rafmacalaba/data-use-mentions-extended huggingface dataset:
3 on different income groups in urban and rural areas. Section 7 presents some possible options for reforming Egyptian food subsidies, and section 8 concludes.

# 2. Role of Food Subsidies in Developing Countries

In the developing world food subsidy programs are used to sell and/or make available certain food items to consumers at below-market prices. These programs can be used to pursue a variety of objectives. In some countries food subsidies are designed as a form of partial wage compensation to public sector employees, including military personnel. In other countries food subsidies are used to try to improve the nutritional level of certain "at risk" groups, like pregnant mothers or school children. Finally, in some countries food subsidies are used to raise the income and nutritional status of larger - and perhaps more ill-defined groups - such as the poor.<sup>4</sup> A key to the effectiveness of all these programs is the degree to which they are able to deliver subsidized food to their intended target group (i.e. pregnant mothers, the poor, etc). Indeed., the whole notion of targeting requires some sort of mechanism by which the general population can be "screened" in order to determnine which individuals should receive food subsidies, and which individuals should not.

In mosl general terms, the screening of individuals can occur in one of two ways: either through administrative targeting or through self-targeting. The first alternative - administrative targeting - typically uses some government agency or bureaucracy to select the beneficiaries of food subsidies. For example, government agencies sometimes select beneficiaries on the basis of income. However, in many developing countries it is often quite difficult to screen individuals on this basis because accurate income data are
two different surveys where individuals are asked to rank mutually exclusive alternatives and are therefore faced with explicit policy trade‐offs.<sup>6</sup> Third, we account for participation in the political process as a mediating factor in the relationship between ageing and expenditure preferences. Larcinese (2007) provides compelling evidence that voter turnout affects redistributive preferences overall, but the possible effects of ageing on spending preferences remains an empirical question.

# **3. Data and Empirical Analysis**

# **3.1. The Data Set and Descriptive Statistics**

This paper uses data from the Life in Transition Survey II (LITS II), collected by the European Bank for Reconstruction and Development and the World Bank in late 2010. Almost 39,000 respondents in 34 countries were asked questions about their preferences over government policy, their subjective well‐ being and their reactions to economic and political change. The Survey covers countries in Eastern Europe and the former Soviet Union, as well as in Western Europe. Table A1 in the Appendix lists the countries included in the Survey and the number of observations in each country.

We measure individual preferences over policy on the basis of responses to the following question: “ _In your opinion, which of these fields should be the first and second priorities for extra government spending? Education, Health care, Housing, Pensions, Assisting the Poor, Environment (including water quality), Public infrastructure, Other.”_ We focus on the policy areas that have been selected as the first or second‐highest priority for government intervention.

Preliminary analysis of the data shows how preferences over policy change with age and across countries. First, health care is the top policy priority area for LITS II respondents, and education, pensions and assistance to the poor are also listed as important priorities (Table 1). These four policy categories will therefore be the focus of our analysis. Second, support for additional government spending on education is high, but it falls with age,
on tax reform from national newspapers or from the International Bureau of Fiscal Documentation (IBFD).

# **3.2 Quality check of the narrative data**

Before proceeding to the main empirical results, a quality check of the narrative data is conducted by extending results in Mertens and Ravn(2013) to a cross-country setting. Critics of the narrative approach in the literature are often skeptical of the quality of these narrative data because the construction of such data relies on the reading of a large amount of narrative sources by a few authors to determine motivations and intentions behind a large number of policy changes. Therefore, the produced dataset are subject to errors and depend heavily upon subjective judgment calls made by the authors. Hence, a quality check is necessary before using the series to study the main research question of this paper.

The quality check takes the form of examining the responses of macroeconomic and fiscal variables to change in personal income tax(PIT) rate and corporate income tax(CIT) rate for a panel of OECD countries. Mertens and Ravn(2013) documents such responses for the United States in a PVAR setting after constructing narrative series for PIT and CIT changes, respectively. The narrative dataset in Dabla-Norris and Lima(2018) provides granular information on tax-based fiscal consolidations, most importantly on tax rate or tax base changes for a panel of OECD countries. It can be seen as an extension of Mertens and Ravn(2013)’s work on narrative data to more countries. Hence, a natural quality check is to extend the results for the responses to PIT and CIT shocks to these OECD countries.

Since we do not have a long enough time-series to carry out PVAR for each country, we aim to extend the PVAR methodology to a panel setting, pooling our estimates of structural parameters(elasticities) while estimating reduced-form VARs for each country. The procedure is divided into four steps and described below: The first step estimates country-specific reduced-form VARs: Consider the following 9
2022) develop an approach to estimating local economic activity using machine learning that is both novel and has considerable potential to guide policies. However, their approach does not incorporate the fact that modern economies consist of heterogenous spatial clusters of economic activity. Further, the R-squared associated with training the model on data from the 2007 boom economy to predict the more fragile 2017 economy is zero. As they acknowledge, their approach may not be suitable to predicting the impacts of business cycles on local economic activity.<sup>25</sup> In this context, it is also worth noting 24 More broadly our results suggest that sub-national units are affected in many different ways (i.e., across different sectors) by aggregate events in a manner that depends on their location.

25 This paper’s focus is not prediction, and we have not attempted to construct a machine learning model that predicts sub-national economic activity in Türkiye. However, the results are relevant to machine learning approaches because they show that clustering over time and space are important characteristics associated with sub-national changes in economic activity.

16
any subject at the WTO, exemplifies the centrality of subsidies in fisheries reform efforts (WTO, 2022).

Despite the near consensus on the connection between fuel subsidies and fisheries depletion, there is no causal evidence of the effect of fuel subsidies on fishing. The absence of empirical evidence reflects the fact that subsidy reforms are typically implemented at the country level, leading to a lack of credible quasi-experimental variation in subsidy levels across producers operating in the same setting. Understanding the effect of subsidies on individual producers’ actions is a key input for designing optimal subsidy reform because producers’ actions can result in both a private channel deadweight loss (caused by a difference between the private marginal cost and the buyer’s willingness to pay) and an external channel loss (caused by externalities in stock dynamics).

In this paper, we compile new vessel-level panel data on the Chinese distant water fishing fleet to estimate the effects of subsidies on production decisions. We leverage vessel-level variation in fuel subsidies that was induced by China’s 2016 fuel subsidy reform. The reform created 25 separate thresholds at which fuel subsidies discontinuously increase. These thresholds depend entirely on predetermined vessel characteristics, which cannot be manipulated to obtain higher subsidy payments. We measure fishing behavior using real-time data from Global Fishing Watch (GFW).

Many features of our data and research design make it a compelling setting to study the impact of input subsidies. First, the fishing effort data from GFW are derived from Automatic Identification Systems (AIS) transponder signals. This means we observe fishing effort in real time at the vessel-level and that our main outcome variable is not subject to misreporting.<sup>1</sup> Second, we leverage vessel-specific quasi-experimental variation in subsidy levels, alleviating concerns about reverse causality (e.g., subsidies being increased when the industry experiences a negative shock) and other sources of omitted variables bias (e.g., subsidizing less-profitable producers). Third, the reform we analyze was in effect for six years, which allows
Notes: Duration is the length of the phase in months (including any period at the end of the phase when rates are on hold). Amplitude is the sum of rate changes in percentage points (pp) over the entire phase (including any rate changes in either direction). Velocity is the total change in the policy rate in percentage points (pp) over the first six months of the phase. Rates in-sync with phase is the mean number or mean size in pp of rate changes in "in-sync" with the phase, i.e., rate increases for a tightening phase and rate decreases for an easing phase. Means are calculated as the average for each economy across all easing or tightening phases. EA is the euro area, with policy rate data starting in January 1999. Individual members of the euro area only include policy rate data through December 1998. Otherwise, policy rate data covers a period from January 1970 through May 2024, with a later start for some economies due to data limitations.

The statistics for individual economies in Table 1 also show substantial heterogeneity across the sample by some measures. For example, in some countries, the average duration of tightening phases is much shorter than that of easing phases—such as 24 and 27 months for tightening phases in Japan and the euro area, respectively, compared to 156 and 138 months for the corresponding easing phases. In other economies different phases have more similar durations—and in Spain and Belgium the average duration of tightening phases is even longer than that of easing phases (albeit this only includes the pre-1999 period). In some countries, the pace of rate adjustments is substantially greater—whether measured by amplitude, velocity, or the average size of rate changes—although this often reflects a larger share of the respective sample occurring during periods when inflation (and corresponding nominal policy interest rates) were higher.

Some of these differences across easing and tightening phases, as well as across economies, could be explained by different shocks triggering the
Because the disturbances for households in the target population are always unknown, we consider estimating the expected value of the indicator given the census households’ observable characteristics and the model of expenditure in (1). We denote this expectation as where ξ is the vector of model parameters, including those that describe the distribution of the disturbances.

In constructing an estimator of μ _v_ we replace the unknown vector ξ with consistent estimators, ξ<sup>_ˆ_</sup> , from the survey-based consumption regression. This yields μˆ _v_ . This expectation is generally analytically intractable so we use simulation to obtain our estimator, μ<sup>~</sup> ν .

The difference between μ<sup>~</sup> ν , our estimator of the expected value of _W_ for the region, and the <u>actual</u> level of welfare for the region reflects three components. The first, ( _idiosyncratic error_ ), is due to the presence of a disturbance term in the first stage model which implies that households’ actual expenditures deviate from their expected values. This component becomes increasingly important as the target population becomes very small. The second component of our prediction error is due to variance in the first-stage estimates of the parameters of the expenditure model ( _model error_ ). We calculate the variance due to model error using the delta method (see Elbers et al 2002, 2003). The third component of our prediction error is due to inexact method to compute μˆ ( _computation error_ ). This component can be set arbitrarily small by choosing a large enough set of simulation draws.

> 8 In the case of Madagascar and Cambodia, we also include regressors from tertiary datasets in the regression model (see Mistiaen, Ozler, Razafimanantena, Razafindravonona, 2002, and Fujii, 2003),

11
. The problem of right-censoring is accounted for in the Cox estimation procedures<sup>13</sup> . Left-censoring presents a more serious problem. Given the short time span, our approach is simply to ignore left censoring in our main estimations. As a robustness test, we drop all observations which are left-censored and determine the sensitivity of our results to left-censoring<sup>14</sup> .

# 4 Data

## 4.1 African Firm Data

Our analysis relies on a novel dataset collected within the frame of the Export Survival Project, implemented by the International Trade Department of the World Bank<sup>15</sup> . The dataset combines ...rm level export data collected by customs authorities in ...ve African reporting countries–Ghana, Mali, Malawi, Senegal and Tanzania. The dataset

> 12Distances are calculated as the sum of the distances between the biggest cities of both countries, weighted by the share of the population living in each city.

> 13Stata incudes a dummy variable taking vaue one if the spell is still existing the last year of the sample.

> 14Results not reported, available upon request.

> 15We thank Denisse Pierola and Paul Brenton.

12
expanded upon proven success, as was the case for SOE reform in which private firms gradually increased their participation in the workforce through joint venture activities (Figure 12).

**Figure 12 China Workforce by Firm Type**

<!-- Start of picture text --> Evolution of total workforce: 1998-2007 (1998 = 100)<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Domestic public firms Domestic private firms<br>Year<br>Total workforce index<br><!-- End of picture text --> Source: Du, Harrison, and Jefferson (2011) The ability to learn from country experiences rests heavily on the quality of those data collected. There is still much work to be done in improving these data, such as in reducing comparability problems, understanding data inconsistencies, reducing biases in data collection, and better linking across types of data. At the macro level, research has long had an important role in creating the tools, especially the data, needed for monitoring overall development effectiveness at country and sub-national levels. At the micro level, credible evaluations of development policies and projects (both ex ante and ex post) are accompanied by continuing methodological and data-related challenges. Methods will vary depending on the nature of the interventions that are being studied. There have been important advances in the use of both experimental and non-experimental evaluation methods for assigned programs (meaning that some observational units get the programs and some do not). However, these tools can be misleading for informing development policy (including scaling up) and may not be appropriate 23
Table 4: Factor covariates, trade and distance weight matrices

|Variable|Trade we|ight matri|x|
|---|---|---|---|
||I<br>II|III|IV|
|∆TFP|0.945||3.131|
||(2.840)||(1.920)|
|Uncertainty|-0.149||-0.134|
||(-1.840)||(-1.600)|
|Real interest rate||-0.391|-0.413|
|||(-1.490)|(-1.260)|
|No. of obs.|43<br>45|45|43|
|_R_<sup>2</sup>|0.165<br>0.078|0.034|0.343|
||Distance w|eight matr|ix|
|∆TFP|5.435||4.958|
||(2.940)||(2.530)|
|Uncertainty|-0.221||-0.142|
||(-1.950)||(-1.270)|
|Real interest rate||-0.027|0.127|
|||(-0.070)|(0.360)|
|No. of obs.|43<br>45|45|43|
|_R_<sup>2</sup>|0.285<br>0.134|0.000|0.345|

Notes: The dependent variable is the common factor from the full sample estimates in Table 3. ∆TFP is the first difference of total factor productivity (TFP), Uncertainty is the log of the U.S. economic policy uncertainty index taken from Baker
POLICY RESEARCH WORKING PAPER 2365

# Summary findings

Everhart and Duval-Hernandez present a method for forecasting growth cycles in economic activity, measured as total industrial production.

They construct a series which they aggregate into a composite leading indicator to predict the path of the economy in Lithuania.

The cycle is the result of the economy's deviations from its long-term trend. A contractionary phase means a decline in the growth rate of the economy, not necessarily an absolute decline in economic activity.

The indicator they select for economic activity is usually the Index of Industrial Production, plus a group of variables that, when filtered and adjusted, becomes the composite leading indicator that forecasts the reference series. Variables include economically and statistically significant financial, monetary, real sector, and business survey data.

They base selection of the components of the leading indicator on the forecast efficiency and economic significance of the series. Once selected, the relevant variables are aggregated into a single composite leading indicator, which forecasts the detrended Index of Industrial Production.

They apply the Hodrick-Prescott filter method for detrending the series. This is a smoothing technique that decomposes seasonally adjusted series into cyclical and trend components. One advantage of the HodrickPrescott filter is that it provides a reasonable estimate of a series' long-term trend.

The OECD uses a system of leading indicators to predict growth cycles in the economies of its member countries. These exercises have been very effective in their forecasting ability and accuracy - but for the technique to work it is essential to have an adequate statistical system that provides many economic variables in a precise and timely manner, preferably monthly. The authors extend the OECD technique and present an application to a country of the former Soviet Union.

- This paper a joint product of the Poverty Reduction and Economic Management Sector Units, Europe and Central Asia - and Latin America and the Carribean Regions, and the Mexico Country Management Unit is part of a larger effort in the Bank to foster the development of macroeconomic monitoring techniques. Copies of the paper are available free from the World Bank,
2010)  and  Davidson,  Matusz  and  Schevchenko  (2008),  we  also  find that  the  workforce  composition  improves  systematically  in  exporting  firms  in  terms  of innate  (time  invariant)  worker  ability  and  in  terms  the  quality  of  the  worker-­‐firm  matches.

**Keywords:** Trade   liberalization,   firm   heterogeneity,   linked   employer-­‐employee   data. **JEL   Classification:** F16

> *   Our   special   thanks   to   Paulo   Furtado   de   Castro   for   help   with   the   RAIS   and   SECEX   data,   to   Marc-­‐ Andreas   Muendler   for   sharing   three-­‐firm   random   aggregates   from   the   PIA   data,   and   to   the Department   of   Economics   and   Academic   Computing   Services   at   the   University   of   California,   San Diego   for   assistance   with   data   access.   Pravin   Krishna   and   Jennifer   Poole   were   visiting   scholars   in   the International   Trade   Division   of   the   Development   Research   Group   of   the   World   Bank   while   work   on this   paper   was   conducted   and   acknowledge   the   funding   support   of   the   World   Bank   executed   Multi-­‐ Donor   Trust   Fund   on   Trade.

1
Figure 1.1—Returns to schooling in the Philippines <!-- Start of picture text --> 5.5<br>5<br>4.5<br>Female<br>4<br>Male<br>3.5<br>Some Elementary Some high Hhigh Some College Post<br>elementary completed school school college completed graduate<br>completed<br><!-- End of picture text --> Source: Labor Force Survey, October 2009 round. Using the pooled sample, the log daily wage regression was estimated with the female indicator; educational attainment indicators (shown in the graph, “no education” being omitted) interacted with the female indicator; and age, age squared, and region dummies. The graph shows estimated constant term + female effect (zero if male) + education effects (differentiated by gender). The estimation sample consists of men and women aged 20 to 49.

29
employment, level of education and other factors can render households more resilient against shocks.

The theoretical causes and impacts of hyperinflation are well known, and provided in the seminal work of Cagan (1956) and Nordhaus (1973). A more recent review and update was conducted by Fischer et al. (2002). A historic overview can be found in He (2017). Recent studies focus on the causes and policy options (for example, Acemoglu et al., 2003 and Reinhart & Savastano, 2003), and historic dimensions often in the context of Zimbabwe (for example, Coomer & Gstraunthaler, 2011). Given the dearth of micro-data in countries with high- or hyperinflation, only very few studies look at the direct welfare impacts of high- or hyperinflation. Fajardo & Dantas (2018) study the impact of hyperinflation on investment behavior in Brazil. However, they do not touch on welfare or livelihood impacts. Larochelle et al. (2014) use a small-area-based approach with an asset index and find that rural poverty also increased in Zimbabwe’s hyperinflation period. In contrast, Kurasha (2021) uses micro-data from several years before and after hyperinflation, but finds that rural poverty fell while urban poverty increased, while asset inequality dropped during the hyperinflationary period. Health indicators worsened for both urban and rural as well as access to electricity, safe drinking water, improved toilets and health care.

In this paper, we assess the shorter-term impacts of high inflation on household livelihoods in urban South Sudan. Longitudinal micro-data for a representative sample of households is used to understand the changes in livelihoods between 2015 and 2017, accompanied by continuous price data collected across South Sudan. The novel data sets based on a set of innovative high-frequency surveys allow the use of a difference-in-difference approach providing a stronger identification than can currently be found in the literature. Furthermore, the paper identifies resilient households to draw conclusions for social protection programs and policies.

The next section of this paper presents the data and methodology in more detail.