rafmacalaba/data-use-mentions-extended huggingface dataset:
expenditure survey from 2010 with the unit record population census data from 2008 using the ELL approach. This poverty map serves as our benchmark. We then ignore the population census data and obtain a second poverty map for Malawi by combining the household expenditure survey data with predictors of poverty derived from publicly available remote sensing data.

An important motivation for using data from Malawi is that we have access to the exact coordinates of the survey PSUs. This enables us to produce the remote-sensingbased poverty map both using the publicly available geographic coordinates (that have been subjected to random offset to protect the anonymity of households) and exact coordinates (that are not publicly available) to investigate whether the random offsets – might undermine the approach and inspect the correspondence.

Our findings are two-fold. First, assessments of the correspondence between the remote-sensing-based poverty estimates and the census-based poverty estimates depend on how the correspondence is evaluated. The two approaches reveal the same patterns in the geography of poverty in Malawi; it is hard to tell the difference in a side-by-side comparison of the two poverty maps. The statistical correlation between the two different small area estimates of poverty is above 0.9, again confirming their close correspondence. However, there are districts for which the two approaches obtain markedly different estimates of poverty. Accordingly, poverty maps obtained using remote sensing data can be expected to do well when the decision maker is interested in estimates or comparisons of poverty between assemblies of areas. Yet, the decision maker may not be served as well by remote-sensing-based estimates when the focus is on estimates for specific small areas. Second, the random offsets embedded in the geographic coordinates of the public use data do not meaningfully alter the estimated geography of poverty. But they may, however, lead to an under-estimation of the standard errors of the small area poverty estimates (i.e. an over-estimation of precision). The reason for this is that the random offsets partially destroy the spatial correlation structure in the data and error term.

The decision to restrict attention to publicly available
poverty in India has exceeded the pace for the developing world since 2005 Since 2005, India has exceeded the pace of the developing world as a whole – as well as middle‐ income countries (MICs) and lower middle‐income countries (LMICs) – in reducing extreme poverty defined by the international poverty line of $1.90/day (2011 PPP).<sup>23</sup> This marks a change from the decade of the 1990s, when extreme poverty reduced in India at a rate on par with the developing world but lagged behind MICs and the sub‐group of LMICs that India is a part of (Figure 12). Comparisons with individual countries in South Asia and India’s MIC peers illustrate this positive trend, which however falls short of the rate of progress seen among the best performers. Extreme poverty reduction in India since 2005 exceeded the rate of progress in its neighbors Sri Lanka and Pakistan and some of its MIC peers (e.g. Indonesia, Bolivia and the Philippines), but lagged behind strong performers like Vietnam, Bhutan and Nepal (see Figure A‐ 4, Annex).

19 This correlation also holds for the share of population living below twice the poverty line.

20 Jammu and Kashmir and Assam are strong exceptions to this trend, while the low‐income states of Uttar Pradesh and Jharkhand experienced a mild decline in the elasticity of poverty reduction to NSDP growth from 1994‐2005 to 2005‐2012.

21 Refer to Figure A‐ 3, Annex.

22 This is consistent with past literature that finds a high degree of state‐level variation in the association between growth and poverty reduction in India, with no statistically significant relationship between growth rates and growth elasticities of poverty during the period 1994 to 2000 (Datt and Ravallion 2002).

23 See Ferreira et al (2015) for details on the international poverty line based on 2011 PPP, including methodology and results.

14
Policy Research Working Paper 8984

# **Abstract**

This paper investigates to what extent media impacts political decisions. A viable practical approach to test the relationship between mass media and political actions is through the use of the World Bank’s _Doing Business_ data, specifically, by assessing local media coverage of _Doing Business_ and implementation of business regulatory reforms. The tested hypothesis is that countries with higher media coverage of _Doing Business_ tend to carry out more business regulatory reforms, assuming one- and two-year lags between media coverage and reform implementation. To achieve this objective, the study put together a comprehensive data set that encompasses country-specific local media coverage of the _Doing Business_ report in 190 economies. The study finds that local media coverage of _Doing Business_ has a significant influence on regulators’ actions. First, the analysis shows that the number of local media articles tends to increase the probability of whether a country does any reform. Second, countries with greater media coverage of _Doing Business_ indicators tend to have higher numbers of implemented reforms.

This paper is a product of the Global Indicators 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 rramalho@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
a descriptive level (Ali _et al._ 2017; Holden and Tilahun 2020). We show that econometric analysis of spatially aggregated registry data can help identify impacts of policy reform that elude a traditional DID approach, in addition to remedying some of the shortcomings of household survey data. Establishing systems to provide such data routinely in land programs could thus enhance accountability; improve tracking of outcomes and understanding of the channels for impacts to materialize; and provide insights long after interventions have wound down.

The rest of the paper is structured as follows. Section 2 provides conceptual framing of the general challenges of evaluating land registration projects as well as the specific characteristics of the project to be considered here. Section 3 discusses descriptives based on administrative data and describes in detail the empirical methodology for evaluating the impact of this intervention using both household survey and administrative data. Section 4 discusses econometric evidence from analyzing household survey and administrative data, compares results from these, and draws out methodological and conceptual conclusions. Section 5 concludes by highlighting some implications for future research.

3
.4|
|Ethiopia||||964.0|||188.0|1152.0|
|Gabon||15147.8||196.3|||2876.0|18220.2|
|Côte d’Ivoire||9384.2|319.5|214.1|||1459.0|11376.8|
|Kenya||2359.0|694.3|190.1||7266.1|2404.0|12913.6|
|Liberia||590.7|320.3|17.2|||4834.0|5762.2|
|Madagascar|4038.4|1736.0||515.8|2394.1|5021.1|513.7|12483.0|
|Tanzania|7998.9|525.5|821.1|760.1|916.4|8332.0|1457.2|20811.1|
|Uganda|||1184.6|||12966.5|3778.8|17929.8|
|Zimbabwe|203.5|||83.4||1081.4|585.0|1953.3|
|Total|12324.1|110619.8|3359.8|4548.0|3310.5|34667.1|76696.9|245182.7|

Source: Authors’ computation based on UNCTAD (2016) With reference to G7 countries’ direct investment; France, Italy and UK tend to have a large presence in countries where they have political, cultural or social ties, especially, in their former colonies or formerly occupied territories (Table 4). For example, Italy has a large presence in Ethiopia, while France has a strong presence in Chad, the Republic of Congo, Gabon, Côte 18
for _n_ ( _τ_ ): where 1( _a > b_ ) denotes the indicator function that equals 1 if _a > b_ and 0 otherwise. In practice of course _β_ and _σ_ will have to be replaced with their respective estimators _β_<sup>ˆ</sup> and _σ_ ˆ. Similarly, an estimator for _n_ ( _τ, y_ ) can be obtained: Given _n_ ˆ( _τ, y_ ) and _n_ ˆ( _τ_ ), we may construct the estimators _F_<sup>ˆ</sup> 2( _y_ ) = _n_ ˆ( _τ, y_ ) _/n_ ˆ( _τ_ ) ˆ and _λ_<sup>ˆ</sup> = _n_ ( _τ_ ) _/n_ . Combined with the estimator for _F_ 1( _y_ ), which is estimated using DB-1 (i.e. the household income survey), we have all we need to estimate _F_ ( _y_ ) (see eq. 1), the income distribution for the complete population. This in turn is all we need to compute any inequality measure of choice.

No assumptions have been made about the distribution of _xh_ at this point. Let us assume that the top end of the distribution of _xh_ can be described by a Pareto distribution.

**Assumption 2** _Let G_ 2( _x_ ) _denote the distribution function of x conditional on x > x_ 0 _. It is assumed that G_ 2( _x_ ) _follows a Pareto distribution with shape parameters α:_

For ease of exposition let us also assume that the income threshold _τ_ is set sufficiently high such that _Y > τ_ implies _X > x_ 0.

# **Assumption 3**

9
the dynamics of income change over time in adjusting the vulnerability line. Depending on income levels and the poverty line, the bottom 40 percent of the income distribution can accommodate a variety of poverty scenarios, ranging from consisting of all the poor to very little of the poor (we empirically illustrate this later). Thus by explicitly focusing on the population below the poverty line rather than on the bottom 40 percent, the second approach can better track the change in the size of the poor group. Put differently, the second approach can perhaps be considered more propoor than the first approach. Finally, since the income distribution varies by country, the fixed thresholds under the first approach are country-specific, which can then be supplemented with region-wide vulnerability lines that can be derived under the second approach.<sup>11</sup> Regardless of which approach is employed, given two income thresholds ( _zj_ and _vj_ ), we can extend expression (2a) to analyze the mobility across welfare categories. For example, the percentage of poor households in the first period that escape poverty but still remain vulnerable in the second period (joint probability) is More generally, expression (4) also represents the percentage of the population in the lowest income group in the first period that move to the middle-income group in the second period.

# **III. Data, Setting Welfare Thresholds, and Regional Context**

# **III.1. Data**

In this paper we analyze household surveys for six Arab economies, including Egypt, Jordan, Palestine territories, Syria, Tunisia, and Yemen. These surveys have been harmonized for

> 11 We could consider the bottom 40 percent of the region-wide income distribution, but this may not add much value to country-level analysis since income levels substantially differ across countries in the MENA region.

10
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. 1 The hypothesis of weak instruments cannot be rejected for specification (6) Source: Authors’ calculations using data from ENTIC, INEGI 19
(0.107)|(0.207)|(0.0898)|(0.134)|(0.0663)|(0.0845)|
|N|6,107|6,107|6,107|6,107|6,107|6,107|6,107|6,107|
|R-2|0.798|0.777|0.914|0.912|0.674|0.675|0.468|0.552|
|Municipalities|2,118|2,118|2,118|2,118|2,118|2,118|2,118|2,118|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. The nonmonetary dimensions are expressed in 0–100, understanding 100 as 100% of coverage. Unweighted results match with Annex 5. Weighed results show the same model adding importance by size of municipal population.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 42
Figure 29: _p_ -values for Rainfall and Temperature by Weather Product <!-- Start of picture text --> p>0.90 p>0.95 p>0.99 95% C.I.<br>Rainfall Temperature<br>1 1<br>.9 .9<br>.8 .8<br>.7 .7<br>.6 .6<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>CHIRPS CPC MERRA-2 ARC2 ERA5 TAMSAT MERRA-2 ERA5 CPC<br>Share of Significant Point Estimates Share of Significant Point Estimates<br><!-- End of picture text --> _Note_ : The figure displays the share of coefficients for rainfall and temperature variables, by weather product. These are aggregated over weather metric, country, outcome variable, and specification. Only one extraction method is included. The rainfall panel includes 1,728 regressions, with each column representing 288 regressions. The temperature panel includes 648 regressions, with each column representing 216 regressions.

90
Becker, Gary S. and Nigel Tomes (1979), “An equilibrium theory of the distribution of income and intergenerational mobility”, Journal of Political Economy 87:1153-1189.

Becker, Gary, & Kominers, Scott Duke & Murphy, Kevin M. & Spenkuch, J ̈org L., 2015. “A Theory of Intergenerational Mobility,” MPRA Paper 66334, University Library of Munich, Germany.

Binder, Melissa and Christopher Woodruff. 2002. “Inequality and Intergenerational Mobility in Schooling: The Case of Mexico.” Economic Development and Cultural Change, Vol. 50, Iss. 2, pp. 249-267.

Behrman, J., A. Gaviria and M. Szekely (2001), “Intergenerational Mobility in Latin America,” Economia, Vol. 2 (1): 1 44.

Behrman, Jere R., (1999). “Labor markets in developing countries”, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 43, pages 2859-2939 Elsevier.

Bjorklund A and K. Salvanes. (2011). “Education and Family Background: Mechanisms and Policies, Handbook in the Economics of Education vol 3, E A Hanushek, S Machin and L Woessmann (es.), The Netherlands: North Holland, 2011, pp. 201-247.

Bj ̈orklund, A., Lindahl, M., & Plug, E. (2006). “The origins of intergenerational associations: Lessons from Swedish adoption data.” The Quarterly Journal of Economics, 999-1028.

Bossuroy, T and Denis Cogneau, 2013. “Social Mobility in Five African Countries,” Review of Income and Wealth, vol. 59, pages S84-S110, October.

Black, S, & Paul J. Devereux & Petter Lundborg & Kaveh Majlesi, 2015. “Poor Little Rich Kids? The Determinants of the Intergenerational Transmission of Wealth,” NBER Working Papers 21409, National Bureau of Economic Research, Inc
research in a number of ways (Neumark and Wascher 2007). First, new econometric techniques were applied to the (continually longer) time-series data. Second, new research designs were introduced based on variations in within-country minimum wages (e.g., U.S. states). Third, new data sources were employed, including, for example, the employer phone interviews used in the well-known Card-Krueger-Katz studies. Fourth, more and more research was carried out in developed countries besides the U.S. and in an increasing number of developing countries, which meant that the minimum wage could be studied in a variety of economic and institutional settings.

An important development in extending—and complicating—the minimum wage literature was the series of studies by Card, Krueger, and Katz in the early-to-mid 1990s.<sup>14</sup> Their research exploited state-level variations in minimum wages, using Current Population Survey data (e.g., Card 1992) or phone survey data from fast-food establishments (Katz and Krueger 1992; Card and Krueger 1994) to model employment impacts. The studies consistently found significant positive employment effects of minimum wages, with elasticities ranging from .35 (teenagers) (Card 1992), to .73 (Card and Krueger 1994), all the way up to 1.70-2.65 (Katz and Krueger 1992).

14 For a complete treatment of this research, including the particularly influential New Jersey-Pennsylvania fast food study, see Card and Krueger (1995).
variable shocki,t is a dummy variable for a recession event (or banking crisis or epidemic), the main variable of interest. Lagged potential growth yi,t-1 controls for the history of potential growth.

# IV.2 Results

Long-term effect of recessions. Even five years after recessions, potential growth as measured by the production function approach is estimated, on average, to have been 1.4 percentage points lower than if a recession had not occurred (figure 8.A). Coefficient estimates for the recession dummy are statistically significantly negative for the first five years after a recession. The effect was somewhat stronger and more persistent for EMDEs, with 1.6 percentage points lower potential growth five years after a recession compared to 1.3 percentage points for advanced economies (figures 8.B.C).

These results are broadly robust to the choice of potential growth measure and the definition of recessions. Four to five years after recessions, potential growth as measured by most methods other than the production function approach is estimated to have been 0.2-1.3 percentage points lower than if a recession had not occurred (annex E).<sup>8</sup> Recessions could alternatively be defined as years of negative output growth, regardless

> 8 The only exceptions are, for advanced economies, forecast-based estimates from the IMF World Economic Outlook database and, for EMDEs, multivariate filters and Hodrick-Prescott-filtered estimates. One possible reason for the unresponsiveness of some forecast-based measures might be that forecasters’ perception of long-term growth is stickier for advanced economies than for EMDEs.

13
the destination country. Therefore, part of the variation in unit export values can be explained by these country-specific covariates. In this regard, we use country-year dummies to control for all time-varying destination market aggregates, such as GDP and exchange rate. Further, Manova and Zhang (2009) demonstrated a positive relationship between the revenue exporters earn and the prices that they charge in the export market. For this consideration, we also include the total revenue that the firm earns in the export destination.<sup>14</sup> Another concern in estimating (2) is that export revenues and quantities may suffer from measurement error. To remedy this concern, we removed outliers, firm-product-destination combinations with export values below the 1<sup>st</sup> percentile or above the 99<sup>th</sup> percentile of the distribution, to reduce the bias associated with measurement error. The extensive fixed effects in our specifications also attenuate measurement error concerns.<sup>15</sup>

# **_<<A>>_ RESULTS**

After removing all observations lacking quantity or export value information, roughly 450,000 observations remain.<sup>16</sup> All of the regression results are derived from a panel-data fixed effect estimation in which the panel is a firm-product-destination triplet. All of the specifications include country-year dummies to control for aggregate variation in the export market. Table 5 contains the main results derived from estimating equation (2). In column (1), we report our estimates without controls. We include our controls in the remaining specifications. The variable log (exportrevenue)fdt denotes the total revenue that firm _f_ earns in market _d_ , log (exportrevenue)ft denotes the total export revenue of the firm, log (m)pdt is the average unit import value of the six-digit HS product _p_ in a particular destination, and log (employment)ft is the number of workers that firm _f_ employs. The availability of employment data determines
Harvard University Press.

- Bernard, A., Redding, S., & Schott, P. (2007). Comparative Advantage and Heterogeneous Firms. _Review of Economic Studies_ , 74(1): 31-66.

IMF. 2012. Regional Economic Outlook: Sub-Saharan Africa. Washington, October.

- ___. 2013a. Trade Interconnectedness: The World with Global Value Chains. August

___.2013b. Regional Economic Outlook: Sub-Saharan Africa. Washington, October ___. 2014. Regional Economic Outlook: Sub-Saharan Africa. Washington, October.

Johnson, R., & Noguera, G. (2012). Accounting for Intermediates: Production Sharing and Trade in Value Added,” _Journal of International Economics_ , 86(2): 224-236.

23
# Reliability of Recall in Agricultural Data

Kathleen Beegle Calogero Carletto Kristen Himelein

**Key words:** agriculture, measurement error, recall **JEL classifications:** Q12, O12

The authors are in the Poverty and Inequality Unit of the Development Research Group at the World Bank. The authors thank seminar participants at the Survey Design and Measurement in Development Economics conference and anonymous referees for very useful comments. This analysis was supported by the Bill and Melinda Gates Foundation Trust Fund for Improving the Quality and Policy Relevance of Household-Level Data on Agriculture in Sub-Saharan Africa at the World Bank and the Economic and Social Research Council, United Kingdom. All views are those of the authors and do not reflect the views of the World Bank or its member countries.
The great advantage of the database (which incidentally also makes the calibration possible) is its size: in the Freedman-Oostendorp’s “summary” (compendium) of the ILO sources,<sup>22</sup> there are more than 72,000 observations of average occupational wages. For each of the three indexes of inter-occupational wage inequality which we calculate (Gini coefficient, standard deviation and absolute mean deviation from the median), inequality indexes are calculated only for the country/years that contain more than 15 occupational wages (of the “calibrated” type). After this “filter” and a few others (dropping data for a number of small island economies and dependencies), we are left with 680 observations (country/years) covering the 1983-99 period and 118 countries. The average Gini is about 23.8, the median 21.7, with the standard deviation of about 10. A summary of the data is given in Annex 1 (Table 1). These inequality statistics can be, according to Freeman and Oostendorp, regarded as both indicators of occupational wage inequality and skill premium.<sup>23</sup> Figure 1 shows the distribution of annual changes in the calculated Gini coefficients ( _dginioww_ ) over the 1984-1999 period. As we observe, the distribution is close to being symmetrical and normal, with the mean which is slightly positive (0.17 Gini point) and a zero median.

> 22 The Freeman-Oostendorp database is indeed a “summary” of ILO data since the data on occupational wages have been collected by the ILO since 1924 while Freeman-Oostendorp data begin with 1983. 23 Implicitly, the greater the dispersion of inter-occupational wages, the greater the return to skills.

25