costs (MC") and a very large amount, a', from a firm with low costs (MC'). These abatement levels are not close to the optimal levels, and they result in losses of societal net benefits measured by the large lined triangles. In contrast, an abatement standard, a, will come much closer to the optimal level of abatement when casts are either high or low, and the resulting welfare loss is smaller (measured by the small darkly shaded triangles). The comparative advantage of a standard to a tax emerges because of the relatively steep slope of the MS of abatement curve.

67
**Figure 3.5: Inequality in Mexico, rural and urban areas,** **<u>Measured by Gini coefficients, 1992-2002</u>**

<!-- Start of picture text --> Urban<br><!-- End of picture text --> <!-- Start of picture text --> 1992 1994 1996 1998 2000 2002<br>Source: World Bank 2004.<br><!-- End of picture text --> This contrasts the development of income inequality in urban areas that experienced an inequality reduction. The higher-productivity agriculture experienced rapid growth and so did high productivity non-farm income, which both contributed to increased rural inequality. Moreover, Mexico as a whole experienced a decrease in returns to tertiary education of workers in the labor market since around the middle of the 1990s and rural areas experience relatively little of this fall as very few of the rural population hold a university degree.

The poor have increased their access to government supplied services and infrastructure such as electricity, water and sanitation in the past decade. Ownership of household durables has also increased, but large inequalities remain in for example the ownership of housing and financial savings (World Bank 2004). Moreover, despite progress, there is evidence of Mexico lagging behind comparators in East Asian in infrastructure provision (World Bank 2004).

# **4. Poverty Profile**

After counting the poor we need to know who they are, where they live, and what they do. Comparing average levels of poverty for different categories is useful for learning about which population groups are falling behind or catching up in terms of poverty. This is useful for the design of policies: we would like to know not only whether, for example, more- or less-educated people are more likely to be poor in rural Mexico, but how the relative odds of being poor have evolved for these groups. This section traces the evolution of the P0 for various population groups during 1992-2002.

16
Authority, SESTIBA -State-owned Loading and Unloading Company- and the Commercial Registry.

The econometric specification of the long-run total cost function is the following: where: _yi_ = Amount of product i, _pi_ = Price of productive factor i, _m_ = Number of product, _n_ = Number of productive factors, _T_ = Time trend, _Di_ = Firm dummy, _N_ =Number of firms. All the variables marked with a horizontal bar reflect the value of the entire sample mean.

In fact, it is a quadratic function deviated from the sample mean where firm dummies have been included to capture specific effects as well as a time trend crossed with all the variables which reflects a possible technical change. Using the iterative technique modified by Zellner, the authors estimate a system of equations made up by total cost function (21) and the equations of expense in factors resulting from the application of Shephard’s lemma, which allows determination of as many additional equations to cost function as variable productive factors are involved in cost estimation, without the need to introduce any additional parameter.

# **5.2. Product definition.**

In almost all empirical applications, the authors have had to deal with the problem of defining the product. The reason is that, although most economic production activities are multi-productive, it was not until _Baumol et al., (1982)_ in the 80’s that there appeared a systematized theoretical body enabling the deep analysis of multi-productive activities nature with definition of new specific concepts related to multiproduct that could be contrasted empirically. In spite of the Baumol et al. contribution, the initial empirical analyses of multiproductive activities continued to rely on an aggregate to represent the product, or used attributes to capture the multi-productive nature that was being omitted. In some cases, real output vector dimension makes it impossible to estimate a flexible functional form and, thus, forces some kind of aggregation even though this results in loss of information. However, as shown by _Jara-Díaz (1982)_ , this simplification implies not only loss of information but also 18
group of countries. The multiplier is 0.26 on impact (and signi...cantly di¤erent from zero), reaches a peak of 0.60 after three quarters, but then becomes insigni...cant after about seven quarters.

This linear (or single) multiplier provides us with a benchmark to ...rst revisit the AG exercise of computing multipliers in expansions and recessions and later examining whether it matters if government spending is going up or down.

# 3.2 Multipliers in recession and expansion

Based on speci...cation (2), we now compute the ...scal multipliers in economic expansions and recessions for OECD countries. The multipliers associated with expansion and recession are illustrated in Figure 2, panels A and B.

As in AG (2012) we ...nd that the ...scal multiplier in recessions is larger than in expansions. In the case of recessions, the multiplier is 0.73 on impact (and signi...cantly di¤erent from zero) and reaches a peak of 1.28 after four semesters. In contrast, in expansions the multiplier is 0.09 (and not signi...cantly di¤erent from zero) on impact and remains insigni...cant for any horizon.

The nice point to emerge out of this exercise of is that one can think of the single multiplier illustrated in Figure 1, panel A, as an “average”of panels A and B in Figure 2 that masks the estimate that we are presumably most interested in (i.e., the multiplier during recessions when policy makers are trying to stimulate the economy in order to raise output and employment).

Taken at face value, the policy implication of this ...nding is clear: increasing government spending in periods of recessions (as Keynesian considerations would call for) would stimulate output whereas increasing it in times of expansion would have essentially no e¤ects. Further, since we have made no distinction between increases and decreases in government spending, it would also follow that reducing government spending in recessions (as many developing countries have historically done and many industrial countries are currently doing) would be 13
Table 5: Modern contraceptive use at endline

||||Covariate at baseline|:|
|---|---|---|---|---|
|**Outcome:**|All|MIL opposed to FP|Valued concealibility<br>of FP method|Found FP embarrassing<br>to use|
|**Using modern FP method**|(1)|(2)|(3)|(4)|
|Own Voucher|0.058|0.058|0.056|0.036|
||[0.037]|[0.041]|[0.051]|[0.048]|
|BAF Voucher|0.059|0.022|0.011|-0.018|
||[0.036]|[0.039]|[0.053]|[0.044]|
|Own Voucher _×_ Covariate||-0.003|0.013|0.031|
|||[0.111]|[0.078]|[0.076]|
|BAF Voucher _×_ Covariate||0.153|0.098|0.205**|
|||[0.121]|[0.075]|[0.085]|
|Observations|599|596|599|596|
|Endline control mean|0.123|0.123|0.123|0.123|
|p-values:|||||
|Own = BAF|0.992|0.450|0.446|0.301|
|Own _×_ Covariate = BAF _×_ Covariate||0.288|0.351|0.074|
|Own + Own _×_ Covariate = 0||0.595|0.242|0.260|
|BAF + BAF _×_ Covariate = 0||0.126|0.042|0.011|

_Notes:_ All columns are fully-interacted regressions where all controls are interacted with the covariate used to examine heterogeneous effects; the main effect of the covariate is also included as a regressor. All columns also include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variable for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-inlaw, FP denotes family planning, and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01.
24 Fig. 2.5. Household plans to improve housing conditions in the near future (by quintiles based on per capita provision with floor space; % of each quintile) <!-- Start of picture text --> SO SO so<br>45 45 45-<br>40 40 40<br>35 35 35<br>30A 1a 10<br>30 -30 30-<br>25 l 25 25 11.7<br>6.7<br>20 20 620-<br>b. Household15 i.m15<br>10 ~ 710 310<br>0 ~ .4*~00<br>1 2 3 45 12 3 45 12 34 5<br>Moscow, 1996 Vladimir, 1996 Gorodets, 1996<br><!-- End of picture text -->

**Note: I** - **lowest quindile, 5** - highest **quintile**

**13o acareb0 ss[on**

<!-- Start of picture text --> freo-hag ai<br><!-- End of picture text --> Sources: **_1996._** vaitin The (Fg 2.6). **Moscow Longitudinal** Household **Survey, January** 1996; Vladiyir and Gorodets Longitudinal Household Survey, **Decenber** It is noteworthy that wheasin yMoscow the lowest quntile, which in all cities experiences the greatest urgency in its housing needs, is clearly dominated by those who count on free housing (30.6% of persons on the waiting list as compared to 17.6% of persons favoring market methods), in the oMter cities and towns such hopes are less common: 20-25% of the households in this **queintile** are prepared to buy it on the market.

# **_b. Household incomes_**

The impact **of incomes** on the households' miarket attitudes makes itself felt in all three cities, albeit with variations (Fig. 2.6).

In Moscow, which has the highest apartment prices in Russia and a considerable income disparity, the low- and middle-income households favor receipt of housing free-of-charge. Only in the highest income qwntile **is** the proportion **of** households who can afford to buy an apartment significantly higher, reaching 19. **1%/.** However, the wealthy no longer need housing quite so urgently as they used to, as is shown by the findings **of** a survey **held** in Moscow in early 1994 (Fig. 2.7). Many such people have probably already purchased apartments, as a result of which demand was largely satisfied (as **will** be shown below, in the past two or thre years, the mobility of the high income groups was quite, high). Poor households **in** Moscow also demonstrate less interest in obtaining housing because **this** group of Muscovites consists mainly **of** pensioner **households,** who do not generally have a pressing need to change their living conditions. As for middle-income households, they unanimously demonstrated increased interest in improving their living conditions.