rafmacalaba/data-use-mentions-extended huggingface dataset:
Dhaka and Sylhet leads to multiple, relatively short power outages while Rangpur experience longer but fewer electricity disruptions. Outages in Dhaka tend to affect the largest number of consumers on average, which is explained by the high population density in this area<sup>21</sup> .

Figure 3.8 displays a large difference between the number of days of outage and the number of outages. For example, in Dhaka, a storm seems to be causing on average 17 outages while in Chittagong, a storm is reported to be responsible for barely more than one power disruption. While this could be the sign of discrepancies between the quality of the grid at the division level, this difference could be due to reporting practices being inconsistent across districts and divisions. Therefore, it is important to bear in mind that the analysis is not meant to be representative at the country level. In what follows, we will favor the use of dated storms inducing outages when matching power disruptions with climate data, rather than the number of outages associated to each event. The magnitude of a blackout will be inferred from information on duration and number of consumers affected.

**Figure 3.8: Spatial distribution of outages**

<!-- Start of picture text --> 300 400 358.4<br>260<br>350<br>250<br>300<br>200<br>250<br>150 200<br>109 115<br>150<br>100<br>55 100<br>50 5 18 50 11.4 0.4 9.7 7.3 10.8<br>0 0<br>Total number of storms that caused outages over the period 2000-2017 Average number of outages per year<br>Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet<br><!-- End of picture text -->

> 21 For the purpose of validation, newspaper coverage was sought for outages
attriting households. In Columns 5 and 6 we restrict the cohort effects to be equal for the two samples, while Columns 7 and 8 allow them to differ. Conditional mobility is found to not differ between the two groups when we restrict the cohort effects to be the same for non-attritors and attritors. However, once we allow the cohort effects to vary, the attritors do show somewhat greater conditional mobility than the non-attritors. A 10 percent difference in income between two households with the same fixed effect would be reduced to a 8.2 percent difference after one quarter in the non-attritors sample, and a 7.5 percent difference in the attritors sample.

Overall these results show that attrition has a rather small impact on measurement of mobility, and therefore that measurement error is the main source of bias in the OLS genuine panel estimation. While those who attrit are exhibit slightly more income mobility, the fact that 35 percent of households attrit over the five quarter panel leads us to speculate that changes in income are only one of a large number of reasons why households attrit. A host of idiosyncratic reasons for non-response, temporary absence, and refusal to answer are likely to mitigate the impact of attrition arising from income changes.

# 5.2 Mobility in Full Income and in Expenditure

The above analysis has been for mobility of household labor earnings in urban Mexico. We can compare mobility in labor earnings with mobility in total household income and in expenditure using Mexico’s national income and expenditure survey, the Encuesta Nacional de Ingreso-Gasto de los Hogares (ENIGH). The ENIGH has been carried out in third quarter of the year on a biannual basis since 1992, and we use the six surveys from 1992 to 2002. Each round surveys a new random sample of approximately 10,000 to 14,000 households, so we do not have a panel of households. We can, however, form cohorts based on the same five year birth intervals and three levels of education of the household head as above, and follow cohorts over
- Feyrer, James, Erin T. Mansur, and Bruce Sacerdote. 2017. “Geographic Dispersion of Economic Shocks: Evidence from the Fracking Revolution.” _American Economic Review_ 107 (4): 1313–34.

- Flegg, Anthony T., and Timo Tohmo. 2011. “Regional Input-Output Tables and the FLQ Formula: A Case Study of Finland.” _Regional Studies_ 47 (5): 703–21.

- Flegg, Anthony T., and C. D. Webber. 2000. “Regional Size, Regional Specialization and the FLQ Formula.” _Regional Studies_ 34: 563–9.

- Flegg, Anthony T., C. D. Webber, and M. V. Elliott. 1995. “On the Appropriate Use of Location Quotients in Generating Regional Input-Output Tables.” _Regional Studies_ 29: 547–61.

- Fosu, Augustin Kwasi. 2017. “Oil and Ghana’s Economy.” In _The Economy of Ghana Sixty Years After Independence,_ edited by Ernest Aryeetey and Ravi Kanbur, 137. Oxford: Oxford University Press.

- Kowalewski, Julia. 2013. “Regionalization of National Input–Output Tables: Empirical Evidence on the Use of the FLQ Formula.” _Regional Studies_ 49 (2): 1–11.

- GSS (Ghana Statistical Service). 2006. _2003 National Industrial Census Report, Background and Results._ Accra, Ghana: Ghana Statistical Service.

- GSS (Ghana Statistical Service). 2016. _Integrated Business Establishment Survey: Regional Spatial Business Report_ . Accra, Ghana: Ghana Statistical Service.

- GSS (Ghana Statistical Service). 2018. _Integrated Business Establishment Survey Phase II: Comprehensive Sectoral Report_ . Accra, Ghana: Ghana Statistical Service.

- GSS (Ghana Statistical Service). 2021. _The Supply and Use Tables, Year 2013_ . Accra, Ghana: Ghana Statistical Service.

- Harrigan, Frank J., and James McGilvray. 1988. “The Measurement of Interindustry Linkages.” _Ricerche Economiche_ 42: 325
era—resulting in five prefectures in the Taiping provinces being classified in the control group. We have tried a robustness check in which being occupied largely before or after 1860 is the criterion for the classification of the Early or the Late Taiping areas.<sup>35</sup> _Wasteland_ **.** Wasteland between the Taiping Rebellion era and 1915 is measured by the share of wasteland in total arable land in 1915 from the Ministry of Commerce and Agriculture (1915), which provides county-level data for the Taiping provinces. Ideally, we need wasteland both before and after the rebellion to apply the DID approach. However, because the national agricultural survey in China started at the beginning of the 1910s, only cross-sectional regressions are feasible.

_Likin_ **.** Provincial-level annual _Likin_ revenues from 1861 to 1925 are available from two sources: those for 1861 to 1908 are from Luo (1936); those for 1920-1922 and 1925 are from The Second Historical Archives of China (1996) **.** Since _Likin_ was about spatial transportation costs, we normalize _Likin_ by the area of the region: _Likin_ per 1,000 square km.

_Control Variables._ We include key controls of prefecture characteristics that have implications for long-term development, such as geography, natural resources, and political importance. Proxies of geography include the distance to the Yangzi River, to the coastline, and to the Grand Canal (i.e., a major canal linking the north and the south), and the number of neighboring provinces of the prefecture. Such indicators of geography are important to control for because we need to filter out favorable geographical conditions under Taiping areas, and because we later rely on the longitude as the instrumental variable for Taiping areas based on the war-routing strategy of the Taiping Army. The number of neighboring provinces is controlled for because prefectures near multiple provincial borders were less tightly controlled by the provincial government; they were also more prone to rebellions, due to free-riding and monitoring difficulties stemming from a
no effect on eventual repayment (columns 4-6) once we control for agricultural profits and original loan size. Coefficient estimates that were previously statistically significant (in Online Appendix Table 4) are now uniformly smaller in magnitude and not statistically significantly different from zero. Indeed, the previously significant coefficients on the fingerprint * quintile 1 interaction across the columns

> 25 We limit ourselves to the 520-observation sample because of the need to control for profits, which was only observed among those in the August 2008 survey. These results should therefore be compared with Online Appendix Table 4, which is also for the 520-observation sample.

25
affected by many more factors than income (see e.g. Masera et al. 2000; Heltberg 2004, 2005; Gupta and Köhlin 2006; Gundimeda and Köhlin 2008). The availability of multiple fuels enables policies encouraging fuel switching, e.g. from charcoal to electricity in urban areas. However, only few studies estimate the cross-price elasticities of demand for various fuels for larger samples that could shed light on the potential for policies to promote fuel switching. One example is Gundimeda and Köhlin (2008), using Indian NSSO data (110000 households). It reported for rural areas: cross price elasticity of wood with respect to kerosene ranging from 0.49 to 0.71<sup>6</sup> ; of wood with respect to electricity ranging from 0.38 to 0.45; and of wood to LPG ranging from 0.56 to 0.84. In urban areas: cross price elasticity of wood with respect to kerosene ranging from 0.43 to 0.54; of wood with respect to electricity ranging from 0.32 to 0.47; and of wood with respect to LPG ranges from 0.38 to 0.65. The cross-price elasticities are thus quite low, reducing the potential for sizable impact of tax and subsidy schemes.

> 6 Thanks to the large number of observations, the elasticities were calculated for low, medium and high income groups.

2
estimate the number of food shocks experienced in a year as a function of temperature shocks in the previous year to account for timing of harvest and storage. We conduct this analysis for a full year as well as by agricultural growing season. We do not find evidence that the previous year temperature shocks we document in our data are leading to significant increase in current year food insecurity (Table 5). These results suggest that our results are unlikely to be driven by a nutrition channel.

# **6.3 Heterogeneity in Effects by Gender**

In our test score data, female students perform relatively poorly compared to male students, on average. Given these baseline differences, we explore whether the effects of temperature on test performance vary by gender. To do this, we add an interaction term between temperature bins and a female dummy to our main empirical specification.

Table 6 shows that coefficient of the interaction term between temperature and female dummy is predominantly positive. Moreover, these effects are positive and statistically significant for temperature bins above 30<sup>_◦_</sup> C across english, math, and the Total score. This indicates that the effects of heat matter less for female students compared to their male peers. Notably, female students appear able to fully offset the negative effects of temperature on their performance in math. This resilience displayed by female students, despite prevailing societal norms that place them at a disadvantage during weather shocks, is remarkable (Bj ̈orkman-Nyqvist, 2013).

There are many potential explanations for this including physiological differences in resilience to heat between females and males, and changes in societal norms that benefit females. However, we have limited data to rule out many of these possibilities. One hypothesis that we find particularly interesting is that these exams may be higher stakes for women as the consequence of going to college versus not may be greater for them. As a result, women may put in more effort relative to men, which may compensate for the negative impact of heat
br>Percent of total debt<br>30 Interquartile range Mean Median<br>25<br>20<br>15<br>10<br>5<br>0<br>2010Q4 2019Q2<br><!-- End of picture text -->

## F. Non-resident holdings of local-currency debt

Source: Ha, Kose, and Ohnsorge (2019); International Monetary Fund; World Bank.

A. Unweighted average of total external debt-to-GDP ratios for 31 EMDE commodity exporters and 30 EMDE commodity importers.

B. Based on data for 61 EMDEs.

C. Unweighted average of current account balance-to-GDP ratios for 88 EMDE commodity exporters and 56 EMDE commodity importers.

D. Based on data for 144 EMDEs.

E. Based on data for 48 EMDEs. Dark blue bars show minimum and maximum values. Assessing reserve adequacy (ARA) metric is based on IMF (2011) which determines the appropriate reserve cover on a risk-weighted basis covering short-term debt, medium and long-term debt, and equity liabilities. Broad model and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure. Values above 1 suggest that countries are fully able to meet balance of payments needs using reserves.

F. Based on data for 22 EMDEs.

27
coverage rates, by decomposing those selected out of the sample into children no longer in school, children with excessive delays, and those missed due to PISA sampling issues. It should be obvious from these magnitudes that any international comparison of countries with vastly different coverage rates must seek to address the problem in some way, and we suggest two alternatives in Section 3.

# _Ancillary household survey data sets_

Our proposed procedure to examine the sensitivity of inequality measures to sample selection, which is described below, relies on using information on fifteen year-olds from general-purpose household surveys. While these surveys may have their own sampling issues, these are not dictated by school enrollment or delay status, or by school closures, openings and reforms. We obtained such household surveys for the four countries with the lowest coverage rates in the 2006 PISA sample: those reported in Table 2. For Brazil, we used the Pesquisa Nacional por Amostra de Domicílios (PNAD) 2006. For Indonesia, we used the SUSENAS 2005. For Mexico, the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) for 2006 was used. For Turkey, the Household Budget Survey (HBS) 2006 was used.

All four are large-sample household surveys with national coverage and representative down to the regional level, which are fielded on an annual basis by each country’s national statistical authority. The PNAD 2006 collected information from a sample of about 119,000 households and 410,000 individuals; SUSENAS 2005 from 257,900 households and 1,052,100 individuals; the 7
- Tanzania National Bureau of Statistics (TNBS) (2017). Tanzania National Panel Survey 20142015, Wave 4. Public Use Dataset. Ref: TZA ~~2~~ 014 ~~N~~ PS-R4 ~~v~~ 03 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/2862` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2021). Tanzania National Panel Survey 2019-2020 - Extended Panel with Sex Disaggregated Data. Public Use Dataset. Ref: TZA ~~2~~ 019 NPS-SDD ~~v~~ 06 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index. php/catalog/3885` on 9 February 2024.

- Tanzania National Bureau of Statistics (TNBS) (2023). Tanzania National Panel Survey 20202021, Wave 5. Public Use Dataset. Ref: TZA ~~2~~ 020 ~~N~~ PS-R5 ~~v~~ 02 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/5639` on 9 February 2024.

- Taraz, V. (2018). Can farmers adapt to higher temperatures? evidence from India. _World Development 112_ , 205–19.

- Tarnavsky, E., D. Grimes, R. Maidment, E. Black, R. P. Allan, M. Stringer, R. Chadwick, and F. Kayitakire (2014). Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present. _Journal of Applied Meteorology and Climatology 53_ (12), 2805–22.

- Tesfaye, W., G. Blalock, and N. Tirivayi (2021). Climate-smart innovations and rural poverty in Ethiopia: Exploring impacts and pathways. _American Journal of Agricultural Economics 103_ (3), 878–99.

- Tzachor, A.
sup>

# **3.3 Correlates of crises**

The probability of crises is estimated using logit and probit models based on a panel dataset of 139 EMDEs on annual data from 1985-2018. The variables selected are based on empirical findings in the early warning indicators literature on crises (see Kaminsky, Lizondo, and Reinhart 1998; Frankel and Saravelos 2012; and Chamon and Crowe 2012 for an extensive review) and Kose et al. (2021). The panel includes data on debt (both public and private), the balance of payments, and the real, banking, and financial sectors (table A5).

# **<u>4. Results</u>**

# **4.1 Decomposition of movements in U.S. interest rates**

The sign-restricted Bayesian VAR model described above is used to decompose movements in U.S. bond yields into the contributions of their respective shocks. Evolving expectations about

> 6 The 5-year maturity is used based on data availability and as a compromise between capturing information in both the 2-year and 10-year yields. Prior to 2003, the inflation expectations series is based on model estimates by Haubrich, Pennacchi, and Ritchken (2012).

> 7 The models exclude observations between 2008Q4-2009Q4 to account for the period of the global financial crisis.

> 8 The historical decomposition divides the 2-year yield into the contribution of each of the shocks to its evolution over time.

8
and all expenditure modules used a recall approach. The 2017 sample counts 48,639 individuals representing a population of 692,895, while the sample size in 2022 is 52,822 individuals, out of a population of 650,118.<sup>1</sup> Both surveys are representative at the district level (there are 20 districts, or _Dzongkhags_ , in Bhutan) (Bhutan National Statistics Bureau 2017, 2022).

The intertemporal analysis of poverty requires the welfare measure of choice – in Bhutan’s case, household consumption – to be computed in a consistent way across surveys. The comparable consumption aggregate is constructed following the principles outlined in Deaton and Zaidi (2002) and Mancini and Vecchi (2022), and includes all required expenditure components, save for the consumption flow from durable goods, for which consistent information is not available across BLSS waves (Amendola et al. 2023). Finally, it is worth remarking that the 2017 and 2022 questionnaires do have a couple of discrepancies that affect the analysis presented in this paper: some variables that would be relevant in explaining households’ living standards and mobility, namely household income sources and individual labor market characteristics, were unfortunately not collected in both waves, and therefore cannot be used within the synthetic panel framework.

# **3 Methodology**

The study of the dynamics of poverty in Bhutan between 2017 and 2022 poses two distinct challenges. The first one is virtually unavoidable when performing this kind of analysis, and it concerns comparability: even when survey instruments are sufficiently similar, one cannot assume that poverty comparisons are accurate in the absence of consistent poverty lines, that is, monetary amounts that correspond to a reference level of utility that remains constant in the two periods (Ravallion and Bidani 1994, Ravallion 1998). This issue turns out to require special attention in the case of Bhutan: the process used to obtain consistent poverty lines is summarized in section 3.1. The second challenge concerns the absence of panel data: section 3
available budget within the household, the subgame perfect Nash equilibrium is one in which the share of total income devoted to children is the same no matter which of the parents migrates. This is because remittances act as a device in the hands of the migrant to offset the decisions of the spouse left behind.

I tested the predictions of this model on data from Indonesia, where female migration is particularly widespread. To account for endogenous selection into migration, I exploited information about previous migrants’ earnings and built measures of expected returns and risks of, respectively, male and female migration to use as instrumental variables. I thus showed that households prefer to send away the member who is expected to earn more upon migration and whose earnings will be less volatile. The estimation results revealed 27
limited because they may reflect erratic changes in expectations. To what extent this is actually the case may be difficult to gauge, because existing asset price models are based on unobserved variables; their empirical predictions are subject to wide margins of error. This makes it difficult to identify the "right" price (reflecting, say, future profit growth rates or productivity shocks) and therefore what is an erratic movement or speculative bubble. In such conditions, incorporating asset prices systematically in monetary policy feedback rules may be unwarranted (see Fuhrer and Moore (1992)). Moreover, the risk premium that is typically embedded in asset prices tends to vary over time. Basing monetary policy on a broader, asset-based measure of prices or monetary conditions may actually lead to greater variability in current and future output and inflation.

# 6 Recent Experiences

As noted in the introduction, in recent years a number of industrial and developing countries have adopted an inflation targeting regime. To put matters in perspective, consider the results of the survey of monetary policy frameworks conducted in late 1998 by the Bank of England and summarized by Sterne (1999). 91 countries, of which 43 were considered developing countries, were covered. Almost all the central banks surveyed targeted either the money supply, the exchange rate, or inflation directly. Several of them (in both industrial and developing countries) stated that instability in the velocity of money (induced in many cases by financial liberalization) led them to abandon money targeting. In 1998 there were 54 countries with an explicit inflation target (compared to 8 in 1990), 37 with an explicit monetary aggregate target, and 47 with an explicit exchange rate target. Between 1990 and 1998, explicit targets (whether for money, inflation, or the exchange rate) became more widespread. Of the 54 countries that had inflation targets, 13 (or 14 percent of the sample) had inflation targets only; thus, many countries had several explicit targets.<sup>41</sup> The majority of
Margin _×_ incumbent victory|–3.62|–11.6|0.47|–2.26|–5.37|–26.6*|–5.13|–4.85|
||(3.81)|(7.21)|(2.92)|(3.20)|(10.4)|(15.7)|(5.59)|(5.33)|
|Mean of dep. var.|0.25|0.25|0.11|0.08|0.30|0.42|0.19|0.17|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variables in columns marked “NPA” are based on conflict involving the New People’s Army. The dependent variables in columns marked “LE” are based on conflict with other lawless elements. Our sample does not contain any casualties in incidents with the Moro-Islamic Liberation Front or Abu Sayyaf Group. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

31
engagement on voting using a panel of precinct-level voting data for 2013 and 2018.

Our findings suggest that local digital participation on the issue of local roads is associated with ‘political dividends’ to the incumbent. Engagement with the city government through digital technologies (measured in our study by pothole complaints) results in an increased number of votes and a higher margin of victory for the incumbent mayor. Our identification strategy is based on the engineering literature and direct evidence from the city authorities. It exploits arguably exogenous variation in intra-city weather conditions during the heating season, which differentially affects 2