rafmacalaba/datause-encoder-data huggingface dataset:
# **Sudden Stops: Are Global and Local Investors Alike?**<sup>*a</sup>

## **César Calderón** , **Megumi Kubota**

> a _The World Bank, 1818 H Street NW, Washington DC 20433, USA_

**JEL Classification:** F32, F41

**Key Words:** Sudden Stop, Gross Capital Flows, Reversals

### **JEL Classification:** F32, F41

**Key Words:** Sudden Stop, Gross Capital Flows, Reversals

> *Calderón: The World Bank, Office of the Chief Economist, Latin American Region. E-mail: <u>ccalderon@worldbank.org. Kubota was a consultant for the Office of the Chief Economist in the Latin America and</u> the Caribbean region during the preparation of this paper. We are grateful to Augusto de la Torre and Eduardo Cavallo as well as participants at the 2010 INFINITI Conference in Dublin, Ireland (Trinity College) for their comments and suggestions. The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank or its Boards of Directors.
Table A.10: Consumption

||**PC total e**|**xpenditure**|**PC food e**|**xpenditure**|**PC non-fo**|**od expenditure**|
|---|---|---|---|---|---|---|
||APL|BPL|APL|BPL|APL|BPL|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Expected dowry_|160.05|122.77|-6.13|53.10|158.30|83.85|
||[708.13]|[333.11]|[81.45]|[181.74]|[671.57]|[223.88]|
|_Firstborn girl_|-7,402.23*|1,367.72|-3,017.97***|-1,036.98***|-2,465.83|-1,457.76*|
||[3,786.08]|[1,092.65]|[792.89]|[326.73]|[3,853.80]|[809.42]|
|N|1,463|1,176|1,451|1,162|1,451|1,162|

NOTES: This table reports the coefficients corresponding to specification (2) estimated for households where all children have the same mother, separately for above poverty line (APL) and below poverty line (BPL) families. Each column is a separate regression. The dependent variable is total per capita consumption expenditure in columns (1) and (2), per capita food expenditure in columns (3) and (4), and per capita non-food expenditure in columns (5) and (6). _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.
Firms can be analyzed at an aggregate level based on several observable characteristics. Such observables include the number of different destinations and products that an exporting firm deals with. The distribution of firms along these characteristics provides some natural category bounds, which are used to investigate how much of the total export USD-value is taken up by each category. Figure 3 shows yearly shares for three firm categories based on the number of destinations to which firms export (1, 2-5, and 6+ destinations) and the number of products that firms export (1, 2-29, and 30+ products), as well as period averages for 2006-2009. The data show that a large share of exports is carried out by fairly sophisticated firms, having six or more destinations (73 percent) and 30 or more products (67 percent). These results, slightly skewed by the mining complexes that send equipment abroad for servicing, show the picture of an economy whose exports are dominated by larger firms.

**Figure 3:** Shares of the total export value taken by each firm type: by number of products exported and number of destinations served (yearly in 2006 to 2009, and period averages)

<!-- Start of picture text --> 100% 100%<br>80% 80%<br>70.0% 75.0% 69.5% 73.2% 80.4% 65.3% 64.6% 63.3% 66.7%<br>60% 80.4% 60%<br>40% 40%<br>20% 17.5% 16.9% 21.8% 18.1% 20% 22.4% 28.2% 30.9% 26.1%<br>12.7% 12.9%<br>6.9% 12.5% 8.1% 8.7% 8.7% 6.7% 12.3% 7.2% 5.8% 7.2%<br>0% 0%<br>2006 2007 2008 2009 06-09 2006 2007 2008 2009 06-09<br>1 dest. 2-5 dests. 6 or more dests. 1 prod. 2-29 prods. 30 or more prods.<br><!-- End of picture text --> _Source:_ Authors‟ calculation based on Lao PDR customs data The degree of sophistication of the national export basket as well as its potential for expanding into a wider range of products (diversification is a major concern for a developing economy like Laos (Record and Nghardsaysone 2009)), is another area where the customs data can shed light. Figure 4 shows how yearly export volumes are divided among products with low, medium and high sophistication (as measured by their PRODY value) and similarly for the products‟ potential for diversification (PATH values). On the left side, we see the picture of an economy dominated by products with a low income potential (produced mostly by low-income countries), and a rather stable composition in time. In 2006 there seems to be a reversal between low and midlevel PRODY, but this is largely an artifact caused by a type of copper product with a very high PRODY value and which was produced in large quantities that year.

_Exporting from a small landlocked economy_ _9_
an aging population, generates a rate of reduction of skilled workers slightly higher than that of unskilled workers, resulting in a (negative) gap of 3 percentage points.

Figure 4: Growth rates of labor by skill, sector and country/region <!-- Start of picture text --> 90.0 NGA<br>SSA<br>70.0 MNA<br>50.0<br>TUR LAC IDN IND SAS<br>MEX<br>BRA ZAF<br>30.0<br>LKA EAP<br>CHN<br>10.0<br>ECA USA<br>YHI<br>EUR<br>-10.0<br>RUS<br>-30.0<br>-30.0 -10.0 10.0 30.0 50.0 70.0 90.0<br>Unskilled<br>Skilled<br><!-- End of picture text --> Source: Authors’ calculations based on GIDD projections. Note: the growth rates are expressed as the cumulative growth for the period 2030-2012. The red dots represent regions; the blue dots are individual countries.

These changes in the labor supply of different education levels constitute the exogenous shock defined in the baseline scenario. While labor supply is likely to respond to wage changes, the strong assumption that labor supply is exogenous and that it follows demographic developments is adopted here. In fact, most economic models likewise treat population growth as exogenous and, in this exercise, labor supply by education level is indirectly derived from the aging of the population and by assuming that the education attainment of the young cohorts in the future will adhere to the same attainment rate observed in the current data. This assumption is conservative because some countries will likely do better. That is, their education policy may bring about better educational attainment among the young generations in the future when compared to those in the present period. The education wave, if such improvement is accounted for, would be even larger than what is currently defined in the baseline.

Table 4 summarizes the scenarios used for this analysis. Against the baseline (education wave), a “no education wave” is set up as the main counterfactual simulation, which is a what-if scenario in which the education trend of the baseline is removed. Two additional scenarios aim to address potential concerns related to technological changes that may be biased towards skilled 17
# **4 Data and methodology**

This section describes the nine data sets that we analyze, and the methodology that we use to measure targeting performance, informed by the framework laid out in section 2.

# _4.1 Data_

We analyze nine data sets from six countries, presented in Table 1. Two main criteria were used in choosing these data sets. First, we looked for data sets from Sahelian environments, to compare across poor, homogeneous settings. Second, the data had to include information on implemented targeting schemes of social safety nets programs. Third, data had to have been collected prior to the intervention. The data sets obtained included information on programs that employed CBT, PMT or both. The reports from the original studies for which the data were collected (Table A1) indicate several issues in the implementation of the targeting operations (e.g., CBT thresholds deviating from the original target in Cameroon). These implementation gaps are inherent to targeting and to CBT selection in particular (de Sardan & Piccoli, 2018). Since we study actual targeting outcomes, the results reflect these implementation issues. On the other hand, studies suggest that data quality is high.

In all data sets, information was collected on households in the areas of intervention, after a geographical targeting process. This geographic selection process varied across data sets: it was very narrow in Cameroon (15 villages in the poorest _commune_ of the country) but not as focused on the poorest areas in Senegal 2 and Burkina Faso 2.<sup>19</sup> Most data sets include information on per-capita consumption and on the food consumption score (FCS). These well-being measures have the advantage of being quasi-continuous. However, the Household Dietary Diversity Score (HDDS) is available in two data sets only, and in Chad, no household consumption is available (Table 1). CBT selection thresholds vary from 21% to 68%, reflecting the wide variation in coverage rates within program areas. As discussed, this calls for considering the selection threshold when comparing targeting outcomes, and for harmonizing selection thresholds within and (when possible) across countries when conducting targeting analyses.

Our data also include information on household characteristics, although the variables collected vary across data sets. We use a limited number of household demographic and housing characteristics (e.g., household size, roof material) when exploring the determinants of beneficiary selection across methods.

_4.2 Methodology: Measures and metrics_

> 19 There are two data sets each for Burkina Faso, Niger and Senegal, which we differentiate by a number (1 or 2). There is only one data set each for Cameroon, Chad and Mali.

12
this paper. Because the IMFWorld Bank framework ignores uncertainty, some changes are needed to make the comparison possible.

The Senegal DSA includes baseline projections for external debt, real GDP growth, implicit interest rate, growth rate of GDP deator in U.S. dollars, non-interest current account, and FDI ows until 2030. Given these values, I recover the debt shock as explained in Section 2. Next, I obtain the residuals _ε_ � _t_ for _t_ = 2011 _,_ 2012 _, ...,_ 2030 that make the predictions from the IMFWorld Bank DSA consistent with the econometric model characterized by equations (3.1) and (3.2). While still ignoring uncertainty, this approach is equivalent to view the projections from the DSA as being derived from a particular realization of the econometric model (3.1)(3.2). Finally, I compute the standard deviation of the six determinants of debt using their values over 2001-2010.

To compute the stress tests, I proceed as follows. The stress test according to the IMF World Bank framework consists of adding a negative shock equal to one standard deviation of the corresponding residual during 2011-2012. For example, in the case of GDP growth, I subtract one standard deviation from the baseline projection only during the years 2011 and 2012. The remaining years and variables are set as in the baseline projection. A stress test consistent with the methodology proposed in this paper is more involved. Consider the case of GDP growth. On average, shocks to GDP growth are correlated with shocks to the other variables. To take into account these correlations, I adjust the ve remaining residuals in such a way that they are, on average, consistent with the covariance matrix of residuals. Specically, let _π_ denote the proposed one standard deviation shock to GDP growth that lasts for two periods and let _ζt_ for _t_ = 2011 _,_ 2012 _, ...,_ 2030 denote a new vector of residuals � for the proposed stress test. For _t_ = 2013 _,_ 2014 _, ...,_ 2030 , this vector satises _ζt_ = _εt_ . For � _t_ = 2012 and 2013 , the vector of residuals is given by _ζi,t_ = _εi,t_ + (Ω1 _,i/_ Ω1 _,_ 1) _π_ where subindex _i_ denotes the ith element of the corresponding vector and Ω _i,j_ is the ( _i, j_ ) element of the estimated covariance matrix of reduced form shocks.<sup>14</sup> Finally, to compute the projected

> 14Formally, I project the change in each reduced form shock into the change in the rst reduced form shock. This is the same approach proposed by Pesaran and Shin (1998).

23
of the probability to be time poor. The rest of the paper is structured as follows. In section 2 we will briefly review the relevant literature. After outlining our analytical framework in section 3, we present empirical results obtained with the Guinea survey in section 4 on the extent of time poverty in that country. The concluding session will highlight the policy implications of our approach.

# **2. Conceptual framework**

The idea that time, and not just income, is an input to the production of commodities, and provides therefore direct utility, was already clear in the seminal work of Gary Becker (1965). Becker’s approach recognizes the importance of time in household production and consumption activities—the household maximizes its utility through consumption of commodities that are themselves produced by combining inputs of time and market goods. The theory implies that in choosing the desired combination of commodities the household also decides how to allocate the time of its members between market work and household production and consumption.

While Becker’s theory has the clear merit of recognizing that household activities are productive and that the efficiency and allocation of time crucially affect the household’s utility, it has been criticized by feminist scholars for treating the ‘household’ as a unit and disregarding the complex internal dynamics that are profoundly shaped by gender roles. The work of gender and feminist economists has also emphasized the importance of taking into account women’s disproportionate engagement in unpaid activities (such as subsistence farming) and household labor. For example, Ester Boserup (1970) stressed the strong demand on women’s time of tasks such as fetching water and wood, food production, and other subsistence activities. The importance of measuring unpaid work in the subsistence sector, informal sector, domestic work, and even volunteer work has been advocated in order to obtain a better measure of economic activity (it has for example been argued that unpaid work should be incorporated in GNP statistics), but also to better understand the survival strategies of the poor (Lourdes Beneria, 1999; Sylvia Chant, 2003). Clearly, the relative specialization of women in unpaid work is related to comparatively lower opportunity costs of women’s labor (e.g. Francine Blau and Marianne Ferber, 1986).

The sociological literature, especially in the context of developed countries, has also devoted a lot of attention to ‘time poverty’ and the use of time in general. In particular, a large 3
**Table 6** : Cross-section regression estimates (OLS) of the effect of per capita income, trade, FDI net inflows on the occupational gender wage gap by occupational skill level and country income (standard errors in parentheses)

||**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|---|---|---|---|---|---|---|---|---|
||**Low an**|**d lower mid**|**dle income co**|**untries**|**High an**|**d higher mid**|**dle income co**|**untries**|
|Log GDP per capita|0.03|0.02|0.07|0.02|-0.01|-0.01|0.02|0.01|
||(0.01)|(0.01)|(0.05)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Trade (% GDP)|||||||||
|* Low skill|-0.001<br>(0.0003)||||-0.0006<br>(0.0001)||||
|* High skill|0.0001||||-0.0002||||
||(0.0003)||||(0.0001)||||
|Trade (% GDP, constant LCU)|||||||||
|<br>* Low skill||-0.001||||-0.0007|||
|||(0.0003)||||(0.0001)|||
|* High skill||0.0004||||-0.0003|||
|||(0.0003)||||(0.0001)|||
|Sector Trade (% sector output)|||||||||
|* Low skill|||-0.00005||||-0.0001||
||||(0.0001)||||(0.00003)||
|* High skill|||0.0002||||0.0004||
||||(0.0002)||||(0.00004)||
|FDI net inflows (%GDP)|||||||||
|* Low skill||||-0.01||||-0.004|
|||||(0.003)||||(0.002)|
|* High skill||||0.005||||0.006|
|||||(0.003)||||(0.002)|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies Cyprus, Japan and|N/A|N/A|N/A|N/A|Yes|Yes|Yes|Yes|
|<br>Korea|||||||||
|Dummy for (ex)Communist|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2246|2242|334|2244|7424|7236|2359|7187|
|Adjusted R<sup>2</sup>|0.19|0.19|0.13|0.17|0.56|0.56|0.72|0.56|

Note: standard errors are corrected for clustering within the same country/occupation observations. NA = not applicable.

41
contraction of both exports and imports during the financial crisis and the recovery of both flows that started on the third quarter of 2009. While both imports and exports have been growing at the margin exports have rebounded at a slightly faster pace. The main products leading this recovery have been rice, textiles like men’s shirts of cotton, women’s apparel, petroleum oil and bed and bathroom linen, as well as cotton yarn.

**Figure 2 Export growth**

<!-- Start of picture text --> Index of export growth: 1980-2010<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Pakistan World Low income South Asia<br><!-- End of picture text --> <!-- Start of picture text --> Imports and Exports of Goods and<br>Services 2008-2011*<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Exports f.o.b Imports f.o.b<br>Source: Pakistan's Central Bank. *2011 Quarterly revision.<br>Millions of $<br><!-- End of picture text --> Pakistan’s recent trade performance is one of stagnation, as indicated by a decrease in its tradeto-GDP ratio over the last decade. At the same time peer countries have leapfrogged with high growth rates.<sup>3</sup> Pakistan’s position below the predicted line indicates that it “under-trades”

> 3 The trade-to-GDP ratio is one of the most basic indicators of openness to foreign trade and economic integration. It weighs the combined importance of exports and imports of goods and services in an economy. The ratio gives an

4
# **1 Introduction**

The COVID-19 crisis has renewed the interest of economists in the determinants of firms’ ability to adjust to economic shocks. A relatively large literature has analyzed different types of firm-level characteristics such as innovation activities, management practices and market power that determine the extent of adjustment to such shocks (Gupta, 2020; Hyun et al., 2020; Ding et al., 2021). Despite yielding important findings on firms’ resilience to shocks, these studies may provide limited guidance to policy makers as many factors that are not directly affected by policies can explain the variations in these characteristics across firms.

In this respect, the role of trade policy is a particularly important gap in the literature, given the increasing connectedness of firms through global value chains. Access to foreign intermediate inputs can increase the amount and quality of firm-level exports by exposing firms to new inputs as well as technologies (Goldberg et al., 2010; Fan et al., 2015; Halpern et al., 2015; Feng et al., 2016). As a result, a firm that is an exporter is also more likely to be an importer. Faced with a sudden change in demand for their goods, exporters need to optimally change their inputs and/ or input sources. To the extent that import measures change the relative costs of such adjustments, they could have a prominent impact on the resilience of to shocks.

This paper helps to fill this gap by examining the role of trade policy, both non-tariff measures (NTMs) and tariff measures, in affecting the resilience of exporters to a demand shock. To do so we match novel data on Indonesia’s trade policy measures at a highly disaggregated product level with data on the universe of Indonesian exporters.

While data on import tariffs are relatively more available, a key innovation of this paper is the extensive coverage of NTMs, which represent increasingly important but still under-researched trade policy instruments (Nicita, 2012). Specifically, we address the data limitation by assembling a novel hand-collected database on NTMs from different regulatory agencies in Indonesia for the period 2014-2018. We then link these NTMs (as well as tariffs) to firm-level customs data at the 10-digit HS level, the most detailed product classification 1
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# Displacement and Social Empowerment: Evidence from Surveys of IDPs in Iraq, the Philippines, and Uganda<sup>*</sup>

Patrick Vinck,<sup>†‡</sup> Thomas O’Mealia,<sup>§</sup> Carol Wei<sup>¶</sup> , Abdulrazzaq al-Saiedi,<sup>||</sup> Muslih Irwani,<sup>**</sup> and Phuong N. Pham<sup>††</sup>

## **_JEL Codes:_** D74, F22, C83, N47, N45, O15, R23

## **_Keywords:_** Displacement, IDPs, Social Cohesion, Political Empowerment, Surveys

> *Vinck and O’Mealia are co-first authors. Acquisition of the data used in this manuscript was supported by the Ministry of Foreign Affairs of the Netherlands (Iraq), the John D. and Catherine T. MacArthur Foundation (Uganda) and the World Bank and World Food Programme (Philippines). The funders played no role in the analysis, interpretation or writing of the results and decision to submit the manuscript.

This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts.” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

- †Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA

- ‡Corresponding author

- §PhD Candidate, Department of Political Science, University of Michigan, USA

- ¶Research Consultant, Department of Emergency Medicine, Brigham and Women’s Hospital, USA ||Fellow and Visiting Scientist, Harvard Humanitarian Initiative, USA

- **Associate Professor of Public and Social Policy, American University of Iraq – Baghdad, Iraq

- ††Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA