rafmacalaba/data-use-mentions-extended huggingface dataset:
transportation. Firms with a larger baseline in terms of revenue were more likely to shutdown.

Despite enduring such a dramatic impact on business activities and earnings in April and May, household businesses recovered quickly. We find an immediate rebound across both intensive and extensive margins, with the number of suspensions and closures reduced by over 50 percent as soon as June, and HB work hours and earnings making almost a full recovery by September. Overall, while the dramatic impacts observed almost immediately after the economy-wide lockdown suggest the financial struggle faced by many household businesses in the country, the observed rapid recovery indicates the resilience of this sector during the pandemic.

More broadly, the paper introduces a unique high-frequency dataset of tax registration and filings, which provides comprehensive and novel measures for different household business’ operating decisions, namely entry, exit, and

> 25To check for the robustness of our DiD findings, we estimate the same regressions using an alternative commune-level F2F index set that is constructed using the 2018 sectoral composition of HBs (instead of 2019). The results, presented in Table A.7, are robust and consistent to our main findings in Table 4.

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means that a 10 percent increase in the minimum wage triggers a job loss of 1.3 percentage points.

In Asia, Indonesia is one of the main developing countries to be the subject of several studies on the implications of the minimum wage for the labor market. The minimum wage was introduced in Indonesia in the 1970s. Since then, the country has set multiple minimum wages, corresponding to each of the provinces (27 levels). In terms of the minimum wage relative to the average wage, Indonesia has one of the highest ratios of all the countries in Southeast Asia, at nearly 0.6 (Del Carpio, Pabon, 2014). The differences in the minimum wage level by province and the sharp rise in the minimum wage during the 1990s made it possible to analyze the potential effects of the minimum wage on the economy.

The results of these analyses are mixed. Based on panel data from the provinces, Rama (2001) found a negative effect for smaller businesses (with elasticities ranging from −0.77 to −1.30) and a positive effect in medium and large enterprises. The existence of monopsonistic power in the medium and large companies actually favored an increase in the demand for workers.

Alatas and Cameron (2008) studied the effect of a minimum wage increase in the area of Botabek between 1990 and 1996 (before the crisis) on the employment of workers engaged in the manufacture of textiles and footwear. The results showed a reduction in the employment of workers in small companies, with elasticities ranging from −0.31 to −0.55, whereas in the large companies the effect was not even statistically significant. Some of the estimates actually showed increases in employment in the large enterprises, which would suggest that employment in the small companies was shifting to the large ones. Furthermore, the large companies appeared to be paying above the minimum wage, so that increases in the minimum wage would not affect employment levels. On the other hand, Harrison and Scorse (2010
Talvi and Vegh (2005) who argue that, in the presence of political distortions, the larger the variability of tax revenues, the more pro-cyclical fiscal policy will be. We use the square of the cyclical component of real GDP from Hp filter (parameter 6.5) to measure output variability. We also control for the characteristics inherent to the level of development of countries using the logarithm of GDP per capita. This allows to know whether the behavior of fiscal policy depends on the level of development.

We control for the level of public debt. Controlling for this variable seems somewhat compulsory since public debt is an important element of decision when designing public expenditure programs. Finally, the literature on Dutch disease suggests that the abundance of natural resources in a particular country and the mismanagement of resource rents could have detrimental effects on public finances (Torvik (2009)). Hence, we use the ratio of natural resource rents to GDP as a control variable. Appendix 1 recapitulate the variables used and the source of data.

Appendix 2 displays basic descriptive statistics on the whole set of variables employed in the model. We also add at the bottom of the table, descriptive statistics on literacy rate of adults (LRA) and life expectancy at birth (LEB), (obtained from World Bank WDI), that we use, in the section 6, as instruments for institutional quality, in addition to the lagged variables of institutional quality.

The correlation matrix in Appendix 3 reports the degree of correlation between the different variables. It suggests that multicolinearity is not likely to be an issue and hence authorizes the introduction of the different variables in the empirical model.

Since our variable of cyclicality of fiscal policy is generated using LGWOLS, we display in figure 1 the Kernel density plot to view the distribution of the variable. On the right, the figure presents the same plot for the distribution of bootstrapped estimates of the cyclicality (with 200 replications). The distribution of the generated cyclicality coefficients is roughly normal.

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cantons were identified using small area estimates of poverty, derived from combining survey and geospatial auxiliary data along the lines of the studies discussed above, instead of small area estimates of wealth. The paper does not address this question directly, because Canton-level poverty estimates derived from combining survey data on per capita consumption with geospatial data was not considered in the set of feasible options.

The first feasible alternative considered simulated the provision of a uniform transfer to all individuals within the poorest prefectures. There are only 40 prefectures in Togo, however, as opposed to 397 cantons. This means that the simulated prefecture transfer differs in two ways from the simulated Canton procedure. First, it is based on many fewer prefectures, which reduces targeting accuracy because no attempt is made to distinguish hypothetical recipients within prefectures. Secondly, the prefecture estimates are estimates of predicted per capita consumption instead of wealth, which all else equal should improve targeting, since a measure of predicted per capita consumption is used for evaluation. Comparing the bottom two rows in Table 6 indicates that in this case, targeting based on less wealthy Cantons is more accurate than targeting based on poor prefectures, as the benefits of more disaggregated targeting outweighs the disadvantage of targeting based on predicted wealth. However, this difference is not large, given the much smaller number of prefectures, as the difference in rank correlation is only 0.04. This suggests that geographic targeting could be further improved if the 100 poorest cantons were determined based on estimates of per capita consumption rather than wealth.
|63.8|57.0|79.0|83.8|
|Percent of seat-km in medium or<br>smaller aircraft|42.7|0.0|50.6|42.5|28.3|32.8|
|Percent of carriers passing IATA/IOSA<br>audit|33.3|0|0|100.0|100|33.3|
|FAA/IASA audit status|Failed|No audit|No audit|No audit|No audit|Passed|

_Source:_ Bofinger 2009Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data).

_Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent. FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit. n.a. = Not applicable.

# **Challenges**

The air transport sector is not immune from Zimbabwe‘s political and economic challenges. Air Zimbabwe is struggling financially, and anecdotal evidence of poor schedule reliability and service quality show an airline with many operational challenges. The economic difficulties faced by the airline are potentially affecting its ability to import spare parts for maintenance.

Zimbabwe‘s overall spending needs for the transport sector are estimated at $218 million annually for a decade, which would restore the road network to good condition and make improvements to other transport modes. By far the largest component of transport spending needs is rehabilitation of the road network, which accounts for 87 percent of total transport needs (table 13). Moreover, as much as 70 percent of total road sector spending
## **Macroeconomic variables**

- **Interest rate risk** : Uncertainty in market rates is expected to translate into higher spreads as banks try to hedge interest rate risk by applying a risk premium. We use the NBT refinancing rate (in level) to proxy for interest rate risk and expect a positive sign for this variable.

- **Inflation:** This is a widely used variable to proxy for macroeconomic volatility. It is expected to have a positive relationship with net interest spreads because higher inflation introduces economic uncertainty that needs to be compensated for by higher margins. We use quarterly changes in the Consumer Price Index rate.

## **Institutional variable**

- **Legal environment** investor protection and bottlenecks in the credit infrastructure, especially insolvency and debt resolution. To reflect this feature in the empirical analysis, we use the Regulatory Quality index compiled by Kaufmann, Kray and Mastruzzi (2006). This is an index capturing perceptions of the ability of the government to formulate and implement sound public policies and regulations. The index, which ranges from –2.50 (weak) to 2.50 (strong) covers general aspects of government regulation in all areas of economy (not only banking) that permit and promote private sector development. A higher index value indicates better regulatory quality and is expected to be associated with lower margins; hence, we expect a negative sign for this variable.

Figures 5 to 14 provide an overview of the independent variables used in the analysis (quarterly banklevel data are weighted averages, with weights corresponding to banks’ total assets).

# IV. ESTIMATION RESULTS

We present the estimation results of Equation 1 in three steps. In the first step, we run the model on only the bank-specific variables (Table 1, columns 1–4). In the second step, we run the model, including the macroeconomic variables (Table 1, columns 5–8). In the third and final step, we run the
repeated values in the independent variables (which have annual frequency) without necessarily introducing new information<sup>17</sup> .

To compare firm level annual shipments with product level annual shipments (reported in Comtrade), the former are aggregated by product, year and destination. This procedure allows observing that the Tanzanian firm level datasets reports from 87 to 100 percent (depending on the year considered) of the exports recorded in Comtrade at the product level. For this reason, it is not necessary to clean or reduce the database.

For the analysis, the firm’s price is proxied by the fob unit value of every annual shipment, which is the value of shipment divided by quantity of shipment, at the firm-product-destination level. More ∑valuefpdt specifically, the fob price is calculated as: ~~.~~ This proxy for price has been largely used in ∑quantityfpdt previous literature, as actual prices are typically not observed. Since the value of the shipments in the dataset is fob value, it should be seen as the “farm gate” price, not including the costs for shipping, handling, storage, marketing, or the tariff paid in the final destination. Therefore, the analysis is conducted under the assumption that this price should only include the mark-up applied by the firm and its marginal costs<sup>18</sup> .

With regard to the firm, value, quantity and prices are the only information available. The dataset is then merged with destination and product specific characteristics such as, annual GDP, GDP per capita (from the Word Development Indicators Database); distance (from CEPII); and the Rauch classification<sup>19</sup> (to classify products as homogeneous and differentiated goods).

Before discussing the econometric analysis, some preliminary statistics are presented. Table 4 shows that the number of firms and products has increased constantly over the 2003-2009 period, while the number of destinations oscillates somewhat, but remains quite stable overtime. Also the average number
# **Struggling with the Rain: Weather Variability and Food Insecurity Forecasting in Mauritania**<sup>∗</sup>

Paul Blanchard Thibaut Humbert _The World Bank The World Bank_ Oscar Ishizawa _The World Bank_ Rafael Van der Borght _The World Bank_

**Keywords** : food security, drought, early warning system, adaptive social protection, climate vulnerability, probabilistic risk modeling JEL classifications: Q54, O55, I31

> ∗We are grateful to the Government of Mauritania and the World Food Programme in Mauritania for sharing the Food Security Monitoring Survey data. We thank Javier Baez, Nicola Fontana, Ruth Hill, Edmundo Murrugarra, Juan Carlos Parra, Emmanuel Skoufias, Eric Strobl, as well as participants to Trinity College Dublin PhD seminar for useful comments. We also thank Dieynaba Diallo, Matthieu Lefebvre, Franck Muller, Mira Saidi and Samantha Sarria for their comments and invaluable support. This work has been financed by the Sahel Adaptive Social Protection Program (SASPP) which is funded by a multi-donor trust fund (MDTF) with contributions from German Federal Ministry for Economic Cooperation and Development (BMZ); Agence Fran ̧caise de D ́eveloppement (AFD); Denmark Royal Ministry of Foreign Affairs; and the United Kingdom Foreign, Commonwealth, and Development Office (FCDO) and the Sub-Saharan Africa Disaster Risk Analytics Program which is funded through the Global Facility for Disaster Reduction and Recovery (GFDRR). This paper is a product of the Urban, Disaster Risk Management, Resilience Land GP. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author(s) may be contacted at pblanchard@worldbank.org or paul.baptiste.blanchard@gmail.com.
,760.8 per adult equivalent per year, which was deflated by using a mixed deflator based on CPI raw price data and survey weights from the original poverty line of January 2013 (GH₵1,314). The official poverty headcount ratio in Ghana in 2016/17 is 23.4 percent, where urban and rural poverty is 7.8 percent and 39.5 percent, respectively (GSS 2018). Gini coefficients are 41.6 at the national level, 36.5 in urban areas, and 40.5 in rural areas.

### _Price data_

For this study, the main price data sets we use are the market price survey data from GLSS7 and CPI price data.<sup>18</sup> The market price survey was collected from 398 clusters (or EAs in the GLSS7 data collection), of which 209 clusters are located in urban areas and 189 clusters are located in rural areas (Table 3). Those clusters cover 174 districts in all the 10 regions of the country. Out of 109 food items found in the GLSS7 consumption module, the market price survey has price information for 96 of them. The CPI price data are regularly collected from 44 markets, covering all the 10 regions of the

> 17 The previous rounds of GLSS surveys were conducted in 1987/88, 1988/89, 1991/92, 1998/99, 2005/06, and 2012/13.

> 18 Survey unit values cannot be calculated based on the GLSS7 consumption module due to the prevalence of nonstandard units and lack of conversion factors.

13
can be tracked across seasons, unlike plots. This is not the case for the plot-crop-level dataset which is at the _plot_ and _crop_ level.

- In both Mali and Niger, perennial crops were provided at the household and crop level. In order to retain this information to some degree, and for merging purposes, each household-crop pair was associated with a bespoke ID. These lines can be identified by cases where the plot link identifier begins with “missing_”. To limit confusion in the plot dataset, however, these observations were not taken into account.

_Construction of the harmonized household and individual datasets_ This section describes the HP household and individual datasets, which mostly contain variables deriving from the household questionnaires (see Figure 2). <mark>An important aspect of these datasets is that they include</mark> _<mark>all</mark>_ <mark>households that are listed in the household questionnaire cover module, regardless of their agricultural activities. They therefore include close to 30,000 households in the seven countries which are not in the plot-crop or plot-level datasets.</mark> The household-level dataset <mark>consists of variables from the household questionnaire that are key to analyzing livelihood and welfare outcomes. Included are access to electricity, household dependency ratios – defined as the ratio of individuals younger than 15 or older than 65 to the other members of the household, whether the household operates a nonfarm enterprise, and household size. Critically, the dataset also contains information on household consumption. The variables included are the total consumption value of the household in LCU and in USD and the consumption quintile of the household (where the quintiles are defined within country-waves). The consumption aggregates are provided in LCU by the data producers are part of the publicly available survey data. More information on the steps taken to calculate the consumption aggregate can be found in the surveys’ online documentation (see Table 1). We also compute a household asset index using a principal component analysis (PCA)
for supply chains trading more complex products.<sup>20</sup> Though the paper does not attempt to nail down the mechanism behind this somewhat unexpected finding, a plausible explanation is that firms that produce more complex products value supplier-specific relationships more and take great care to preserve them when faced with uncertainty and disruption.

The importance of relationship-specific investments in global supply chains, and international trade more generally, has been emphasized in the literature (see 2020 WDR for an overview). Seen through this lens, it is perhaps not surprising that the US firms that appear in the Panjiva data and trade internationally did not on average sever existing relationships during the pandemic. Furthermore, the additional demand for certain products, e.g., face masks or electric batteries, may have led them to seek new trading partners. At any rate, though supply side shortages due to lockdowns contributed to net separations, overall the extensive margin of trade resulting from the combination of supply and demand forces indicates--- contrary to popular claims---strong resilience during the pandemic, as shown in Figure 5.

This message is reenforced in a few other papers that explore the economic effects of the pandemic. Stumpner (2022) studies regional lockdowns in China and shows that with the exception of the Shanghai lockdown of April 2022, the other regional disruptions in China due to COVID had no effect on international trade. He concludes that regional lockdowns could <mark>still reduce aggregate trade, but only if they were exceptionally stringent and implemented in an economically important province such as Shanghai.</mark> Using data for 64 countries, Bonadio et al (2021) document a large contraction of economic activity (an average 29.6% drop of GDP) during the pandemic. But only a small fraction of this contraction (ca. 23%) can be attributed to foreign shocks that are intermediated through final and intermediate goods trade. Moreover, simulations based on a global network model indicate that global supply chains alleviated the
Policy Research Working Paper 10936

# **Abstract**

This paper discusses the inclusion of usufruct rights in household wealth estimates, emphasizing the theoretical rationale for adjusting traditional estimates that do not include this component. It then applies this concept to the Italian context using Survey on Household Income and Wealth data. Despite usufruct being relatively rare (around 3 percent of residential homes), adjusting for it impacts the wealth distribution, with some increases characterizing younger households. Overall, adjusted estimates show a significant decrease in the concentration indices compared with unadjusted ones. These findings suggest the importance of considering this component in standard household wealth estimates, particularly in countries where use rights are more common.

This paper is a product of the Development Data 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 gdalessio@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
search assistance and training (140). Conversely, public works reforms were the least common among all ALMP reforms, but they were the second-most popular reform in low middle- and low-income economies (Figure 8a).

**Figure 7. Number of UB policies during the Covid-19 pandemic**

<!-- Start of picture text --> a. By policy and income b. By region<br>44 Increase in the amount of UB 58<br>45 Increase in the coverage of UB 60<br>40 Increase in the duration of UB<br>50<br>35 Change in the application process of UB<br>40<br>30 25 30 29<br>25 22 30 24<br>20 17 20<br>9 9<br>15 10 9 10 10<br>10<br>3 4 4 3 4 3 0<br>5 1 0<br>0<br>HIC UMIC LMIC<br><!-- End of picture text --> Source: World Bank's Social Protection and Jobs Policy Inventory (SPJPI). Data cover March 2020 – January 2022. Note: LIC did not have any UB reforms captured by the SPJPI.

**Figure 8. Number of ALMP programs during the Covid-19 pandemic**

<!-- Start of picture text --> a. By program and income group b. By region<br>Adjustments in labor regulations<br>177<br>180 Wage subsidies 250 233<br>160 Job search assistance and training<br>141<br>Public works 200<br>140 125 168<br>148<br>120 150 132<br>110<br>100<br>78 100<br>71<br>80<br>61 49<br>60 42 50 25<br>34 33<br>40 27<br>17 20 19 0
extrapolation but it recognizes that the degree of containerization is limited and the growth will eventually start to decrease. This drives the choice of the functional form to a semi-logarithmic function (i.e. the degree of containerization (Y) is explained by the logarithm of a time trend (T): Y=.61+.04 InT) but the specific parameters of this equation are picked by experts rather than simply estimated analytically from past data . Jointly with the first step, it yields an overall regional containerization forecast which is an average figure since the experts recognize that its evolution will vary for the various departure/destination combinations. This is then used to generate a traffic forecast for containers in the corridor. In 1996, it was expected to increase from 140.6 million tons in 1995 to 184.5 in 2000 and 227.5 in 2005.