rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Policy discussion**

Containing public debt vulnerabilities has typically led to a policy discussion that puts a lot of emphasis on debt management issues such as: (a) policies/mechanisms to enhance debt-capacity building and transparency —enhancing procedures of public debt recording, monitoring and reporting to avoid “debt surprises”— and (b) strengthening debt management practices —where the World Bank can play a role in disseminating best practices in the management of risks associated to currency and interest rate fluctuations, refinancing, contingent liabilities, and SOE debt, among others.

Debt management, however, is only one dimension of a more holistic strategy to reduce debt vulnerabilities. That strategy should include: (a) practices that help manage the risks of the balance sheet of the government —including public assets and non-debt liabilities (contingent liabilities), and (b) policies that foster the efficiency of public investment.

## _Building debt management capacity_

Several countries in the region have a narrow coverage of public debt data (for instance, data on domestic arrears and contingent liabilities of state-owned enterprises) and limit data transparency. The lack of data and/or transparency hinders the debt sustainability analysis and the assessment of fiscal risks, thus elevating the probability of debt surprises (e.g. Mozambique, Republic of Congo, and Zambia). Resorting to borrowing from non-traditional creditors has also led to opacity on the terms and conditions of loans (especially, collateralized loans).

In this context, international financial institutions can provide technical assistance and develop tools to build capacity to record, monitor, and report debt in Sub-Saharan African countries. The World Bank should continue improving upon its tool to monitor a country’s capacity and performance on debt recording, monitoring and reporting —that is the Bank’s Debt Management Performance Assessment (DeMPA). There is also scope to enhance accessibility and user friendliness of the public debt data, and encourage countries to improve reporting and compliance with established statistical standards.

There is concern of greater
the household survey, it appears that the household survey under-represents the top earning households, particularly the top earning senior executive households.

Similarly, in Vietnam the top salaries recorded in their household survey are less than half of average executive salaries obtained from corporate salary surveys (World Bank, 2014). In the case of Argentina, Alvaredo (2010) finds that while the tax data have almost 700 observations with incomes exceeding 1 million USD, there are none in the Argentine household survey. In a comparison of 16 Latin American household surveys, the ten richest households have incomes similar to a managerial wage, which is arguably substantially smaller than the incomes of top capital owners (Sz ́ekely and Hilgert, 1999).

# **4 Empirical application**

This section presents our empirical application to Egypt. As outlined in the methodology section we combine data on household expenditures with data on house prices. The household expenditures are obtained from the 2009/10 Egypt Household Income, Expenditure and Consumption Survey (HIECS), which is also used for Egypt’s official estimates of poverty and inequality. The house prices represent listing prices for houses that have been put up for sale via two large real estate firms operating in Egypt. We use the real estate database to estimate the top end, defined as the top 5 percent, of the income distribution. The “bottom” 95 percent of the income distribution is estimated using the HIECS.

The following practical decisions and assumptions are made: (a) we restrict the analysis to urban Egypt only (this can be extended to apply to all of Egypt under the assumption that rural households do not rank in the top of the income distribution in Egypt), (b) it is assumed that house price quotes are proportional to (imputed) rental values (as the household expenditure survey contains data on rents only, and we rely on the survey to identify the relationship between house value and household income),<sup>17</sup> (c) it
context, where we find that -0.5 is close to the optimal rate for Africa.

Figure 5 shows the differences in PPD worldwide by country; where within each country we take a weighted average of each city’s PPD. Blank areas are countries without cities in the data set. It is clear that African countries, in general, have very high PPD, as well as parts of South and East Asia. The question is whether that is just from high overall population density. We use simple regressions with dummies for regions of the world to answer that. Our regression results are presented in Tables 1a and 1b, where the top panel of each table gives the basic results controlling just for city ruggedness from Nunn and Puga (2012) and will represent what the raw data tell us. The bottom panel additionally controls for GDP per capita from the Penn World Tables (PWT 7.0), to see the extent to which differences in levels of development explain the patterns.

13
in the boundary over time or survey representativeness is different, efforts are needed to maintain a long panel of data to have comparable statistics spatially and over time. Such efforts could be (1) regroup areas to a new area that matches the previous definition of areas, or (2) a higher level of geographical disaggregation over time. In this version of the panel data, on average a country has data for 14 geographical areas over the period of 3 years.

We also employ poverty data from different sources available at the country level and subnational level. The first is taken from the World Development Indicator (WDI) which provides different measures including the poverty headcount ratio, poverty gap, and number of poor at both international and national poverty lines. Our measures of interest are poverty headcount ratio at US$1.90 a day. It is calculated by the percentage of the population living on less than $1.90 a day at 2011 international prices. For richer analysis, we also use other poverty lines including the poverty headcount ratio at $3.20 and $5.50 a day.

As an alternative source of subnational poverty, we exploit the annual GRP data provided by Kalkuhl and Wenz (2020), which is available from 1981 to 2016 for more than 1,500 regions in 77 countries worldwide. The dataset, however, includes only a few countries in Africa. We calculate the incidence of poverty by assuming the poverty line of $1.90, $3.20, and $5.50 for all countries in our sample.<sup>14</sup> We also exploit annual gridded datasets for GDP per capita (PPP) from Kummu _et al._ (2020) which covers 26-year period from 1990 to 2015 for 82 countries. In this dataset, each grid cell is recorded at 5 arc-min resolution. We then apply a similar exercise as in the dataset of Kalkuhl and Wenz (2020) and measure the incidence of poverty at different thresholds.
positive effects (see Ottaviano et al., 2018 and Kerr et al., 2015 for examples). However, most of these studies focus on the impacts of highly educated migrants in developed countries with small informal sectors. The effects of migration on firm outcomes are also sizable in these studies. Ottaviano et al. (2018), for instance, find that a one percentage point increase in the share of immigrant workers in the local labor force produced a 2.8 to 3.4% increase in firm exports and a 3% effect on labor productivity. The sign difference between their results and ours may be explained by the type of migrants we are analyzing. Particularly, highly educated individuals are typically absorbed by the formal sector whereas IDP are predominantly absorbed by the informal sector.

Overall, our estimates suggest that larger IDP inflows are detrimental for the intensive and the extensive margins of production of formal firms in Colombia. We explore the potential mechanisms driving these effects in the next section.

# **V.2 Effects on prices**

We next explore the effects of IDP inflows on firm prices. Considering that IDP are a population shock, it is plausible as was pointed earlier that they may be impacting output or input prices directly through an increase in consumption; that is, through an aggregate demand shock in output and input markets. We consider this possibility by estimating equations (1) and (2) using the average sale or purchase value of all outputs and inputs used or produced by all formal firms in the manufacturing census between 1995 and 2010. All estimates include firm and year fixed effects as well as product fixed effects, which correspond to the four-digit classification of the International Standard Industry Classification (113 four-digit codes). The results of this exercise are presented in Table III. Although some of the results are statistically significant for nominal output prices, these effects are not consistent and disappear once a more robust set of controls is included in column (3). Consequently, our results suggest
received little empirical attention. Fourth, the gender dimension of temporary migration and its gender-differentiated effects, for example on adult household members and children left behind, is an area that remains significantly understudied via rigorous causal analysis.

Another important limitation of available evidence on the impacts of temporary migration on origin countries is that it is largely based on static models and analysis. While providing very useful insights, cross-section data and analysis do not shed adequate light on the dynamic effects of temporary migration. These dynamic effects are expected to impact workers and their families’ entire life cycle, and to differ prior to, during and after the migration episode. Recent papers attempt to identify some of these time-varying effects, but rigorous answers to these questions require a dynamic modeling approach with granular data on employment and migration history. For example, when looking at the impacts of temporary migration on the outcomes of household members left behind, the analysis would greatly benefit from having information on the full employment and migration history of temporary migrants, but also of other household members.

For policy purposes, the main drawback of the non-experimental reduced-form approach to temporary migration is its inability to examine the impacts of policy changes on the welfare of migrants themselves and of the origin country more broadly. In contrast, a more structural and dynamic approach allows to model the complex linkages between the decisions and outcomes of migrants at different stages of the migration life cycle. This allows the researcher to simulate the impacts of a change in policy parameters on the migration decisions and outcomes of workers and to estimate impacts on specific aggregate variables of interest in the origin or destination country. For example, Bossavie, Gorlach, Ozden, and Wang (2021) simulate the impacts of changes in migration recruitment costs, domestic loan interest rates, and information provision on emigration rates, migration duration, repatriated savings and the extent of business creation in Bangladesh.

Existing microdata typically capture temporary migrants at one point in time in one location (abroad or at home after return), while a dynamic analysis of
by increases in the price of fuels. They also note that the NBR results are consistent with their general equilibrium results under the assumption where land is fixed, namely that it is not easy to change allocation of land between crops, which is likely to be the case in the short term.

In addition to the Arndt et al (2008) study a few other researchers have examined related aspects of the food price spike in the Mozambique context. Nhate, Massingarela and Salvucci (2013) examine the price transmission from international markets to Mozambique, the degree of integration across various markets in the country, and the political economy of the government policy responses to the 2007-08 price rise. They find that domestic prices for rice and wheat are only partially integrated with the international prices, and that the domestic prices remain high even after international prices had declined. Within the country, they find that the prices in the largest market in Maputo, the capital, transmit partially to some markets (Nampula), but not others (Beira).

Minot (2014) also undertakes a price analysis of agricultural commodities in 11 countries, including Mozambique. The analysis covers the period from January 2003 to December 2010, and includes beans, cooking oil, maize, millet, rice, and sorghum. The author notes that the retail prices in some of the markets experienced high volatility in the lead-up and during the food-price spike, particularly maize, though it is lower than in countries where the governments actively attempted to intervene in the markets in an attempt to stabilize prices.

# **3. Data and descriptive statistics**

# **3.1 Overview of the data**

This paper uses data from two main sources: (i) the 2014/15 round of the national household income and consumption expenditure survey, the Inquérito sobre Orcamento Familiar (IOF), conducted by Mozambique's National Institute of Statistics and (ii) farm level data from the 2015 Integrated Agriculture Survey (IAS) done by the Ministry of Agriculture
<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>UA RU EE LV PL RO SI SK CZ BG HU DK NO FI SE ES GR IL IT CY PT BE CH DE UK FR NL IE AT<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% over-qualified<br><!-- End of picture text -->

**Figure 11 Over-qualification among formal and informal workers, 2004-2009.** _Source:_ Calculation with ESS data.

**_Sectoral composition._** Previous research on undeclared work (Riedmann and Fischer, 2008: p.23; Williams and Renooy, 2008: p. 9-10; Pedersen, 2003: Figure 4.2)  has revealed construction, household and personal services, trade, and hospitality as the most popular activities among informal workers, as well as the ones with the highest proportions of all workers involved in undeclared work; yet the same studies suggest that countries and European regions might substantially differ from each other in terms of sectoral distribution of undeclared work. For instance, Williams and Renooy (2008) report that construction activities account for only 3% of undeclared work in Southern Europe, whilst in the Nordic countries this proportion is 27%. However, given the sensitive nature of the questions and relatively small samples in the underlying surveys, country-specific findings should be subject to caution. ESS-based results, reported in Table 6 by categories similar to the ones used in Williams and Renooy (2008), suggests a much smaller geographical variation in sectoral distribution of informal work; in particular, the share of construction is about 10% in the South and 13% in the North. On the other hand, within European regions we find substantial differences between undeclared employees and informal self-employed. In particular, the former are much more concentrated in education and
a different Local Government Area (LGA) within the country or in a different country.<sup>37</sup> Table 1 reports simple descriptive statistics that show how to perform some preliminary back-of-theenvelope calculations to assess and address the ‘rare event’ issue in household surveys. A ‘migrant individual’ is an individual that was present in the previous interview but has now moved either internally (at least to a different LGA) or internationally. A ‘migrant-sending household’ is a household where there is at least one migrant individual in a given survey wave. In the case of the NUPD there is a mere 3 percent of migrant individuals, implying that migration is a statistically rare event in the individual sample. Conversely, almost 20 percent of households sent at least one migrant, meaning household-level migration is roughly 7 times greater than individual-level migration. The household-level share cannot be considered a ‘rare event’ outcome if we use a rule of thumb of 10 percent threshold for the underrepresented class.

**Table 1: Migration-related descriptive statistics for Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|Migrant individual|0.030|0.170|0|1|121,690|
|Migrant-sending household|0.195|0.396|0|1|19,249|
|Household size|6.322|3.451|1|34|19,249|

This simple exercise makes an important point: it is almost always true that there is a ‘rare event’ issue in standard household surveys for the study of (climate) migration, but only when looking at _individual_ migrants. Shifting to the household as the unit of analysis,
and their small land area means that a single disaster can have a systemic impact.

The main type of climate-related natural disasters<sup>3</sup> which affect most SIDS—both in terms of incidence and especially in terms of the economic damage caused—are tropical cyclones, which accounted for 95 percent of the total damages from climate-related natural disasters that SIDS in the Caribbean and Oceania suffered from 1995 to 2022 as recorded in the EM-DAT. SIDS are also affected by riverine floods and droughts, although the economic damages they cause are generally much less than those caused by tropical cyclones.<sup>4</sup>

> 3 These are classified as meteorological (e.g. tropical cyclone, invective storm), climatological (e.g. drought, wildfire) and hydrological (e.g. landslide, riverine flood, coastal flood, flash flood) in the EM-DAT.

> 4 Some SIDS are also highly exposed to geological hazards such as earthquakes and volcanic eruptions. These risks also need to be incorporated into the disaster risk planning and management of governments and the private sector.

4
0.00270)|(0.00145)|(0.0150)|(0.0188)|
|VIX Index|-0.0588|-0.384*|-0.00399|0.0775|-0.00540|-0.0278*|-0.0494|-0.392*|
||(0.0499)|(0.203)|(0.0229)|(0.102)|(0.00765)|(0.0162)|(0.0425)|(0.224)|
|US Economic Policy|0.00170|0.00490**|0.000684|-0.000951|-0.000163|0.000230|0.00118|0.00503*|
|Uncertainty|(0.00388)|(0.00241)|(0.00178)|(0.00100)|(0.000594)|(0.000164)|(0.00330)|(0.00266)|
|Commodity Terms of|-0.276|0.0157|-0.228|0.0257|0.0466|0.00354|-0.0950|-0.00337|
|Trade (in logs)|(1.384)|(0.0927)|(0.635)|(0.0367)|(0.212)|(0.00604)|(1.178)|(0.102)|
|Oil Price|0.0933|-0.328|0.00481|0.0656|-0.00990|-0.0200|0.0984|-0.323|
|(in logs)|(0.104)|(0.239)|(0.0478)|(0.101)|(0.0160)|
Figure 8: Political Influence Concentration Index and Poverty Headcount Index <!-- Start of picture text --> EAS ECS LCN<br>MEA SAS SSF<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Poverty Headcount Index<br>1<br>)Π<br>.9<br>.8<br>.7<br>.6<br>Political Influence Concentration Index, I(<br>.5<br><!-- End of picture text -->

# **5.3 Linking Arithmetics with Politics**

Figure 9 shows the relationship between the Arithmetics of DRM, as measured by _I_ ( _P_<sup>_δ_</sup> ) (with _δ_ = _∼p_ in the left panel and _δ_ = _r_ in the right panel), and the Politics of DMR, as measured by _I_ (Π). In particular, the vertical and horizontal lines correspond to the average value of the two indices among the analyzed countries, dividing the space in four quadrants. The West quadrants in each figure correspond to the set of countries where there is lower capacity to mobilize resources for poverty eradication. The South quadrants in both figures, on the other hand, define the set of countries which are more likely to enact some redistribution, thanks to a lower concentration of political influence.

Among those countries with a poverty eradication capacity close to one, where a small transfer from the non-poor or the rich would be enough to solve poverty, we expect those in the South-East quadrant to have more difficulties in actually mobilizing resources than those in the North-East quadrant, due to the higher concentration of political power. This should be even more striking when, under redistribution rule (ii), only the rich are responsible for eradicating poverty, since the optimal marginal tax rate would be higher, and this would foment stronger opposition. When we check the
temperature and precipitation within each day could include important information to identify extreme or unusual weather conditions (Deschênes and Greenstone, 2007; Fisher et al., 2012; Schlenker et al., 2006; Schlenker and Lobell, 2010; Schlenker and Roberts, 2009).

# **2.3 Regression analyses**

In the literature there are two econometric approaches used to estimate the income effects of weather variation (Dell et al., 2012; Deschênes and Greenstone, 2012, 2007; Fisher et al., 2012; Mendelsohn et al., 1994): Differences between households and locations are estimated from a cross‐section of data observed at one point in time following a Ricardian approach. Alternatively, changes over time are estimated from panel data observing the same households or locations at various points in time.

The data set used for this study allows estimating the following regression model: where Y denotes per‐capita income observed for individual _i_ in commune _j_ in year _t (i.e._ 2010, 2012, 2014 _)_ . W measures weather conditions n commune _j_ in year _t_ using five sets of weather variables as described in section 4. _β_ is the parameter of interest that indicates the income effects of weather variation. _X_ is a set of household‐specific controls that vary over time, such as education, labor and land endowments.<sup>5</sup> _T_ measures time‐fixed effects to neutralize common trends over time. _Z_ includes commune‐specific effects that do not change over time. _U_ is a random, idiosyncratic error term.

A particularity of this data set is that for some households information is available for two or three survey years. These households form a _Panel_ data set. Other households were only observed in one of the three years. Using this cross‐section data as well and treating all observation as independent observations provides a _Pooled_ data set.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias (