rafmacalaba/data-use-mentions-extended huggingface dataset:
10 for the months in between these observed postings for which we assume the establishment was operational, but not hiring. In total, 23.5% of all green technology-related postings in our sample countries in this period are matched to a firm with both CDP energy consumption and Factset sales data.

# **3. Differential impact of the energy crisis**

## **3.1 Background on the energy crisis**

The 2022 global energy crisis began in the aftermath of the COVID-19 pandemic, when natural gas prices in the EU and Asia started to increase in response to a supply-demand gap and a tight liquefied natural gas (LNG) market. Rising prices subsequently escalated into a widespread global energy crisis following the Russian invasion of Ukraine in February 2022. The invasion, and the international sanctions that followed, resulted in a reduction in natural gas imports from Russia, the suspension of the Nord Stream 2 pipeline’s certification and the halting of gas deliveries through the Nord Stream 1 pipeline. In the first half of 2022, Russia’s gas exports via pipeline to the European Union and the United Kingdom fell by nearly 50 percent compared with the previous five-year average (EIA, 2022). These supply shortages were exacerbated by uncertainty and concerns of further reductions as well as reduced hydroelectric and nuclear electricity output that pushed gas consumption for electricity (Tertre, 2023).

This led to soaring natural gas prices in parts of the world dependent on Russian imports and in other countries that competed in the global LNG market. In Europe the benchmark natural gas price peaked in August 2022 at a record high of $69.98 USD per MMBtu, a $42.09 increase from the January 2022 price, while the benchmark LNG price in Asia increased by $27.33 over the same period. Natural gas prices were comparatively stable in countries not reliant on imports, with the Australian benchmark price increasing by $4.79, the US benchmark by $4.55, and the Canadian benchmark falling
Policy Research Working Paper 9209

# **Abstract**

A rigorous understanding of the developmental effect of fiscal transfers to subnational governments remains an important policy research issue globally. This paper exploits a novel dataset of 20 years of municipal poverty maps and local public finances to study the effects on local welfare of a large fiscal transfer fund earmarked for social investment in more than 2,000 Mexican municipalities. Results show a positive but modest effect on the average household income, and positive effects on seven nonmonetary welfare measures. In contrast, these funds have no significant impact on extreme and moderate monetary poverty. These results provide important lessons for policy on the effects of earmarked funds to reduce territorial poverty and inequality in terms of incentives to design formulas to distribute earmarked fiscal resources to subnational governments.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at crodriguezc@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **c. Mortality and morbidity**

Data on mortality, years of life lost, and morbidity have been derived from the Global Burden of Disease (GBD) Project as of 2016. For each country, the years of life lost have been calculated as the total number of years lost because of premature deaths attributable to smoking among the working-age population of women and men below age 65 (table 3).

Table 3. Years of Life Lost, 2016

|_Country_|_Smoking-relate_<br>|_dyears of product_<br>|_ive life lost_<br>|
|---|---|---|---|
||_Males_|_Females_|_Total_|
|Bangladesh|346,044|5,340|351,514|
|Bosnia and Herzegovina|15,656|5,805|21,591|
|Chile|21,925|12,148|34,203|
|Indonesia|1,124,484|83,549|1,208,163|
|Moldova|29,075|2,661|31,866|
|Russian Federation|1,260,265|196,823|1,457,218|
|South Africa|66,823|33,915|100,863|
|Ukraine|517,300|64,192|581,622|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, <u>http://ghdx.healthdata.org/gbd-results-tool.</u> _Note_ : Smoking-related years of productive life lost are calculated based on Global Burden of Disease Study data by age-group among men and women below the minimum value of age 65 or life expectancy at birth..

# **d. Tobacco-related medical costs**

To maintain methodological consistency across countries, direct medical expenditures have been adapted from the calculations of Goodchild
the 1970s to 1990s. Another important aspect in which rural India and rural China differed in this period is their schooling systems, with private schools playing a

> 2For recent contributions on China, see, among others, Fan et al. (2020), Park and Zou (2019), Sato and Li (2007), Emran and Sun (2015a)), on India see, among others, Azam and Bhatt (2015), Emran and Shilpi (2015), Asher et al. (2018), Ahsan et al. (2019), Emran et al. (2020). For cross-country analysis, see Behrman (2000), Hertz et al. (2007), and Neidhofer et al. (2018), among others. Among the few contributions on intergenerational persistence in health, see Bhalotra and Rawlings (2013).

> 3Educational mobility in this paper relates to schooling attainment. For a discussion on the broader concept of education in and outside the classroom as input to human capital production, see, for example, Behrman (2019).

> 4The rural population in India was 742.62 million in 2001 (72.16 percent of the total), according to the census of India 2001. In China, the rural population in 2000 was 807.39 million (64 percent of the total), according to the China Statistical Yearbook, 2011.

> 5China opened up its economy gradually from 1978, and the rural economy went through substantial policy changes starting with the household responsibility system that introduced market incentives at the margin. India liberalized its economy with a big bang in 1991, but the rural economy was relatively less affected as the initial trade protections were focused on the industrial sector as part of a planned industrialization strategy.

1
Chinese panel data (CFPS) we can only measure migration as changes in home province or county, in the Egyptian panel data (ELMPS) we additionally account for moves between ”sheyakha”, a relatively small administrative division. We return to this more in detail at the end of this section, where we show that we find similar results when we separate observations by distance of migration.

We next ask whether this relationship still holds once we condition on basic observable characteristics. First, we consider age and gender. Figure 5 shows that our conclusion generally holds when we separate between female and male respondents, and group observations into five age bins.<sup>12</sup>

> 11Average internal migration rates are 6.8% for the self-employed, 9.9% for wage workers, and 11.6% for the unemployed. Average international migration rates are instead 0.7%, 1%, and 0.9%, respectively. 12We do not show results for Egypt, as we observe no instance of migration for female respondents in the 18 to 24 age category. We similarly omit data from Tanzania because the sample does not include migrants for several gender-age groups.

9
**Table 3. Intention-to-treat effects on total investment in physical capital and land (past 6 months)**

||Total invest<br>|ment (log)<br>|
|---|---|---|
||All|Existing|
|Post|||
|Cash|0.160***|0.155***|
||(0.014)|(0.013)|
|Matching|0.004|0.008|
||(0.010)|(0.009)|
|ANCOVA|||
|Cash|0.151***|0.151***|
||(0.011)|(0.011)|
|Matching|0.010|0.010|
||(0.010)|(0.010)|
|Control group mean (in XOF, millions), all|0.029|0.027|
|Standard deviation|0.133|0.122|
|Control group mean (in XOF, millions),|0.154|0.273|
|investment>0|||
|Standard deviation|0.145|0.252|
|Standard error (S.E.) clustered|Municipality|Municipality|
|Controls|Yes|Yes|
|Block fixed-effects|Yes|Yes|
|R2 (Post)|0.142|0.128|
|R2 (ANCOVA)|0.122|0.122|
|N (Post)|1,032|960|
|N (ANCOVA)|952|952|

_Note:_ ***p < .01, **p < .05, *p < .1. Investment has been trimmed at the 99th percentile. All control variables come from the baseline survey. The list of controls include a dummy that equals one if
take this into consideration while making their own trade policies with these countries going forward.

- The results of this study show that a positive Chinese trade and growth shock has a positive effect on most, but not all, ASEAN countries. The magnitudes of the positive effects are also different. This indicates that the channels through which trade translates into growth are different for different ASEAN countries. The policymakers of the respective countries could take this into consideration for conducting negotiations under different trade agreements involving these countries, including the Regional Comprehensive Economic Partnership Agreement (RCEP) and the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP). The development of these trade policies will remain critical for the China-ASEAN relationship in the future.

- Finally, more dependence on trade with China may also indicate greater vulnerability of the ASEAN countries to a negative Chinese economic shock. For some countries where the spillover effects are larger (i.e., Singapore), the risks associated with a negative macroeconomic shock are also larger. The heightened geo-political tensions due to the Russian Federation-Ukraine war and any possible future trade war between the United States and China could magnify the economic woes of the ASEAN countries due to the existing (and often difficult to replace) value chain system with China.

The outcome of this paper also sheds light on the direction of future research involving this region. In conducting econometric analysis with time series data of ASEAN countries, researchers often consider them as one group. The results of this study indicate that heterogeneity among countries should be considered more seriously while conducting empirical research. This creates the need for more research on the interaction between these countries to better understand the economic integration process. Finally, due to limitations involving such large-scale multi-country models, investigating the transmission mechanism of shocks 20
**Table A6 continued**

|**Category**|**What we look at?**|**Why we look at it?**|**Characteristics**|
|---|---|---|---|
|Locations|A database of postcodes/zipcodes and<br>the corresponding spatial locations<br>regarding latitude and longitude (or<br>similar coordinates in an openly<br>published coordinate system). The data<br>has to be available for the entire<br>country. The Index drew on work of<br>theUniversal Postal Union to develop<br>this category.|Open location data shows<br>the addresses of public and<br>private buildings. While<br>mainly used to route postal<br>services, this data has many<br>use cases: to calculate the<br>number of persons in a city<br>district, to provide homes<br>with services, or for direct<br>mailing and marketing.|Following data must be online to qualify for<br>assessment:<br>•<br>Zipcodes Addresses (required, if<br>zip code does not include the<br>address)<br>•<br>Coordinates (latitude, longitude)<br>•<br>Data available for entire country -<br>Note: To qualify, data must<br>contain geographic projections<br>that enable to interpret<br>coordinates|
|National<br>statistics|Key national statistics on demographic<br>and economic indicators such as Gross<br>Domestic Product (GDP), or<br>unemployment and population<br>statistics. These statistics can be<br>published as aggregates for the entire<br>country.|As Open Data Watch states<br>"Official statistics provide
were applied. Dumai and Samarinda are both located in natural-resource rich region of Riau and East Kalimantan. In turn, localities such as Sarmi and Yahukimo of a lagging province in Papua received only around a half of the “ideal” allocation.

Figure 3 implicitly assumes that the formula-based distribution is correct. However, the design of a formula might be equally open to question. The preference of one formula over another can be based on a range of arguments. The original DAU formula separately measured expenditure needs for population, area, poverty gap, and regional cost levels. For example, Lewis (2002) argued that costs should ideally be used to multiply expenditure needs factor as this actually reflects the spending requirements that need to be adjusted by cost. Others have claimed that poverty should figure more prominently in the distribution of the DAU. The 2006 DAU has dropped the poverty measure in favor of the HDI and per capita GRDP.<sup>18</sup> Table 2 presents a number of plausible allocation scenarios in the policy debate. Simulation one to four refers to a number of alternatives for expenditure needs formula design. The 2006 formula substituted the Human Development Index (HDI) for the previous poverty gap measures. Simulation three stresses the importance of interregional cost differences in financing services by using the cost index to multiply the population, area, and poverty indicators. Simulation four stresses the poverty gap measure as the sole expenditure needs indicator. All simulations used the 2006 fiscal capacity definitions and keep all other variables as defined in Table 1.<sup>19</sup> Simulations five to six refer to political or hold-harmless compromises. Option five would allocate all the

> 18 The rationale is that HDI measures the state of welfare of the people and GRDP per capita measures economic potential and activity of one region (Warta Keuangan, 2<sup>nd</sup> Edition 2005).

> 19 Simulation two uses poverty indicators from DAU 2005 basic data from MoF.

17
<!-- Start of picture text --> 65<br>55<br>BAU<br>45<br>Actual<br>35<br>25<br>Panel A: China<br>65<br>60<br>South - BAU<br>55<br>50 South - Actual<br>45<br>40 North - BAU<br>35<br>North - Actual<br>30<br>25<br>May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09<br>May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09<br><!-- End of picture text --> Panel A: China Panel B: South and North China Data source: Authors’ own data (CNRS dataset) Figure 3. Monthly share (%) of rural labor force with off-farm employment in China, actual and under the assumption of Business as Usual (BAU), May 2007 to April 2009 34
In Appendix A we examine the determinants of matching success and conclude that the matched sample is not representative of our study sample. Matched households are more likely to be larger and their heads are more likely to be male, older, educated and to work in agriculture. The fact that the likelihood of being matched is not random will affect our ability to generalise findings to the general population and will therefore need to be kept in mind when interpreting our findings. Notwithstanding the differences between the matched and unmatched sample, the matched sample is balanced across treatment and control arms, both in terms of their socio-economic characteristics and in terms of their SBM subsidy eligibility criteria. This can be seen in Tables 2 and 3.

Table 2 focuses on socio-economic characteristics of client households and informs us that the matched MF client sample is largely hindu (73%), with the remaining client households being either muslim or buddhist. The average household is reported to have 5 members, with a male head aged 47 years, who is on average able to read or write (around 70%), having had less than 6 years of formal education.

Turning to Table 3 we describe households’ official VG and subsidy eligibility status obtained from the SBM data. We find that 29% of the matched MF client sample (without a toilet at survey baseline) is officially classified as being BPL. An additional 51% is considered to be marginalised APL, so that overall 80% of MF client households without a toilet at survey baseline in our sample are officially categorised as VG. Given that we focus here on households without a toilet at survey baseline these VG households should in principle have all been eligible for a subsidy. However, the data reveal that 16% of VG households that do not have a toilet at the time of the survey baseline (which was conducted only a month after SBM was launched) are recorded to have one in the SBM data, classifying them officially subsidy in-eligible. We re-visit this matter in Section 5 where we
0.24|na|
|Banks<sup>(4)</sup>|0.22|39.4|0.11|39.4|0.74|na|

**Notes:**<sup>(1)</sup> Retail,<sup>(2)</sup> QRE retail,<sup>(3)</sup> loans to public institutions and state-owned enterprizes, and<sup>(4)</sup> loans to credit institutions. Data are taken from BIS (2006).

The main point of reference in our proposed stress testing approach is the steady-state of the macroeconomy which is associated with the equilibrium through-the-cycle PDs and LGDs. We then model departures from this steady-state under the PIT and Stress scenarios using a mapping that relates the changes in overall macroeconomic conditions to the changes in credit risk factors as captured by the PDs and LGDs. This is implemented by estimating the elasticities of NPLs with respect to the four main macroeconomic variables of interest. This systemic component of credit risk factors is discussed in the next section. The role of individual bank characteristics, i.e., the idiosyncratic component of risk factors, is described in Section 4.6.

24
for instance Autio et al. 2000, Bottazzi and Secchi 2006, Goedhuys and Sleuwaegen 2010, Copass 2013, Holzl 2014, Daunfeldt and Halvarssonin 2016, and Bianchini and Secci 2016, Haltiwanger et. al., 2017). The growth measures using turnover or number of employees are the most available in enterprise surveys worldwide, which make it possible to compare HGFs across countries. We can classify these approaches in two groups: i) Absolute percentage growth (with fixed growth rate or fixed share of top performing firms); ii) Combined growth and initial size measures.

A well-known HGF definition in the first group is provided by OECD (2007). It defines HGF as all enterprises with average annualized growth greater than 20% per annum, over a three-year period, measured by number of employees or turnover. Moreover, this definition uses a 10employee minimum threshold to eliminate firms with a small number of jobs, for which a small change (e.g. 1 job) can lead to high growth rates. This threshold is contested in some circumstances, because it can significantly influence the number of HGFs in the data set.<sup>8</sup> An

> 7 The OECD report Entrepreneurship at a Glance (OECD 2017) has been publishing several indicators of HGF annually, but mostly concentrated on OECD countries.

> 8 For example, Clayton et al. (2013) and Daunfeldt et al. (2015) find that at least 75% of firms in the US private sector and 95% of surviving firms in Sweden, respectively, are smaller than 10 employees.

3
respect to the macroeconomic data lend importance to the information provided by sample surveys. Here development economists owe a debt to the initiatives of the great Indian statistical school, to which we also owe important theoretical insights. Both Deaton and Strauss and Thomas discuss the figures collected by such surveys as the very early Additional Rural Incomes Surveys, as well as the intensive ICRISAT surveys of the International Crops Institute for Semi Arid Tropics, carried out by investigators who live in the sample villages, and of the very useful data generated by the World Bank's Living Standards surveys, which cover for an increasing number of countries a broad array of variables in addition to the standard budget data. The _Handbook_ authors stress that there is no reason to question the quality of the sample data, whose accuracy matches that for developed countries. Such surveys are still few in number unfortunately, and have all too often been stopped after a few years; expansion of this work is desirable.

This development has been complemented by significant progress in "micro econometric" methods which both Deaton's and Strauss and Thomas's chapters describe. New statistical techniques have evolved, and valuable experience has accumulated about how to control selection and other types of bias. Thanks to this work we are now much better informed about such important topics as the impacts of education on productivity, and of investment in hospital facilities on health.

- 20 Srinivasan (1994); on the China/India growth race; see Timberg (1997), which comments on a volume comparing the two countries, edited by Srinivasan (1993).

- 21 The recent Boskin report (1996), co-authored by economists with a high reputation, which suggests that US national accounts data has underestimated by 1.3 percent the rate of growth of consumption, shows that GDP growth rates are subject to doubt even in countries whose statistical system is highly developed.

14
The Economic Census is the primary data source on all non-agricultural enterprises in Indonesia – from micro and small enterprises to medium and large firms.<sup>4</sup> It collects detailed information on various aspects of an enterprise’s operations such as workforce characteristics, assets, cashflows, and income. These two factors – universal coverage of non-agricultural firms and the wide range of data collected – are key to identifying and mapping the distribution of economic activities across the country. For this reason, the Census also functions as the sampling frame for other enterprise surveys in Indonesia (Lestari & Wikaningsih, 2011). But as the Census is only conducted decennially, it is neither designed to monitor business activities regularly nor to study enterprise dynamics.

To fill the gaps left by the Economic Census, Statistics Indonesia conducts other surveys during intercensal years. As mentioned earlier, there are two main ones – the IMK Survey, and the Manufacturing Survey. The IMK Survey, which began in 2009, is conducted annually and covers 1 percent of all micro and small enterprises in Indonesia.<sup>5</sup> It includes a range of questions on production and output, capital, labor, and technology use. The Manufacturing Survey is also conducted every year, but unlike the IMK Survey, covers the entire population of medium and large manufacturing firms.<sup>6</sup> This unique feature enables researchers to construct a panel dataset of all manufacturing firms with 20 or more workers. However, the data also comes with several caveats. As Marquez-Ramos (2020) explained, information on some of the variables in the questionnaire (e.g., capital stock, R&D) may be limited due to non-response. Aside from missing data, there are also issues with sectors needing to be surveyed in certain years, typographical errors and outliers, as well as consistency in variable definitions (Marquez-Ramos, 2020). More

> 4 In Indonesia, firm size is determined in two ways. The first