rafmacalaba/data-use-mentions-extended huggingface dataset:
score was the highest in the North East, but the mean values are quite similar in all regions. A CES-D index of above 10 is regarded as a threshold level for significant depressive symptoms. In our sample, 28 percent of the respondents have a score above this level, so poor mental health is indeed a common issue in the regions studied.<sup>16</sup> Looking at the demographic characteristics in table 1, panels a and b, we can see that household size is largest in the North East and smallest in the South South, and the fraction of female household heads is lowest in the North East and highest in the South South. Similarly, household heads are least educated in the North East and most educated in the South South. Twenty-one percent of all households in the sample are polygamous, with the highest share of polygamous households being in the North East and the lowest in the South South. These descriptive statistics illustrate the regional differences between Nigeria’s poorer north and relatively wealthier south.

# 4.   Empirical framework 4.1.   Panel data sets

For the analysis, we created two panel data sets: one for the consumption measures, including poverty, and one for food insecurity as outcome variables. The outcome variables were measured at different points in time. Figure D.1 in appendix D illustrates the timing of the data in terms of the outcome variables as well as the telephone survey.

For consumption, we have data from three waves that have two visits each; one visit is in the autumn (post-planting), and the other is in the spring (postharvest). The consumption aggregate is the median of the consumption level of those two visits. Hence, we have a measure for consumption at three points in time (2010–11, 2012–13, and 2015–16). Our food insecurity measure, the CSI, comprises the seven-day recall questions administered during each visit of the GHS—that is, altogether six times during the GHS panel
# **Figure 6. Notre Dame GAIN indicators of vulnerability to climate change**

<!-- Start of picture text --> A. Climate change exposure by region<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are bottom<br>and top of box, respectively<br>B. Vulnerability index, 1995‐2017<br>60<br>55<br>50<br>45<br>40<br>35<br>Note: Country median per year. 75th and 25th percentiles in dotted lines<br>D. Readiness index, 1995‐2017<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>Notre Dame exposure index (0‐100, high)<br>Notre Dame Vulnerability Index (0‐100, high)<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>Notre Dame readiness index(0 ‐100, high)<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Country median per year. 75th and 25th percentiles in dotted lines

- C. Vulnerability index by region

<!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>EAP ECA LAC MENA SA SSA<br>
16 On the other hand, the unit prices of Cambodia’s main export products are lower than the world average. For EU-15 exports, unit prices in 2005 were lower than in India, Sri Lanka, and Vietnam, but higher than in Bangladesh, China, and Pakistan (Tewari 2008). One of the reasons for the low prices of Cambodian apparel exports is that Cambodian apparel manufacturers concentrate on the production of basic products, while other countries export higher-value products.

In both Cambodia and Sri Lanka, unit values show declining trends following the end of the MFA. Figure 3 contains the weighted averages of the dollar-value square-meter equivalent apparel imports into the United States from Sri Lanka and Cambodia. The unit values for both countries fall after the end of the MFA, but recover after the great trade collapse of 2008. As Cambodia entered the global market (1998–2001), unit values rose sharply. They fell in Sri Lanka over the same time period. China’s entrance into the WTO corresponds with a drop in unit values in Cambodia, while unit values rose in Sri Lanka. U.S. import prices experienced sharper declines than those of the EU. Between 2004 and 2008 the average price of apparel exports to the United States fell by 25 percent, from US$52 to US$39 per dozen. Sri Lankan apparel export unit prices to the United States declined from US$59 to US$42 per dozen items over this period. The average price of apparel exports to the EU-15 declined by 7 percent, from €13.4 to €12.5 per kilogram between 2004 and 2008 as shown in figure 4 (U.S. International Trade Commission (USITC) and Eurostat data; see Staritz and Frederick 2011a).
indicate an increase in average household consumption per capita, although most of them are either not fully comparable to the official CES, or do not cover a period long enough, or a geographical coverage wide enough, to assess the evolution of household consumption and, more precisely, poverty rates. We then adopt two methods to estimate poverty in 2017/18. First, we use a surveyto-survey methodology to impute consumption into the Social Consumption Survey for Health 2017/18 using a model estimated on the CES 2011/12. Our method builds on Newhouse and Vyas (2019), adding explanatory variables such as household energy consumption and demographic household characteristics, and modifying the functional form of the rainfall shock, but not including a time trend. Second, we project the CES 2011/12 forward using national accounts growth rates combined with a pass-through factor that adjusts for the difference between growth in national accounts and household surveys. Borrowing from Lakner et al. (forthcoming), we use a machine learning algorithm to estimate the pass-through rate and allow for changes in inequality.

Using the survey-to-survey method, we estimate a national poverty rate (at the international poverty line of $1.90 per person per day) of 9.9 percent in 2017, with a 95 percent confidence interval of between 8.1 and 11.3.<sup>35</sup> With the preferred pass-through rate of 0.67, we obtain a national poverty rate of 10.4 percent in 2017, with a confidence interval between 10.0 and 10.8. Despite using very different data sources and methods, the estimated poverty rates are strikingly similar with overlapping confidence intervals. Within urban and rural areas, the differences are somewhat larger, but the confidence intervals again overlap, and there is no evidence that one method is systematically biased in one direction. Using the survey-to-survey method, rural poverty is estimated at 10.5 percent [8.8, 12.0], some 1.5 percentage points lower
capita))|(0.157)|(0.205)|(0.106)|(0.195)|(0.668)|(0.717)|
|Observations|18,647|7,600|38,659|6,714|31,523|2,091|

Note: ***p < 0.01, **p < 0.05, *p < 0.1. Standard errors in parentheses. Standardized weights are used that give each country equal weight. The standard errors are clustered at a sector × country level. Measures for GVCB share and GVCF share are standardized. All regressions include controls for technology (computer use, computer use squared), skills, education, age, gender, sector FE, and country FE. The wage data for Canada, China, Hungary, Macedonia (FYROM), Peru, Serbia, Singapore, Sweden, and Türkiye are unavailable; therefore, these countries are excluded from the sample. Source: Authors’ calculations based on PIAAC, STEP, CULS (tasks), World Bank (GDP), EORA data, and Borin and Mancini (2015, 2019) (GVC participation measures).

At the same time, the relationship between GVC participation and wages is significant only in some types of occupations and sectors. First, higher forward GVC participation is associated with higher wages among workers in non-offshorable occupations in the industrial and business services sectors. This could indicate high value-added tasks such as R&D upstream in GVCs which are more difficult to offshore and thus pay high wages. Second, more backward GVC participation is associated with higher wages only in business services – positively in non-offshorable occupations, and negatively in offshorable occupations.

Due to the unavailability of wage data, we had to exclude Canada, China, Hungary, Macedonia (FYROM), Peru, Serbia, Singapore, Sweden and Türkiye from the previous analysis. Table 4 thus replicates the results of Table
expenditures.<sup>12</sup> Separate surveys were administered to the leaders of each locality on services and programs available at the locality.

For the health outcome analyses we use the _Encuesta Nacional de Nutricíon_ (National Nutrition Survey) collected by the _Instituto Nacional de Estadística y Geografía_ (INEGI) (National Institute of Statistics and Geography) and the _Secretaría de Salud of Mexico_ (Secretary of Health) in the last quarter of 1999.<sup>13</sup> The survey interviewed 7,180 rural households in 767 different localities. The survey collected general information on all members of the household and more detailed information, including anthropometric measures and illnesses in the past 2 weeks, for females between 12 and 49 years of age, and for all children 12 years or younger.

The climate data used in this paper come from the Mexican Water Technology Institute ( _Instituto Mexicano de Tecnología del Agua_ —IMTA). The IMTA has compiled daily weather data from more than 5,000 meteorological stations scattered throughout the country. The data span a very large period of time—from as far back as the 1920s to 2007—and contain information on precipitation, and maximum and minimum temperature.

The meteorological stations registered these variables on a daily basis and we used this information to interpolate daily values of these variables for a geographic centroid in each of the country‘s municipalities<sup>14</sup> . The centroid was determined as the simple average of the latitude and longitude coordinates of all the localities listed in INEGI‘s 2005 catalogue corresponding to each municipality, which resulted in a locality-based centroid. We chose this method over a population-weighted average because that alternative would bias the interpolation towards urban rather than rural areas. The interpolation method used is taken from Shepard (1968), a commonly used method which takes into account relative distance and direction between the meteorological stations and the centroids (see Appendix 5).

We carried out an independent interpolation for every day between 1950 and 2007, for each
Table 1 also gives a regional breakdown of the estimated impacts of differential mortality on the 2001 headcount index. Since the headcount index is not (of course) around 20% in all regions, the estimates of _DRD_ from the DHS data do not strictly apply to each region separately. One would be drawn toward lower values of DRD for the poorer regions. But even focusing on _DRD_ =1.2, it is clear that the impacts are highest for Sub-Saharan Africa. The trend rate of poverty reduction in SSA has been close to zero over 1984-2001 (indeed Chen and Ravallion report a regression coefficient of the region’s $1 a day headcount index on time of 0.0!). So it is not meaningful to express these impacts as a proportion of the annual rate of poverty reduction. However, the impacts are clearly sizeable. Indeed, these calculations suggest that without differential mortality, the headcount index in SSA would be rising at a non-negligible rate over time, at about 0.2 percentage points per year at _DRD_ =1.2, rising to 0.8 at _DRD_ =2.

The region with the second highest impacts is South Asia. At _DDR_ =1.2, differential mortality adds 0.06 percentage points to the “$1 a day” headcount index in South Asia, representing 15% of the long-run trend since the mid-1980s implied by the Chen-Ravallion estimates. This rises to 0.3 points per year at _DRD_ =2 though this appears to be an implausibly large value for South Asia.

Turning next to the impact of differential fertility, the calculations in the previous section also suggest a range for _BRD_ of 1.2 to 2. Table 2 gives the corresponding results for the impacts of differential fertility (analogously to Table 1). At _BRD_ =1.2, the poverty rate in 2001 under random fertility would have been reduced by 0.1 percentage point, representing 16% of the trend rate of poverty reduction. Again, the impacts are
|**Country**|**Survey(s)**|**Year(s)**|
|---|---|---|
||Integrated Household Survey|2000-06|
|Mexico|Encuesta de Gastos de Hogares|2000, 02, 04-<br>06|
|Micronesia|Income and Expenditure Survey|2005|
|Moldova|Household Budget Survey|2001-08|
|Mongolia|Income and Expenditure Survey|2005|
||LivingStandard Survey|2000,05|
|Montenegro|Integrated Household Survey|2001-03|
|Morocco|Enquête de vie des ménages|2006|
|Mozambique|Inquérito aos Agregados Familiares|2002|
|Myanmar|Income and Expenditure Survey|2002,06|
|Namibia|Income and Expenditure Survey|2003|
|Nepal|LivingStandard Survey|2003|
|Nicaragua|Encuesta de Hogares|2001,05|
|Niger|Enquête Budget-Consommation|2007|
||Enquête sur le conditions de vie|2005|
|Nigeria|LivingStandard Survey|2003|
|Pakistan|Integrated Household Survey|2001|
|Panama|LivingStandard Measurement Survey|2003|
||Encuesta de Hogares|2000-06|
|Papua New Guinea|Income and Expenditure Survey|2005|
|Paraguay|Encuesta de Hogares|2000-07|
|Peru|Encuesta de Hogares|2000-07|
|Philippines|FamilyIncome and Expenditure Survey|2000,03,06|
|Poland|Household Budget Survey|2000-02, 04-<br>06|
|Romania|Household Budget Survey|2003,06-07|
||FamilyBudget Survey|2001-03|
|Russia|Household Budget Survey|2000-04, 06-<br>07|
|Samoa|Income and Expenditure Survey|2002|
|São Tomé and<br>Principe|Enquête sur le conditions de vie|2000|
|Senegal|Enquête auprès des ménages|2001|
|Serbia|Household Budget Survey|2003-04, 06-<br>08|
||LivingStandard Survey|2003|
|
**PanelJ. C**|**osta Rica**||||
||Annual location premium growth<br>2001/03-2008/09(before sorting)||Annual location pre<br>2008/09|mium growth 2001/03-<br>(after sorting)|
|γ0<br>b|-0.00904<br>-0.0245***|γ0<br>ab|-0.0421***|-0.0218**|
||(0.0107)<br>(0.00886)||(0.0129)|(0.0102)|
|Obs.<br>|30<br>49<br>||30<br>|49<br>|
|R<sup>2</sup><br>|0.025<br>0.140<br>||0.276<br>|0.088<br><br>|
|Source: D’A<br>initial locati<br>characteristi<br>sorting, esti<br>characteristi<br>characteristi<br>all periods.<br>periods. In<br>metropolita<br>surveys.|oust, Galdo, and Ianchovichina (2022). Note:<br>on premium before sorting. These are the es<br>cs but are net of survey-year fixed effects as<br>mated with model (2.1). These are the estimate<br>cs (age, age squared, gender, human capita<br>cs) and are also net of survey-year fixed effect<br>In the case of Brazil, census data is used for<br>the case of Colombia, we use official aggre<br>n municipalities. For the rest of the countri|The resu<br>timated<br>
br>economy|Public spending on infrastructure would reduce income inequality in the short-<br>run, whereas it would increase in the long-run.|
|Sasmal and<br>Sasmal (2016)|Panel data analysis for Indian states|The lager is the public expenditures on the development of infrastructure, the<br>higher is per capita income and lower poverty headcount|
|Chotia and Rao<br>(2017a)|Auto-regressive distributed lag (ARDL)<br>bound testing approach|Infrastructure development reduces poverty in India|
|Chotia and Rao<br>(2017b)|Pedroni’s panel co-integration test and<br>panel dynamic ordinary least squares<br>(PDOLS) method|Infrastructure development and economic growth lead to a reduction of poverty<br>and urban-rural inequality in BRICS countries|
|Hooper et al.<br>(2018)|US state-level panel data for 60 years<br>(1950-2010)|Infrastructure investment reduces income inequality; the relationship is<br>stronger at the bottom 40 percent (by income) population|

# **5. Some Issues Related to Infrastructure and Economic Growth or Inequality**

There are several issues related to the relationship between infrastructure and economic growth, as well as inequality. These include: how much the quality of infrastructure matters; is the marginal effect of infrastructure diminishing? What are the channels through which infrastructure affects economic growth and inequality (such as increased productivity of firms, employment, or labor market). Short-term vs. long-term impacts of infrastructure, infrastructure, and institutions, and so on. Each of these issues could be the topic of a separate paper and cannot be discussed in this paper. Below, we highlight a few of them. Some of these issues require further studies for better understanding and are highlighted in the next section.

# **_5.1 Quality of Infrastructure_**

The existing literature on infrastructure and economic growth deals mostly with the quantity of infrastructure. Studies
Table 5 reports correlations between the aggregate market share of politically connected firms, family owned (in panel A) and broadly defined (in panel B), and various industry characteristics during 1993–96 and 2000–09. The sorting of connected firms across industries is by no means random. Industries with higher levels of political connections – measured either by the market share of Suharto family firms or firms with broad connections – generate significantly more output, have more tangible assets, and have lower exports. Politically connected firms thus seem to sort into non-tradable industries. In addition, industries in which broadly defined politically connected firms are more important tend to import more. They have lower foreign ownership but higher state ownership penetration.<sup>16</sup> The market share of the largest four firms (market concentration) tends to be higher, and there are fewer firms in such industries.

Churning is negatively correlated with political connectedness. Both entry, exit, and the natural rate of entry are negatively correlated with political connectedness measures, albeit that the correlation between exit and family connections is not significant. Connections seem to repress entry.<sup>17</sup> Overall, political connections appear especially important in industries that are less competitive. Our summary competition index, Z, is negatively correlated with the aggregate market share of firms with both family and broad connections, but only significantly so for the latter group.

Regime change has resulted in a significant increase in the correlation between Suharto family connectedness and the aggregate competition index, albeit only at the 10% level. Competition thus appears to have improved somewhat in family connected industries, though we cannot reject the null hypothesis of no correlation between Suharto family connections and competition in either the Suharto era or the post-Suharto era. The negative correlation between broad connectedness and the summary competition index also attenuates slightly after Suharto’s fall, but not significantly so.

> 16 Note the contrast with the results from the firm level descriptive statistics, which show that politically connected firms
mobility trends (e.g. Retail and recreation, Grocery and pharmacy, Parks, among others). From Figure 2, it is quite clear that global mobility indicators have slowed. We use these data to test the correlation with industrial production . Analysts can use the change in industrial production to back out estimates for annual GDP growth.

Unfortunately, some of these data go back only to February 15, 2020. To increase the degrees of freedom in the analysis, we backcast the mobility data using daily weather and pollution data. The assumption is that pleasant weather and low pollution are correlated with an increase in mobility.

The rest of the paper is structured as follows: In Section 2 we describe recent nowcasting literature. The overall methodology is discussed in Section 3, which is followed in Section 4 by a discussion of estimating and approximating the models via a Kalman filter. In Section 4.2 we describe the approximation done using pollution data, while Section 4.3 discusses the links to industrial production. In Section 5 we present the results and Section 6 concludes.

# **2 Literature review**

The seminal papers of Geweke (1989), Stock and Watson (1989), and Bai and Ng (2002) have placed the dynamic factor model (DFM) as the predominant framework for research on macroeconomic forecasting using high-frequency indicators. Overall, this framework allows us to study large panels of time series through a few common factors, especially, when the data series are strongly collinear.

The available methodologies for estimating DFMs can be divided into two groups. The first group of estimators entails nonparametric estimation with large N using cross-sectional averaging methods, primarily principal components. Principal components analysis (PCA) is the most popular factor extraction method in the treatment of dynamic factors models. PCA is appealing because of its computational advantages and asymptotic properties in large data sets, see Bai (2003). Unfortunately, for many empirical applications the PCA 2
living in her father‘s household, a woman will work on the family land and eventually obtain use rights on a plot. As a wife, she‘ll work on the land of her husband‘s family and might also have use rights on a particular plot. If she is in neither of these positions, she simply won‘t have access to land. This in part explains the high remarriage rate following widowhood or divorce. Whether this fact contributes to gender inequality in terms of well-being is a question we want to explore in this paper.

The data used here come from an original survey entitled Pauvreté et Structure Familiale (Poverty and Family Structure, henceforth PSF) conducted in Senegal in 2006/2007. The PSF survey stems from the cooperation between a team of French researchers and the National Statistical Agency of Senegal.<sup>13</sup> The survey is described in detail in De Vreyer et al. (2008).

The PSF is a nationally representative survey covering a sample of over 1800 households spread over 150 clusters drawn randomly from the census districts so as to insure a geographically representative sample. About 1,750 household records can be exploited, covering 14,450 individuals. The survey describes a population of which the majority (57.1%) live in rural areas, 48% is male and 95% is Muslim—statistics that accord well with other sources (World Bank, 2009). Urbanization over time is evident in that, amongst those adults who had a father in rural areas, 22% now live in urban areas. A similar percentage of those whose mother lived in rural areas also do so.

Senegalese households are large, with slightly more than eight members on average in the PSF. The families are typically multigenerational. Polygamous unions are common, with 24% of married men and 37% of married women engaged in such unions, which mostly comprise a husband and two wives (only 20% of polygamous unions have more than two wives)