rafmacalaba/data-use-mentions-extended huggingface dataset:
using data on real local currency GDP from the World Bank (2007), World Development Indicators.

Following Calvo, Izquierdo and Mejia (2004) and Calvo, Izquierdo, and Loo-Kung (2006) we keep in our sample only countries that are integrated to global financial markets (emerging and developed economies) since the literature on sudden stops focuses on reversals in private capital flows that are likely different to the fluctuations in aid flows and remittances that constitute an important part of capital flows to poorer countries.

The sample of 47 developed and developing countries considered in this study, and the detailed list of episodes of capital account reversals, recessions, and sudden-stops experienced by each of them are reported in Table 1 and summarized in Figure 1. Most of the countries in the sample have experienced a recession during 1975-2003, and almost all emerging economies considered have experienced a capital account reversal. Only two developed countries that have experienced no recession during these 28 years (Austria and Norway). Two emerging economies have experienced capital account reversals but no recession according to our definition (Egypt and Pakistan). Four emerging economies experienced recessions but no capital account reversal a la Guidotti, Villar, and Sturzenegger (2004) (Algeria, Colombia, India, and South Africa). As noticed by other authors (see Calvo, Izquierdo, and Mejia, 2004), there is clustering of these three types of events around the Latin American Debt Crisis, the Mexican Tequila Crisis, and the Asian ad Russian Crisis (see Figure 1). However, these are clearly not the only events in the sample, since as noticed in the literature (see Guidotti, Villar, and Sturzenegger (2004) and Rothemberg and Warnock (2006), among others) sudden stops are common. In fact, since 1980, there are only four years where no country in our sample of 47 was experiencing a sudden stop episode.

13
)|
|Union|0.591***|0.103***|1.128***|0.844***|
||(0.125)|(0.010)|(0.135)|(0.054)|
|Urban|0.322***|0.043***|0.294***|0.265***|
||(0.053)|(0.008)|(0.050)|(0.075)|
|ln(GDP/capita)|0.033|0.016|0.037|0.105|
||(0.204)|(0.011)|(0.263)|(0.177)|
|Adj. _R_<sup>2</sup>||0.487|||
|Pseudo-_R_<sup>2</sup>|0.185||0.343|0.345|
|Number|6,375,826|6,375,826|6,375,826|6,256,021|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors clustered at the country level are in parentheses. All specifications include year, country, and sector fixed effects. Significance: *** 1%, ** 5%, * 10%.

27
# **VI. Conclusions**

In this paper we have used a unique set of surveys measuring cognitive and noncognitive skills in low- and middle-income countries to examine differences in earnings between men and women. Focusing on seven low- and middle-income countries (Armenia, Bolivia, Colombia, Georgia, Kenya, Ukraine, and Vietnam), our objective was to determine if these newly available skills measures could explain the gender wage gap beyond what is explained by traditional measures of schooling and experience. While gender wage discrimination has been extensively studied in various contexts, our use of measures capturing different dimensions of human capital extends what we know about the gender wage gap in developing contexts and contributes to our understanding of the role of cognitive and noncognitive skills in research related to labor market outcomes. The findings from this paper are summarized next.

Mean decompositions with and without occupation controls can be indicative of the role of sorting in earnings differences between men and women. Controlling for occupation, we find that in Vietnam, openness to experience and emotional stability are important factors in explaining gender wage gaps. Our decomposition results indicate that men receive a reward for scoring higher, on average, on openness and emotional stability traits. The sorting argument would suggest that men sort into occupations that require the appearance of more openness and emotional stability. Controlling for occupation, however, we can infer that sorting perhaps does not explain the entire difference between men’s and women’s returns to noncognitive skills in Vietnam. Mueller and Plug (2006) found similar results for men with lower agreeableness scores than women in the United States, and Nyhus and Pons (2005) found that emotional stability scores positively affect wage setting for women. Our mean decompositions also show some evidence for the influence of cognitive skills in Georgia and Kenya.

Findings from the quantile decompositions indicate that the wage gap follows a “sticky floor” pattern in the Latin American countries as well as Kenya and Vietnam but follows a “glass ceiling” effect in the ECA countries. Previous
development practitioners have been faced with an acute lack of data and evidence on how to design and implement support policies. This lack of evidence is especially acute in developing countries. This paper addresses this gap by providing novel evidence using recently collected firm-level data covering more than 120,000 firms across 60 (mostly developing) countries.

The paper’s results show that there are significant gaps to be addressed in order to improve the reach, targeting, and effectiveness of policy support. Smaller firms, especially those owned by women in sectors such hospitality, are facing some of the largest declines in sales _and_ the most limited access to policy support – raising concerns about widening inequality. Similarly, the likelihood of receiving support for firms in poor countries is several times less than for similar firms in highincome countries. While governments appear to have prioritized minimizing exclusion concerns over strict targeting in the earlier stages of the pandemic, this has resulted in a large number of firms benefiting from public assistance without having experienced any adverse COVID-19 shock – an issue that will demand more attention as fiscal space becomes more constrained. Lastly, there is indicative evidence that some types of policies (i.e. liquidity injections direct or through credit and wage subsidies) have been successful in mitigating liquidity constraints and reducing layoffs – but much more rigorous analysis will be needed to provide more precise guidance to policy makers.

Going forward, we see four main avenues for future research. First, understanding better how firms manage to receive public support and the extent to which connections may explain access to public resources. Second, our results so far present some novel associations but we limited ourselves to mainly presenting conditional correlations. Future research, relying on additional data collection and stronger identification strategies, should address more carefully the question of the effect of receiving public support on subsequent firm results. Third, some of the policies being implemented may have important spillover effects, especially when targeted firms play an important role in supply chains and production, which will be an important area for future
average firm will run out of cash in about 10 weeks.

<!-- Start of picture text --> Figure 5: Mean Survival Time based on Fixed Costs by Country<br><!-- End of picture text -->

**Figure 5: Mean Survival Time based on Fixed Costs by Country**

<!-- Start of picture text --> Ukraine<br>Kazakhs ...<br>Greece<br>12 16 20 24<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> 28<br><!-- End of picture text --> <!-- Start of picture text --> 32<br><!-- End of picture text --> <!-- Start of picture text --> 36<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> _Note_ : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345 <!-- Start of picture text --> 40<br><!-- End of picture text --> 11
|0.395|1,740**|0.345|
|100% bandwidth|618|0.278|13,900|0.185|702|0.107|
|200% bandwidth|754|0.322|3,718|-0.058|276|0.056|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

20
<mark>UNHCR policies guiding targeting of assistance to Syrian refugees have changed since 2013/14, and therefore policy recommendations for Syrian refugees in Jordan cannot be based on the findings presented here. Rather, our study shows that it is possible to apply a gender lens to available data and produce results that quantify differences between male and female headed households’ incidence of poverty and identify some of the demographic characteristics that are linked to greater poverty risk. Moreover, our findings indicate that it is of utmost importance to distinguish between different types of female and male principal applicant households to understand poverty in the setting of the Syrian refugees in Jordan.</mark> More generally, our findings suggest that unless gender disadvantage is considered in the design of development policies to replace humanitarian assistance the poverty reduction gains it achieves may not be sustained. In the short‐term it will be important to build the capacity for women to move into the labor market so that they can have access to economic opportunities which can replace the value of assistance or the advantages that an adult male brings to a household. Including cash transfers and other types of social protection that can reach the households that are especially vulnerable – unaccompanied children, siblings and single caregivers‐ is equally important. Finally, the strong protective role that education plays underlines the importance of ensuring remedial action is taken so that all children and young adults who have missed education due to displacement can make up the schooling they have missed.

<mark>Finally, although the UNHCR data enabled us to examine some of the impacts that gender differences have on household poverty, there are some important data gaps. The JD‐HV data show the proportion of both male and female principal applicants who are employed and do daily or irregular work. However, employment details of other members of the household and more detailed gender‐disaggregated data on labor market participation (that includes the numbers of days/hours worked, daily or hourly earnings, sector of employment, and type of work) are needed
than 500 meters were omitted, as well as the roads serving less than 0.01% of the total rural population.

**Step 2** : The network of connected roads is then overlaid with the population layer, and the number of people living within 2km of each tertiary road or track is calculated. Tertiary roads and tracks are then ranked according to the number of people per kilometer of road.

The first road from this list is recorded and marked as upgraded to all-season, the new RAI is calculated, and the algorithm starts at Step 2 again, until no more improvement to accessibility can be achieved given the restrictions outlined in Step 1.

# **3. Results and discussion**

## **3.1. Global Rural Accessibility Index estimates**

Results for rural accessibility are displayed in Figure 3. While most European countries have an RAI close to 100 percent, most developing countries are below 60 percent, all Sub-Saharan countries are below 51 percent, and 24 countries are below 20 percent.

> 3 Data were considered good enough if the completeness score on Open Street Map was higher than 75 percent.

7
pandemic and early recovery.

The remainder of the paper is structured as follows. Section 2 provides an overview of the existing evidence and simulations on the distributional impacts of the COVID-19 pandemic and a conceptual framework that draws out the implications for inequality of the initial impacts and of the recovery patterns. Section 3 provides details of the HFPS data that the bulk of the empirical analysis relies upon, as well as other sources of data, such as the Oxford Policy Stringency index, and data on the size and targeting of government policy response packages. Section 4 presents the main findings of the analysis of the distributional patterns of the initial impacts of the pandemic for the countries in the HFPS sample. Section 5 builds on the patterns observed in the HFPS data to present a first account of the global inequality impacts of the pandemic, based on a simulation exercise. Section 6 examines the distributional patterns associated with the early recovery by linking them to the evolution of the policy stringency measures. Section 7 concludes with some reflections on the longer-term impacts and the importance of having an equity lens for the building back better policy agenda.

# **2. How COVID-19 can amplify existing inequalities, with potential longer-term impacts**

The impact of COVID-19 on the global income distribution is driven by its effects on inequality both _between_ and _within_ countries. On between-country inequality, simulations based on economic growth projections as well as the evidence from HFPS and other phone surveys suggests that household level socioeconomic impacts have been larger for poorer countries, widening the divide between households in rich and poor countries.

# 2.1. Impact on between-country inequality

The incidence (or distribution) of income growth across the global income distribution can be simulated using projected GDP growth rates for countries, under the assumption of no change in inequality within countries since 2019. This assumption is necessary since income distribution within countries has not been observed in the data since the pandemic. Figure 1 shows the results based on simulations using distribution3
Finally, we explore whether distance and wealth have different effects depending on whether the potential destination is urban or rural (column 6). To do so, we include a dummy variable indicating the urban status of the destination as a regressor, as well as its interactions with distance and wealth. Urban destinations are substantially less likely to be chosen, but controlling for whether the destination is urban or rural, we do not see any differential effect in the pull effect of the destination’s wealth. The deterring effect of distance also remains, but is twice as strong for rural than for urban destinations.

We explore these differences between urban and rural migrants further by altering how we form the dyads. In Table 5 we link all urban migrants to all 47 urban locations. For urban migrants selecting urban locations distance and wealth are both significantly associated with destination choice, with coefficients roughly of equal absolute magnitude. At a per standard deviation change the absolute magnitude for distance (sd=0.91 in this dataset pairing urban migrants with urban destinations) is 1.6 times greater than that for wealth (sd=0.50). In Table 6 we link all rural migrants to all 31 rural locations. Again, both distance and wealth matter, but expressed in standard deviation change (sd=1.03 for distance and a much lower sd=0.20 for wealth in the rural-rural dyadic dataset) distance outweighs wealth by a factor of 7.5.

For urban migrants, interactions with poverty and educational status at the outset further show that the pulling power of urban destination wealth is reduced for poorer households. Better educated individuals, on the other hand, tend to find wealthier urban destinations more attractive than lesser educated individuals and also tend to be less deterred by distance. Nonetheless, the deterring effect of distance remains, even for highly educated individuals (Table 5). It is only by around three years of postsecondary education that distance loses its effect completely. Urban migrants with such high levels of
question but reports helping without pay in a household enterprise or farm in the secondary employment status question. She should be coded as active. This is the same adjustment shown for Uganda in Table 3 which compared LFPR based on primary activity only to the LFPR including secondary activity.

- b. _Use additional information from the survey to verify and recode those who still remain inactive_ . Where there is a household enterprise module, identify household members working in the enterprise. If they have been coded as inactive, recode them as active, and their primary activity as a contributing family worker in the nonfarm enterprise sector.

- c. Use the relationship to head information to code all domestic servants as _economically active, and as wage workers_ .

- d. For those who are still inactive, look for _time use or household chores modules_ . Code as active those who reported economic activities in either the detailed household chores or time use modules (e.g. activities such as agriculture, or fetching water or wood) _._

- e. Identify agriculture households (i.e. where at least one household member reports agriculture work) and code all “inactive” women who reported ‘domestic duties’ as reason for inactivity as _active_ , in the agriculture sector. Mostly likely, they did participate, but were doing unpaid and unrecognized work, and this should be captured.

- f. For those still coded inactive or missing use the education, health, and age information to verify reasons for inactivity.

> 19 Many surveys check for participation in household chores unpaid but considered “economic” (such as fetching water) in a separate section on household chores, not in the screening section.  This approach works as long as the analyst is careful to include this information when constructing indicators of LFP.

22
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 3_ survey data, for instance by leveraging high-frequency phone interviews to collect data to inform developmental responses to emergencies (Hoogeveen and Pape, 2019). In practice, a rapid survey system hardly produces complete data, particularly when deployed in difficult settings such as during economic turmoil, conflict, or natural disasters, when it is particularly important to understand possible drastic shifts in economic variables. A great deal of innovation is aimed at the question of how to design such surveys to ensure sufficient response rates, and how to process the data to produce correct estimates (Pape and Wollburg, 2019; Pape, 2021; Khamis et al., 2021).

To overcome some of the challenges with high-frequency surveying, Andr ́ee (2021) developed an approach for real-time imputation of ongoing surveys. Specifically, the paper proposed a matrix-completion algorithm based on multiple machine learning models that simultaneously estimates missing entries using information contained in other responses. This works well when the survey tracks multiple correlated variables, and is specifically suitable to impute a price index, as prices of different goods are typically interrelated. The imputation can be applied in high-frequency settings in which only incomplete and intermittent data can be collected.

The aim of this paper is to continue the line of investigation into the ability of machine-learning techniques to impute ongoing surveys in near-real-time and produce continuous data that yield insights into the evolution of possibly fluid events in data-scarce contexts. The paper focuses on a challenging nowcasting objective. It attempts to track inflation in fresh produce prices at the local market level in Papua New Guinea (PNG) using monthly survey data obtained from the International Food Policy Research Institute (IFPRI). The application is made particularly challenging by high intra-month price volatility in fresh produce items, low cross-market price correlations owing to a lack of overall market integration, and weak price trends. The application cross-validates the imputation strategy under different designs in terms of numbers of markets, food items and time periods covered, and shows that when the survey
Policy Research Working Paper 6032

# **Abstract**

Albania’s radical farmland distribution is credited with averting an economic crisis and social unrest during the transition. But many believe it led to a holding structure too fragmented to be efficient, and that public efforts to consolidate plots are needed to lay the foundation for greater rural productivity. This paper uses farm-level data from the 2005 Albania Living Standards Measurement Survey to explore this quantitatively. The analysis finds no support for the argument that fragmentation reduces productivity. However, producers fail to utilize about 10 percent of the country’s productive land, and, in the majority of cases, this land has been idle for at least five years. Farmers quote inefficiently-small plots as the reason for this in few cases, casting doubt on the scope for land consolidation to solve this issue. Instead, the data are consistent with the notion of land market imperfections, which can be traced to gaps in the legal and policy framework, as well as inefficiencies in registry operations, leading to land abandonment on a large scale. To maintain the productive potential of Albania’s rural economy and, if and when needed, the ability to conduct consolidation in a cost-effective and sustainable manner, it will be critical to complement the emphasis on consolidation with an effort to address those gaps and inefficiencies on a priority basis.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org, gcarletto@worldbank.org; sara.savastano@uniroma2.it.

_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