rafmacalaba/data-use-mentions-extended huggingface dataset:
20 investing across borders: do fdi-specific regulations matter?

# 1.4.2 _Arbitrating Commercial Disputes_

Most foreign companies prefer resolving disputes through arbitration over lengthy litigation in local courts. The indicators assess the strength of legal frameworks for alternative dispute resolution by combining the Ease of Arbitration Process and the Extent of Judicial Assistance indices to represent the quality of FDI-specific institutions. This index directly deals with how foreign investors prefer to resolve contractual or commercial disputes. It is also strongly correlated with the rule of law, government effectiveness, corruption, and regulatory quality pillars of the World Governance Indicators that summarize a country’s institutional regime in general (Figure 5).<sup>21</sup> Specifically, the index on the Ease of Arbitration Process scores, among others, best practice provisions for party autonomy and tribunal integrity. Autonomy assesses whether the laws allow parties to choose arbitrators or arbitral institutions, the language of proceedings, and whether foreign counsels can represent investors. Tribunal integrity measures whether there exist provisions to ensure that arbitrators remain independent and impartial and whether the proceedings remain confidential. The other index on judicial assistance evaluates whether local courts follow a “pro-arbitration” policy, whether tribunals decide the jurisdiction of disputes and whether the courts assist tribunals by requiring the appearance of witnesses and production of evidence.

What are the bases for these indicators? Investors need to be assured that their investments will not be unjustly expropriated and that in instances of disputes, there is a predictable course for resolution. An effective arbitration regime for FDI mitigates risk by providing legal security to investors (including assurance

> 21 The scatter plots in Figure 5 exclude outliers – Afghanistan and the Solomon Islands – that score zero on the Ease of Arbitration Process index and the Extent of Judicial Assistance index. Serbia is excluded from the WGI data set. This leaves 84 country observations.
at the household level, an approach that captures use but not ownership and tends to result in higher estimates for penetration, especially among youth and women. In addition, the Global Findex survey includes adults age 15 and above, while other surveys often use 16 or 18 as an age cutoff.

Third, many of the most recent individual- or household-level surveys on financial use in a given economy or region were carried out several years ago and may not reflect recent reforms or expansions of financial access.

Two commonly cited cross-country user-side data collection efforts are the FinMark Trust’s FinScope initiative, a specialized household survey in 14 African countries and Pakistan,<sup>9</sup> and the European Bank for Reconstruction and Development’s Life in Transition Survey (LITS), which covers 35 countries in Europe and Central Asia and includes several questions on financial decisions as part of a broader survey.<sup>10</sup> The Global Findex country-level estimates of account penetration are generally higher than those of the FinScope surveys, perhaps because of the difference in timing (most of the FinScope surveys were carried out in the mid-2000s) and the variation in the definition of an account. The Global Findex country-level estimates of account penetration are within 7 percentage points of the LITS estimates for the majority of economies, with discrepancies perhaps explained by the fact that the LITS financial access questions focus on households, not individuals, and are less descriptive than those of the Global Findex survey.<sup>11</sup> On the provider side, Beck, Demirguc-Kunt, and Martinez Peria collected indicators of financial outreach (such as number of bank branches and ATMs per capita and per square kilometer as well as the number of loan and deposit accounts per capita) from 99 country regulators for the first time in 2004.<sup>12</sup> These data were updated and expanded by the Consultative Group to Assist the Poor (CGAP) in 2008 and 2009 and by the International Monetary Fund in 2010
**Figures 4 and 5. Deposits and Loans per Population against Branch Networks**

<!-- Start of picture text --> 2,000 600<br>UAE 500 Lebanon<br>1,500<br>400 Oman<br>Lebanon<br>1,000 Oman 300<br>Jordan<br>Algeria Tunisia 200 Tunisia Jordan<br>500<br>Syria EgyptMorocco 100 Syria Egypt<br>0 Yemen 0 Yemen<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Branches per 100,000 Adults Branches per 100,000 Adults<br>Deposit accounts per 1,000 Adults Loan accounts per 1,000 Adults<br><!-- End of picture text -->

**Figure 6. Deposits/population vs. Per Capita Income.  Figure 7. Deposits/population vs. State Bank Networks**

<!-- Start of picture text --> 2,000 2,000<br>UAE UAE<br>1,500 1,500<br>Lebanon Lebanon<br>Tunisia<br>1,000 Oman 1,000 Oman<br>Jordan Jordan<br>Algeria<br>Tunisia<br>500 500<br>Egypt Egypt<br>Morocco Morocco<br>YemenSyria YemenSyria<br>0 0<br>0 10,000 20,000 30,000 40,000 50,000 0.0 0.2 0.4 0.6 0.8 1.0<br>Per Capita Income Percentage of state branches<br>Deposit accounts per 1,000 Adults Deposit accounts per 1,000 Adults<br><!-- End of picture text --> Source: CGAP and World Bank Financial Access 2010

**While branches facilitate a range of financial services, and can enable a personalized**

**service to clients, ‘branchless
can be obtained from a model where lobbying is given micro-foundations, such as in Grossman and Helpman (1994), provided that the ownership of the speci fi c factors is concentrated.<sup>54</sup> In GrossmanHelpman each industry has the same relative weight in the government’s objective function provided that it is organized into a lobby. In our sample it is reasonable to assume that all industries are organized because the available industry data we use to identify this weight is aggregated. Moreover, in the Grossman-Helpman setting, the extra weight ω −1 has a structural interpretation: the marginal rate of substitution between social welfare and contributions in the politician’s objective. Therefore, the estimates of the coefficient on the variable ∆x, which represents the extra weight ω − 1, are

> 52 More speci fi cally, we calculate the Durbin-Wu-Hausman statistic. They are reported in Table 2 in the row labeled “Endogeneity p-val”. The value of 0.04 applies to the equivalent of speci fi cation (4) from table 2 when the initial tariffs are added as a regressor.

> 53 More speci fi cally we estimate Cragg’s “heteroskedastic OLS”, which is more efficient than OLS in the presence of heteroskedasticity of unknown form because it uses the orthogonality conditions of the excluded instruments. The excluded instruments are the same we use for the IV estimates.

> 54 Please see Staiger (1995).

32
<!-- Start of picture text --> Figure 1: FCV Pass-through rate for Syria 2009-2020<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Share of time since 2009 as FCS Pass-through Rate<br>Source: Corral et al. (2020)<br><!-- End of picture text --> An alternative nowcasting approach, which bypasses the distribution neutral assumption, relies on growth elasticities of poverty (GEP). Growth elasticities of poverty reveal the percentage change in poverty associated to a 1 percent growth in GDP per capita. Implicitly, the elasticity takes into account both the impact of growth on average welfare as well as changes in the distribution of welfare (Shorrocks, 2013; Datt and Ravallion, 1992). Still, this approach relies on the strong assumption that the relation between growth and poverty does not change over time.

In some cases, poverty nowcasting builds on more articulated microsimulation models. More complex examples of microsimulation models are based on computable general equilibrium (CGE) or general equilibrium macroeconomic models that rely on social accounting matrices or time series macroeconomic data to create “linkage aggregate variables” (LAVs) that are fed into microsimulation models that build on household survey data. However, the high information demands of these models makes them hard to apply in data deprived contexts. A less data demanding approach to microsimulation relies on building behavioral models on household surveys (typically linking labor market outcomes and remittances to individual and households’ incomes and consumption) which are then linked to aggregate and sector level macro-economic projections.<sup>8</sup> Table 3 below provides a summary of the results and methodologies employed in the limited number of studies that have attempted to estimate poverty in Syria during the post-conflict period. Despite their notable limitations, all these studies consistently indicate substantial increases in poverty.<sup>9<
and the innovation activities of firms. _International Journal of Industrial Organization_ 28: 191-202.

Damijan, J.P., Kostevc, C. and Polanec, S. (2010) From innovation to exporting or vice versa? _World Economy_ 33: 374-398.

De Rosa, D., Gooroochurn, N., & Görg, H. (2010). Corruption and Productivity: Firm-level Evidence from the BEEPS Survey. World Bank Policy Research Working Paper 5348, The World Bank, Washington DC.

De Mel, S., McKenzie, D., &Woodruff, C. (2011). What is the Cost of Formality? Experimentally Estimating the Demand for Formalization. Working Paper, University of Warwick, Coventry.

Distinguin, I., Rugemintwari, C., & Tacneng, R. (2016). Can Informal Firms Hurt Registered SMEs’ Access to Credit? _World Development_ , 84: 18 – 40.

Djankov, S., and Murrell, P. (2002). Enterprise Restructuring in Transition: A Quantitative Survey. _Journal of Economic Literature_ 40(3): 739-792.

Dollar, D., Hallward-Driemeier, M., & Mengistae, T. (2006). Investment Climate and International Integration. _World Development_ , 34 (9): 1498 – 1516.

37
certainly does matter, the wage adjustment process in this context is dependent on multiple factors not captured in household surveys.

Secondly, our interpretation of trends over time assumes that the composition of consumption baskets, and hence the COICOP category weights, remained constant over time and across changes in income levels (zero elasticity).

Thirdly, the calculation does not differentiate between purchased and home-produced consumption items, as well as price differentiation stemming from spatial differences, such as urban and crop-producing regions, or income-dependent behavioral differences, for example, price hunting. This is a limitation, but future iterations and work on disaggregating inflation impacts will seek to model this nuance explicitly in the estimation strategy.

# **3. RESULTS: THE DISTRIBUTIONAL IMPACT OF INFLATION**

Descriptive statistics show significant heterogeneity in consumption baskets of Pakistani households across income groups. Data compiled from the HIES (2018-19) for the 12 COICOP categories reveals that households from the bottom of the income distribution spend more than two-thirds of their budget on food, housing, and utilities (Table 1). More specifically, households in the first decile allocate around half of their budget for food and non-alcoholic beverages. The top decile, in contrast, spends around 28.4 percent on food and non-alcoholic beverages. Similarly, poor families allocate less than 1 percent for education compared to 7.26 percent by families in the richest decile. Similarly, the budget shares increase across deciles for COICOP categories for housing, utilities, and transport. For instance, the top decile spends more than double (9 percent) on transport compared to spending by the lowest decile (4.2 percent). Overall, the poorest families (decile 1) spend 72 percent of their budget on basic necessities such as food, housing, health, and education.

The fact that the consumption structure varies across the welfare distribution implies that high inflation rates for some categories affect some households more than others, depending on their consumption patterns. This inflation heterogeneity depends on the sources of inflation. For instance
Fourth, to attain greater quality control during the data collection process, enumerators recorded the answers via _Computer-Assisted Personal Interviews_ (CAPI) or _Computer-Assisted Telephone Interviewing_ (CATI) software, and we regularly monitored the data collection process using standard algorithms to analyze the consistency of the data.<sup>10</sup>

> 9These procedures are in line with suggestions of good practice for implementation by (Bloom et al., 2016). We use online surveys only for Georgia and Croatia. In Georgia, we partnered with the National Statistical Office, which resulted in exceptionally high response rate. Face-to-face interviews were not possible during the pandemic. See Table A.16 for the mode and date of data collection in each country.

> 10Randomized survey experiments with household surveys have demonstrated that a large number of errors observed in _Pen-and-Paper Personal Interview_ (PAPI) data can be avoided with CAPI or CATI (Caeyers, Chalmers and De Weerdt, 2012). For Georgia and Croatia, we used Computer Assisted Web Interviewing (CAWI).

12
understanding development outcomes pertaining to women, both within and outside the household.

In our case, the administration of individual interviews using a common roster of agricultural parcels within the IHPS (T5) sample permits the examination of possible discrepancies in spouses’ reported ownership/rights for specific agricultural parcels. In this part of the analysis, we focus on the subsample of IHPS households in which the members of the principal couple were both interviewed, and estimate the following equation at the parcel-level:

> 25 “Even with the documentation, the intrahousehold truth regarding who exerts control over a given asset may not line up with which household members are listed in the records as owners” (Doss et al, 2019: 22) for various reasons including (i) lags in the updating of cadastral records following inter-personal parcel transfers, (ii) temporal variation in intrahousehold control of the parcel in question and (iii) the potential disconnect between _de jure_ legislation (prohibiting gender discrimination in ownership of and rights to land) and local _de facto_ arrangements that may ultimately prevail over state laws and that may result in gender discrimination in a way that exhibits spatial variation in accordance with social norms.

18
Table 4: Summary of counterfactual scenarios for transport investments

|Scenario|Country|Road infrastructure||Policies|
|---|---|---|---|---|
|||Baseline<br>|||
|1|Djibouti, Ethiopia,<br>Kenya,Somalia|Speed and road conditions from latest<br>surveys|High <br>lays|Border De-|
||With t|ransport infrastructure investments|||
|2.1|Kenya,<br>Somalia,<br>Ethiopia|Corridor 1: Kismayo, Lamu and Mo-<br>gadishu Corridor with 3093km of re-<br>habilitation or new roads|High <br>lays|Border De-|
|2.2|Djibouti, Ethiopia|Corridor 2: Assab and Djibouti Cor-<br>ridor with 649km of rehabilitation or<br>new roads|High <br>lays|Border De-|
|2.3|Somalia, Djibouti|Corridor 3:<br>Berbera and Djibouti<br>Corridor with 1003km of rehabilita-<br>tion or new roads|High <br>lays|Border De-|
|2.4|Djibouti, Ethiopia,<br>Somalia|Corridor 4: Mogadishu, Berbera and<br>Bossasso Corridor with 2550km of<br>rehabilitation or new roads|High <br>lays|Border De-|
||With|border infrastructure investments|||
|3|Djibouti, Ethiopia,<br>Kenya, Somalia|Corridors 1–4|50% <br>borde<br>borde<br>corrid|reduction in<br>r<br>times<br>at<br>r posts along<br>ors 1–4|

We create counterfactuals using the transport networks from each country described in Section 3. Figure A10 in the appendix shows the new corridors and investments
study uses the elimination of pre-existing cross-sectional variation in tariff uncertainty that came with China's accession to identify the impact on trade flows. They find that this reduction in trade policy uncertainty can explain 22-30 percent of China's subsequent export growth to the US.

Crowley et al. (2018) provide the first empirical evidence that “tariff scares” – threats to raise tariffs in the future – have negative impacts on trade even when the threatened import tariff hikes never actually materialize. The analysis uses the universe of transaction-level Chinese customs data and focuses on the foreign market entry decisions of Chinese firms in response to antidumping duties imposed against China by 17 economies over 2000-2009. They study not how Chinese exports to a country fall when a country imposes a tariff, but rather how entry into new foreign markets by Chinese firms declines when other countries impose these special tariffs. They find that the average number of Chinese missing entrants because of tariff uncertainty per year is 1,718. The total number of missing entrants grew from 670 in 2001 to a peak of 2,920 in 2008.

Our paper complements this strand in the literature, because it examines the impact of economic policy uncertainty and trade policy uncertainty for a broad cross-section of major traders over a significant period of time, rather than for specific countries or specific episodes. Furthermore, previous work has not extended the analysis of economic policy uncertainty to encompass the recent trade slowdown.

5
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 12. Burkina Faso has made progress on improved water supply and sanitation**

<!-- Start of picture text --> a. Water b. Sanitation<br>90 100<br>80 90<br>70 80<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>1993 1999 2003 2007 2009 1993 1999 2003 2007 2009<br>Improved Unimproved Improved Unimproved<br>Percentage of population Percentage of population<br><!-- End of picture text --> _Source:_ WHO Joint Monitoring Program 2010a and 2010b, from Demographic and Health Surveys for 1993, 1999, and 2003; Questionnaire a Indecateur de Base du Bien-Etre for 2007 and 2009.

Most of the progress in water supply is to be found in urban areas. In 2001 the Office national de l’Eau et l’Assainissement (ONEA), the corporatized public water utility serving urban areas, implemented a performance-based service contract for its commercial and financial activities—without transferring management control to the private sector—linking a portion of the service contractor’s revenue to measurable efficiency improvements (Fall and others 2009). The results were impressive. Under a costrecovery tariff, Burkina Faso’s ONEA essentially recovered full costs with collection rates. At around $0.76 per cubic meter (m<sup>3</sup> ) for residential consumers and $2.15 per m<sup>3</sup> for nonresidential customers, as of 2003 Burkina Faso’s water tariffs were substantially higher than those found in other African countries with scarce water resources, yet they were affordable for most of the customers.

**Table 9. Evolution of
specification in table 2, but since it is at the firm level, we can bring in more firm-level control variables to rule out other possible effects. The coefficients of the interaction term are statistically significant with negative signs. Other control variables are statistically significant and of the expected sign: the higher the share of ownership of the firm that is foreign; and the higher the number of skilled workers in the firm in the previous period, the greater the firm’s labor productivity. We find that services firms tend to be more productive, but this may be marred by the very low proportion of firms in the WBES sample that are in the services sectors.<sup>7</sup> We believe that the enabling characteristics of services in the productivity of other firms is better captured in the sectorial regression results (tables 2-8).

**Table 11: Sectoral FDI has a positive impact on firm productivity**

|Dependent Variable: ln labor<br>productivity **at the firm level**|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_ln (FDI)_|0.038***|0.038***|0.051***|0.061***|0.061***|0.059***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|_ln (FDI) × GVC_||||-0.052**|-0.058**|-0.059***|
|||||(0.02)|(0.02
<u>governorate and quintiles of spatially adjusted and nominal monthly per capita expenditure</u>_ <!-- Start of picture text --> spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Aleppo Rural Damascus Idleb Damascus Homs Hama Lattakia<br>Al-Hasakeh Dar'a Tartous Deir-ez-Zor Ar-Raqqa As-Sweida Quneitra<br>Q5<br>Q4<br>Q3<br>Q2<br>Q1<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP May 2022 Demographic and WASH survey
Are the traditional banks really failing to exploit the potential gains to the poor from nonfarm rural development? Is this new class of banks doing any better? The answers are unclear on a priori grounds. It is not even clear that there will be any real gains from facilitating sector switches. The higher levels of living of rural non-farm households, compared to farming households, may be due entirely to heterogeneity in characteristics which are conducive to success in rural non-farm enterprises, such as education or access to urban markets. Poor farmers may well be the most credit constrained-but they may also lack the education or other attributes which allow a new entrant to the non-farm sector to succeed. Credit market failures do not necessarily imply that efforts to lend more for non-farm rural enterprises will be pro-poor. Controlling for heterogeneity, the supposed gains may well vanish. Or there may well still be large unrealized gains, but favoring the non-poor, leaving the new micro-credit banks with little scope for helping the poor.

This paper attempts to assess whether banks are responding to the potential gains to Bangladeshi farm households from switching to the non-farm sector. To test for such a response we study the placement of bank branches. In an underdeveloped rural economy, branch location is bound to be an important factor in determining access to credit. While other relevant indicators might be identified (such as a bank's attempts at targeting poor households within a given area), branch location also has the advantages of being well measured and of being consistent between banks. By combining information on branch location with independent household survey data, we aim to better understand the(potentially diverse) motives behind bank behavior.

There appears to have been very little previous work on the location decisions of microcredit banks. We know of only one previous empirical study, namely by Khandker et al. (1995), 3
<!-- Start of picture text --> School attendance, age 10-14<br>a<br>.<br>es<br>a<br>e<br>Bio<br>2.<br>ca<br>‘a<br>a<br>ast<br>a<br>TM<br>a<br>2020 2020 ae Shon 2021n-Maer 2021jun m071du l ‘eonow 2021 2019sr 2020 2021r 020 202 2021<br>Ln Female @ Male<br><!-- End of picture text -->