rafmacalaba/datause-encoder-data huggingface dataset:
36

# **Table 3**

## **Base regression**

_(measure of current account reversals: BP5)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||probit|probit|probit|probit|linear|linear|linear|
|CA<br>RES|-5.301<br>[0.641]***<br>-0.216<br>[0.527]|-7.188<br>[0.848]***<br>-1.946<br>[0.918]**|-7.417<br>[0.911]***<br>-1.942<br>[0.906]**|-7.385<br>[0.893]***<br>-2.005<br>[0.957]**|-1.206<br>[0.138]***<br>-0.311<br>[0.132]**|-1.214<br>[0.137]***<br>-0.494<br>[0.211]**|-1.233<br>[0.138]***<br>-0.468<br>[0.211]**|
|EXTD|-0.027|0.162|0.087|-0.001|0.007|0.006|0.095|
||[0.082]|[0.134]|[0.145]|[0.153]|[0.027]|[0.027]|[0.041]**|
|SHORTD|0.117|0.052|0.337|0.478|0.007|0.007|0.007|
||[0.449]|[0.604]|[0.644]|[0.670]|[0.088]|[0.088]|[0.088]|
|CREGRO|0.003|0.001|0.005|0.003|0.001|0.001|0.001|
|PPPGDP|[0.016]<br>0.053|[0.019]<br>0.202|[0.019]<br>0.170|[0.019]<br>0.123|[0.003]<br>0.018|[0.003]<br>0.016|[0.003]<br>0.018|
||[0.019]***|[0.089]**|[0.100]*|[0.111]|[0.017]|[0.017]|[0.017]|
|OPEN||||0.600|0.090|0.106|0.107|
|||||[0.455]|[0.064]|[0.067]|[0.067]|
|OT||||1.039|0.127|0.135|0.066|
|||||[1.415]|[0.266]|[0.265]|[0.268]|
|CONCD||||-0.298|-0.027|-0.021|-0.001|
|||||[0.667]|[0.080]|[0.079]|[0.079]|
|RES^2||||||0.257|0.263|
|EXTD^2||||||[0.264]|[0.266]<br>-0.022<br>[0.007]***|
|Country fixed<br>effects|NO|YES|YES|YES|YES|YES|YES|
|Year dummies|NO|NO|YES|YES|YES|YES|YES|
|Observations|1874|1874|1874|1861|1861|1861|1861|
|R-squared|0.08|0.16|0.20|0.21|0.17|0.17|0.17|

_Notes_ : the dependent variable (BP5) takes the value 1 if a reduction of a current account deficit of at least 5 percentage points of GDP and equal to at least half of the initial current account deficit occurs at time t, and zero otherwise. Robust standard errors in brackets. All explanatory variables are described in Table A1 and lagged one period. For the probit models we report the pseudo – R<sup>2</sup> . * significant at 10%; ** significant at 5%; *** significant at 1%
# **4 Materials and Methods**

## **4.1 Data**

**Wealth data–** Our wealth data comes from recently published estimates of relative wealth on a 2.4 _×_ 2.4 km grid for populated areas in 103 countries around the world.<sup>25</sup> These data use machine learning algorithms to combine satellite data, mobile phone data, and data from Facebook users to estimate relative wealth (RWI) within countries. The algorithms are trained on Demographic and Health Survey (DHS) collected measurements of wealth. The gridded wealth data provide estimates for each grid-cell of how wealthy that grid-cell is relative to others in the same country as well as an estimated error for that estimate. The relative wealth indicator ranges from -2.5 to 2.5 over the full data in our sample, with smaller ranges within individual countries.

As a robustness check we also re-estimate our model with the Demographic and Health Survey (DHS) derived wealth indices that the gridded wealth estimates were trained on.<sup>36</sup> **Pollution data–** We use estimated pollution concentrations from the global dataset produced by the Van Donkelaar research group<sup>29</sup> . This provides monthly average pollution estimates on a 0.01 _×_ 0.01 decimal degree covering the entire planet. We use the average of estimates spanning 2015 to 2020 and for each wealth grid cell assign it the value of the nearest PM2 _._ 5 gridcell. To calculate the average we collapse the monthly pollution estimates in three different ways. In our primary analysis we calculate the average across all years and months from 2015 to 2020. Secondly, we calculate the median across all months and years. Finally, we use the maximum monthly average level of particulates for each grid-cell for the month-of-year that had the highest average pollution from 2015 through 2020. For example, Delhi is assigned the average December PM2 _._ 5 concentration because that is the month-of-year in which average particulate values are highest. In the analysis of the correlation between wealth and particulate pollution from non-dust and sea salt sources we use the dust and sea salt removed estimates produced by the Van Donkelaar group at the annual level and match to RWI points in the same way.

As a robustness check we also re-estimate our models with available ground monitor measurements of PM2 _._ 5. All ground measurements come from the OpenAQ network ( `https: //openaq.org` ).

**Elevation data–** We use data from NASA’s ASTER project that provides a global digital elevation model (DEM) that provides elevation at 30m resolution comprehensively around the world.

**Pollution source data–** We use data from the EU EDGAR database that provides information on contribution of different economic sectors to particulate pollution for each point on a grid covering the whole planet. We aggregate these sectoral figures into broad categories (e.g. power generation) and calculate the total contribution of each broad category within each coun-

13
This specification allows us to study the association between regulation and agricultural output after controlling for agricultural inputs (labor, land, livestock, human capital), country characteristics (average precipitation, landlocked-ness) and regional fixed-effects. The available sample has 59 countries.<sup>***</sup> Following Huber (1967) and White (1980) we employ OLS regression with robust standard errors to address heteroskedasticity.

The adjusted R<sup>2</sup> for all OLS regressions exceeds 0.75, which suggests that the fitted regression outcomes explain the variation in cross-country agricultural outputs by a significant level. Table 5 displays the results of one baseline regression (OLS1) where we have excluded the regulatory measures to maximize sample size. Further, we report 3 estimations of our model performed using our overall (OLS2), quality (OLS3) and efficiency (OLS4) regulatory measures as explanatory variables.

**Table 5.** Estimates of the aggregate agricultural production function

|||Depende<br>|nt Variable: ln<br>|AGVA<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|OLS2|OLS3|OLS4|2SLS|
|lnEBA||1.064**|||4.343***|
|||(0.397)|||(1.421)|
|lnEBAEFF|||0.676**|||
||||(0.260)|||
|lnEBAQUAL||||0.973**<br>(0.456)||
|lnLABOR|0.273***|0.226*|0.220|0.232*|0.216|
||(0.0560)|(0.133)|(0.134)|(0.125)|(0.196)|
|lnLAND|0.498***|0.505***|0.508***|0.518***|0.407**|
||(0.0701)|(0.130)|(0.135)|(0.128)|(0.165)|
|LIVESTOCK|6.53e-09***|4.81e-09*|5.46e-09**|3.78e-09|5.31e-09*|
||(2.14e-09)|(2.60e-09)|(2.59e-09)|(2.83e-09)|(3.04e-09)|
|EDUCATION|1.093***|0.723*|0.792**|0.768*|0.0582|
||(0.281)|(0.375)|(0.373)|(0.393)|(0.608)|
|RAIN|0.000226*|0.000239|0.000245|0.000165|0.000673|
||(0.000127)|(0.000271)|(0.000282)|(0.000270)|(0.000435)|
|LANDLOCK|-0.354**|-0.592**|-0.617**|-0.621**|-0.310|
||(0.163)|(0.243)|(0.241)|(0.249)|(0.327)|
|Constant|7.486***|5.452*|6.565**|5.621*|-3.422|
||(1.530)|(3.003)|(2.843)|(2.941)|(5.267)|
|Observations<br>|140|59|59|59|59|
|Adjusted R<sup>2</sup>|0.860|0.723|0.721|0.712|0.411|

Region fixed-effects included but not reported Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

> *** See Annex I for a detailed list of countries in the sample.

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# **Table 12: Health**

## **OLS estimates, standard errors in parentheses, p-values in braces**

|**Any Ebola Case Within: **|**Household**<br>**Aged**|**Family**<br>**Network**<br>**12-17 at B**|**Friends**<br>**Network**<br>**L**|**Household**|**Family**<br>**Network**<br>**Aged**|**Friends**<br>**Network**<br>**18-25 at**|**IPV**<br>**(same relationship)**<br>**BL**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|**Disruption Effect**|-.008|-.007|-.004|.004|.043|.055|-.116*|
||(.026)|(.071)|(.074)|(.023)|(.057)|(.061)|(.060)|
|**Treatment Effect in High Disruption**|.011|-.008|-.014|.006|-.018|-.025|.106*|
||(.033)|(.075)|(.079)|(.029)|(.057)|(.063)|(.054)|
|**Treatment Effect in Low Disruption**|-.023|-.045*|-.050*|-.016|-.020|-.021|-.025|
||(.014)|(.025)|(.028)|(.017)|(.032)|(.035)|(.034)|
|**Difference [β3, p-value]**|{.365}|{.645}|{.675}|{.535}|{.978}|{.952}|{.046}|
|**Control Mean at BL**|.034|.152|.178|.032|.144|.139|.531|
|**Observations**|2,657|2,894|2,894|2,617|2,617|2,617|1,899|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. The sample includes those individuals tracked from Baseline to Endline plus respondents resampled at endline that have been residing in the same village since before the Ebola outbreak. For Column 7, the estimation sample includes all women that were tracked between baseline and midline and did not change partner during the study period. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. Only Column 7 controls for baseline value of the outcome variable. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
## **_4.3 Cost of the PDS_**

The cost of the PDS is assessed here as the direct cost of the subsidies in the PDS, measured as the difference between the open-market price at the time and the subsidized price at which grains are released through India’s Fair Price Shops. This cost abstracts from the direct costs of the scheme, such as administrative costs, costs of transport to final consumers, or any costs associated with leakages or fraud—all of which are outside the scope of the model used. The cash flow of the PDS subsidies is equal to the distributed volumes times the difference between market and subsidized prices: where the different categories of households benefiting from the PDS—households above the poverty line (APL), below the poverty line (BPL), and the “poorest of the poor” households receiving grain subsidized under the Antyodaya Anna Yojana (AAY) scheme—are indexed by �∈����, ���, ����; �� is the distributed volume; and ����� the subsidized price. Consistent with the modeling of stock releases in Section 5.1.1, �� are assumed to be constant and sum to Θ, the total volume released through the PDS. The values can be found in Table 1.

**Table 1. Values used to assess the cost of subsidies in the PDS**

||**Above thepoverty line**|**Below thepoverty line**|**Antyodaya Anna Yojana**|
|---|---|---|---|
|**Subsidized**<br>**price,**<br>��<br>���|6,100|4,150|2,000|
|**(Rs/t)**||||
|**Share of volume released,**<br>�� **(% of**�**)**|51|32|17|

_Source:_ Food Corporation of India.

The measured costs of the PDS do not appear in the national welfare measure, since it is considered to be a pure transfer that does not affect the overall economic welfare measure used in this representative-agent model. Clearly, the targeted nature of this intervention may result in a substantial increase in social welfare if the social welfare function attributes greater marginal utility of income to the beneficiaries relative to those paying the costs of the scheme.

# **5 Price stabilization policies**

This section begins by incorporating India’s current wheat policies in the analytical model. As noted, these policies target the three main channels for price stabilization—production, storage and trade. Next, the derivation of optimal price stabilization polices is outlined, followed by proposals for some optimal simple rules that might serve as a more realistic, practical policy alternatives.

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In Equation (14), the parameters<sup></sup> _j_<sup>and</sup>  _j_ are estimated from Equation (1), and  can be estimated using an approximation of the correlation of the cohort-aggregated household consumption between the two surveys (<sup></sup> _yc_ 1<sup>_y_</sup> _c_ 2 ). In particular, given an approximation of<sup></sup> _yc_ 1<sup>_y_</sup> _c_ 2 , where c indexes the cohorts constructed from the household survey data, the partial correlation coefficient  can be estimated by Note that the standard errors of estimates based on the synthetic panels can in fact be even smaller than that of the true (or design-based) rate if there is a good model fit (or the sample size in the target survey is significantly larger than that in the base survey; see Dang and Lanjouw (2013) for more discussion).

Equation (14) can be extended to the more general case of vulnerability. For example, we can estimate the percentage of poor households in the first period that escape poverty but still remain vulnerable in the second period (joint probability) as Other formulae and more detailed derivations for other measures of vulnerability dynamics are provided in Dang and Lanjouw (2017).

# _Illustrative Example 5_

Table 5 provides the estimates for both unconditional and conditional poverty mobility using the synthetic panels, and then compares these estimates with those based on the true panels. Several remarks are in order for this table. First, the synthetic panel estimates approximate those of the 32
Policy Research Working Paper 5408

# **Abstract**

Adverse shocks to rich countries often have a large and persistent negative impact on investment and output in developing countries. This paper examines a transmission mechanism that can account for this stylized fact. The mechanism is based on the existence of international financial frictions. Specifically, if a small, developing country has to collateralize its assets to borrow funds to invest, falling asset prices caused by a negative shock in an advanced economy worsen the developing country’s collateral value and reduce its ability to borrow and reinvest. Hence, investment in the developing country declines, and international investors repatriate capital to the advanced country. As less capital now can be pledged as collateral, the developing country’s credit constraint is further tightened, which leads to another round of decline in investment. This generates a downward spiral that may cause large output losses to the developing country. The mechanism finds empirical support in the 2008–2009 crisis data.

This paper—a product of the Macroeconomics and Growth Team, Development Research Group—is part of a larger effort in the department to understand crisis transmissions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hanguyen@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
Owing to the improved growth record since 1998, there is a general consensus that poverty in Bulgaria is on the decline.<sup>2</sup> However, welfare comparisons over an extended period of time, such as between the period of economic volatility of 1996-97 and relative prosperity of the period leading up to the EU membership in January 2007, have been impeded due to lack of reliable and comparable measures of welfare over time. For Bulgaria, the most relevant data come from the household surveys that were undertaken in 1995, 1997, 2001, and 2003.<sup>3</sup> However, income and consumption aggregates derived from these surveys are not comparable over time and thus do not allow assessing the household welfare dynamics. As a result, it is not entirely clear whether the living conditions of Bulgarians have improved, deteriorated or stayed the same between the mid-1990s and 2000s. This study argues that the analysis of the stock of durable goods owned by individuals and households can provide a useful and more reliable alternative approach to examining the trends in poverty and inequality in Bulgaria during this period.

There are several advantages to relying on durable goods, as opposed to or in addition to income and consumption aggregates, to analyze individual and household welfare dynamics over time. Some of the data and measurement advantages of stock variables such as durable goods are obvious. Unlike income or consumption aggregates, the ownership status of durable goods is less subject to measurement errors as they can be readily observed and verified. In household survey instruments, questions related to durable goods are straightforward and less affected by respondents’ recollection ability. Furthermore, as questions on durable goods tend to be near identical from survey to survey, comparability over time is reasonably ensured.

The other advantages of relying on durable goods are related to the welfare of individuals and households owning them. The stock of durable goods that households and individuals control largely determines their structural position in society and their likelihood of avoiding or escaping persistent poverty and vulnerability (Lastrapes and Potts, 2005). Some durable goods can be inputs to generate income and offer the collateral base for consumption expenditures and productive investments. Accumulation and increased ownership of durable goods may also be

> 2 See, for example, _Bulgaria: The Road to Successful EU Integration—The Policy Agenda_ , The World Bank, Washington DC,  November, 2005

> 3 A new and similar multi-topic household survey, a joint effort between the Open Society Institute—Sofia and the World Bank’s Bulgaria Poverty Monitoring team, is currently underway.

- 2 -
Especially employees in the formal sector, both private and public, have smaller than average cost-of-living differences between Abidjan and other cities.

The data also permit to calculate a cost-of-living index by poverty status, and here the differences are more pronounced (Table 5). Between Abidjan and other cities, the costof-living differential narrows with rising total expenditure: for the very poor it is 14%, while for the non-poor it is only 7%. In contrast, in the rural areas, the differential widens with rising expenditure: for example, in West Forest the price differential for the very poor - is 7%, but for the non-poor it is 12%. This implies that the very poor who are mostly located in rural areas - fail to benefit from the lower cost of living in rural areas as much as other groups. This also means that the deflation of expenditures (or income) with a single index will underestimate real expenditure levels for urban poor and overestimate those of rural poor. By implication, if real expenditure are used as a welfare measure to estimate poverty (as we have done earlier in this paper), the simple index will overestimate urban poverty and underestimate rural poverty.

The cost-of-living differences reflected in Table 5 are of course only those due to different expenditure patterns across groups. The ICP data permit to estimate regional price differences only on an average basis, since the prices are collected from selected points of sale without taking into account the characteristics of actual purchasers. There could be further cost-of-living differences if unit prices differ systematically across socio-economic or poverty groups. In particular, it has been argued that the poor pay higher prices than the non-poor for many food items because they have to buy in small quantities. Also, price differences may occur if the poor and non-poor buy at different types of outlets or markets, and if there is systematic price variation across these. Such differences cannot readily be captured by conventional price data collection, but are best relegated to household surveys. Questions on actually paid prices can then be related to the household's socio-economic characteristics captures in the survey.

13
## stimulus.

Adverse effects through multiple channels. Recessions affected potential growth through multiple channels. Four to five years after an average recession, the annual growth of investment, employment, and productivity remained significantly lower than in “normal” years (by 3 percentage points, 0.7 percentage point and 0.7 percentage point, respectively). This contrasts with banking crises, which tended to be associated mostly with lasting losses of productivity growth, and epidemics, which were mainly associated with lasting employment losses, possibly reflecting economic shifts caused by behavioral responses to epidemics.

Different features of potential growth estimates. The comprehensive database also allows a comparisons across potential growth measures. Forecast-based estimates tend to be systematically higher than other estimates, and estimates based on univariate filtering techniques systematically lower. Estimates based on filtering techniques tend to be the most volatile and to track actual growth most closely, as expected. Estimates based on the production function approach tend to be the most stable and the least correlated with actual growth as they capture slow-moving drivers of potential growth.

The study proceeds as follows. Section II presents the database. Section III describes movements in potential growth around the world in recent decades. Section IV estimates the effects on potential growth of recessions. Section V documents the channels through which these operates. Section VI concludes.

# II. Database

Three main methods of estimating potential growth estimates have been used in the literature, and several different measures can be derived using variants of them. The comprehensive database developed here allows a comparison of the behaviors of such measures.

The database includes nine measures of potential growth for up to 173 countries over periods as long as 1981-2021. The baseline measure of annual potential growth, estimated using the production function approach, is available for up to 30 advanced economies and 64 EMDEs for 1998-2021 (table 1, annex A). Six univariate and multivariate filter-based estimates of potential growth, which require quarterly data, are available for up to 37 advanced economies and 52 EMDEs for 1980Q1-2022Q1 (annexes B and C). IMF World Economic Outlook-based estimates of potential growth are available for up to 37 advanced economies and 136 EMDEs for 1990-2022 (annex D). Consensus forecast-based estimates of potential growth are available for up to 34 advanced economies and 44 EMDEs for 1990-2022.

The database also includes projections for a subset of measures. For the production function approach, projections are available for 2022-32. These projections and the methodology on which they are based are presented and analyzed in Kilic Celik, Kose, and Ohnsorge (2023). For the filter-based estimates, forecasts are available up to 2024Q4.

This study discusses aggregates for the global economy and for particular country groups.

5