rafmacalaba/datause-encoder-data huggingface dataset:
## **3.3 Other Data**

### **3.3.1 Night Lights Data**

We employ night light luminosity (NTL) data constructed by Elvidge, Zhizhin, Ghosh, Hsu, and Taneja (2021) as a proxy for electrification. More specifically, we use the average VIIRS (Visible Infrared Imaging Radiometer Suite) annual cloud-free composite VNL version 2 for the year 2015. The resolution of these data is 15 arc seconds (approximately 0.0327 km around the Equator). Thus, after extracting it for SSA, we aggregate the NTL measure at the level of each GAEZ grid.

### **3.3.2 Water-Usage Data**

Large variations exist in water-usage patterns within countries. We employ annual countrylevel freshwater-withdrawals data for agriculture, industry, and municipal (i.e., domestic) use.<sup>5</sup> Annual freshwater withdrawals refer to total water withdrawals, not including water loss due to evaporation from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Using this data, we calibrate country-level thresholds needed for crop irrigation purposes. By subtracting the freshwater needed for sanitation and industrial production purposes from the total freshwater consumption of the country, the thresholds represent the share of freshwater that is available for irrigation purposes. The agricultural water withdrawals is collected at the country level. As a result, we are unable to account for within-country variation in access to water for sanitation.

### **3.3.3 TerraClimate Data**

To incorporate climatic factors into our yield estimations, we utilize the dataset curated by Abatzoglou, Dobrowski, Parks, and Hegewisch (2018). This dataset provides monthly climatic information at a spatial resolution of approximately 4 km (1/24th degree). For each of the GAEZ grids, we extract data on mean monthly temperature, total monthly precipitation, and monthly Palmer Drought Severity Index (PDSI) for 2010. We then calculate the quarterly averages of the temperature and PDSI, as well as the quarterly sums of the total precipitation. Additionally, we consider the squared values of the mean quarterly temperature and the total quarterly precipitation.

# **4 Descriptive Statistics**

## **4.1 Groundwater Access and Availability in SSA**

The first exercise we undertake is to map the existing groundwater availability in SSA.

> 5For access to the data, refer to: https://ourworldindata.org/water-use-stress#freshwater -withdrawals-by-country.

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added is U-shaped. More specifically, at low levels of income, economic activity diversifies as countries grow, but then, at around 9,000 (constant 1985$) GDP per capita, economic activity concentrates as countries grow richer. Klinger and Lederman (2004; 2006), Hesse (2008) and Cadot et al. (2011) also find a U-shaped pattern between measures of diversification of exports and income per capita.

<!-- Start of picture text --> pattern between measures of diversifi-<br><!-- End of picture text --> <!-- Start of picture text --> cation of exports and income per capita.<br><!-- End of picture text --> <!-- Start of picture text --> Growth Variability, Development, and Economic Concentration<br><!-- End of picture text --> Figure 1: Growth Variability, Development, and Economic Concentration <!-- Start of picture text --> . .<br>] ]<br>2 | 22 ° E 2 9<br>Bq] 2 a ey | hed<br>:6:<br>E: E 0<br>< ‘e 5 = =<br>a a<br>Oo i] Oo<br>r=] e a r=) o ®<br>a4 fa s<br>= e 8 ic %<br>zB " kn @ : B : @ a<br>z eo ~ * 3 = Oo CO 3 rs8<br>5 ae eG 5 os<br>5 | sgtaaaeh<br>oSee (Soees a}.e or 2 oeCageSs Recmes=? ©<br>o Ss ° * ae &o® ¢ Ss «6<br>=<br>& to) e “8 = S<br>ee<br>6 8 10 12 6 wi a) 4 1<br>‘GDP per capita in 1970, log Gini of exports in 1970<br><!-- End of picture text -->

<!-- Start of picture text --> Source: Authors’ calculations based on data from<br><!-- End of picture text --> <!-- Start of picture text --> Penn World Table and UN COMTRADE.<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Table and UN COMTRADE.

Bringing both sets of literature together, this paper studies the patterns of economic diversification over the past 57 years. The contribution of this paper to the literature is fourfold. First, by taking advantage of longer and expanded data series, this paper updates our understanding of diversification patterns across countries and over time to 151 countries over 57 years. Second, it investigates whether the observed patterns are more likely driven by cross-country characteristics or by within-country trajectories. Third, we expand the study <!-- Start of picture text --> characteristics<br><!-- End of picture text --> <!-- Start of picture text --> or by<br><!-- End of picture text --> <!-- Start of picture text --> trajectories.<br><!-- End of picture text --> <!-- Start of picture text --> we expand the study<br><!-- End of picture text --> 2
( _spatial adjustments_ ) after floods because these events destroy local suppliers and raise the cost of sourcing local inputs (Kato and Okubo, 2018). Nevertheless, these firms are also larger and better managed and hence, conceptually, they could be more resilient. There are not many studies that have considered this distinction in a systematic manner.

By comparison, single-person firms appear to be more resilient to crises. They are more likely to reopen in the first 3 months after the shock partly because (i) employment is flexible with family participating in business operations (ii) absence of other means of income opportunities for the owner and family members; and (iii) ability to react faster when making such decisions than larger firms. This was substantiated in a study of 673 Mississippi establishments that were tracked weekly in the year following Hurricane Katrina, (LeSage et al., 2011). Nevertheless, given that these firms are, by definition, small they are the hardest hit by the shock. This could be because the firms that disappear are not the same as those that emerge after the crisis.

# _Informality_

Poor people who run informal firms are the most likely to set up production in risky places because of the low demand for locating on such land parcels. Clear tradeoffs emerge between encouraging entrepreneurship while protecting people from engaging in excessive risk taking. Lowering the costs of entry for firms fosters competition and can contribute to achieving upward income mobility for those entrepreneurs who succeed. At the same time, informal production areas are most likely to emerge in areas exposed to flood risk and mudslide risk.

With the ever improving access to administrative data, we foresee that researchers in more and more developing countries will be able to recreate firm demographic longitudinal data to observe firms being born and closing. Empirical benchmarks of a failure to adapt include firm closings, new formal business formation, firm growth and a lower average product of labor in areas facing more weather shocks. Evidence from India using data on both formal and informal firms suggests that floods led to significant decline in employment in the formal sector, especially in the least productive establishments, and towards informal household-run enterprises (Hossain, 2020). This is consistent with the view that such reallocation results from survival strategy of workers who ended up unemployed and suffer from labor market contraction in the formal sector (Tybout, 2000; La Porta and Shleifer, 2014). Given the large productivity gap across formal and informal sectors, such climate-induced reallocation generates a significant reduction in aggregate productivity.

# **Industry dynamics and adaptive capacity**

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<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>Cassette Sales Any War (D̅≥.3)<br>Any War(D̅<.3)<br>.35<br>1500<br>.3<br>1000<br>.25<br>.2<br>(Million USD)<br>500<br>.15<br>Proportion of Countries in Conflict<br>Value of Global Music Cassette Sales 0<br>.1<br><!-- End of picture text --> FIGURE A6: CASSETTE SALES AND INCIDENCE OF CONFLICT _Notes:_ Cassette sales data are in millions of USD and taken from IFPI (2009). Any war is a dummy variable for whether a country has experienced civil conflict in that year taken from NQ’s dataset. �<sup>�</sup> is the proportion of years in which a country received any wheat aid shipments from the US in the NQ replication file. The median value of �<sup>�</sup> is 0.3, so that the conflict trend is split by countries that are above and below the median propensity to receive aid. All data come from the NQ replication file.

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Another relevant question is whether the credit ratio should take into account other denominators besides GDP, such as financial assets or total assets of the private sector. Although GDP is correlated to a significant extent with private sector income and therefore serves as an indicator of the ability to repay a given amount of loans, holdings of financial assets (deposits and securities investments) and nonfinancial assets (for example, real estate) are also relevant to the evaluation of excessive credit.

Chart 18 presents credit gaps with alternative denominators (GDP and financial assets and total assets of the private sector) calculated using the HP filter on data for bank loans in the Czech Republic with a high lambda (400,000) as proposed in Basel III. The filter is applied to quarterly data for the period 1998–2010, which, however, is regarded as relatively short from the international perspective (Basel III recommends at least a 20-year period). The estimates indicate that the current level of bank loans is below the long-term trend. However, the trend estimate is subject to a range of problems related to the short time series—above all to extraordinary factors linked with a fall in credit volume in 1998–2002, which was caused by a banking crisis in the 1990s, and the cleanup of bank balance sheets ahead of the privatization of large banks.

**Chart 18. Credit gaps in the Czech Republic Chart 19. Credit-to-GDP ratios for a similar with alternative denominators (in %) level of economic development (in %; GDP per capita in 2005 USD = USD 17,000 approx.)**

<!-- Start of picture text --> 100<br>20 90<br>15 80<br>70<br>10<br>60<br>5 50<br>0 40<br>30<br>-5<br>20<br>-10 10<br>-15 0<br>03/98 03/00 03/02 03/04 03/06 03/08 03/10 CZ IT NL AT FR ES DE<br>Credit-to-GDP gap (2009) (1986) (1984) (1984) (1985) (1990) (1983)<br>Credit-to-GDP gap (actual period estimation)<br>Credit-to-financial-assets gap<br>Source: IMF IFS, authors' calculations<br>Credit-to-assets gap<br><!-- End of picture text --> Source: CNB, authors' calculations As regards simulating possible macroprudential policy in the past, it makes more sense to apply the HP filter recursively: in each past period using only the data that were available in that period. (At the end of 2005, for example, the trend value and therefore also the gap between the observed credit level and the trend is calculated on 1998–2005 data.) This simulates the situation that the macroprudential policy maker would hypothetically have found itself in had it been required to decide whether excessive credit growth was emerging. The calculated credit gaps expressed as a percentage of GDP indicate that the Czech Republic would have found itself in a situation of excessive credit as early as 2004 (Chart 18). However, the aforementioned drawbacks of the HP filter play an even greater role in the calculated gap, as the problem period of 1998–2002 influences the trend.

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2 dedicated SHI delivery system, as in some Latin American countries, or by forcing an institutional separation of the ‘purchasing’ of health care, which would be done by insurers or the a SHI agency, and the delivery of health care, which could remain the responsibility of the health ministry’s job, as in, say, Vietnam, or could be contracted out to the private sector, as in, say, Argentina.

There is actually no evidence showing that in Europe SHI countries enjoy better quality health care than countries with tax-financed health systems. For example, survival rates of cancer patients are relatively poor in Denmark and England, but good in Iceland and Sweden, which also operate tax-financed systems; the latter two, in fact, have better survival rates than France, Germany and the Netherlands, which all operate SHI financing systems (Micheli et al. 2003). In any case, other system variables are likely to matter too; the bivariate associations point, for example, to survival prospects for cancer patients in Europe being better in richer countries, and in countries that spend more on health care. In Latin America, where dedicated SHI facilities or private providers deliver care to SHI members and health ministry facilities deliver care to the rest of the population, both types of provider have been criticized for delivery poor quality care. SHI providers, for example, have acquired a reputation for “focusing a disproportionate amount of resources on high technology and curative care to the near total exclusion of primary health care” (Fiedler 1996). But public providers have been argued to be unresponsive and inconvenient, and, especially at lower levels, to deliver poor quality care (cf. e.g. Lewis, Eskeland and Traa-Valerezo 2004).

As far as cost is concerned (or at least health care spending as a share of GDP), what emerges from the OECD evidence is that it is not so much the source of revenues that matters but rather the characteristics of the delivery system: the role of the public sector in health care
||School attendance rates of 15-17 year olds|X|X|X|2002|
|---|---|---|---|---|---|
||Unemploymentrate of 15-24yearolds|X|X|X|2002|
||Activity rate (in school or working) of 10-17 year olds|X|X|X|2002|
||Share of 10-17 year olds without any activity|X|X|X|2002|
||Share ofworking16-24years oldsin formalsectorjobs (_com carteira assinada_)|X|X|X|2002|
||Share of 16-17 year olds who vote|X|X|X|2002|
|Socioeconomic|Share of people below the poverty line|||X|2002|
|Conditions|Averagehouseholdincome percapita|||X|2002|
||Proportion of the workforce with a signed work contract (formal sector)|||X|2002|
||Number of physicians per 1000 habitants|||X|2002|
||Share ofsinglemother households|||X|2002|

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in the country.<sup>1</sup> Ceará’s capital, Fortaleza, is Brazil’s fifth largest city, with 2.8 million inhabitants. The Military Police have classified 82 of its 402 _favelas_ as risk areas. Fortaleza is characterized by large internal disparities. The most developed neighborhood has a Human Development Indicator comparable to that of industrialized countries (0.916), while the least developed ones show values similar to countries in Sub-Saharan Africa (0.338). The three neighborhoods included in the survey are among the poorest in Fortaleza.

# _3.2. The survey_

The survey implemented by the World Bank covered three neighborhoods in Fortaleza: Autran Nunes, Edson Queiroz, and Pirambú. Each has a population of around 20,000 people and around 4,500 houses. Approximately 500 questionnaires were applied in each neighborhood and, as a result, the sample contains about every ninth household in each of the three neighborhoods.

The data were collected by two sets of teams: the research team, which included statisticians from the local university, psychologists, NGOs, human rights spokespersons, youth and other people living in the selected neighborhoods; and the interview team, composed of one general coordinator, three neighborhood coordinators, and the interviewers, recruited in each of the neighborhoods. The Institute of Organized Research of Ceará (IPOC), a local NGO operating in the poor neighborhoods, led the interview process. Both the pilot and final interviews were carried out by youth from the neighborhoods. Some of the interviewers were part of the MH2OCE (Organized Hip-Hop Movement of Ceará); this organization, which works on violence prevention in poor neighborhoods in Fortaleza, facilitated the team’s access to the most violent areas. The interviews were conducted from June 19 to 29, 2003.

The survey instrument covered five major areas: socioeconomic background; education; health and sexuality; social capital and violence; and employment and economic activity. More details on the data collection procedures can be found in Verner and Alda (2004).

> **1** Section 3 draws heavily on Verner and Alda (2004) and Cardoso and Verner (2006).

4
> <sup>20</sup>

with bankers and industrialists sufficient to spur economic growth, such efforts are the exception rather than the rule.

The second contention of the paper is that the effects of politics on financial sector development extend beyond the formal institutions of competitive elections and political checks and balances. The evidence in Tables 2a and 2b supports this contention. The ordinary least squares estimates in Table 2a indicate large effects of both the continuous years of competitive elections and average newspaper circulation. A one standard deviation increase in the initial number of continuous years of competitive elections is associated with an increase in the rate of growth of the financial sector of 0.6 percentage points per year and with an increase in the size of the financial sector in 2000 of as much as 25 percent of GDP. Results for newspaper circulation are still larger. These findings are robust to controls either for initial financial sector development or initial income, both of which are highly correlated with the two political variables. They imply that government actions supportive of financial sector development, including the security of property rights, are public goods whose provision is sensitive to government incentives to provide public goods.

If these variables accurately proxy the credibility of pre-electoral political promises and voter information, respectively, the results in Tables 2a and 2b underline the importance of a different kind of credibility for financial sector development. When average citizens do not believe the promises of political competitors to provide such public goods as secure property rights or are unable to monitor the fulfillment of such promises, financial sector development slows.

Table 2b investigates the sensitivity of these results to the potential endogeneity of the political variables to financial sector development. Although the political variables remain highly significant, this exercise is less successful than in Table 1b. The _F_ -statistics
Ruel, M., Quisumbing, A., & Balagamwala, M. (2018) Nutrition-sensitive agriculture: What have we learned so far? _Global Food Security_ 17: 128-153. https://doi.org/10.1016/j.gfs.2018.01.002.

Sharma, I., Prima, S., Essink, D., & Broerse, J. (2021). Nutrition-sensitive agriculture: A systematic review of impact pathways to nutrition outcomes. _Advances in Nutrition_ , 12(1): 251-275. <u>https://doi.org/10.1093/advances/nmaa103.</u> 19
per person per day, while the non-poor have consumption or income greater than $3.10 per person per day.<sup>21</sup>

# **3. Findings**

# **3.1. Poverty is disproportionally rural**

As shown in Figure 3, 18.2% of rural residents subsist on less than $1.90 a day, and 45.6 percent of rural residents are either extremely or moderately poor, and therefore live on less than $3.10 per person per day. The corresponding rates for urban residents, in contrast, are 5.5% and 16.2%. This large gap in poverty rates, combined with the concentration of the general population in rural areas, translates into a startling disparity in the number of poor people. About 80% of the extreme poor and 76% of the moderate poor live in rural areas, as compared to only 44% of the non-poor. Given that there are 655 million extreme poor and a little over a billion moderate poor people represented in the sample, the rural sector accounts for a total of 525 million extreme poor and additional 789 million moderate poor. Of course, these absolute numbers underestimate the true global figures, since the sample of 89 countries is only representative of 86.5% of the population of the developing world, and 73% of the population of the globe, in 2013.

**Figure 3: Poverty rate by residential sector Figure 4: Share of population by residential sector**

<!-- Start of picture text --> 90 83.8<br>80<br>67.7<br>70<br>60 54.4<br>50<br>40<br>27.4<br>30<br>18.2 19.9<br>20 10.7 12.5<br>10 5.5<br>0<br>Urban Rural Total<br>Extreme poor Moderate poor Non poor<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>44.2<br>55<br>60% 80.1 75.7<br>40%<br>55.8<br>20% 45<br>24.3<br>19.9<br>0%<br>Extreme Moderate Non‐poor Total<br>poor poor population<br>Urban Rural<br><!-- End of picture text --> This finding suggests a slightly higher level of concentration of the poor in rural areas than previously thought. The study most comparable to this one is Olinto et al. (2013), which applied the $1.25 extreme poverty line (2005 PPP) in 2010 to a different set of harmonized household 21 These figures are generally calculated using the PPPs from 2011. But as of June 2016, The 2011 PPP conversion factors had not yet been adopted for 5 countries: Bangladesh, Cabo Verde, Cambodia, the Lao People’s Democratic Republic, and Jordan. In these cases, we use the $1.25 and $2.00 dollar-a-day poverty lines (measured in 2005 PPP), which are roughly equivalent to the $1.90 and $3.10 lines in 2011 PPP terms. (Ferreira et al. 2015).

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economic theory tells us that it is optimal to subsidize an activity if it generates positive externalities -- i.e. the activity benefits agents other than those directly involved in the activity itself. The potential for positive externalities from FDI surely exists and available evidence exists that often this potential is realized. Incentives to attract FDI may be justified on the grounds of such externalities from inward FDI. However, such policies are not typically what proponents of industrial policy have in mind – indeed the thrust of such arguments is typically in favor of encouraging the development of _indigenous firms_ . It is worth keeping in mind that investment incentives and tax breaks to multinational investors work against their local competitors. Thus, if there exist local firms that could potentially compete with multinationals, the adverse effect of tax incentives to multinationals on such firms needs to be taken into account. The efficacy of investment incentives is also dubious – such policies can end up transferring rents to foreign investors without affecting their investment decisions.

One final insight from the literature on linkage effects of multinational firms is worth mentioning. As Rodriguez-Clare (1996a) notes, we should “...expect multinationals to generate more linkages when they come from regions that are farther away.” The implication for industrial policy with respect to multinationals is clear – if a country does want to attract FDI, it ought to focus on firms that come from far off regions and are interested in serving the local market.

In a recent paper, Barrios et. al. (2004) construct a model wherein the competition effect generated by a multinational is eventually dominated by the positive externalities it generates. Using plant level panel data from the manufacturing sector in Ireland (a country whose economic development has been influenced greatly by multinational firms) they show that such a model indeed describes the Irish experience with FDI. Alfaro and Rodriguez-Clare (2003) use plant level data from several Latin American countries and they make the important point that many empirical studies testing existing theoretical models often use inappropriate measures to evaluate the linkage effects of multinationals. More specifically, empirical studies often use the share of inputs purchased locally by a multinational to measure its impact on linkages. They argue that requirements, may have costs above world prices, raising the possibility that greater linkages imply lower GDP per capita.

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