rafmacalaba/data-use-mentions-extended huggingface dataset:
# I – Introduction

In the early months of the COVID-19 pandemic, health authorities around the world scrambled to inform the public about how to reduce the risk of infection. Modes of message-sending varied substantially depending on local contexts, and multi-mode campaigns were common. Television announcements and dedicated news segments were widely used, complemented where feasible by smartphone alerts, radio broadcasts, social media services, newspaper reports, and even personal appeals from health care workers.

With more than 5 billion unique mobile phone subscribers in the world,<sup>2</sup> COVID-19 information campaigns also turned to short message service (SMS) text messages in many countries. A standardized message bank for these initiatives was quickly developed and prompted by the World Health Organization and other international organizations.<sup>3</sup> The text messaging approach had several key strengths, especially in reaching out to geographically disperse populations, and in low and middleincome countries where advanced smartphone penetration remained limited. In such situations (i.e. large numbers of both smartphone and basic mobile phone users) text messaging was often the “lowest common denominator” to reach the maximum audience. These constraints were highly relevant to the setting of this study. Data from the Listening to Tajikistan survey shows that before the outbreak, mobile phone coverage was quite high, with about 95 percent of the population reporting access to at least one mobile phone in their immediate household (and 108 connections per 100 people in 2019 according the GSMA industry association). However, the number of _mobile internet connections,_ which are required for most smartphone functions and social media applications, was much lower, standing at just 55 connections per 100 people in 2019 according to the same source.

Designs for SMS-based COVID-19 messaging campaigns have been widely documented since the start of the pandemic. Details of campaigns in the United Kingdom,<sup>4</sup> Taiwan, China (Huang, 2020), Saudi Arabia (Hassounah, Hafsa, and Mohammed Alhefzi, 2020),
transfers. These transfers are allocated to the three household groups based on their share of transfers in the SAM. The model thus includes a rather simple mechanism through which the income from migration is transmitted through village linkages.

To summarize the discussion so far, we are analyzing the response of four different types of households, distinguished on the basis of their access to agricultural income and income from outside province migration. Analyzing sources of income pointed to imperfect labor and land markets. These are accommodated by estimating household-specific shadow prices, introducing profits earned on off-farm employment and renting of land, and by modeling household production and consumption decisions as nonseparable.

# **2.3 The village equilibrium model**

Despite introducing nonseparability of household production and consumption decisions, the mathematical structure of the model closely resembles macro level general equilibrium models. Consumption decisions are modeled through a linear expenditure system, while production is modeled by nested CES functions. Table 3 summarizes the key substitution elasticities for each activity. The estimation procedure for obtaining these substitution elasticities exploits the inter-household variation in the survey data. The nesting structure differs across activities and is determined by statistical testing based on pairwise comparisons. Kuiper (2005) provides full details of this method.

The village model does not attempt to treat two-way flows of commodity trade with the outside world. Households consume farm output, but do not purchase these goods from outside the village, nor from other households in the village. Household sales to outside village markets are thus equal to total production minus household consumption.

Village markets exist for traction by draught animals or tractors, and for locally produced consumption goods. Of these village markets, only animal traction has an endogenous village price in the model. The SAM indicates that only limited use is made of 16
According to official statistics, Argentina has 11 million students enrolled in four education levels: preschool (ages 3–5, 15.6 percent); primary (ages 6–11, 41 percent); secondary (ages 12–17, 35.5 percent); and tertiary (ages 18–22, 7.9 percent). The majority of these students (71 percent) attend public schools (DiNIECE 2015).<sup>6</sup> Between 2003 and 2013, student numbers increased by approximately 10 percent, while the number of teachers increased by more than 20 percent over roughly the same period (DiNIECE 2004, 2015). This allowed Argentina to reach a pupil-teacher ratio of 11, the lowest in Latin America after Cuba (OECD 2016), although it is worth noting that this ratio varies considerably across provinces.

Although there have been large successes in terms of increasing coverage, the Argentinean education system fails to provide high-quality education (at least as measured by standardized test scores). While other countries in the region improved learning outcomes since 2000—measured by the OECD’s Program for International Student Assessment (PISA)—Argentina’s scores show no progress (at best) in Science, or even experienced a marginal decline between 2000 and 2012.<sup>7</sup> According to the 2012<sup>8</sup> study, Argentina ranked amongst the lowest of the participating countries (59 out of a possible 65) (OECD 2014).<sup>9</sup> CABA, being the wealthiest jurisdiction in Argentina, exhibits some specific features; namely, a lower share of students attending public schools (49 percent) and higher levels of student achievement (DiNIECE

> 6 These figures do not include special and adult education.

> 7 According to De Hoyos, <u><mark>Holland, and Troiano</mark> (2015), there is a gradual increase in Argentina’s Science scores
# **Does Hotter Temperature Increase Poverty and Inequality? Global Evidence from Subnational Data Analysis**

Hai-Anh H. Dang, Minh Cong Nguyen, and Trong-Anh Trinh<sup>*</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _June 2023._ This version is updated on _October 2023._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

# **JEL Classification:** Q54; I32; O1

**Key words** : Climate change; temperature; poverty; inequality; subnational data

* Dang (hdang@worldbank.org; corresponding author) is a senior economist with the Living Standards Measurement Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Nguyen (mnguyen3@worldbank.org) is a senior economist with the Global Poverty Practice, World Bank; Trinh (trong-anh.trinh@monash.edu) is a consultant with the Living Standards Measurement Unit, Development Data Group, World Bank, and a research fellow at the Centre for Health Economics, Monash University, Australia. We would like to thank Carlo Azzarri, Edward Barbier, Romina Catavassi, Andrew Dabalen, Benjamin Davis, Stephane Hallegatte, David Johnston, Wojciech Kopczuk, Anke Leroux, Paulina Oliva, Paul Raschky, Russell Smyth, Jevgenijs Steinbuks, and participants at the Frontiers in Development Policy Conference (KDI) and seminars at Food and Agriculture Organization and Monash University for useful comments on earlier versions. We would like to thank Matthias Kalkuhl for helpful advice on data and Brenan Andre for assistance with the global maps. We would also like to thank the UK Foreign Commonwealth and Development Office (FCDO) for funding assistance through a Knowledge for Change (KCP) grant for the World Development Report 2021 “Data for Better Lives” and the Data and Evidence for Tackling Extreme Poverty (DEEP) Research
appears to suffer from horizontal dominance. To take but one example, the 100,000 workers at the automobile firm GAZ, which is one of the more innovative large-scale businesses, produced 220,417 vehicles in 1997, compared to the 125,000 workers at Chrysler that made 2 million cars. Goskomstat data of 800 enterprises reveals that firm size is negatively correlated with sales revenue per employee (see Appendix).

# **2. Vertical Integration**

Second, many of the dominant enterprises in Russia are also highly vertically integrated (or have exclusive buyer-seller relationships). Excessive levels of vertical integration superimposed on (horizontally) concentrated product markets can foreclose the entry of rival firms. The high degree of observed vertical integration largely reflects inertia of the uncertainties and chronic shortages of the old Soviet supply system. Engines for trucks are still made only by the Zavolzhye engine factory and bought only by the GAZ vehicle factory. Heavy locomotives are still produced only by the Novocherkassk electrical equipment factory, and the output is purchased completely by the Railways Ministry. Vertical integration is also increasing, occurring usually through mergers and acquisitions rather than through _de novo_ expansion. In the oil industry Tyumen Oil was created in 1995, bringing together oil production associations, refinery operations, and oil product marketing firms. Vertical integration also is increasing through the creation of industry-led (as opposed to bank-led) FIGs. A case in point is ALKOR, which encompasses aluminum smelting, production and distribution of finished products. Data from the WBRAS survey reveal that 46% of the firms indicated that their customers purchased supplies from only 1 or 2 suppliers, and that 23% of the suppliers control more than 65% of the relevant input market To be sure, there are economies of scale and a savings in transactions costs in combining successive stages of production under one corporate roof. The classic case of continuous steel-casting is well-known: it would be economically inefficient (if not technologically unfeasible) to have three separate firms heating iron ore, rolling it into ingots, and
program as one comprising several strands of activity which could work through diverse pathways to produce changes in what women are able to do. While our data do not allow us to trace the pathways from mechanisms to outcomes, they do nonetheless help make it clear why programs with multiple strands might impact diverse aspects of life quality and contribute to understanding why, as a result, these programs have grown in popularity with women in India.

The remainder of the paper is structured as follows. In section 2, we briefly discuss the Indian SHG movement by way of background before offering a description of the SHG program investigated in this paper. Section 3 then presents the main methods and data used. Discussed first is the estimation of average treatment effects (ATEs), using propensity score matching (PSM) before moving on to a consideration of the capability data developed through a program administration survey. Section 4 provides information about the data gathered and presents some descriptive results. In section 5, we present the main ATE results together with some additional results to consider robustness. Finally, in section 6 we offer some concluding remarks that consider the empirical and methodological contributions as well as the conceptualization of selfhelp and some topics for future research related to limits of the current research design.

# **2. Self-Help for Empowerment and Human Development**

# **2.1 SHGs in India – Some Background**

The policy environment in India has been supportive of SHGs and the ideas of microfinance, at least since the late 1960s when banks were required to earmark funds for poverty alleviation and development programs, and they have evolved rapidly as a result. Research into early initiatives, for example, Harper (2002) suggested that 5
# **3 Evidence on the emergence and dissemination of green technologies identified in patents**

In this section, we present the main findings of our analysis of innovation and diffusion of green technologies based on patent data from the WIPO green database. This analysis allows us to assess the dissemination of the most novel and influential green technologies. As discussed above, we follow a recent literature seeking to infer innovation and dissemination of technologies using general-text recognition methods for identifying patents associated with specific technologies.

Figure 1 reports the share of each WIPO green sector in the total mentions of green technologies in patents. The evidence in this figure reveals that green technologies belonging to the WIPO green sectors energy and construction account for the vast majority of mentions in patents over the period 2000-2021, accounting for about 80 percent of total mentions in most years. Transportation is the third most important sector, and its relative importance experienced significant growth in the mid-2000s, remaining relatively stable thereafter. Technologies from the PMP and Farming and Forestry sectors are the least important, accounting together for less than 10 percent of total patent mentions in most years.

Figure 2 reports the percentage of patents mentions by technology within each WIPO green sector over the same period. Among green technologies from the construction sector, we observe a clear downward trend for the relative importance of air conditioning, and a growing importance of LED technology, sensors, and construction material. Since 2005, LED technology accounted for about 40 percent of total mentions in patents within the construction sector, while the other technologies stabilized at around 20 percent each. In the energy sector, we observe a growing importance of biofuels, geothermal energy, and inverter technology. In transportation, drones accounted for a progressively higher share of total mentions, reaching about 80 percent in the early 2000s and stabilizing around 70 percent between 2015 and 2020. Among the PMP sector, carbon capture became the most important technology since 2005, reaching about 60 percent of total mentions at the end of the sample period. In Farming and Forestry,
and the cost and time challenges involved in implementing standard household expenditure surveys. The Survey of Well-Being via Instant and Frequent Tracking (SWIFT) methodology (Yoshida et al., 2015) provides poverty estimates in situations where no expenditure and consumption surveys are available.

SWIFT-plus trains a model to predict household expenditure using data from past household expenditure surveys. It was developed to address two major methodological challenges: model stability over time and model capacity to capture shocks. The model identifies key poverty correlates and uses the model coefficient and errors to impute household expenditures in a survey where no household expenditure data was collected. Additionally, the SWIFT-plus approach integrates Machine Learning (ML) and CrossValidation (CV) steps to improve the stability of the estimated model and expand the list of variables for poverty modeling/data collection to include dummies on consumption of key items.<sup>4</sup>

> 4 See Yoshida et al (2022). The Concept and Empirical Evidence of SWIFT Methodology. World Bank.

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between 1990 and 2001 and totaling over 1.2 millions individual observations. To illustrate the indices proposed in this paper, we picked the longest panels available including the German Socio-Economic Panel (SOEP, 1991-2000), the British Household Panel Survey (BHPS, 1991-2000), the Polish Household Budget Survey (PHBS, 1994-2000) and the Hungarian Household Budget Survey (HHBS, 19921997).<sup>4</sup> Data for Germany were further split into East and West Germany. This is because Germany reunited in 1989 and we expected Eastern and Western Germans to behave differently in relation to feelings of satisfaction and deprivation with income. The subdivision between East and West Germany follows the pre-1989 regional administrative division but it should be considered that many Eastern Germans migrated to West-Germany and, vice-versa, some Western Germans took residency in Eastern regions during the 1990s. Also, Eastern Germans are likely to have followed a process of adaptation to the new social and economic conditions and we should expect the behavior of Eastern regions to converge towards the behavior of Western regions.

The period considered (1991-2000) also coincides with the first decade of transition towards a market economy for East Germany, Poland and Hungary, a period characterized by recessions and deep institutional changes. In essence, our data provide the unique opportunity to observe and compare Western and Eastern European countries during a decade of extraordinary changes that we expected to be associated with significant swings in satisfaction and deprivation.

The variables of interest are income, predicted incomes and satisfaction with income. Income is measured in local currency, net of taxes and contributions and adjusted for inflation using the 2000-based deflators published by the IMF (World Economic Outlook report, October 2009). Predicted income is estimated with an OLS regression

> 4 Note that the time period considered for Poland includes two separate panels, 1994-1996 and 19972000.

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in the typical way, providing a direct measure of dietary diversity and food access at the household level. Each food group is given a score from zero to seven, depending on the

> 18Following the guidance of Nigeria’s National Bureau of Statistics (NBS), we dropped Borno state from the analysis. The 2018/19 NLSS is not representative of Borno state because violence prevented the survey field teams from accessing all of the sampled enumeration areas. For further details see NBS (2020).

> 19The sample is also representative at the national and zonal level.

> 20Further details on the construction of the consumption aggregate with the 2018/19 NLSS can be found in World Bank (2020b).

> 21Specifically, the infit statistics should range between 0.7 and 1.3; in the 2018/19 NLSS data they range from 0.85 to 1.15 for each of the eight FIES components. Similarly, the Rasch reliability statistic is 0.77, placing the 2018/19 NLSS data within the range of most other datasets considered in FAO’s cross-country analysis (e.g., FAO 2016). For more details, see FAO (2016).

> 22As a robustness check, we also calculate moderate and severe food insecurity at the zone level by directly applying the full Rasch model to FIES module in the 2018/19 NLSS to better account for zone differences. This is implemented using the RM.weights package in R (Cafiero et al. 2018b).

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cross-border trade flows, it is important to address some caveats that could limit the significance of a relationship between the two variables. First, it should be noted that the data resulting from the analysis of the text of national laws only captures ‘de jure’ protectionism. As with many coding of legal texts, there may be discrepancies between the text of the law and its implementation. Our investigation explores whether the legal provisions have empirical bite in reducing trade openness.

Furthermore, the text analysis only provides a static picture of the national law on government procurement as of 2021-22, whereas the annual trade data cover the 2000-2019 period. Although we know the year a decree was first promulgated, we have no information about possible amendments introduced or how the law looked before the decrees entered into force. Therefore, in our empirical analysis, we are assuming that the characteristics coded in 2021-22 apply over the entire period of the trade data. Any deviation from this assumption, which is not systematically related to the determinants of trade, could introduce measurement error and work against finding a significant association between our protectionism indicators and trade openness.

Another partial discrepancy between the law and the trade data concerns the object of discrimination, which can refer to either firms or the goods and services being procured. In 9
proportion of Tier 1 capital to have a greater impact in reducing risk in countries that impose greater stringency. The results suggest that a higher proportion of Tier 1 is less effective in reducing risk in the ECA region in both high-income and developing countries (Table 3, columns 2 and 4).

These results highlight the importance of how capital is defined. In particular, we find that simple leverage ratios are more informative than regulatory capital when it comes to explaining bank risk. Lower quality capital that is not common equity has lower loss absorptive capacity. Properly measuring risk exposure for computing risk-weights is also very difficult especially for large and complex financial organizations. There is also much discretion in the calculation of risk-weights, which creates the possibility of manipulation. As a growing number of countries in ECA have adopted or implemented components of Basel II and III, more banks (especially large banks) are relying on internal models for the calculation of risk-weights. Figure 14 below shows Basel implementation in each ECA group. While smaller, low-income countries have been shifting out of Basel I, most have adopted Basel II/III compared to other developing countries.

# **Figure 14: Basel Implementation as of 2016**

<!-- Start of picture text --> EU CE & BC WB SC CA Russia Turkey EE RoW<br>Basel I Basel II Basel III<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for
Provincial Controls||||Yes||||
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year FE|Yes|Yes|Yes|Yes||||
|Province-Year FE|||||Yes|Yes|Yes|
|Provincial Trend<br>||||Yes||||
|City Trend|||||||Yes|
|<br>#of Observations|3642|3642|3642|3642|3642|3642|3642|
|#of Cities|281|281|281|281|281|281|281|
|Adj R<sup>2</sup>|0.15|0.15|0.27|0.38|0.60|0.66|0.65|

_Notes_ : The dependent variable is the difference between reported GDP growth and predicted true GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls, i.e., the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights. In column (6), we control these city-level economic variables flexibly by allowing for provincespecific coefficients. Leader controls also include a set of dummies indicating educational background, ranging from secondary school to Ph.D. in addition to the ones shown in the table. Provincial controls include the corresponding characteristics of the provincial party secretaries, provincial growth target, and three dummies indicating the timing of the provincial Party Congress as well as one year before and after the Congress. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

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1988 and<br>1993<br><!-- End of picture text -->

Figure 5 strongly suggests the converse: There is an inverse relationship between industrial pollution and economic development, most strongly in the case of air pollution. Sichuan is the clear leader, with both sulfur dioxide (S02) and total suspended particulate (TSP) emissions exceeding 1,000,000 tons/year in both 1998 and 1993. Although it has substantially reduced organic water pollution (chemical oxygen demand - COD) in recent years, Chongqing remains, with Liaoning, the most highly polluted province in this category as well. Despite its huge industrial base, Shanghai has far lower pollution loads, while Beijing and Guangdong are also modest emitters by comparison.<sup>4</sup> _4_ Analysis of air emissions data for all twenty-nine provinces reveals the same pattern: The poorest provinces have significantly higher total emissions.

**_5_**