rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9460

# **Abstract**

Using a novel, nationally representative data set on fraud victimization, this paper examines the impact of credit constraints on fraud victimization and potential underlying mechanisms in Chinese urban areas. After controlling for other household characteristics and regional fixed effects, households facing credit constraints are associated with 2.3 percentage points higher probability of becoming fraud victims, and have 20.4 percent higher subsequent economic losses from fraud when they are approached. The results are robust when dealing with the endogeneity of facing credit constraints and when addressing potential sample selection bias. Further analyses show that the personal discount rate (impatience) and the need for social network expansion are critical pathways via which credit constraints affect fraud victimization. The findings suggest that improving financial development is an effective way to reduce fraud victimization.

This paper is a product of the Development Research Group, Development Economics. 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 authors 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
variable, using the delineation of that period, obtained from the Global Human Settlement Layer. The altitude variable is also obtained from the Global Human Settlement Layer and is measured as the number of meters above sea level. We perform Wu-Durban-Hausman tests after our estimations to gauge whether urban density can be treated as exogenous variable. As shown in the tables in Section 4, the used instruments are relevant (not weak) and exogenous.

Although there may be a problem of reverse causality from a theoretical point of view, this does not necessarily mean that there is a problem from an econometric point of view (Combes et al, 2008). If indeed the independent variable of interest is exogenous, then the Mundlak estimation, which is a more efficient estimator, should be preferred over its 2SLS counterpart. In addition, estimating 2SLS models including interaction effects is a challenge in and of itself without having good instruments for the interaction effects.

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alia_ accounting for endogeneity issues. Furthermore, in this context the brief references to ancient times made earlier are suggestive of the need to probe further with an historical perspective.

Given the constraints in data availability at the city and country level, in this initial research inquiry we did not include some other important institutional dimensions, such as decentralization and local finance. Obviously it cannot be ruled out that some of the significance of the governance variables for which we did have measures at the city and country level, such as the corruption-related ones, may be picking up in part some of the importance of other governance and institutional variables. Future research would need to 19 In essence we could conceptualize of another hypothesis, where the ability of cities to translate global opportunity into local value depends on governance, and that cities compete with each other on the basis of governance and not on the basis of initial endowments (infrastructure, culture etc). Cities with better governance remain globalized for longer periods of time (attract higher flows of capital, people, firms, finance, products) or attract such flows systemically over time (growth or change in volumes of these flows over time). This test would allow us to distinguish between those cities that are highly global and then drop in their rankings over time. For such a test, we would need at least two time periods. A possible model specification could include: Δ Y = f (X1, X2, X3)+ ε where Δ Y = change in the vector of governance indicators, to capture improvements or deterioration in governance over time which is a proxy for staying power in translating global opportunity to local value; X1 = a dummy for an alpha city from the Taylor database; X2 = dummy for a beta city, and X3 = dummy for a gamma city. With such a specification one could investigate whether alpha and beta type cities have better governance compared to the gamma type cities.

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By aggregating cost-related moments over household types and center qualities we identify and estimate these location effects. For variable costs, we modify the parameters of the corresponding lognormal distributions to allow for: where _ψ_ 0( _l_ ) _, ψ_ 1( _l_ ) are the scale effects in variable costs in location _l_ .

As for fixed costs, in the aggregate model they are calibrated outside the estimation procedure using industry cost reports (see footnote 23). We scale these calibrated values by location to take into account possible cost differences among locations. We use the _Census Data for Residential Rents in 2000_ , which reports residential median gross rent at the county level. This is a price measure with national coverage, suitable granularity, and related to a critical fixed cost item for center-based providers, namely rent. We assign counties to our four locations based on population and income as described in Section C.2.2. We then take the mean of the county median rents for each location. The ratios between these means and the aggregate mean are the scaling factors reported in the “Rent” column of Table D.1 and are the ones use to scale fixed costs _{F_<sup>_L_</sup> _, F_<sup>_H_</sup> _}_ .

Third, we scale the single-mother transfer to account for cost-of-living differences across locations. We build the scaling factor with the same procedure used for the fixed costs 68
‐ clines—as well as with Engel’s Law of less‐than‐unitary income elasticity for food commod‐ ities.<sup>**8**</sup> Thus, the negative sign of the income elasticity (‐0.5 in the case of the panel model) should be interpreted as the difference between the effect of income on nominal food prices and the effect of income on the deflator. The former is generally lower than one, the latter greater than one.

Though it has often been argued, especially in the context of the 2008 food price spike, that the changing consumption patterns of emerging economies, especially China and India, were key drivers of rising prices,<sup>**9**</sup> the evidence points to a lower impact than

> **7** Based on the same tests, all the statistics but one confirm co‐integration at the 1 percent level of significance (co‐integration statistics are not reported here). Detailed stationarity tests of all variables can be found in Baffes and Etienne (2016), who used the same data within a panel autoregressive distributed lag model to estimate the long‐run impact of income on commodity prices.

> **8** The declining terms of trade of primary commodities versus manufactured goods were first observed in the mid‐19th century, when the German statistician Ernst Engel observed that poor families spent a greater pro‐ portion of their total expenditure on food compared to their wealthier counterparts, leading to Engel’s Law of less than unitary income elasticity of food commodities (Engel 1875). Almost a century later, Kindleberger (1943, p. 349) argued that “[t]he terms of trade move against agricultural and raw‐material countries as the world’s standard of living increases ... and as Engel’s Law of consumption operates.” Kindleberger’s thesis was empirically verified by Prebisch (1950) and by Kindleberger (1958)
**Table 1. Panel A: Variable Description and Data Sources**

|Variable|Description and Data Source|
|---|---|
|_Firm-Level Variables_||
|Access to finance<br>Firm size (employees)|Dummy variable equal to 1 if the firm has access to bank finance<br>(loan, overdraft or line of credit) from World Bank Enterprise Surveys.<br>Number of permanent full-time employees from World Bank<br>Enterprise Surveys.|
|Manufacturing<br>Exporter<br>Foreign-owned<br>Government-owned|Dummy variable equal to 1 if the firm is in the manufacturing sector<br>from World Bank Enterprise Surveys.<br>Dummy variable equal to 1 if 10% or more of sales are exported<br>directly or indirectly by the firm from World Bank Enterprise Surveys.<br>Dummy variable equal to 1 if 50% or more of the firm is owned by<br>foreign organizations from World Bank Enterprise Surveys.<br>Dummy variable equal to 1 if 10% or more of the firm is owned by the<br>government from World Bank Enterprise Surveys.|
|Firm age|Age of the firm inyears from World Bank Enterprise Surveys.|
|_Country-Level Variables_||
|Lerner index|Lerner index constructed using variables from Bankscope (see<br>Appendix 2).|
|Concentration 3<br>Herfindahl index<br>Inflation rate<br>Financial development<br>Credit information index<br>Share of government-owned banks|Fraction of total assets held by top 3 banks from Bankscope.<br>Herfindahl index calculated as the sum over all banks in the country of<br>the squared market share (in terms of assets) of each bank from<br>Bankscope.<br>Inflation calculated as the annual change in the GDP deflator from the<br>World Development Indicators (WDI) World Bank.<br>Domestic
# **4. Descriptive Statistics**

In this section, we report some key descriptive statistics from the Domah survey.

## **4.1 Countries with Autonomous Electricity Regulators**

Table 1 shows that by 1998 just under half the countries in our sample had an autonomous electricity regulator<sup>22</sup> – mainly in Latin America. However, in the following three years 3 African and 1 Caribbean country joined the set. Asia provides an exception to the spread of autonomous regulators with only 1 country (Philippines) having an autonomous regulator before 2001.

By 2001, a majority of countries had regulators classified (at least in legal terms) as autonomous.

**Table 1:  The Trend towards Autonomous Regulators (by Continent)**

||||**1998**||**2001**|
|---|---|---|---|---|---|
||**Total**|**Ministry**|**Autonomous**|**Ministry**|**Autonomous**|
|**Africa**|5|5|0|3|2|
|**Asia**|4|3|1|3|1|
|**Carrib**|6|3|3|2|4|
|**Latin America**|13|4|9|4|9|
|**Total**|28|15|13|11|17|

Source: Domah 2001 survey, supplemented and updated by authors

## **4.2 Countries with Electricity Regulatory Laws**

Even where there was no autonomous regulator, laws for the reform of the ESI Including regulatory reform were being passed. Table 2 shows the regional distribution of electricity reform laws for those states without autonomous regulation. According to the Domah data, all the countries with autonomous regulators
# **5 Conclusion**

We use administrative corporate tax records from 10 low- and middle-income countries to simulate how the COVID-19 pandemic impacts firm profits and tax payments. Modeling the lockdown-triggered revenue shock with simple and transparent assumptions, we predict that less than half of all firms will remain profitable by the end of 2020, about 5-10% of the aggregate annual payroll is lost, and exit rates double. We then show that payroll subsidies are unlikely to protect formal employment, especially in lower-income countries. Overall, we expect that governments’ tax revenue shortfalls from the corporate sector will reach at least 1.5% of baseline GDP, and that tax revenue losses as a share of GDP are larger in richer countries.

Our study illustrates the benefits of using harmonized administrative tax data across countries for justin-time policy advice. This work is part of an effort to make such data more widely used in comparative policy analysis. Our simulations will be improved in the coming months with the release of monthly and quarterly data (notably on VAT payments). This will allow us to refine the calibration of sector-specific demand shocks based on realized shocks. After corporate income tax declarations for 2020 are filed, we will also be able to compare our predictions to realizations, and to analyze the shortcomings of the simple assumptions we imposed.

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provided by FINCA DRC agents could therefore be viewed as a better fit for the urban poor than for other groups. We derive similar results when we replace the cash-in variables with the cashout variables on the left-hand-side of our sub-sample regressions, and so we do not report those results in the paper.

# **_6b. Monitoring of FINCA Agents_**

FINCA agents receive unannounced visits from agent network officers to assess how well they are following FINCA protocols and to suggest areas for performance improvement. Agents are supposed to be visited on a monthly basis, though the monitoring reports that we have indicate that visits are less frequent than that for a large number of them. Agents are rated in four broad categories: liquidity management, client service, transactions performance, and branding. Within each category, agents receive scores in multiple sub-categories.<sup>32</sup> For example, for liquidity management agents receive a score for the management of “e-float,” that is, money in the agent’s account that can be transferred to a client’s account in exchange for cash, and “cash float” (which can be exchanged for e-float). For client service, agents receive scores for state of shop, connectivity/service availability, and operator availability. For branding, scores are given for agent visibility, posting of FINCA images, and the quality of FINCA signage outside the shop.

We sum the scores from the sub-categories to derive a score in each of the four broad categories, which we include in Table 7 in the regressions that we presented earlier.<sup>33</sup> Although we lose a large number of observations because most agents did not receive visits in all months for which we have transactions data, the total number of observations in the regressions still

> 32 It should be noted that this assessment of the agent is not in any way linked to the agent’s performance in terms of number of transactions or volumes transacted. These assessments are carried out in the
The Doha Development Agenda (DDA) negotiations, sponsored by the World Trade Organization, experienced a blow in Cancun, Mexico, precisely over the question of rich country agricultural support and its potential impacts on poverty in developing countries. The Doha negotiations are now emphasizing the need to better understand the linkages between trade policies – particularly in rich countries – and poverty in the developing world. Moreover, poverty reduction is now widely accepted as a central focus for development efforts and has become the main mission of the World Bank and other development institutions. For example, the “Millennium Development Goals” commit the international community to halve poverty by 2015, and locate several key means to this goal in international trade.

With this high level of policy interest, it is hardly surprising that the issue of trade and developing-country poverty has become a focus of much research activity over the last several years. This paper summarizes a research program that offers the first comprehensive analysis of the national poverty impacts of specific policy reforms proposed under the auspices of the WTO.<sup>1</sup> It combines the results from several strands of research in a novel way. First, it draws on an intensive analysis of the July 2004 DDA Framework Agreement, particularly of potential reforms in agriculture, which, as we shall see, have special significance to the poor. The scenarios analyzed below are built up from newly available tariff line data on bound and applied tariff rates. Similarly detailed analysis is undertaken in the case of domestic support for agriculture and export subsidies, as well as for non-agricultural market access.

> 1 Most of the studies in the program are included in the World Bank Policy Research Working Paper series. All are available at https://www.gtap.agecon.purdue.edu/poverty

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2 and 3. These statistics show that timeliness and institutional intensity are on average higher for intermediate than for consumption goods. In particular, 22 percent of U.S. intermediate imports are shipped by air compared to 18 percent of consumption goods, and 65 percent of intermediate goods are not reference priced products compared to an average of 78 percent in the case of consumption goods. In addition, table 4 shows the correlation between factor intensities and the share of intermediate goods in industry exports. This correlation is positive and significant for capital intensity, not significant for skilled labor intensity, and negative and significant for both institutional intensity and timeliness.

> 9 The data is accessible at http://www.stanford.edu/%7Echadj/HallJones400.asc and a number of studies have used the same database. These include Romalis (2004), Levchenko (2007) and Bernard, Jensen, Redding and Schott (2010). 10 Indicators on enforcing contracts measure the efficiency of the judicial system in resolving a commercial dispute. Time is recorded in calendar days. Cost is recorded as a percentage of the claim, assumed to be equivalent to 200% of income per capita.

> 11 This index is based on a worldwide survey of operators on the ground (global freight forwarders and express carriers) and it captures the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology).

> 12 Time to export is recorded in calendar days and measures the time required to compile all procedures required for exporting.

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this measure may be limited in scope. Our second index of women’s empowerment, including additional dimensions, increases significantly by 0.38 SD (FWER-corrected p-value < 0.0005) compared to the control group, indicating large impacts when other dimensions of women’s empowerment are considered. These results are driven by increases in three dimensions: (i) an increase of 0.34 SD (p-value < 0.0005) in the index measuring women’s participation in decisions about their own body and time, including fertility and mobility (time use, finding a job outside, opening a business), and effective access to inputs and resources including ownership of a mobile phone and having savings or loans; (ii) an increase of 0.29 SD (p-value < 0.0005) in the index on participation in income-generating activities, including paid incomegenerating activities and being the owner or manager of a self-employment enterprise; and (iii) a 0.33 SD increase (p-value < 0.0005) in the index on political involvement and social capital, which includes having an ID, attending community meetings, and reaching out to a community leader. The two additional indices reported smaller positive increases, both statistically insignificant: The index on children’s investment decisions (education, health, marriage) increases by 0.08 SD (p-value 0.157) and the index on social norms – measured through aspirations for daughters in education and enrollment – decreases by 0.01 (SD p- value 0.906).

Two things are apparent from these results. First, they are mainly driven by changes in indicators related to economic opportunities and access to inputs, which is expected given the focus of the intervention. However, while participating in income-generating activities and having a financial account in their own name are impacted positively, and are highly linked to components of the TUP intervention, interestingly, outcomes not targeted, such as ownership of a mobile phone, having a paid job, and owning
**Figure 3.4: PPML HDFE Gravity estimates of Morocco’s Trade Agreements and Furniture Trade**

<!-- Start of picture text --> Agadir Agreement<br>EFTA-Morocco<br>EU-Morocco<br>PAFTA<br>Turkey-Morocco<br>United States-Morocco<br>GSTP<br>-4 -2 0 2 4<br><!-- End of picture text -->

**Source:** Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO). Plotted estimates are incidental-biascorrected coefficients and standard errors following Correia et al. (2019).  The solid vertical line indicates the estimated value of the “average” agreement as described in equation 1 when the dependent variable is bilateral apparel trade (HS61 and HS62). The estimates of the “average” agreement are basically identical across most agreements because Morocco’s individual agreements have little influence on the average estimate of the other 262 agreements (Morocco is not one of the largest trading countries).

The main takeaway of these results is that trade agreements in Morocco have made modest contributions towards the goal of boosting trade flows. Exports have increased both in absolute terms and as a share of GDP, but there is not strong evidence that trade agreements were driving these changes. It is still possible that the changes in exports have affected local labor market outcomes. The next section builds on these results by exploring how the increase in trade flows affected local labor market outcomes.

# **4. Shift-Share (Bartik) Analysis**

Our main question in this section is how exports have affected local labor market outcomes in Morocco. Since exports have increased, the next step is to estimate how trade flows affect local labor market outcomes. One of the most prevalent current approaches is known as the shift-share (Bartik 1991) approach. Following the current 16 | P a g e
2|25.6|24.6|25.4|26.0|24.6|23.1|17.6|
|Relative impact<sup>c</sup>|0.66|2.89|2.25|1.90|1.57|1.24|1.06|0.89|0.65|0.43|0.13|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income (in %).

Regarding the impact of reducing concentration on poverty and inequality, Table 8 shows that moving from a concentrated to a more competitive market (and the price changes derived from this change) reduces the poverty headcount by 0.66 percentage points and the poverty gap by 0.53 percentage points. As shown in Figure 8, short-run estimates—corresponding to the first step of the stepwise simulation—suggest that the poverty headcount falls by 0.45 percentage points as the market share of incumbent firms decreases by 50 percent. The extreme case of perfect competition estimates an upper bound reduction of 0.88 percentage points. Inequality will also decline since households at the lower end of the income distribution have larger expenditure shares in corn products than those at the higher end of the distribution. Finally, the improvements in inequality are modest relative to the gains in poverty reduction.

**Table 8. Estimated effects on poverty and inequality of changes in competition in corn products**

||**Pov**|**erty indic**|**ators**|**Ine**|**quality indicators**||
|---|---|---|---|---|---|