rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4:  Number of firms that export a non-negligible amount**

<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>No. of firms exporting > 10 000 USD / year<br>No. of firms exporting > 100 000 USD / year<br>No. of firms exporting > 1 million USD / year<br><!-- End of picture text --> _Source:_ Statistics in this an all following figures and tables are calculated on the firm-level dataset that is constructed by merging administrative information from three data sourced that covers the universe of registered firms in the agriculture, mining, and manufacturing sectors of Rwanda (for details see description in the data section).

In this perspective, it can be considered a positive development that the ratio of small to large exporters has increased over time. In particular, while the number of firms that exported more than 1 million USD grew from 20 to 36 over the 9 year sample period, the number of firms that exported less than 1 million USD almost tripled, increasing from 23 to 65. This can be seen directly from the widening gap between the black and the orange lines in Figure 4.

We show the number of small and large exporters separately by sector in Figure 5. Not surprisingly, the number of large exporters, i.e. firms that sell more than 1 million USD abroad in a year, is relatively stable over time. The right panel of Figure 5 barely shows any change for agricultural firms, rising from 8 firms to a maximum of 12 in 2015. The pattern is similar for mining firms between 2008 and 2013, but in the last three years the number of large exporters suddenly jumped. Quite remarkably, only a single manufacturing firm managed to sell more than 1 million USD abroad in 2009 or 2010, but this became a little more common over time. In 2012 and 2013 all three broad sectors counted approximately 10 large exporters, but since then the sectoral experience diverged again. But unless the sudden increase in large mining exporters proves durable, it seems that the three sectors are converging somewhat.

The importance of the three sectors is notably different for small and large exporters. In fact, the relative importance of the three sectors is exactly reversed if we focus on smaller exporters. In the mining industry, there are more firms exporting more than 1 million USD than firms exporting less than this amount. This is especially remarkable given that Rwanda’s mining industry specializes in high-value minerals that are extracted and exported in relatively low quantities. If mining were mostly concentrated in large-scale extraction of basic ores using a highly capital-intensive process, a high market concentration would be expected. However, the most important mining exports for Rwanda are tin ore, coltan, wolfram 10
# **2. Literature Review and Theoretical Expectations**

# **2.1 Recent literature**

The literature on the gender-level impacts of armed conflicts has been growing in recent years due to improved access to household level data for conflict-affected economies. This section briefly reviews studies that address the effect of armed conflict and large-scale economy-wide disruptions on the education and labor market outcomes of individuals that were either exposed to the conflict as civilians or through participation in the military.

The research on the relationship between armed conflict and education began with an examination of cross-country differences in aggregate enrollment rates in developing and developed countries (Stewart, Huang and Wang 2001). Once individual and household level datasets became available, researchers turned to the examination of the impact of conflict on differences in educational attainment across birth cohorts and regions (Merrouche 2006; Akresh and de Walque 2008; Shemyakina, 2011). These studies observe a decline in the education of affected cohorts but do not reach the same conclusions. Akresh and de Walque find that the education of boys from wealthy households suffers due to the genocide in Rwanda, while Shemyakina’s analysis indicates that in Tajikistan the impact is stronger for older girls from affected household as compared to younger girls from similar households. These studies contemplate that the observed decline in education may be related to school closure, migration and displacement, quality and availability of school facilities and shocks to income and security. The studies also note that the observed decline in education is likely to have a negative impact on the future productivity and wages of affected cohorts.

Two recent studies connect large economic and political shocks to labor market experience and education. Meng and Gregory (2007) investigate the impact of the Chinese Cultural Revolution on the earnings of the cohort who lost a substantial number of years of education due to the Revolution. They find that the earnings of the individuals who did not receive university degrees (but would have if they had been raised during a different period) were about 46-76 percent lower. Blattman and Annan (2010) 4
# **Crime and Growth Convergence: Evidence from Mexico**

Ted Enamorado<sup>∗</sup> Luis F. López-Calva<sup>†</sup> Carlos Rodríguez-Castelan<sup>‡</sup>

**Keywords:** Crime, convergence, growth, spatial inequality **JEL codes** : O12, O54, H70, D74 **Sector Board** : Poverty

> ∗ Princeton University. E-mail: tede@princeton.edu

> † World Bank. E-mail: lflopezcalva@worldbank.org

> ‡ World Bank. E-mail: crodriguezc@worldbank.org

The authors would like to thank Louise-Cord , Samuel Freije-Rodriguez and Mary Alexander Sharman for valuable comments and edtis. The findings, interpretations and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent.
disasters and where often most of the poor live (World Bank, 2003, Benson and Clay 2004). Disasters only rarely affect a whole country; Honduras Mitch is probably the one disaster that came closest to affecting the whole country’s territory and population. But even in Honduras the effects were much more intense in certain rural areas than in the major cities. As can be seen in the sample of events presented in Table 4, with the exception of Honduras, even massive disasters have tended to affect less than 25% of the country’s population. The greatest and most massive earthquake ever measured- Chile 1960- which caused damages and death in as far away as Japan and the Pacific Islands, affected mainly one-third of the country, while the rest was basically unscathed.

A related issue is that most disasters in rich and middle-income countries destroy much more physical capital than human capital. Human lives are usually spared in these countries because prevention mechanisms are in place to protect lives and physical capital tends to be highly concentrated to exploit economies of agglomeration which means that the potential for physical capital destruction is higher than in the case where it is more dispersed. By contrast, in poor countries the disasters affect human lives more with somehow lower damage to physical capital which is scarcer in the first place and is much more geographically dispersed than in developed countries (ADPC, 2005). Natural capital is affected in similar ways in both poor and rich countries but poor economies tend to be much more dependent on natural capital as a productive asset than rich countries.

Often the most serious impacts of disasters fall upon the poor, mainly the rural poor (World Bank, 2003, Benson and Clay 2004). Table 4 illustrates this tendency for selected cases where the information for the sector impact of disasters has been quantified. As can be seen in the table, most disasters have affected much more intensely the agriculture, fisheries and forestry sectors than the more modern enterprise sector. The rural poor tend to live in vulnerable areas and they are economically most dependent on natural resources for their survival. Several authors have pointed out that the vulnerability of the poor relates not only to their geographic location but also to the likelihood that the incomes losses caused by the disaster may propelled them into poverty traps ultimately rendering them destitute, unable to sustain themselves over the long run. Carter et al., 2007, provide micro empirical evidence for this phenomenon for Honduras and Ethiopia (see also IMF, 2003; Hallegate and Dumas, 2009).

8
# Figure 1. **Brazil's fiscal performance, 1990-2003**

In per cent of GDP

## **A.  Indebtedness and fiscal stance**<sup>**1**</sup>

<!-- Start of picture text --> 6 70<br>Primary budget balance<br>5 (at time t+1, left axis) 60<br>Public debt (at time t,<br>4 right axis) 50<br>3<br>40<br>2<br>30<br>1<br>20<br>0<br>-1 10<br>-2 0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> <!-- Start of picture text --> B. Fiscal stance over the business cycle 2<br>6<br>4<br>2<br>0<br>-2 Overall budget balance<br>-4 Primary budget balance<br>-6<br>-8<br>-10<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> <!-- Start of picture text --> C. Central government revenue and<br>D. Central government outlays on pensions<br>expenditure<br>and discretionary programmes<br>24.5 18.0 7.5 5.0<br>24.0<br>17.5 7.0 4.5<br>23.5<br>23.0<br>17.0 6.5 4.0<br>22.5<br>Revenue (left scale) 16.5 6.0 3.5<br>22.0<br>Pensions (left scale)<br>21.5 Expenditure (net of<br>intergovernmental transfers, 16.0 5.5 Discretionary spending 3.0<br>right scale) (right scale) (3)<br>21.0<br>20.5 15.5 5.0 2.5<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04<br><!-- End of picture text -->

1. Refers to the consolidated public sector.

2. Refers to the consolidated public sector. The grey areas identify cyclical downturns, defined as the years in which changes in the ouput gap relative to the previous year are non-positive.The ouput gap is calculated as the percent difference between actual and HP-filtered GDP.

3. Measured as "other current and capital spending" (other OCCs).

Source: National Treasury, and author's calculations.

5
# **Tapering Talk: The Impact of Expectations of Reduced Federal Reserve Security Purchases on Emerging Markets**

# **Barry Eichengreen and Poonam Gupta**<sup>**1**</sup>

Keywords: Capital Flows, Emerging Markets, Monetary Policy, Tapering JEL Classification: F32, F41, F42, F62

> 1 University of California, Berkeley and World Bank, respectively.  Gupta thanks Tito Cordella, Zia Qureshi, David Rosenblatt  and Aristomene Varoudakis for useful discussions and comments, and James Trevino for excellent research assistance.  Eichengreen thanks Victoria University and the Reserve Bank of New Zealand for their hospitality while the first draft of this paper was completed. Comments are welcome at eichengr@berkeley.edu; and <u>pgupta5@worldbank.org.</u>
22

# F.BLIOGRAPHT

Barnes, B. 'Papanicolaou Cervical Smears for Screening in Asymptomatic Women." Primaly Care, 8 (1): 131 140, March, 1981.

Barnum, H. 'Cost Savings from Altenr.tive Treatments for Tuberculosis." World Bank Technical Notes Series, Rej%rt No. 86-1, February, 1986.

Bernard, R.P., De Watteville, H., Kessel, E., Ravenholt, R.T., and Kendall, E.N. "International Maternity Care Monitoring: Results of a Pretest." International Journal of Gynaecolonv and Obstetrics, 17 (1): 24-39, 1978-79.

Berry, L.G. 'Age and Parity Influences on Maternal Mortality: United States 1919-1969." Demozra2bv, 14 (3): 297-310, 1977.

Bhatia, J.C. Maternal Mortality in Anantapur District. India: Preliminary Findings. Background paper prepared for the Meeting on Prevention of Maternal Mortality, Geneva, WHO, November 11-15, 1985.

Carrasco, E. 'Contraceptive Practice." In World Fertility Survey Comnarative Studies: Cross National Summaries, No. 9, International Statistical Institute, Voorburg, The Netherlands, 1981.

Chen, L.C., Gesche, M.C., Ahmed, S., Choudhury, A.I., and Mosley, W.H. 'Maternal Mortality in Rural Bangladesh." Studies in Family Planning, 5 (11): 334-341, 1974.

Gorosh, N.E. and Wolfers, D. "Standard Couple-years of Protection." In 3hA Methodology of Measuring the Inmact of Family Planning Programmes on Fertility, New York: United Nations, 1979.

Gray, R.H. Effects of Reproduction and Fertility Control on Women's Health. Prepared for the Workshop on Health Consequences of Contraceptive Use and Controlled Fertility, National Research Council, Committee on Population, unpublished draft, August, 1986.

Grimes, D., Peterson, H.B., Rosenberg, N.J., Fishburne, J.I., Rochat, R.W., Khan, A.R., and Islam, R. "Sterilization-attributabld **e** aths in Bangladesh." International Journal of Gvnaecology and Obstetrics, 20 (2): 149-54, April, 1982.

Harrison, K.A. "Child Bearing, Health and Social Priorities: A Survey of 22,774 Consecutive Hospital Births in Zaria, Northern Nigeria." kBiish Journal of Obstetrics and Gynaecology, 92, (supplement 5): 1-115, 1985.

Herz, B. and Measham, A.R. "The Safe Motherhood Initiative: Proposals for Action." Staff Working PaRers (in press), The World Bank, Washington D.C., 1987.

International EncyloRedia of Ponulation. Vol. 1, J.A. Ross (ed. in-chief), New York, The Free Press, 1982.
Policy ReseaRch WoRking PaPeR 4398

# **Abstract**

Using the most recent purchasing power parity data for 44 sub-Saharan African countries, this paper examines the characteristics of long run growth in Africa between 1975 and 2005. The authors find that low and volatile growth is the outstanding defining characteristic of Africa’s growth experience since 1975, but the authors find no evidence that growth volatility is associated with economic performance over the long run. The authors also find that the 1990s may mark a turning point in Africa’s growth; income distribution is becoming more unequal across countries; formation of clubs; initial conditions matter a great deal for income distribution but not for growth; and that geography and natural resources do not seem to matter for growth.

This paper—a product of the Office of the Chief Economist for the Africa Region—is part of a larger effort in the department to investigate long term growth in Africa. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at jarbache@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
_Lockdowni_ is a dummy equal to one if firm _i_ is located in a municipality subject to a lockdown, and _Postt_ is a dummy equal to one after the firm’s municipality enters into lockdown.<sup>50</sup> A credible causal interpretation requires that firms in treated and control areas are similar in relevant pre-treatment characteristics. To evaluate this assumption, we compare firms in both groups along observable variables that predict program participation, such as size, age, and outstanding debt. The comparisons reveal no meaningful differences, lending support to the validity of our identification strategy (Appendix Table 16).

Table 4, Panel A reports the results for program use. For the credit program, the interaction term is not statistically significant, indicating that firms entering a lockdown are not more likely to use that program (column 1). This evidence is consistent with the selection results shown in Table 2, where firms with negative and positive sales growth are equally likely to take a guaranteed loan. Instead, for the employment program, we observe a positive and significant interaction term (column 2). This finding is also consistent with the selection results, in which firms with negative sales growth are substantially more likely to use employment protection than firms with positive sales growth. This result is consistent with the fact that the opportunity cost of using the employment program is lower if the firm resides in a municipality under a lockdown because a lockdown reduces economic activity and, therefore, the potential profits of firms.

To provide a sharper and more exogenous analysis, we restrict the comparison to firms within a short geographical distance. Similar firms tend to co-locate in space, indicating that nearby firms are similar in many economic characteristics. Importantly, because the virus is spread within short distances, nearby firms have similar exposure to the virus. However, around the border of a lockdown, social distancing measures are different: one firm is in lockdown while the other is not. To perform the analysis, we re-estimate Equation (3) by restricting the sample to firms that run along the municipality border. The main results remain unchanged (Table 4, Panel B).

Lastly, we augment Equation (3) by interacting firm risk with the other variables in the equation to study whether risk is correlated with the adoption of the programs within firms under a lockdown.<sup>51</sup> Since the lockdown regressions use CMF financial data rather than the

> 50We estimate this regression using financial data from the Financial Market Commission, rather than the merged dataset with real-sector variables. This is because only the Commission’s records include firm-level geolocation, which is crucial for identifying firms along municipal borders, a key component of our identification strategy, as detailed below. 51 Specifically, we estimate:

> _ProgramUseit_ = _αi_ + _αt_ + _βLockdowni_ + _γPostt_ + _ηRiski_ + _δLockdowni × Postt_

> + _ζLockdowni × Riski_ + _λRiski × Postt_ + _θLockdowni × Postt × Riski_ + _uit._

19
|With control for structure|GDP/Price<br>Agriculture/Price<br>GDP/Agriculture<br>_↓_<br>_↓_<br>_↓_<br>Agriculture<br>GDP<br>Price||_−_0_._62_∗∗∗_<br>_−_0_._03<br>_−_0_._002<br>(0.26)<br>(0.02)<br>(0.14)<br>_−_1_._68_∗∗∗_<br>_−_0_._05<br>_−_0_._22<br>(0.54)<br>(0.05)<br>(0.25)<br>_−_1_._32_∗∗∗_<br>_−_0_._09<br>_−_0_._0004<br>(0.13)<br>(0.10)<br>(0.24)||1.00<br>2.15<br>0.28<br>1.51<br>4.79_∗_<br>0.04<br>22_._42_∗∗∗_<br>3_._19<br>4_._37|ifcance at the 1%, 5%, and 10% level respectively. _†_|ality. Standard errors are reported in parentheses.|
|---|---|---|---|---|---|---|---|
|for structure|rice<br>GDP/Agriculture<br>_↓_<br>Price||0.05<br>(0.14)<br>_−_0_._12<br>(0.20)<br>_−_0_._27<br>(0.17)||3.36<br>0.37<br>19_._65_∗∗∗_|_∗∗∗_, _∗∗_, and _∗_denotes sign|the test of Granger caus|
|Without control|GDP/Price<br>Agriculture/P<br>_↓_<br>_↓_<br>Agriculture<br>GDP||_−_0_._65_∗∗∗_<br>_−_0_._03<br>(0.24)<br>(0.02)<br>_−_1_._87_∗∗∗_<br>_−_0_._02<br>(0.53)<br>(0.04)<br>_−_1_._47_∗∗∗_<br>0_._05<br>(0.12)<br>(0.03)||2.72<br>7.10_∗∗_<br>0.79<br>3.91<br>12_._70_∗∗∗_<br>2.12|ction of Granger causality.|the chi squared statistic for|
|Income group||Long run|High income<br>Upper middle income<br>Lower middle income|Short run_†_|High income<br>Upper middle income<br>Lower middle income|Note: _↓_denotes dire|The values represent|

25
2013; Hansen and Rand, 2014a; Hansen and Rand, 2014b). This study, in line with Beck et al. (2018), moves beyond the traditional focus on credit access, showing the importance of improving more broadly access to financial services, including payment services, as tool to stimulate business performance.

To summarize, the study contributes to the literature in the following ways. First, it uses nationally representative firm-level data for 16 economies across Sub-Saharan Africa, making it one of the most exhaustive studies in the region providing new information on mobile money penetration at the firm level. Second, it affirms the finding that mobile money increases investments by firms that was initially found for four economies in the region mostly in East Africa and finds that the relationship between mobile money use and investment by firms in Sub-Saharan Africa is largely driven by female-owned businesses. Finally, it uncovers suggestive evidence that supplier and customer relationships and the demand for credit may be the mechanisms through which mobile money leads female-owned firms to invest but not male-owned firms.

The rest of the study is structured as follows. Section 2 provides the conceptual framework; section 3 provides data details, summary statistics, and mobile money adoption patterns in the region; section 4 details the empirical strategy; section 5 provides the results; section 6 explores several mechanisms; section 7 provides robustness checks and section 8 concludes.

# **2. Related Literature and Conceptual Framework**

The relationship between transaction costs, access to credit and investment has been well established. In the neoclassical model, the accelerator model, and the Q theory of investment, the optimal level of investment is achieved with zero transaction costs and no budget restrictions; as per the Modigliani-Miller theorem (Modigliani and Miller, 1958), perfect capital markets carry the implication that the firm’s financial structure has no role in investment decisions. Subsequent literature has challenged the assumption of perfect capital markets with asymmetric information leading to credit rationing (Stiglitz and Weiss, 1981) and has thus made the availability of capital 5
# Figure 8. Risks to EMDE growth prospects: Other adverse shocks

# A. Share of countries with growth slowdowns

# B. Spillovers from the United States, euro area and China

<!-- Start of picture text --> Percent Percentage points<br>90 EMDE Global<br>Advanced economies EMDEs<br>0.0<br>80<br>70<br>-0.5<br>60<br>50 -1.0<br>40<br>30 -1.5<br>20<br>-2.0<br>United States Euro Area China<br>C. Spillovers from EM7 D. Number of armed conflicts<br>Percentage points EMDEs Number<br>55<br>0.2 1990-2017 average<br>50<br>-0.1<br>45<br>-0.4<br>-0.7 40<br>-1.0 35<br>30<br>E. Oil production F. Weather-related events<br>Mb/d 1000 2017<br>100<br>Rest of the World MENA 1960-2009<br>800<br>2010-2018<br>80 2005<br>600<br>60<br>400<br>40<br>200<br>20<br>0<br>0 0 50 100 150 200 250 300 350 400<br>2007 2018 Occurance<br>China Russia India Brazil Mexico Indonesia Turkey<br>2015 2016 2017 2018 2019<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2017<br>US$ million<br>Damage per event, Real<br><!-- End of picture text --> Source: Centre for the Study of Civil War at the Peace Research Institute Oslo (PRIO), CRED/OFDA International Disaster Database, Huidrom et al (2019), International Energy Agency, World Bank. A. Slowdowns of at least 0.1 percentage point in annual GDP growth. Data for 36 advanced economies and 146 EMDEs.

B. Median cumulative impulse response of EMDE and global GDP growth after one year to a 1 percentage point decline in U.S. and euro area GDP growth. Based on vector autoregression of world GDP, output growth in the source country of the shock, the U.S. 10-year sovereign bond yield plus JP Morgan’s EMBI index, output in EMDEs excluding China, and oil price as an exogenous variable. The “global” sample includes 22 advanced economies and 19 EMDEs for 1998Q1-2016Q2.

C. See Huidrom et al (2019) for details. Cumulative impulse responses of EMDE growth after one year in response to a 1 percentage point decline in growth in origin of shock.