rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5790

# **Abstract**

This paper develops a self-enforcing contract model to show that better economic fundamentals can help when there is weak rule of law—but with order—to attract foreign direct investment, whereas lowering taxes does not necessarily help. Using a cross-region Chinese dataset, the analysis finds evidence consistent with the theoretical analysis. Regional variations in tax rates and the perceived quality of formal contracting institutions are not correlated with regional inflows of foreign direct investment, but leadership characteristics are. Most conventional economic factors have the predicted effects on foreign direct investment. The finding that foreign direct investment is lower in locations where domestic private firms have better access to finance and where the air quality is poor is new to the literature.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lxu1@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
which equals zero only if _δ_ = 1, i.e. all spillovers are transitory. This would imply coefficient estimates for entries and exits that are equal but opposite in sign. On the other hand, if FDI spillovers are permanent, then _δ_ = 0 and there should be no significant effect from exits. Finally, if the coefficient is between zero and one, then this would suggest the presence of both transitory and permanent spillovers.

Note that in both of these latter two cases, it should be that the estimated effect of new FDI is greater in absolute value than for exiting FDI if entering investment creates spillovers immediately. Thus, by estimating the impact of increases and decreases in FDI separately, i.e. not assuming that _δ_ = 1, we can test for the presence of transitory versus persistent spillovers. Further, recall that in equation (8), we assumed that firm _i_ ’s firmspecific characteristics and the spillovers were additively separable. If this is not the case, then the impact of spillovers will vary across firms. This too is something we will explore in the data. Finally, just as employment growth depends on FDI, so too does capital growth, with investment responding comparably to changes in FDI.

# **3 Data Description and Empirical Strategy**

Our data come from the firm census for 2006 and 2011 from the Department of Statistics in Jordan. These data represent information of about 100,000 firms in Jordan in each year from both, manufacturing and services. We observe 15,465 firms in both periods.<sup>14</sup> These data provide several pieces of firm level information, including the employment of the firm, the share of foreign ownership, the year of incorporation, revenues, capital investment, the region it is located in, and the sector in which it operates. Together, these firms comprise 53% of employment in Jordan as compared to Labor Force Survey (LFS) data.<sup>15</sup>
Firms in Dar es Salaam are significantly less likely to receive any government support than those in Dodoma and Tanga.<sup>13</sup> On average, firms in Tanga are 17 (±5) percentage points more likely to get postdisaster governmental support than their counterparts in Dar es Salaam (table D.9, appendix D). Moreover, across all firms in the sample, every month that passes since the last disaster increases their likelihood of obtaining governmental help by 0.07 percentage points on average. This small but significant effect indicates that post-disaster government support tends to be “too little, too late”.

In summary, these results suggest that firms may lack financial resilience to extreme events. Disasters, such as flooding, are often regional events affecting multiple households and firms; but both community resilience and local governmental support are of insufficient size to be effective coping mechanisms. More transregional and national support mechanisms could help reduce risks and speed up recovery in the aftermath of a disaster.

# 6. Key messages

Leveraging novel data from a specialized firm-level survey, this paper presents new evidence on the impacts of natural hazards on Tanzanian firms. Flooding is the predominant disaster affecting businesses in Dodoma, Tanga, and Dar es Salaam, often destroying valuable production assets of Tanzanian firms. The study also reveals that firms are keenly aware of these risks and adjust their operations accordingly. The data suggest that firms have inefficient inventory holdings due to on-site flood risks or potential supply chain disruptions.

Overall, the findings from this firm survey highlight the potential of policy interventions in multiple areas to mitigate the adverse effects of disaster risks on firms.

## **Investments in more resilient transport infrastructure can ensure job accessibility, protect supply**

**chains, and reduce loss multipliers.** Disrupted transport infrastructure is the key driver of the indirect losses experienced by firms, leaving employees unable to come to work, and causing supply and delivery delays, which multiplicatively propagate through the entire supply network. Investing in
inform creditor and multilateral organization decisions to extend grants/loans, as in the case of the WB/IMF’s DSA.

_2- Lack of efficiency._ Governments face multiple requests from different external agents. At the same time, external agents duplicate efforts to manage, validate, and disclose their own debt statistics, without any systematic reconciliation process with creditors or cross-source validation.

3- _Noisiness in the market._ Different debt data circulating at the same time may confuse _stakeholders_ not familiar with the process (Seiferling, 2020) _._ It may also increase the burden on DMOs, who are often called upon to clarify their debt statistics.

4- _Challenging data reporting requirements (WB, IMF, 2020)._ Debt statistics in LIDCs are usually produced by DMO staff, not by a specialized statistical office. International guidelines such as PSDS are designed with more sophisticated institutional arrangements in mind, with a clear separation of responsibilities between a DMO and a statistical debt office. This is burdensome for DMO officers, who may lack the adequate statistical skills to report under such guidelines (e.g., nominal versus face value). Furthermore, international statistical definitions may be disconnected from those used by DMO staff in their operations, as in the case of the external debt by residency versus currency.

_5- Pressure on resource-constrained DMOs._ The current disclosure framework often forces short-staffed DMOs to divert resources away from their daily debt management activities to address the multiple and demanding requests of indirect reporting. This is a result of the repercussions of the lack of indirect reporting on lending from WB,<sup>39</sup> IMF,<sup>40</sup> other MDBs, and rating agencies.

6- _Discouragement to invest in accurate direct reporting._ As DMOs engage with external agents to produce debt reports, they may see it as a form of outsourcing their reporting responsibilities. In fact, indirect statistics may reduce DMOs’ incentives to produce direct statistics. This effect may be
2002 and 2005, this volume) also present a model based on the signals approach. In a related paper, Boyd, Gomis, Kwak and Smith (2000) focus on the cost of crisis and present a detailed review of macro conditions before, during and after crises, for more than 50 crisis countries, basing their discussion on a general equilibrium model. They highlight the great diversity of economic conditions that precede crises, drawing the conclusion that it is difficult to rule out sunspots, i.e. random events, as the cause of many crises.

9 Using a variant of the multivariate logit model, in which the crisis dummy takes the value of one in the year before the crisis and the value of two in the year of the crisis, Hardy and Pazarbasioglu (1999) also find that macroeconomic indicators were of limited value in predicting the Asian crises. In none of these countries was the precrisis period identified as problematic. They conclude that the best warning signs for these crises were proxies for the vulnerability of the banking and corporate sector.

10 See Demirgüç-Kunt (1989) for a review of this early literature.

11 See Eichengreen and Fishlow (1998) for a review of this literature.

12 This study does not address the question of whether foreign entry leads to a less concentrated banking system, however.

13 It is not possible to control for the quality of regulation and supervision in a panel of data, such as is typically used on banking crisis regressions, because measures of these dimensions are only available after 1999. Results from cross-sectional tests show that countries with more generous deposit insurance design are likely to have experienced crises since the 1980s, even after controlling for supervision and regulation.

14 An additional problem is that changes in the aggregate stock of real credit to the private sector are not a good measure of the flow of credit available to the economy, especially around banking crises, because of valuation effects caused by inflation or exchange rate changes. Also, a decline in the stock of credit may
hours/day|18||20|24|
|Revenue collection|% sales|94.1||—|99.3|
|Distribution losses|% production|34.8||46|28.8|
|Cost recovery|% total costs|89.5|||86.3|
|Operating cost recovery|% operating costs|125.2||—|120.8|
|Labor costs|connections per employee|175.9||89|203.4|
|||**Zimbab**|**we**|**Countries with**<br>**scarce water**<br>**resources**|**Other**<br>**developing**<br>**regions**|
|||**2005**|**2008**|**Mid-2000s**|**Mid-2000s**|
|Residential tariff|US cents / m<sup>3</sup>|24|23|60|3–60|
|Nonresidential tariff|US cents / m<sup>3</sup>|39|82|121||

_Source:_ Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures from the 1999 and 2005 Demographic and Health Surveys; — = Not available.

# **Challenges**

Due to lack of proper maintenance, Zimbabwe‘s water and sanitation network is in dismal condition, constituting a threat to public health. The cholera outbreaks in 2008 and 2009 were a red flag to the poor state. The disease had killed more than 3,000 people and infected around 60,000 by February 6, 2009, according to the World Health Organization (WHO), and spread over most of the country and across the borders into neighboring
employed by Rutherford (1955). As noted above, for several countries (one at a time) he compared the observed quintiles of the distribution of income with their theoretical counterparts derived under the null hypothesis of lognormality. Formally, we exploit the one-to-one mapping that arises under lognormality between the Gini coefficient and the Lorenz curve _L_ ( _p_ ) that describes the relative income distribution.<sup>8</sup> Letting _G_ and σ respectively denote the Gini coefficient, and the standard deviation of log income, Aitchison and Brown (1966) show that lognormality implies and

> where Φ(.) denotes the cumulative normal distribution. Hence a change in the Gini coefficient, and thus in σ, must be reflected in a matching change in the Lorenz curve

> 7 Kraay (2005) uses a filter to eliminate extreme observations from his poverty dataset. This results in the loss of over one-third of his original sample.

> 8 Recall that _L(p)_ is the aggregate income share of the bottom _100p_ percent of the population. Thus, _L(0)_ =0 and _L(1)_ =1.

-4-
Looking at a more compressed period from 2013 to 2014, there is some evidence that host community families changing in their labor market characteristics. For example, the proportion of workers with a permanent contract type declined from 36.2 percent in 2013 to 35.1 percent

> 13 Household size is not reported for the entire household, only the 15+ segment. Dependency ratios and per capita income cannot be computed using the 2014 LFS.

16
proposed by the National Voucher Steering Committee (NVSC). Membership in the forum included public sector representatives at the national, regional and district level as well as representatives of the private sector, particularly fertilizer companies, seed companies, agrodealer associations, farmer organizations, and civil society organizations. The forum’s broad membership was intended to encourage the participation of all concerned groups and to ensure transparency in implementing the voucher system. The forum was chaired by the Minister of MAFC and met twice a year.

The NVSC provided policy guidance and oversees project implementation. The NVSC met every two months and was chaired by the Permanent Secretary of MAFC and included representatives of the Ministry of Finance, Prime Minister’s Office, Regional Administration and Local Government, directors of relevant MAFC departments, and representations of national farmer organizations, agribusinesses that produce, import and market inputs, civil society, and the partner bank National Microfinance Bank (NMB). The NVSC set the criteria for allocating vouchers and guidelines for implementation of NAIVS at all levels, and reviewed the integrity of the voucher system, approved annual work plans, and addressed all implementation issues.

The Agricultural Input Section (AIS) was a section within the Directorate of Crop Development Department of the MAFC and was responsible for day-to-day management of NAIVS and for providing support to NVSC. The Head of Agricultural Input Section (HAIS) served as the NVSC Secretary. He was assisted by staff from other departments and sections of MAFC including one planning officer, one fertilizer and soil nutrition management specialist, one monitoring and evaluation officer, one accounting officer, one procurement officer, and one communication specialist.

# **NAIVS implementation**

At regional level, the Regional Voucher Committee (RVC) supported districts and monitored the implementation of the voucher scheme in the region. The RVC was responsible for allocating vouchers to districts based on established criteria; estimating demand for agricultural inputs, based on historical input-use data, and providing this information to AIS; reviewing information collected from each district, including data on
in Africa, Asia, and Latin America, discrimination in Europe seems to be associated with poverty and economic marginalization. In a recent study on poverty and unemployment rates among the Roma population in 11 EU member states, the European Union Agency for Fundamental Rights concluded that, despite being the largest ethnic minority in the EU, 90 percent of the Roma people surveyed “live in households with an equivalised income below national poverty lines” and that “on average, around 40 percent of Roma live in households where somebody had to go to bed hungry at least once in the last month since they could not afford to buy food” (FRA 2014, 11).

# IV. Indicators and Methodology

This paper proposes six new indicators for the collection of comparable cross-country data on discriminatory legal frameworks and tests the data collection methodology in six pilot economies (Bulgaria, Mexico, Morocco, the Netherlands, Tanzania, and Vietnam).<sup>17</sup> 17 The pilot countries were selected to represent the various regions and legal traditions of the world. They range from low- to highincome economies, and all of them contain segments of the population that are considered minorities and that face discrimination.

Anti-Discrimination Law and Shared Prosperity 9
For the estimate of _β_ , we use the result in column (2) in table 4. Regression equation (9) yields a point estimate of _γ_ of 0.0157, with t-statistic 4.01 and significance at the 1 percent level.<sup>22</sup> We estimate var( _z_ ), var( _y_ ), and cov( _y, z_ ) directly from the sample. With those estimates, we arrive at an optimal weight of 0.67. The first row in table 6 presents from left to right the intermediate calculation results of equations (12)-(14). The second row presents some useful statistics. Notably, the majority of the variance of nighttime light growth can be attributed to noise.

Table 6: Optimal Weight on Nighttime Lighted-Predicted GDP Growth

||_β_<br>_γ_|cov(_y, z_)|var(_y_<sup>_∗_</sup>)|var(_y_)|var(_ε_<sup>_y_</sup>)|var(_z_)<br>var(_ε_<sup>_z_</sup>)|
|---|---|---|---|---|---|---|
|Point estimate|1.55<br>0.0157|4.05|2.62|7.90|5.29|259<br>252|
||optimal weight||var(_y_) de|composition||var(_z_) decomposition|
||||signal|noise||Signal<br>Noise|
|Point estimate|0.67||33%|67%||2%<br>98%|

_Note_ . This table presents detailed calculations of the optimal weight based on equation (11). The sample includes 84 EMDEs between 2013Q2 and 2020Q4. _β_ is estimated in column (2)
The results are very similar.<sup>22</sup> These results highlight the importance of customer-side discrimination in productivity differences between women and men in the workplace (for consumer-facing roles). Prior research on the gender wage gap suggests women receive lower pay partly because they are less productive (Sin et al., 2020; Gallen et al., 2017; Blau and Kahn, 2017; Caliendo et al., 2017). We show that discriminatory behavior—on the part of consumers—can drive these productivity differences. In our context, for women and men to have similar productivity levels, women would need to overcome significant barriers created by consumers’ behavior. These results also suggest that piece-rate wage structures—i.e., rewarding employees for their output levels—could further workplace inequality. While we cannot speak to optimal policy responses, regulation that prevents employer discrimination could limit some consequences of customer discrimination.

An advantage of our study design, which assigns each agent to both treatment sta-

> 19 Statistical power is also likely to be higher in the period directly after these focal events.

> 20 Interpretation of the total-price measures, based on only administrative customer-purchase records, is more challenging due to the mode of purchase potentially being endogenous to treatment status. In practice, we find treatment status affects purchases by agents on behalf of customers and purchases made directly by customers in the same magnitude and direction (see Table A5). This means we very likely _underestimate_ the coefficient on total-price, although the effect relative to the baseline mean is not clear.

> 21 We calculate control group means accounting for agent-month fixed effect cells _c ∈ C_ as ∑ _c∈C_ ( _E_ [ _Y|C_ = _c_ , _D_ = 0) _wc_ for weights _wc_ , cell _C_ , and treatment status _D_ . We do so to correspond to the OLS estimand, _β_<sup>_OLS_</sup> = ∑ _c∈C_ ( _E_ [ _Y|C_ = _c_ , _D_ = 1]
The choice of Sierra Leone as a case-study is also motivated by historical and policy considerations. Sierra Leone’s post-colonial history offers a compelling and dramatic example of the vicious links between poor government performance, popular distrust toward state authorities and violence. Over time, the looting of state resources by predatory elites combined with the institutional weakness of the state apparatus contributed to the collapse of public services. These circumstances quickly catalyzed armed groups’ recruitment efforts, leading to escalation of violence and civil war (Humphreys and Weinstein, 2008; Kpundeh, 1995; Reno, 1995; Richards, 1996). Understanding whether the Government of Sierra Leone’s and donors’ focus on strengthening local service provision has contributed to overcoming longlasting patterns of distrust by driving positive changes in citizens’ beliefs toward political authority is a critical policy input. Focusing on Sierra Leone can contribute to informing ongoing debates on the best available strategies to support sub-national governments and maximize resource allocations in post-conflict states. Given the large amount of government and donor resources channeled to sub-national governments, the policy relevance of such an analysis is important. Drawing on these data, we now turn to empirically test the effects of local government performance on citizens’ beliefs about the trustworthiness of local government authorities.

# **4 Data, Measures and Methods**

## **4.1 Data**

To test our hypotheses and control for alternative explanations, we rely on four sources of data (i) the 2005, 2007 and 2008 National Public Service (NPS) household surveys and the 2007 and 2008 community surveys; (ii) the 2004 Sierra Leone Census of population and Households; (iii) district level data on fiscal transfers from 2006 and 2007 (disaggregated by sector); and (iv) the 2006 and 2008 Comprehensive Local Government Performance Assessment System (CLoGPAS).

The NPS surveys are designed to be nationally representative. The country was stratified by local council areas. Enumeration areas (EA) were then selected in each local council according to the size of
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Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Data source: World Bank survey on trust (2021) Socio-demographics also matter for trust. Figure 6 plots the coefficients of an ordered logit regression of trust in government on relevant geographic and socio-demographic variables. The coefficients show the direction and strength of the correlation between respondents said they do not trust other Moroccans. The least trusted part of society is strangers or people whom they have met the first time: only 19.4 % of respondents said they trust people they have met for the first time. These numbers were consistent with those observed in 2021 and in 2022.

22
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