rafmacalaba/datause-encoder-data huggingface dataset:
������ � ∝��∝� ���������� � ∝� ������� �∝� ������� � �� � �� � ����, (1) The model links the greenfield foreign direct investment, _FDIist_ , flowing into country _i_ in sector _s_ in year _t_ with a range of variables underpinning perceived uncertainty and expected returns. These variables include lagged FDI in sector _s_ ; political violence indicator _Pi(t-1)_ for country _i_ in the previous year; a set of control variables _Xi(t-1)_ , which capture conditions that might confound the relationship between political violence and greenfield FDI; a set of country dummies μi for time-invariant country characteristics; and a vector of time dummies μt.

The country fixed effects capture time-fixed heterogeneity, controlling for effects such as country size, resource endowments, culture, ethno-linguistic fractionalization, as well as institutions because institutions change very slowly over time. Moreover, the country fixed effects control for unobserved heterogeneity, limiting the risk of self-selection bias. We thus consider only within country variation, that is, whether a country attracts less FDI when its level of political violence increases. The time dummies capture time-dependent effects, such as global FDI waves, global commodity price fluctuations, and other global economic phenomena. The lagged FDIis variable minimizes the risk of omitted variable bias because the amount of FDI received in the previous period is one of the best predictors of FDI received in the subsequent period. In addition, this variable makes possible the estimation of the long-term effects of our variables. To reduce the problem of reverse causality, all independent variables are lagged. However, ultimately, this model cannot determine causality, so the results should be interpreted as conditional associations, not causal relationships.

The data on flows of greenfield FDI into developing countries for the period from 2003 to 2012 are obtained from the fDi Markets database, a Financial Times databank tracking cross-border investment in new projects and expansions of existing ventures. The data are collected through Financial Times newswires, internal information and other media sources, project data acquired from industry organizations and investment agencies, and data purchased from market research and publication companies. Each project is cross-referenced against multiple sources. The data set includes 51,800 greenfield investments in developing countries, amounting to US$ 4.62 trillion. Annual FDI inflows are aggregated to the sector level of the receiving country. To test hypotheses three and four, we split 17
Table St The Inulation Equation

# Dependet Variables XU1.

|Country|DSUB|DDCI-|DW|Q|
|---|---|---|---|---|
|31ghPr<br>uwComtrdss|||||
|Argentina<br>Bolivia|0.71<br>1.74|0.31<br>0.37|2.00<br>2.34|2.50<br>15.49|
|Brarfl|0.i793<br>(704)|0.20<br>(213)|1.78|20.92|
|**Chile**|.<br>0.44<br>(293)|.<br>0.05<br>044)|2.11|6.21|
|Domnien<br>Republic|.<br>0.81<br>249|.<br>0.15<br>112|1.40|31.63|
|ypt|(.)<br>0.67<br>(256)|(.)<br>0.13<br>(126)|1.77|27.90|
|Ghana|.<br>0.47<br>(205)|.<br>0.17<br>(235)|1.57|36.67|
|Nieria<br>Nigeria|.<br>(0.64<br>~~~~~~|.<br>0.16<br>~<br>~~(2.12)<br>(1.69)|1.98|14.40|
|<br>Peru|0.96<br>(680)|<br><br>0.18<br>(Z70)|1.61|62.14|
|Tazania|.<br>0.59<br>(205)|.<br>0.11<br>(150)|1.98|25.99|
|Venezela|.<br>0.83<br>(191)|.<br>-0.18<br>(076)|1.56|9.42|
|ZEmbia<br>~dratePr_.<br>Coomtriaas|.<br>0.31<br>(3.31)|-.<br>0.14<br>(2.88)|1.45|37.96|
|Colambia|0.69<br>(4.66)|0.24<br>(2.73)|1.24|76.49|
|eador|<br>0.80<br>(471)||1.39|45.17|
|mexco|.<br>0.67<br>(336)|0.21<br>(165)|1.37|8.65|
|Tntkey<br>Notes$|.<br>0.73<br>(5.60)|.<br>0.29<br>(4.20)|1.94|39.20|

Soarces It8 World Currecy Yearbook. Pick's Currecy earbook. Kaoin(1991) for Argentia.

Data **are quarterly,** ed of period for 1970-1990, *zeept for Argentin. Mexco and Venesla (oco tex. for details). TSo perid for unifid exanbel rates is excludd for **Chile.**

**tiguree** in **paroetbooe are t-.tatiatica**

lUlL, Alit?! flU Deprcdation In the parallel rate. AIMt" AlL eotletion Usaing intruenntal vartAble. Inat omete **are** lagged DOB _and_ laggd or current rate of cbane of M2 (MI for Argntin).
27

|University|0.000|0.000|0.009|0.094|0.025|
|---|---|---|---|---|---|
|**Lived in Dar es Salaam all their**||||||
|**life**|0.762|0.426|0.738|0.440|0.368|
|Marital status||||||
|Never married|0.704|0.457|0.738|0.440|0.202|
|Divorced/separated|0.259|0.438|0.226|0.419|0.203|
|Currently married|0.011|0.103|0.016|0.126|0.430|
|Widowed|0.014|0.119|0.011|0.103|0.598|
|Cohabiting|0.013|0.111|0.007|0.085|0.369|
|**Has at least one child**|0.780|0.414|0.707|0.455|0.005|
|**Neighborhood poverty ranking**||||||
|**(1-5)**|2.691|0.663|2.673|0.706|0.653|
|**Self-ranked social status (1-7)**|3.807|1.571|3.633|1.475|0.057|
|I**ncome**||||||
|Total income (30 days) Tsh - mean<br>|253742|196423|258666|382006|0.790|
|Income from sex work (30 days)||||||
|Tsh - mean|236996|191110|239860|348298|0.866|
|**Place of work**||||||
|Pub/bar|0.431|0.496|0.378|0.485|0.076|
|Guesthouse|0.263|0.441|0.256|0.437|0.784|
|Street|0.076|0.265|0.081|0.273|0.738|
|Night club/disco|0.068|0.253|0.106|0.309|0.026|
|Brothel|0.070|0.256|0.068|0.253|0.906|
|**Clients and condom use**||||||
|Number of clients /week|8.360|6.750|8.383|6.975|0.956|
|Amount earned with a condom|14184|16688|13702|13338|0.604|
|Amount earned without a||||||
|condom|20580|51555|18861|26741|0.540|
|**Ever had an STI**|0.063|0.243|0.068|0.252|0.711|
|**Last time tested for STIs**||||||
|Within the last month|0.023|0.151|0.023|0.151|0.989|
|1-2 months ago|0.089|0.285|0.088|0.284|0.938|
|3-6 months ago|0.157|0.364|0.171|0.376|0.545|
|6 months - 1 year ago|0.129|0.335|0.120|0.326|0.675|
|Over a year ago|0.207|0.406|0.181|0.386|0.276|
|Never|0.395|0.489|0.417|0.493|0.457|
|**Reasons for not testing for STIs**||||||
|Fear of knowing status|0.330|0.471|0.336|0.473|0.895|
|Don't feel at risk|0.140|0.348|0.138|0.346|0.943|
is access to market and market information, which may be related to the isolation of rural households. Thirty-nine percent of rural HEs report distance as a severe challenge, while 24 percent report obtaining market information and access to inputs as a severe challenge. There is a large degree of consistency in reported severe obstacles for male and female operated enterprises and across age groups.

Business concerns and reported obstacles for HEs are different than those of urban SMEs. Table 6 compares the most frequent reported obstacles for peri-urban and rural HEs with those reported by urban SMEs interviewed in 2007. Aspects as taxation, crime, corruption, common obstacles for urban SMEs, are not reported an issue by rural HEs, while infrastructure issues and access to finance are reported as obstacles by both types of enterprises. Loening, Rijkers and Soderbom (2008) also find that Ethiopian HEs report different business obstacles than larger enterprises, and there is also some evidence that they react differently to policy change and economic cycles (Mead and Lindholm, 1998; Schoar, 2009).

**Table 6  Highest ranking severe obstacle for HEs**

|_Rank_|_Urban SMEs in 2007_|_Rural HEs in 2010_|
|---|---|---|
|1|Informal competition|Access to financial institutions and<br>borrowing procedures|
|2|Access to finance|Informal financing|
|3|Crime|Access to electricity|
|4|Tax rates|Quality of cell phone|
|5|Corruption|Road quality|
|6|Electricity|Transport costs|
|7|Transport|Distance to markets|
|8|Tax administration|Road access|

_Source:_ Investment climate survey, 2007, Rural Investment Climate survey 2010 The issue of working capital for start-up or sustainability is one of the most commonly reported issues for HEs in SSA (Fox and Sohnesen, 2012), and Mozambique is no exception. Eighty-two percent of rural households that never opened an HE report that access to or lack of capital is the main reason for never trying. The Finscope survey (2009) reports that households’ access to formal credit is almost nonexistent as less than 2 percent of households have any formal credit. The challenge is not only access to credit for business endeavors, but access to banking in general as only 12 percent of adults use any kind of formal banking product, and only 4 percent of adults in rural areas. In Mozambique, many existing HEs report borrowing procedures, transaction fees and interest as severe challenges to their enterprise (RICS, 2010), which points to the challenge of developing financial products that are relevant to ordinary households. But somehow, households in Mozambique manage. Twenty percent of households report an ability to save in the FINSCOPE data. Many either keep their savings at home or use informal savings mechanisms such ROSCAs to manage to put aside some savings for their business. Although households that did not start an HE in the RICs sample reported lack of capital to be the primary reason, yet those that did succeed in starting up an HE where not significantly wealthier than those that did not in the first 22 | P a g e
**Table 4. Association of per capita woreda block grant recurrent expenditure and poverty rate** **<u>(Pooled OLS)</u>**

||Coefficient (t-ratio)|
|---|---|
|Poverty rate|0.0012***|
||(3.12)|
|Region||
|Tigray|0.0408|
||(0.34)|
|Afar|-0.1522|
||(-1.27)|
|Amhara|-0.1549|
||(-1.31)|
|Oromiya|-0.2029*|
||(-1.72)|
|Beneshangul Gemuz|0.3131**|
||(2.55)|
|SNNP|-0.2080**|
||(-1.76)|
|Gambella|0.2906**|
||(2.08)|
|Harar|Omitted|
|Die Dawa|Omitted|
|Year||
|2008/09|Omitted|
|2009/10|-0.0086|
||(-0.60)|
|2010/11|-0.0049|
||(-0.34)|
|2011/12|-0.0080|
||(-0.56)|
|NumberofObservations|2,204|

Note: *** p<0.01, ** p<0.05, * p<0.1.

**<u>Table 5. Association of service outcomes with head count poverty</u>**

|Indicator|Coefficient<br>(t-ratio)|
|---|---|
||-0.0111**|
|Net enrolment rate 1-8 (1-8NER)|(-2.39)|
||-0.0119*|
|Net enrolment rate 5-8 (5-8NER)|(-1.76)<br>-0.0852***|
|Contraceptive acceptance rate (CAR)|(-6.52)<br>-0.0414***|
|Access to antenatal care services (ANC)|(-3.51)<br>-0.0853***|
|Births assisted by skilled attendants (SBA)|(-5.39)|

Note: *** p<0.01, ** p<0.05, * p<0.1.

32
# **Appendix B. Additional Tables and Analyses on Attrition**

# **<u>Table B.1. Attrition by Treatment Status, ECD Study</u>**

|Follow-upStatus|Program|Non-<br>program|Total|
|---|---|---|---|
|Total Interviewed in Round 1 (baseline)|4786|3136|7922|
|Stayed in Round 1 sample barangay in all three rounds|4197|2577|6774|
|Total Lost to Follow-up|589|559|1148|
|**Reasons for Attrition:**||||
|Outmigrated and not followed-up|276|276|552|
|Outmigrated but tracked in new barangay|206|193|399|
|Refusal|52|36|88|
|Non-availability|28|40|68|
|Deaths|17|11|28|
|No information|10|3|13|

_Note_ : Cell counts refer to number of children.

# **<u>Table B.2. Association between Attrition and Baseline Characteristics, ECD Study</u>**<sup>**a**</sup>

||Treatment|Main Effects|Interactions with<br>Treatment|
|---|---|---|---|
|_In Treatment Area_||||
||-0.244*|--|-0.413*|
||[.035]||[.176]|
|_Child’s Age (years)_||||
|1||0.021|-0.130~|
|||[.084]|[.073]|
|2||-0.031|-0.123|
|||[.086]|[.076]|
|3||0.05|-0.234*|
|||[.085]|[.077]|
|4||-0.188+|-0.11|
|||[.090]|[.077]|
|**_Parent Characteristics_**||||
|_Father’s Schooling_||||
|7 to 11 years||-0.115|0.034|
|||[.073]|[.063]|
|12+ years||0.225+|0.276*|
|||[.111]|[.083]|
|_Mother’s Schooling_||||
|7 to 11||0.092|-0.032|
|||[.068]|[.062]|
|12+ years||-0.061|-0.186|
|||[.107]|[.116]|

27
**Figure 2.3. Annual Infrastructure Investment Needs in the MENA Region**

<!-- Start of picture text -->
1. Total Amount 2. Breakdown by Sector<br>(Billions of U.S. dollar) (Billions of 2005 U.S. dollar)<br>140 25<br>120 OEC GCC OIC<br>20<br>100<br>15<br>80<br>60 10<br>40<br>5<br>20<br>0 0<br>Market IFC (2016) WEF (2015) IMF (2014) Transport ICT Electricity Water and<br>Intelligence Sanitation<br>(2015)<br>Sources: World Bank and International Monetary Fund. Source: Ianchovichina, et al. (2012)<br>Note: ICT = Information and Communication<br>Technology.<br><!-- End of picture text -->

**Figure 2.4 Public - Private Partnerships in the Global Context**

<!-- Start of picture text -->
1. Total value of PPPs in millions of U.S. 2. Total number of PPPs projects per<br>dollar per million capita million capita<br>70 0.25<br>60<br>0.2<br>50<br>0.15<br>40<br>30<br>0.1<br>20<br>0.05<br>10<br>0 0<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>Sources: World Bank, Private Participation in Infrastructure Database; and World Bank staff calculations.<br>Note: Regional grouping follow s the World Bank classification. High income countries are not included. Total value of<br>investment and number of projects are in accordance to year 2016, or latest year available before that.<br><!-- End of picture text -->

Existing PPPs in MENA are not large both in relation to what is needed (see Figure 2.3) and in comparison to other regions<sup>4</sup> (see Figure 2.4). PPP investment in the region amounted to a few billion U.S. dollars as of 2016. These PPPs are mainly in a few countries—Jordan, Iraq, and Morocco. They are concentrated in telecom, electricity and seaports (see Figure 2.5).

> 4 Due to data availability of the source, only low- and middle-income countries are covered in the figure.

6
2006; Chen et al., 2008; Cerra and Saxena, 2008), while in other cases negative consequences are found to persist over longer periods (Blattman and Miguel, 2010).

The increase in the availability of household survey data in the last decade has generated some recent micro-level studies which examine the effects of violent conflict on welfare and economic outcomes of affected populations (e.g., Akresh and de Walque, 2008; Bundervoet et al., 2008; Akresh et al., 2011; Chamarbagwala and Moran, 2011; Merrouche, 2011; Shemyakina, 2011; Akresh et al., 2012; Leon, 2012; Mansour and Rees, 2012;Akbulut-Yuksel, 2014; Grimard and Laszlo, 2014; Valente, 2014; Pivovarova and Swee, 2015; Brück et al., 2019; Martin-Shields and Stojetz, 2019).

The micro studies that have focused on African conflicts have typically examined delayed outcomes observed many years following conflict. For example, Akresh (2008), and Kraehnert et al. (2019) examine the longer-term impacts of the Rwandan genocide on schooling and fertility outcomes.<sup>4</sup> Minoiu and Shemyakina (2012) find strong negative impacts of the 2002-2007 Côte d’Ivoire civil conflict on children’s health status in subsequent years.<sup>5</sup> Similarly, studies of the impacts of the 1998-2000 Ethiopia-Eritrea border conflict find evidence of negative impacts on child health and schooling outcomes in subsequent years (Akresh et al., 2012; Weldeegzie, 2017). In northern Nigeria, Adelaja and George (2019) find that increased intensity of Boko Haram attacks reduced agricultural productivity.<sup>6</sup> Most micro-level studies evaluating the impact of violent conflict suffer from two major data-related limitations. First, because the outbreak of violent conflict disrupts traditional data collection efforts, evaluating the immediate impacts of an active violent conflict proves difficult. Second, tracking the trajectory of outcomes associated with violent conflict may require highfrequency data that are not usually collected in conflict settings. This is particularly crucial for large-scale high-intensity violent conflicts that may evolve in ways that are difficult to foresee. In other words, these limitations lead to delays in reliable data collection, leaving most micro-level

> 4 Similarly, Leon (2012) and Bertoni et al. (2017) find negative impacts of conflict on human capital accumulation and schooling outcomes, respectively, in Peru and Nigeria; while Odozi and Oyelere (2019) examine the impacts of a broader range of conflicts on poverty outcomes.

> 5 Dabalen and Paul (2014) find evidence of reduced dietary diversity for the same period. 6 Other studies in Africa have focused on identifying causes of violent conflicts as in the case of increases in food price levels and extreme climatic conditions leading to socio-political unrest (e.g., Hsiang, 2011; Hendrix et al., 2012; Smith, 2014; Maystadt and Ecker, 2014; van Weezel, 2019). For the case of Ethiopia, Akresh et al. (2012) and Weldeegzie (2017) use panel data to estimate impacts of the 1998-2000 Ethiopia-Eritrea border conflict on child health and schooling outcomes of children.

3
# **4.2 Sensitivity of debt vulnerability to different shocks in SSA countries**

This section analyses the sensitivity of SSA countries’ debt vulnerabilities to shocks affecting key macro variables. To this purpose we summarize how the different debt burden indicators deteriorate, for the whole sample and for specific country groups, under different scenarios imbedded in the DSA. The scenarios we study are detailed in Table 4.1 below and the results are presented in tables 7A through 7C in the Annex.

**<u>Table 4.1: Alternative scenarios used for sensitivity analysis</u>**

|**Publi**<br>|**c DSA**<br>|**Extern**<br>|**al DSA**<br>|
|---|---|---|---|
|Shock/Variable affected|Explanation|Shock/Variable affected|Explanation|
|A2: Primary Balance|Primary balance is set to<br>its last year of history for<br>the entire projection<br>horizon.|A2: External financing<br>conditions|New public sector loans on<br>less favorable terms (200<br>basis points higher) during<br>entireprojection horizon|
|A3: Real GDP growth|Real GDP growth is<br>lowered by a fraction of its<br>standard deviation for the<br>entireprojectionperiod<sup>1/</sup>|B1: Real GDP growth|Real GDP growth is<br>lowered in years t+2 and<br>t+3<sup>2/</sup>|
|B1: Real GDP growth|Real GDP growth is<br>lowered in years t+2 and<br>t+3<sup>2/</sup>|B2: Exports growth|Export value growth lower<br>in t+2 and t+3<sup>2/</sup>|
|B4: Exchange rate<br>depreciation|One-time 30 percent real<br>depreciation in t+2|B6: Exchange rate<br>depreciation|One-time 30 percent real<br>depreciation in t+2|

1/ For additional details see Painchaud and Stučka (2011). 2/ Growth is set at its historical average minus one standard deviation.

Overall, we observe that average liquidity indicators are only marginally affected by shocks for the sample as a whole and for the different groups, despite the fact that small deviations from the baseline tend to be protracted, especially in the case of debt service to revenue.<sup>14</sup> As a result risk ratings on average are not sensitive to liquidity shocks. Further, in the situation pre-shock breaches are on average short lived and small in size (Annex Table 7C), leading to few cases of high risk of debt distress.

On the contrary, solvency indicators show sensitivity to shocks, leading to more protracted and larger breaches on average. In the public DSA shocks lead to protracted deviations from the baseline in general, although the size of the changes in debt-to-GDP ratios varies across groups. The depreciation shock has a lesser effect in general, on average, while shocks to the primary balance (A2) and real GDP growth (B1) affect the Frontier and HIPC groups more. Real GDP shocks are the main source of vulnerability of Fragile and Oil Exporting countries (Annex Table 7A).

Similar effects are found for solvency indicators in the external DSA, although the effect on breaches vis-àvis the threshold is more significant on the duration rather than the size of the breach (Annex Table 7B).

> 14 Deviation from the baseline are not reported in tables 7A through 7C, but available upon request from the authors.

16
Policy Research Working Paper 10534

# **Abstract**

Transport connectivity is an important determinant of agglomeration economies and urbanization. However, measuring its impacts is a complex task when causality is considered. An important empirical challenge comes from potential endogeneity of infrastructure placement. To deal with the endogeneity problem, first, the paper constructs detailed georeferenced connectivity measurements based on micro shipping data collected over 10 years. Then, the system generalized method of moments regression is applied. Using unique data from the Caucasus and Central Asian countries, the paper estimates the impact of transport connectivity on agglomeration economies. It finds that agglomeration economies are significant and persistent in the region. Thus, the existing firm clusters are likely to continue growing. However, a constraint is also found. Large cities exhibit congestion diseconomies. Finally, the paper shows that the improvement of transport connectivity, especially local market accessibility, has a significant effect on agglomeration. By contrast, no clear evidence to support the impact of improved regional connectivity on agglomeration is observed yet. To take full advantage of agglomeration economies at the regional level, further efforts may be needed, for instance, toward increasing efficiency in transportation and logistics, improving the freight load, and/or reducing the time and costs of border crossing, which add to overall transport costs and times.

This paper is a product of the Transport Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at aiimi@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