rafmacalaba/data-use-mentions-extended huggingface dataset:
have strong personal financial incentives to detect non-compliance, which should help deter corruption.

However, these performance rewards may not sufficiently incentivize inspectors to act with integrity. Corruption appears pervasive, possibly due to the virtual absence of sanctions for improper conduct, threats from economic operators, and because compensation is low relative to opportunities for graft (Chalendard et al. 2020). According to a nationwide survey of inspectors that we conducted in 2017, only 23 percent believe that their colleagues act with integrity, only 6 percent claim non-ethical behavior is sanctioned and only 12 percent believe promotions are merit-based. Close to a third of inspectors claim being subjected to threats from economic operators on a regular basis. Undervaluation of imports was widely agreed to be the main type of customs fraud in Madagascar.<sup>8</sup> The inspectors in Toamasina interact with a limited number of customs brokers ( _commissionnaires agrées_

> 7Note that inspectors’ pay does not vary mechanically with the total taxes they collect.

> 8Administrative data on fraud records classify 67.2 percent of all fraud in Madagascar customs as underreporting of value, 27.4 percent as underreporting of quantities, and the remainder as product misclassification (4.9 percent) or misreporting country of origin (0.5 percent).

5
regional leader’s figures would be the resource rich countries (that is, countries that are abundant in metals and minerals). This group is probably the most laggard in terms of export product diversification. Specifically, if these countries were to diversify their export product basket more, their growth rate per worker would increase by 1.18 percentage points. In terms of physical capital accumulation, the average annual gains would be 1.02 percentage points while TFP growth accelerates by 0.51 percentage point annually. Finally, note that non-resource rich countries attain the lowest potential growth benefits (0.30 percentage point per year) from narrowing the gap in terms of export product diversification. The gains in physical capital accumulation and TFP growth are about 0.26 and 0.13 percent per year.

**-** **<u>Table 9. Comparative Statistics, Product concentration (2010 2014)</u>**

|||Region<br>Leader|World excl.<br>SSA (10th<br>percentile)|Industrial<br>countries<br>(median)|East Asia & the<br>Pacific<br>(median)|
|---|---|---|---|---|---|
|Panel A.<br>Growth|SSA|0.40%|0.42%|0.38%|0.07%|
|-0.023|Resource Rich|1.18%|1.19%|1.16%|0.85%|
||Non-Resource Rich|0.30%|0.31%|0.28%|-0.04%|
|Panel B.<br>Capital|SSA|0.35%|0.36%|0.33%|0.06%|
|-0.02|Resource Rich|1.
2 winners and losers in both rural and urban areas, not least because many farm households receive some of their income from non-farm sources. The only feasible option for discerning the net impacts of price-distorting policies on poverty and inequality is to undertake quantitative analysis using economy-wide models with up-to-date price distortion data and ideally detailed household information on the earning and spending profiles of different groups of people, both rural and urban.

The need for undertaking poverty and inequality analysis remains strong, notwithstanding the contributions of policy reforms over the past quarter-century. Partly as a result of those policy reforms and the consequent growth of incomes in many developing countries, the number of people living on less than $1 a day nearly halved over the 19812005 period, and their share of the global population fell from 42 to 16 percent (Table 1). Yet that number of extremely poor people was still almost 900 million in 2005, and it may have risen above that following the eruption of the global financial crisis that began in 2008. Moreover, most of the improvement has been in Asia (especially China), while in SubSaharan Africa the incidence of poverty was little lower in 2005 than in 1981, at around 40 percent (amounting to 300 million people in 2005). Despite the success of China, it still had over 100 million people on less than $1 a day in 2005, 90 percent of whom were rural. And in India the number of extreme poor remains stubbornly close to 300 million – and 74 percent rural, even with large subsidies to their farmers.

Less pressing than extreme poverty but nonetheless still important to the welfare of individuals is the extent of income inequality. In the past it was just inequality at the local level that affected individuals’ utility, but the information and communication technology revolution has increased awareness of income differences not only within local regions but also nationally and internationally. At the national level, there are concerns about rural-urban inequality as well as inequality within each of those broad geographic zones. Within rural
. We therefore drop roughly a third (24) of the 66 districts on which we collected data to ensure that each claim has counterparts at all three tiers of government.

We create a data set where the unit of observation is a claim of a bureaucrat about a district. As described, each federal and regional official is asked about three districts for each indicator (the 25th/50th/75th percentiles of that indicator), and so each bureaucrat-indicator-percentile is an observation for these officials. The claim of the district official about a specific indicator in her district is a single observation. Each response is associated with the corresponding district census ID.

In a very small number of cases, there seems to have been a data entry issue, where additional digits were included or excluded in the recording of responses to non-fractional variables. This leads us to some errors that are off by a very significant factor (of 94 at the 95th percentile of errors). These are not reflective of the wider variation in the data. Two-thirds of absolute errors are less than 1 in proportional terms. The most extreme values seem outside the realm of feasiblity for even the least informed bureaucrat. Since a plausible explanation is that these outliers reflect data entry issues rather than outsized errors, we therefore censor the top 5% of raw errors for each of the variables. The interpretation of the paper is therefore analysis of the lowest 95% of errors that public officials make, rather than of the top 5%.

Absolute values are then taken of all errors. At this stage, we have a data set that is not comparable 37
primary|||||-0.108<sup>***</sup>|-0.042<sup>***</sup>|
|<br>_(% of GDP)_|||||(0.000)|(0.000)|
|Market Concentration|0.00881<sup>***</sup>||0.005<sup>**</sup>|0.0119<sup>***</sup>||0.006<sup>**</sup>|
|_(Herfindahl Index)_|(0.000)||(0.004)|(0.000)||(0.005)|
|Product Concentration|-0.0246<sup>***</sup>||-0.020<sup>***</sup>|-0.0277<sup>***</sup>||-0.020<sup>***</sup>|
|_(Herfindahl Index)_|(0.000)||(0.000)|(0.000)||(0.000)|
|Secondary enrollment rate|0.00951<sup>***</sup>|0.015<sup>***</sup>|0.009<sup>***</sup>|0.00822<sup>***</sup>|0.014<sup>***</sup>|0.008<sup>***</sup>|
|<br>_(initial level, logs)_|(0.000)|(0.000)|(0.000)|(0.000)|
−0.31|0.44|0.31|
|1980–1984|0.09|0.03|−0.22|−0.29|0.38|0.28|
|1985–1989|0.29|0.20|−0.06|−0.22|0.59|0.47|
|1990–1994|0.23|0.18|−0.14|−0.23|0.41|0.37|
|1995–1999|0.19|0.15|−0.13|−0.19|0.28|0.23|
|2000–2005|0.20|0.16|−0.08|−0.20|0.26|0.21|
|_All years_|_0.21_|_0.11_|_−0.15_|_−0.26_|_0.45_|_0.31_|
|No. of<br>countries|74|69|38|34|67|64|

_Source:_ Own calculations based on the data described in the text.

_Notes:_ The figures report _NRA_ and _RRA_ averages for the full sample, autocracies, and democracies in different subperiods. The number of countries refers to the total number in each category in the 1955–2005 period and changes over time due to entry and exit.

Although these statistics demonstrate that the average _NRA_ and _RRA_ values are much higher in democracies than in autocracies, they say nothing about the potential causal effect of democratization on agricultural policies. To obtain further insight on this issue, we examine the _NRA_ and _RRA_ values of countries in the data set that have experienced a political transition from autocracy to democracy. Specifically, figures 1 and 2 present the average _NRA_ and _RRA_ values in the predemocratization and postdemocratization periods for 23 countries that have experienced permanent democratization, as defined by Papaiannou
- U.S. dollar inflation is forecast to be 2 percent a year over the term of the PPA.

- The forecast average capacity of the plant is 200 MW. (This forecast might be derived from data on the maximum capacity of the plant and an estimate of the percentage of the time it will be available, given maintenance requirements and the possibility of breakdowns.)

Finally, we assume that the advice of financial experts suggests that

- The actual or estimated credit rating for this utility (in the absence of government support) by, say, Moody’s is B. (We assume, that is, the utility in this example has poorer credit than the utility in the previous example.)

- As above, the best estimate of the relationship between Moody’s credit rating and cumulative probabilities of default is that given in Moody’s (2017, Exhibit 33).

- As above, the forecast loss given default is 100 percent.

- The risk-free rate of interest in U.S. dollars is 2 percent a year. This rate can be driven from the U.S. long-term government bond yields.

We can then use these assumptions to arrive at an estimate of the expected cost of the guarantee (see Table 2).

- Given the real price of capacity ($60,000/MW every year), forecast U.S. inflation of 2 percent a year, and forecast average available capacity of 200MW, the utility’s forecast payment in year 1 is $12.2 million. In each following year, the forecast payment increases with U.S. inflation by 2 percent.

- In the first year, the assumed probability of distress is 3.573 percent, so the estimate of the government’s expected payment is approximately $0.4 million. Because the loss given default is assumed to be 100 percent, this is also the net payment. The estimated expected net payment increases each year partly because of inflation but mainly
>19</sup> summarizes the cyclical behavior of fiscal policy and indicates whether fiscal policy is countercyclical or procyclical. It is composed of both automatic stabilizers and the discretionary response of governments to economic fluctuations. ββ<sup>20</sup> The residual , captures changes in fiscal policy that are unrelated to the business cycle or any of the control variables. These decisions can be the result of political decisions (such as changes in tax rates or spending associated with the political cycle) or errors in policy (such as , εε mismeasurement of the output gap). The uncertainty associated with the residual can be seen as generating excessive volatility in GDP and, possibly, reduced long-term growth. Following Fatás and Mihov (2013), the volatility of fiscal policy is measured as the standard deviation of the residual in the fiscal policy reaction function ( ). εε Annual data are used for the 1990–2021 period for 184 countries, including 148 EMDEs and 36 advanced ii σσ Annual data are used for the 1990–2021 period for 184 countries, including 148 EMDEs and 36 advanced economies. The choice of the sample period is based on data availability. Of these 184 countries, 94 are commodity exporters and the remainder as commodity importers. Among commodity exporters, only five are advanced economies. Among commodity importers, 31 are advanced economies and 59 are EMDEs.

# **IV.2 Basic features of fiscal policy volatility**

The following stylized facts emerge from a comparison of the measures of fiscal policy volatility for EMDEs and advanced economies. First, over the past three decades, the volatilities of primary expenditures, government consumption, and revenues were all significantly higher in EMDEs than in advanced

> 19 HP-filtered GDP levels are also used as a measure of economic activity to check the robustness of the results. The results using this alternative measure are consistent with the baseline results.

> 20 The specification has potential endogeneity issues since fiscal policy could affect economic activity contemporaneously. While the literature has acknowledged these issues,
inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**The adjustment in the Guaranteed Minimum Income (GMI) significantly impacts the overall transfer coverage, doubling financial assistance.** This shift in transfer coverage signifies a strategic effort to reach a broader spectrum of the population and extend the protective umbrella of social support (Figure 11, panels A and b).

**The GMI increase, in conjunction with the heating allowance, results in a remarkable generosity level, particularly for households in the lower income deciles.** For those in the first decile, this combination accounts for more than 40% of their total income, providing a substantial income boost (Figure 11, panel c). Similarly, the second and third deciles experience increased support, albeit slightly lesser, with close to  4% of their income originating from these transfers. In contrast, the effect is comparatively minor when considering the entire population, constituting only 0.6% of their total income.

**However, despite these transfer coverage and generosity changes, their impact on poverty reduction is relatively moderate** . The poverty rate decreases by 0.85 percentage points, and the poverty gap

29
WPS3648

# **Insurance and Liquidity: Panel Evidence**

### Rashmi Shankar*

### **Abstract**

This paper presents evidence that balance sheet effects are critical determinants of both the likelihood of a crisis and of income losses following a crisis. The paper tests the validity of “insurance” and “liquidity” models of currency crisis. Both models predict that the occurrence of a balance of payments crisis is conditional on the health of the nation’s accounts vis-à-vis the rest of the world. Problems in the balance sheet either cause a financial crisis that develops into a run on the Central Bank, or generate a run on the Central Bank once contingent liabilities exceed reserves and the yield differential moves against domestic assets. Estimations of crisis likelihoods based on several specifications of single and simultaneous equation probit models confirm that output losses following the crisis are persistent and conditional on the balance sheet indicator, i.e. the ratio of the stock of gross external liabilities to assets. Measures of contingent liabilities, capital flight, and financial depth perform well as crisis predictors, and the marginal effects on the probability of a crisis are of the expected sign. The panel data set covers the time period 1973 through 2003 for 90 countries.

**_Keywords: insurance, liquidity, currency crises, panel estimation JEL Classification: F3,F4,E6_**

## World Bank Policy Research Working Paper 3648, June 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http:/
With respect to measurement, a promising area of work relates to producing more reliable FDI statistics that control for profit shifting motives and the role of tax havens. For instance, Damgaard et al. (2019) and Damgaard and Elkjaer (2017) build an alternative measure of FDI which does not include special purpose entities, Casella (2019) develops a method aimed at uncovering the identity of ultimate investors, and Coppola et al. (2021) build a new dataset for cross-border investment by both residency and nationality.

# **3 FDI and Growth since the 1970s**

This section describes how the relationship between FDI and growth has evolved over time. It documents that local conditions (financial depth and education) played an important role for the link between FDI and economic growth in the early years of the sample. However, this result no longer applies for more recent growth spells.

The first part of the section focuses on cross-country regressions similar to those of Borensztein et al. (1998) and Alfaro et al. (2004) but estimated over different time periods. The second part of the section explores the evolution of the relationship between FDI and growth using panel data which allow controlling for country-specific and time-invariant unobserved heterogeneity.

Our focus is on FDI received by emerging market and developing countries. Our main dependent variable is real per capita GDP growth (sourced from the World Bank Development Indicators) averaged over either 10 or 20-year periods and our key explanatory variable is net FDI inflows over GDP, also sourced from the World Bank Development Indicators. The number of countries in our regressions ranges between 72 and 96 and varies depending on the time period and the estimation strategy. The number of observations in the panel regressions ranges between 1091 and 3098. Appendix Table A.1 reports summary statistics for all variables used in the analysis and Appendix Table A.2 shows the countries included in each sample.<sup>4</sup> The data show that average net FDI were just above 1% of GDP and
investments -- and for which industries these constraints bind or not – a benchmarking tool using 163 indicators (including Doing Business and WEF indicators) for 140 countries was developed. The selection of the indicators, however, was only based on expert judgment.

Building on these earlier efforts, we propose in this paper a novel empirical methodology to identify factors associated with respectively foreign investment decisions and international trade success, using publicly available data measuring the environment in which trade and investment happen, with the following aims:

- Reduce the dimensionality of the universe of indicators to a subset of indicators that are significantly associated with trade and investment outcomes (“success”).

- Robustly identify the subset of indicators.

- For the selected subset of indicators, benchmark country and sector performance against the rest of the world and take initial steps toward the building of a predictive model of trade and investment “success”.

This empirical strategy is ultimately designed to help policy makers and economic actors improve their strategic focus on the variables that have the greatest impact on their economic objective, and possibly on sector specific interventions with the greatest value for money.

A review of commonly used investment and trade climate indicators and identification of the strategy environment for trade and investment There is a vast empirical literature investigating the drivers of trade and investment outcomes. We review here briefly the main empirical strategies and analytical frameworks that are the most commonly used in the policy literature, reflecting on the considerable growth of available information on indicators tracking factors of trade and investment climate, while also noting some of the limitations of currently used approaches.

# • Drivers of investment climate and business environment

Derived from the new comparative economics literature, investment climate and business environment indicators rankings aim to exhaustively benchmark the private sector or investment climate environment at the country level. For each country, the performance on individual attributes relative to the rest of the world and best performers is being recorded. The Doing Business Indicator series, which was prepared annually by the World Bank Group between 2003 and 2020 measures
# **Methodology**

To begin, a 0.5x0.5km grid was created over each city. This grid functioned as the base unit of measurement for all the indicator analysis. All spatial data layers were aggregated to this resolution and joined to this grid.

Urban footprints (city boundaries) were created by combining a population density layer (Landscan) with an urban footprint layer (Global Human Settlement Layer ‐ GHSL). Created by European Commission’s Joint Research Centre (JRC), the GHSL offers a systematic and detailed representation of ‘built‐up’ areas, and classifies each 30m pixel as ‘built‐up’ if it intersects with a building or road.<sup>54</sup> In order to make these datasets comparable they were overlaid and summarized to the 0.5x0.5km grid.

Grid cells comprised of more than 10% built‐up land and had more than 100 inhabitants were labeled as urban. These thresholds were created from visual interpretation, and are consistent with previous urban mapping studies.<sup>55</sup> While the city boundary created in this study differs from the administrative border, it more accurately reflects each city’s physical ‘built‐up’ land and hence it is better suited for inter‐city comparisons.

Following the creation of the city footprints, the land cover classification layers were converted to 0.5x0.5km grids. Each grid cell was labeled as a class (vegetation, residential regular, residential irregular, etc.) based on the majority land cover class contained in that cell. The following analyses use only grid cells within a 15km buffer from the CBD.

# **Centrality**

Urban sprawl is defined as areas with low density, and scattered, discontinuous, and commercial strip development patterns.<sup>56</sup> Sprawl reduces inner‐urban connectivity and typically places higher transportation costs on families living in these areas. However, the effects of sprawl are not just felt
of -0.20 indicates that a Category 3 hurricane wind ( , wind speed of 203 kph) would reduce nightlight by 0.04 units ( ≈ −0.20 ∗0.2 )—equivalent to a 0.33 percent reduction—using the inverse of the average level of nightlight in 2000.<sup>28</sup> In model 3 (purple), the effect of the presence of mangroves ff= 0.2 on hurricanes is greater and implies a 0.60 percent reduction in nightlights.<sup>29</sup> As in Figure 5, panels (b) and (c) show that hurricane damage is lower in cells with more than 1 and 2.3 kilometers of mangrove width during the sample period.

**Figure 6. Impact of hurricanes on nightlight by mangrove width, including Cuba**

<!-- Start of picture text --> 1 0.05 1 0.27 1 11.54<br>2 1.20 2 1.68 2 26.49<br>1 -0.20 1 0.17 1 8.37<br>2 0.87 2 0.64 2 20.10<br>3 1.32 3 2.15 3 29.40<br>1 -0.36 1 0.13 1 6.60<br>2 0.50 2 0.43 2 17.61<br>3 0.80 3 0.94 3 22.13<br>4 1.61 4 2.51 4 31.32<br>-2 3 0 2 4 0 20 40<br>a) Estimated impact of hurricanes b) Distribution of mangrove width (km) c) Distance to the coast (km)<br>on nightlights (NL)<br>Source: Authors’ calculations based on data from LECZ database.<br>Notes: In models 1–3