rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 4. Economies with investment growing below its long‐term average**

_The share of EMDEs with investment growth below its long‐term average has risen since 2012. The increase has been especially pronounced for commodity exporters._ <!-- Start of picture text --> A. EMDEs with investment growth below long‐term B. All economies and advanced economies with<br>average investment growth below long‐term average<br>Percent of countries Percent of countries<br>100 Commodity exporters 100 AEs World Share = 50<br>Commodity importers<br>80 Share = 50 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Oxford Economics; World Development Indicators, World Bank. A.B. Long‐term averages are country‐specific and refer to the period 1990‐2008. The world sample includes 157 economies. The AE sample includes 35 economies, and the EMDE sample includes 78 commodity exporters and 44 commodity importers.

# **Figure 5. Investment growth forecasts**

_Short‐term and long‐term forecasts for investment growth in EMDEs have declined steadily since 2010._

## **A. Next‐year forecasts in EMDEs**

## **B.  Forecasts: Five‐year ahead expectations**

<!-- Start of picture text --> Percent Percent EMDEs<br>12 GDP Investment 8 EMDE commodity importers<br>EMDE commodity exporters<br>EMDEs: 2015 actual<br>6<br>8<br>4<br>4<br>2<br>0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2010 2011 2012 2013 2014 2015 2016<br><!-
krais, 46 oblasts, 2 federal cities, 1 autonomous oblast, and 4 autonomous okrugs. We exclude the North-Caucasian (Chechnya) district because civil unrest prohibited surveying. 5 Summary statistics by gender, age, and education (% with secondary degrees) are very similar to those found in the 2002 Russia Longitudinal Monitoring Survey (LSMS), as well as the 2002 Russian National Census. Relative to the census data, however, our survey appears to under-represent individuals in the highest income bracket.  This is likely the result of difficulty gaining access to the highest income individuals, many of whom live in gated housing communities, in order to conduct face-to-face interviews.

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# **2. Motivating Theory**

This section first discusses several general features of malapportioment and then lays out our theoretical argument for the origins and consequences of malapportionment.

A long standing literature in political science (Taagepera and Shugart, 1989; Samuels and Snyder, 2001; Snyder and Samuels, 2004) identifies malapportionment as a formal and often deliberate “pathology of electoral systems”. Malapportionment – a discrepancy between the share of legislative seats and the share of population held by electoral districts – violates the “one person, one vote” principle that authors like Robert Dahl (1971, 1989) consider to be a basic pillar of fair democratic regimes. Even though this principle is often guaranteed by constitutional charts, in many countries it has been disregarded or implemented only after judicial intervention<sup>5</sup> .

As Snyder and Samuels (2004) point out, countries with a bicameral system may display a high level of malapportionment in the upper chamber since this chamber usually represents all the geographical constituencies more or less equally. While upper chamber malapportionment is, therefore, normatively justifiable, there is no _a priori_ reason for weighing the votes of citizens unequally in the lower chamber. This paper thus focuses only on lower chamber malapportionment.

Lower chamber malapportionment can arise spontaneously over time due to migration or different regional patterns of population growth. Western European and North American democracies typically have low levels of malapportionment since they periodically reapportion the number of seats attributed to electoral districts in response to these demographic changes (Snyder and Samuels, 2004).

On the other hand, the data from Samuels and Snyder (2001) reported in Table 1 show that many of the countries with high legislative malapportionment are newly established or consolidating democracies. In addition, data from Snyder and Samuels (2004) on Latin America

> 5 For example, with two verdicts, _Baker v. Carr_ in 1962 and _Wesberry v. Sanders_ in 1964, the US Supreme Court ruled in favor of redesigning electoral districts since they were characterized by high levels
.31|884.60|
|Prob _> F_|0.0000|0.0000|0.0000|0.0000|0.0000|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: First-stage results of IV-FE regressions of (log) employment (for various types of skilled and unskilled workers) on export intensity (exports/sales). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

17
To ensure our measures of food insecurity are as similar as possible over time, we create a standardized FIES score developed by Adjognon et al. (2021) and implemented in Rudin-Rush et al. (2022) that is in addition to our mild, moderate, and severe indicators. The standardized measure counts the number of affirmative answers to FIES questions in the pre-outbreak data by country and uses survey weights to standardize the variable such that its mean is zero and standard deviation is one.<sup>7</sup> Following a similar process, the post-outbreak data are standardized by country across all data rounds. As such, the standardization process facilitates comparison between preand post-outbreak data and across country by ensuring our measure of food insecurity is as similar as possible over time and across countries. This allows for comparisons of deviations from the pre-pandemic mean and the mean of the variables after the onset of the pandemic within each country. Additionally, standardization allows us to interpret estimated coefficients in terms of standard deviations instead of a unitless score.

As seen in Figure 2, food insecurity in all three countries increased substantially between 2019 and the summer of 2020, following the onset of the pandemic. Recovery in food security throughout the subsequent year was slow in all three countries, with about 80 percent of households in Malawi and Nigeria and about 60 percent in Ethiopia experiencing mild food insecurity in almost every month following the outbreak. Prior to the onset of the pandemic, mild food insecurity affected less than 30 percent of households in Ethiopia and about 60 percent in Malawi and Nigeria. Similarly, moderate food insecurity spiked after the COVID-19 outbreak and slowly recovered in subsequent months. Severe food insecurity increased in Malawi and Nigeria in June 2020 and rose slightly in Ethiopia after the initial outbreak period. In all three countries, food security has not returned to pre-pandemic levels.

# **3 Method**

We use two econometric approaches to investigate the causal effect of livelihood diversification on mitigating the impact of COVID-19 on food insecurity. The first approach is
# **2 Data and the definition of a firm**

The number of firms can be directly determined using business registries, censuses, and surveys. This often requires that a firm has a fixed physical location associated to the firm. Although this is generally regarded as the gold standard for data on firms, these data are rarely available for developing countries, particularly in low-income regions of Africa.

Firms can also be assessed indirectly via information on employment. Following Coase (1937), a firm can be understood as the space where a relational contract occurs. Specifically, it is where someone, an employer, hires workers to perform a number of tasks. Under this characterization, _employers_ , regularly reported in labor and household surveys, can be assumed as a good approximation of a firm.

To put this definition into context, it is worth exploring other definitions. OECD defines a firm as “[...] a legal entity possessing the right to conduct business on its own, for example, to enter into contracts, own property, incur liabilities and establish bank accounts.”<sup>1</sup> The World Bank Enterprise Survey (WBES) defines it as the aggregation of _establishments_ , where the establishment is ”a physical location where business is carried out and where industrial operations take place or services are provided.” A firm may be composed of one or more establishments. In most cases, the WBES focuses on formally registered firms with five or more workers.<sup>2</sup> The World Bank’s Entrepreneurship Database (WBED) provides measures on registered firms across countries, which are defined as formally registered private companies with limited liability.

Formal status is an important dimension for the definition of firms in developing countries. However, to understand the broader universe of firms in the Africa context, we also need to incorporate informal businesses, given they represent a large share of the economy. There are very limited data available across African countries providing comprehensive measures of the
contour is equal to w2/w1. Equating the values of AF weights to the marginal rate of substitution of u at z (the slope of the dashed line in Figure 7) does not make sense. Instead, AF parameters (weights w, cutoffs z and identification threshold k) should be selected in a way that the identification contour best approximates indifference curve u (Decerf, 2023b). A similar point can be made for PX weights.<sup>65</sup> <!-- Start of picture text --> x2 Union x2 Intersection<br>z<br>z2 z<br>u u<br>x1 x1<br>z1<br><!-- End of picture text -->

**Figure 7:** AF weights are not marginal weights of substitution

The fact that multidimensional poverty weights are not mere marginal rates of substitution casts serious doubts on seemingly natural methods to select their values. This is clearly the case for methods whose objective is precisely to uncover marginal rates of substitution. For instance, AF weights cannot directly be selected from the methodology proposed (in another context) by Benjamin et al. (2014b) because its aim is to uncover average marginal rates of substitutions for non-monetary dimensions. They can neither be selected directly from the coefficient values obtained from OLS regressions on subjective well-being data.<sup>66</sup> Also, it is unclear whether directly asking subjects for their preferred AF weights would yield meaningful results. Indeed, the link between AF weights and the shape of the identification contour is not trivial.

Rather, we outline a “weights selection” method whose objective is to yield interpersonal well-being comparisons that are best in line with preference u. This method is in line with the spirit of Maasoumi and Xu (2015). For Objective 1, we would like to select ˆ parameters such that the indicator most reliably yields u > u(ˆxi) whenever u > u(ˆxi).
br>2005 onwards|<br>In firstyear, institutional deliveries inthe five districts<br>increasedfram 38per cent to59 percent.4.7% of<br>|deliveries in thegroupwere caesareans. A later report<br>=<br>|statesthatpublic sectorinsitutional deliveries have been<br>= <br>declining|<br>A household-level survey of beneficiaries (n=262) and non- <br>users (n=394) indicated that the scheme iswell-targeted to<br>the poor (only 6% of beneficiaries were non-poor) but<br> |manypoorpeople do not use the services.|<br> |The beneficiaries saved more than Rs 3,000<br>|(USS$75) in delivery-related expenses and<br>were generally satisfied with the scheme,<br>although5% reported notreceiving transport<br>funds and4% reported being askedfor<br>moneyby staff.An average of Rs 654was<br>spend per person on additional medicines.|
|4/OBA, Kenya|Significant increase in uptake of safe motherhood services; <br>limited increase in the uptake ofFP services (possibly<br>driven by socio-cultural/other<br>factors)||Where abilityto pay is low, Marie Stopes pays forthe<br>vouchers. In rural areas,95% ofvouchers purchased by MSK<br>(the service provider)|Households benefitfrom high subsidies (e.g.<br>|200 KES for delivery package, compared to<br>600-1,500KES fordelivery in public sector<br>(plusadditionalcostsfordrugs,ANC,PNC)|
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||_H1: There is a correlation_<br>_between environmental_<br>_commitment and_<br>_liberalisation_|wholesale reform<br>-reform model (pool,<br>wholesale wheeling,<br>competitive bidding)<br>-timing (reform in 1990, 0.5<br>points deducted for each<br>year of delay)<br>retail reform<br>-reform model (regulated<br>TPA, negotiated TPA)<br>-timing (reform in 1990, 0.5<br>points deducted for each<br>year of delay)|-energy independence index<br>(computed from total primary<br>energy supply of each different<br>fuel, total exports of each fuel,<br>indigenous production of each<br>fuel, share of each fuel in total<br>electricity production)<br>(SE)<br>_Result: for the restricted model_<br>_the coefficient is significant at_<br>_the 1% level_<br>-environmental index<br>(constructed from information on<br>Kyoto target increase, renewable<br>energy R&D expenditure/ GNP,<br>implementation of energy/<br>carbon tax, energy related<br>carbon-dioxide emissions,<br>
**Figure 14. Inequality (Dissimilarity Index) in Social, Emotional, and Cognitive Development by Year**

<!-- Start of picture text --> 100<br>90<br>90<br>80<br>70<br>58<br>60<br>51<br>48<br>50 2006/7<br>40 34 2011<br>30<br>2012<br>20<br>20<br>10<br>0<br>ECCE Development Violent Work &<br>Activities Discipline Domestic<br>Work<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on MICS 2006/07, ENPSF 2011, and ONDH panel 2012.

Children have very different chances of successful early social, emotional, and cognitive development for the most and least advantaged (Figure 15). Overall, in Morocco, the most advantaged children benefit most from early childhood care and education, which naturally has implications for inequality in school and then during adulthood. In 2012, the least advantaged child had a 45 percent chance of ECCE compared to a 95 percent chance for the most advantaged. This represents an improvement from 2006/07 to 2012. Disparities in development activities increased, with the most advantaged child having a 79 percent chance of development activities in 2011 compared to 18 percent for the least advantaged. The least advantaged child is almost guaranteed of being violently disciplined (99 percent) while the most advantaged child has a substantial but lower chance (74 percent). The chances of work (including domestic work) are slightly higher for the most advantaged (16 percent) than the least advantaged (8 percent). This reversal is notable for its rarity among all the outcomes.

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# **2.2. Data and Sampling**

Data for variables in (1) come from different sources. _Budget Balance_ data are from the World Bank. Time series end in 2004 when change in fiscal accounting methods and reporting was completed at the International Monetary Fund. Data for _Elections_ are from the International Federation of Electoral Systems and from Block and Vaaler (2004). Election years are adjusted for government fiscal years consistent with Hanusch and Keefer (2011). _Ratings_ data are from _Bloomberg_ , and are measured annually as an average of up to three ratings published by Moody’s, S&P and or Fitch (or CRAs merged into Fitch in the 1990s) on January 1. These raters dominate the industry, including sovereign rating segments, with the broadest country coverage, the most analysts and the largest market shares of ratings for individual bond issuances (White, 2010). Their ratings tend to move together with financial market players following them as a group. Data for other controls include the World Bank’s World Development Indicators ( _Employment, Inflation, Reserves, Curr Crisis_ ) and Database of Political Institutions (DPI) (Beck et al _._ , 2004) ( _Checks_ ), and Freedom House ( _Rights_ ).

We sample from 18 prominent emerging democracies including: Argentina, Bolivia, Brazil, Bulgaria, Chile, Colombia, Ecuador, Indonesia, Mexico, Paraguay, Peru, Philippines, Poland, Russia, South Africa, South Korea, Uruguay, and Venezuela. They uniquely meet three sampling requirements. Starting in different years from 1989 and the fall of the Berlin Wall to 1998 and the fall of the Suharto regime in Indonesia, these countries have: 1) competitive presidential executive electoral systems that are new (e.g., Russia) or recently reformed (e.g., Brazil); 2) ratings from Moody’s, S&P and or Fitch; and 3) comparable budget balance data for years when rated. Sampling from these
from equation (1) and calculate averages of the standard deviation of the residuals over five-year rolling windows. Figure 5 displays the results for the four fiscal policy indicators for the group of EMDEs. While the patterns across the four charts are not identical, there is an overall downward trend in at least three of the indicators: primary expenditures, government consumption, and revenues. The trend for the primary balance indicator is less obvious although there is a clear decline in volatility in the years that followed the GFC which was only interrupted by the volatility associated with the COVID crisis.<sup>14</sup> We reproduce the same analysis now for the group of commodity exporters and also explore the correlation between fiscal volatility and energy prices (Figure 6). The patterns for fiscal volatility indicators are similar, with a clear downward trend in the volatility of government consumption and revenues. The trend is less obvious in the case of primary expenditures or the primary balance although, as in the case of EMDEs, there was a period of low volatility following the GFC and before the pandemic-induced crisis. Further, there does not appear to be any clear relationship between fiscal volatility and energy prices over the sample period although the volatility in primary

> 13 ‘Government size’ is measured as the share of general government total expenditure in GDP (from the IMF WEO database).

> 14 We have reproduced these results for the group of advanced economies, although not shown here for brevity. The pattern is weaker. While there is some decline in the volatility of primary expenditures following the sharp spike in the pre-2000 period, there is almost no decline in volatility for the other three indicators.

9
<!-- Start of picture text --> Selected Middle Income Countries:<br>Real GDP Growth<br>10<br>&<br>6 f —~<br>, w/z<br>-4<br>BS Botswana ——— South Afnca= Namibia ——= Maunitius<br>-6<br>2005 2006 2007 2008 2009 2010 Proj.<br>RegionalSource: IMF,Economic Afncan Outlook,DepartnertApril Database2010. andSubSataranAfica<br><!-- End of picture text -->
Colombia (World Bank, 2021). On the other hand, the percentage of women aged 15-49 years old subject to physical or sexual violence in the last 12 months in Guatemala was 8.9 in 2015, below the rates of countries such as Mexico (9.5 in 2016) and Honduras (11 in 2012), but well above the rates of El Salvador (6.7 in 2014), and some European countries such as France (7 in 2012) and Germany (5 in 2012) (World Bank, 2021).

We closely relate to Bhalotra and Fernandez (2021). Exploring Mexican census data, they assess the role of different drivers of FLFP between 1960 and 2015.<sup>4</sup> During this period Mexico’s FLFP rose from 13% to 47%. Changes in women’s education and in the occupational structure of employment account for the change. But the increase in FLFP of the 1990s is an exception as it remains largely unexplained. The authors conjecture that unobservable factors such as changes in social norms regarding marriage or fertility could be driving these changes. In our paper we can observe proxies of social norms, attitudes towards women and of social policies and we explore their role to explain the within-country variation in FLFP.

> 4 Bhalotra and Fernandez (2021) propose as supply side drivers of FLFP: education, marital status, and fertility. Demand-side drivers include sector and occupational structure of employment at the municipality level.

4
Figure 20. Average hours worked by week, ages 25-59 Figure 21. CME at mean, children in the household ages 0-3 <!-- Start of picture text --> No children Children<br>51<br>50 50 49<br>48 48 48<br>47<br>47<br>45 45<br>43 44<br>43<br>41 41<br>40<br>39 39<br>35<br>2019 2021 2019 2021 2019 2021 2019 2021<br>No higher education Higher education No higher education Higher education<br>Men Women<br>Figure 22. AME, children in the household<br>ages 0-3, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household 0-3 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Figure 24. AME, children in the household<br>ages +3-6, ages 15-59<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>Children in the household +3-6 years<br>Men 2019 Men 2021 Women 2019 Women 2021<br>Pr(labour force=1)<br>Pr(labour force=1)<br><!-- End of picture text --> <!-- Start of picture text -->
1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0 2 4 6 8<br>0.0 Children in the household 0-3 years<br>Men 2019 Men  2021 Women 2019 Women 2021
**Figure 8: Catastrophic health expenditure among low‐income households, 2003 and 2008**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4 2003<br>2008<br>2<br>0<br>%<br>Incidence ratio of catastrophic spending<br><!-- End of picture text --> Source: China National Health Services Survey (NHSS) 2003 and 2008. Note: According to China’s NHSS, out‐of‐pocket health spending is catastrophic when exceeding 40% of annual household (HH) consumption. Low income HHs are those with per capita annual income below 50% of the mean annual HH income in the jurisdiction.

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trade sharein GDP<br>5 10<br><!-- End of picture text -->

**When taking services as inputs into account, Turkey performs in line with its peers.** Services are not only exportable in their own right, but also are critical inputs to manufacturing and GVCs, as seen above. The importance of services as inputs can be seen by examining the value added from domestic service inputs (domestic indirect) and foreign services inputs (foreign) in exports.<sup>7</sup> The left‐hand panel of Figure 2.7 shows domestic indirect services value‐added together with the exports of services firms (domestic direct, which is what is shown in Figure 2.6, but in value‐added terms).<sup>8</sup> Overall, Turkey is in the middle of the pack, although the domestic indirect share is well below the OECD average. The share of foreign value‐ added is also relatively smaller (see right‐hand panel in Figure 2.7). Turkey’s low rate of participation in GVCs and foreign inputs (Figure 2.5) is reflected in the relatively low foreign services value‐added in exports. _However, one critical omission in the data is that it does not include services produced in‐house by manufacturing firms, which is the focus of the following section._

> 7 Based on statistics from the OECD‐WTO Trade in Value‐Added (TiVA) database.

> 8 To be precise, the exports picked up in Figure 2.6 cover in large part trade in services on a cross‐border basis, which is called Mode 1 services supply in WTO parlance. It also covers Mode 2 (i.e. consumption abroad) and part of Mode 4 (i.e. temporary movement of labor), but does not cover Mode 3 (sales of foreign affiliates). Mode 3 trade however is covered in Figure 2