rafmacalaba/datause-encoder-data huggingface dataset:
A note of caution is warranted here: the principal components procedure normalizes the mean of the index to zero for each country. Therefore, when comparing the "poor" in Kenya to the "poor" in Turkey or India it is important to keep in mind that the measure is relative, and 40 percent of the individuals are defined as living in "poor households" in every country. This paper does not attempt to generate an absolute poverty measure based on the asset index approach.<sup>9</sup> As a rough benchmark, Table 2 reports the percentage of the population living below the national poverty line, the dollar-a-day and the two-dollar-a-day poverty lines for the countries analyzed here as reported in the World Bank's World Development Indicators database (World Bank, 1999). The percent who live below a dollar a day clearly varies tremendously across countries, from below two percent in Morocco to almost 90 percent in Haiti. In an (unweighted) average across these countries the percentage living below this internationally comparable poverty line is about 40 percent - the percentage defined as the "poorest group" in the analysis in this paper. National poverty lines produce a much more stable proportion of each country defined as poor: again, the cross-country (unweighted) average is again about 40 percent.

What to take from this? Although using an asset index approach does not provide an internationally comparable cutoff (in the sense thait a dollar-a-day day does) it does identify a group of individuals in each country whose size is comparable to other breakdowns that are frequently made. In particular, using the 40 percent cutoff in this paper corresponds approximately to the percentage of people living below the national poverty line in many countries (Cameroon, Bangladesh, India, Nepal, Kenya, Philippines) or the percentage living under a dollar-a-day in Zimbabwe two-dollars-a-day in Brazil.1<sup>0</sup>

**9** An attemp **t** o do this would require benchmarking the index derived in each country to an international standard, or pooling the data to derive weights. Attempting this interesting work is left for a separate research endeavor and the interpretation of the present analysis is limited to relative gaps within a country. **10** We do not simply include the list of variables that make up the index to "control" for wealth in the regression as advocated by Montgomery et al, 1997, as for a substantial part of the paper we will interested in the effect of wealth per-se. More applications of this "asset index" approach using the DHS can be found

9
first QAT after the first major expansion of the program: phase-3, which increased the number of program schools from 194 to 676 schools or by 248%.

The sample for the learning dynamics analysis is restricted to phase-3 and phase-4 program schools. This is for two reasons. First, large numbers of schools entered into the program in these phases, and these schools have been in the program for a sufficiently long period to examine short-run dynamics (phase-3 schools have been subjected to five QATs and phase-4 schools four). Second, by phase-3, PEF had formalized the program entry qualification process, introducing standardized application forms, unannounced school inspections by PEF staff with standardized inspection forms, and an entry test, called the Short-Listing Quality Assurance Test (SLQAT), which is a pared-down version of the QAT. As a result, pre-program information on schools from the entry qualification stages is available for phase-3 and phase-4 schools, allowing the examination of the correlations between pre-program characteristics and the learning levels and changes, as well as their use as a form of specification testing in the causal analysis of the effects on learning from program design features that introduce stick and carrot incentives.

The data were provided by PEF in QAT-specific databases. Each QAT database is at the student level, with the number of points scored and/or the percent score in each relevant subject. Information on the school and grade of the tested student is also provided. Schools in the databases for QAT 5−QAT 8 were identified by both their names and unique identification numbers provided by PEF. School-level summary statistics on QAT performance—such as the number of test takers, test score means, test score standard deviations, and test pass rates— derived from the student-level data were linked across QAT rounds using the unique school identifiers. There were no issues in the linking process: when a school in a given QAT was expected to be also found in another QAT, it was. Program schools in the QAT 4 database were however only identified by school name (at times augmented by location markers inserted into the school name field). Thus, schools in QAT 4 were linked to the schools in QAT 5 by visually matching on school names. There were 674 program schools that took QAT 4; 658 of them were expected to be found in QAT 5. Matching on school names yielded 625 linked schools (a success rate of 95%).

Table 3 presents the number of phase-3 and phase-4 program schools that took each of the QATs. It also presents the number of phase-3 and phase-4 schools that qualified for entry and 14
line data corresponding to the eight-digit tariff code in a manner analogous to equations 1 and 2. In general, there will be multiple ten-digit tariff lines corresponding to a single eight-digit tariff line; so we will need to average over multiple ten-digit tariff lines in such cases. Further, we continue to use data at the most disaggregated level available, i.e., the country level. So for any ten-digit tariff line we use the quantity and value of imports from all countries from which Russia had positive imports in all of the three time periods of our base period.

While we hope that the explanation above is clear and intuitive, the remainder of this section is devoted to the mathematical description, and this requires the introduction of considerable notation. The reader who is interested in the results should be able to skip the remainder of this section.

# **Definition of Matched Tariff Lines**

Define At = the set of all ten digit tariff lines in the Russian tariff code in year t, t= 2009, 2010, 2011*, 2011,..., 2020 (denote the first half of 2011 as 2011*);

> Define _A_ 2011* = the set of ten digit tariff lines in the Russian tariff code 2011*, the first half of 2011;

Define the set B = A2009  A2010  _A_ 2011* = the union of the three sets of tariff lines in the three periods comprising the base period; Define AMt = B  At = the intersection of the sets of all tariff lines in year t and the base period, t= 2011,..., 2020. We call this the set of matched tariff lines in the year t with the tariff lines for the base period. Note that for a ten digit tariff line to belong to the set AMt, it is sufficient for the tariff line in year t to match with one tariff line from any of the three periods in the base period.

The vast majority of tariff lines are matched with a tariff line in at least one of the three years of our base period, but not all tariff lines have a match in the base period. For 2011 (A2011), there are 632 tariff lines that are not an element of AMt. For 2012 there are 347 tariff lines that are not an element of AMt, i.e., they do not match at the ten digit level with any tariff line in any of the three years of our base period.

# **Tariff for Matched Tariff Lines in 2011** .

We need to develop separate procedures for tariff lines h in the set AM2011 and for those not in the set AM2011. First consider those tariff lines in the set AM2011. Using the tariff rate data from the Customs Union, we can follow a procedure based 10
# **Table 2 - Corruption in Developing Countries**

||Developing Country<br>Sample|
|---|---|
|Government size|-0.0938<br>(-0.26)|
|Complexity of Tax<br>System|-0.0346<br>(-0.09)|
|Degree of closed<br>economy|0.3911***<br>(4.63)|
|Lack of competitive<br>market structure|-0.0217<br>(-0.17)|
|Laxity of bureaucratic<br>controls|-0.1220<br>(-0.67)|
|Judicial fairness|0.0752<br>(0.52)|
|Democratic Institutions|-0.1985**<br>(-2.63)|
|Decentralization|-0.1773<br>(-1.14)|
|Lack of service<br>orientation in<br>bureaucratic culture|0.2918***<br>(3.76)|
|Colonial Past|2.4636<br>(0.60)|
|Ethnic Heterogeneity|0.0778<br>(1.02)|
|Government Pay|-1.3091<br>(-1.39)|
|Social Development|-0.0029<br>(-0.01)|
|Sample Size|20|
|Adj. R sq.|0.9658|

The dependent variable is corruption index of TI where higher value of the index corresponds to more corruption. Inverse of standard deviation of corruption index is used in the weighted LS regression. Errors are White-Heterocedasticity consistent.

t-statistics are in parenthesis – standard errors are White-corrected for heterogeneity. *** significant at p<0.01 ** significant at p<0.05 * significant at p<0.10 22
19 Departing from equation (1), our first counterfactual is: ln _wij_ 1 = _X ij_ 88 β 88 + _I ij_ 88 * _wp sj_ + ( _I ij_ 88 * _S ij_ 88 ) _sp_ 88 _j_ + _F_ 88 − 1 (θ _i_ 88 ) (6) where _wp sj_ = ( _T j_ 95 − _T j_ 88 )γ ˆ _wp_ and γ ˆ _wp_ are the estimated coefficients in the second-stage regression (3) for the industry wage premia. This first simulation therefore corresponds to changes in the wage distribution due only to those changes in industry wage premia which are mandated by changes in the exogenous trade variables included in the secondstage equation (3). The second counterfactual is: The power of the preceding counterfactuals to simulate the changes in the wage distribution that arise from trade reforms depends entirely on the ability of the linear second stage equations to identify the impact of changes in tariffs and exchange rates on wage premia and employment probabilities. They also miss, so far, a key theoretical channel through which a trade liberalization is likely to impact on wage differences in the

> 28 As noted above, the ten occupational categories specified in equation 2 were more aggregated than the 22 industries for which wage and skill premia are estimated. In constructing _G(w_<sup>_3_</sup> _),_ workers whose predicted occupations differed from those observed in 1988 due to changes in λ0j were allocated to specific industries (within the broad sector to which they were mapped by the multinomial logit) by random draws with probabilities derived from the 1995 employment distribution across industries.
average temperatures from 1980–2008. We use these temperature differences to estimate heterogeneous, nonlinear firm responses along multiple dimensions of firm performance. Our identifying assumption is that temperature deviations are as good as random after conditioning on country by sector by year fixed effects, firm size (a general proxy for firm quality), and firms’ location-specific historical mean temperatures.

Another novel feature of our study is that the standardized firm-level data include manufacturing and services firms, as well as a rich set of firm-level characteristics and measures of the business environment. For instance, it includes measures of firms’ main sales markets, the resilience of firms’ production processes, firms’ access to finance, the quality of electricity services, the level of local security and political stability, and the quality of the local regulatory environment. Interacting these measures with location-specific weather data allows us to describe which types of firms and policy reforms are best able to “weather the storm”.

We document large, negative impacts of higher-than-average temperatures on revenues for firms in low and low-middle income countries. Small and medium-size firms as well as startups are most vulnerable to rising temperatures. These firms exhibit a 12% decline in revenues when annual average temperatures are 0.5<sup>_◦_</sup> C higher than the historical average. The impact is equally significant for manufacturing and services firms and driven by declines in labor productivity, which also result in lower wages. Firms in heat-sensitive sectors and with less resilient production processes are more affected, supporting our empirical identification. Consistent with the findings in Carleton et al. (2022), we find that income significantly flattens the temperature-revenue curve. In low and low-middle income countries, firms facing limited access to finance, burdensome local business regulations, and unsafe conditions are more vulnerable to temperature increases, weakening economies’ resilience to climate change.

Taken together, our results highlight two novel facts about economic resilience to rising heat. First, the performances of most manufacturing and services firms in low- and lowmiddle income countries has been affected by rising temperatures, with substantial differences in the degree of vulnerabilities depending on firms’ specific characteristics. Second, local 3
- Shilliday, A. and J. Lautenschlager (2012). Data for a global ICEWS and ongoing research. _Tech. rep., 2nd International Conference on Cross-Cultural Decision Making: Focus 2012_ .

- Topalova, P. and A. Khandelwal (2011). Trade liberalization and firm productivity: The case of India. _Review of Economics and Statistics 93_ (3), 995–1009.

- Ulyssea, G. (2018). Firms, informality, and development: Theory and evidence from Brazil. _American Economic Review 108_ (8), 2015–2047.

- Ulyssea, G. (2020). Informality: Causes and consequences for development. _Annual Review of Economics 12_ , 525–546.

- Utar, H. (2024). Firms and labor in times of violence: Evidence from the Mexican drug war. _The World Bank Economic Review_ , lhae037.

- Verwimp, P., P. Justino, and T. Br ̈uck (2019). The microeconomics of violent conflict. _Journal of Development Economics 141_ , 102297.

- WHO (2002). World Report on Violence and Health. _World Health Organization, Geneva_ .

29
**Table 10: Correlation of Level 1 variables across tools**

|**A. SDI v**|**s. Stallings (N=153)**|Share of<br>time<br>teaching|Share of time<br>teaching and<br>learning<br>**Stall**|Materials and<br>infrastructure<br>**ings**|Classroom<br>environment|||
|---|---|---|---|---|---|---|---|
||Share of time teaching|**0.80**|**0.66**|0.29|0.00|||
||Share of time teaching and learning|**0.66**|**0.70**|0.25|0.04|||
||Good Teacher Demeanor|**0.36**|**0.31**|0.22|0.07|||
|**SDI**|Good Pedagogical Practise|**0.33**|0.25|0.19|-0.03|||
||Classroom environment|0.06|0.06|-0.02|**0.69**|||
||Materials and infrastructure|0.20|0.24|0.21|0.04|||
||Use of materials|**0.39**|0.27|0.21|0.00|||
|**B. SDI v**|**s. CLASS (N=129)**|||**CLASS**||||
|||Emotional|Classroom|Instructional|Overall|Student||
|||Support|Organisation|Support|CLASS|Engagement||
||Share of time teaching|0.23|**0.55**|0.24|**0.37**|0.31||
||Share of time teaching and learning|0.19|**0.47**|0.26|**0.34**|0.29||
||Good Teacher Demeanor|0.20|0.24|0.20|0.24|0.19||
|**SDI**|Good Pedagogical Practise|0.18|0.23|0.20|0.24|0.28||
||Classroom environment|0.05|0.02|0.02|0.03|0.01||
||Materials and infrastructure|-0.04|0.17|-0.03|0.02|-0.05||
||Use of materials|0.16|0.19|0.14|0.18|0.15||
|**C. SDI v**|**s. Teach (N=130)**|||**Teach**||||
|||Share of|Share of time|||Socio-||
|||time|teaching and|Classroom||emotional|Overall|
|||teaching|learning|culture|Instruction|Skills|Teach|
||Share of time teaching|**0.50**|**0.39**|-0.02|0.19|0.00|0.09|
||Share of time teaching and learning|**0.33**|**0.32**|0.04|0.14|-0.02|0.07|
||Good Teacher Demeanor|0.09|0.11|0.09|0.14|0.10|0.15|
|**SDI**|Good Pedagogical Practise|0.07|0.08|0.12|0.18|0.08|0.21|
||Classroom environment|-0.08|-0.05|0.02|0.01|0.06|0.04|
||Materials and infrastructure|0.03|0.00|0.07|0.12|-0.10|0.05|
||Use of materials|0.09|0.09|0.06|0.30|0.23|0.29|

continued...

49
15 is larger for post-payment income than for pre-payment income. This reflects the fall in the share of income absorbed by health spending over this period in Vietnam.<sup>20</sup> Figure 5: Out-of-pocket payments and poverty, Vietnam 1998 <!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 500 999 1498 1997 2496 2995 3494 3993 4492 4991 5490 5989<br>Households ranked in ascending order of total consumption<br>Food-based poverty line = 1,287 thousand Dong p.a.<br>Post-OOP HH consumption<br>Pre-OOP HH consumption<br>HH consumption as multiple of food PL<br><!-- End of picture text --> Source: Wagstaff and van Doorslaer<sup>3</sup> .

Results for rural China over the same period show a reduction in the difference between pre-payment and post-payment headcounts.<sup>21</sup> However, Gustafsson and Li<sup>22</sup> find the opposite result in their analysis of changes between 1988 and 1995: the poverty headcount fell by 2.2 percentage points at the dollar-a-day poverty line if health expenditures are not deducted from disposable income and by only 0.7 percentage points if they are. This reflects the fact that the share of income spent on health care increased in rural China during the period 1988-95.

A couple of studies have looked at trends before and after the introduction of a reform. Limwattananon et al.<sup>10</sup> find that rates of impoverishment in Thailand were lower
to better services or education opportunities, even if they invest less in fostered children relative to biological children.<sup>3</sup> There are a handful of studies which examine the living arrangements of children, and a subset of studies explore how these arrangements have evolved over time in Sub-Saharan Africa. Although it is clear that HIV-related adult mortality has fundamentally altered basic demographic patterns with respect to the mortality rates and life expectancy in some countries, it is less clear how these changes translate into impact on caregiving patterns. Moreover, patterns may change even in low prevalence countries. The existing evidence suggests considerable resilience among extended families in absorbing orphaned children. Significant increases in the number of child-headed households or children living outside of the household environment (e.g. street children) have not been observed (Heuveline 2004; Monasch and Boerma 2004). The most commonly cited consequences of rising adult mortality on childhood living arrangements is an increasing propensity for children to not reside with any parent, but with other relatives.<sup>4</sup> Bicego, Rutstein and Johnston (2003) document an increase in orphan caregiving by grandparents in Niger, Tanzania and Zimbabwe from the early/mid-1990s to the late 1990s, but a decrease in Ghana and Kenya over the same period. Monasch and Boerma (2004) observe a shift in caregiving from other relatives (this excludes siblings and is presumably mostly aunts and uncles) to grandparents in Tanzania, Namibia and Zimbabwe, but an opposite trend in Kenya and Uganda. Ardington (2008) documents how extended families remained a source of support to orphans in South Africa between 1993 and 2005—a time of rapid increase in the number of orphans—and shows that grandparents played an increasing role as caregivers over this period. Zimmer and Dayton (2005) explore the patterns of older adults residing with children and grandchildren across several countries in Africa.

The increasing number of orphans raises the question of whether extended family networks are capable of caring for increased orphan burdens (as questioned by Foster, 2000, and Nyambedha, Wandibba and Aagaard-Hansen, 2003, and investigated—and contested in the case

> 3 Orphanhood patterns can also have implications or spill-over effects for living arrangements of non-orphans. Evans (2005) studies the extent to which other household members are impacted by orphans with whom they reside. He uses 42 DHS data sets from Sub-Saharan Africa and finds little evidence that this negative impact exists. 4 Another pathway by which being orphaned impacts living arrangements is through earlier marriage (and, therefore, residing outside the extended family network). Beegle and Krutikova (2007) find evidence that orphanhood increases the propensity of girls to enter into early marriage in Tanzania. Since the focus here is on orphans under 15, very few have married.

4
maintain capacity reserves—generally set about 15 percent above the forecast, coincident, non-interruptible demand peak. The cost of such reserve capacity is traditionally recovered through an insurance premium against interruption in the rates paid by retail customers. In most countries, the optimal market equilibrium with respect to capacity and reserve margin is not reached much of the time. Also, it is widely accepted that that the social cost of overinvestment (higher electricity cost) is small relative to the social cost of underinvestment (shortages and blackouts that can have hugely disruptive effects with severe economic and social consequences). Thus, if some deviation from the optimum level of capacity is inevitable, society should err on the side of overinvestment.

Sub-optimal use of generation capital is not unique to Eskom. State-owned utilities, in particular, have historically had difficulty making economically efficient new generation capacity investment decisions.<sup>53</sup> However, Eskom’s capacity expansion program was too excessive relative to demand growth. From 1980 to 2000, its reserve margin rates far exceeded those dictated by reliability and adequacy considerations. In the early 1990s, those reserve margin rates fluctuated just below 40 percent (figure 16). The presence of excess capacity over and far above the generally required rate of 15 percent reserve margin for such a long time period clearly signifies gross inefficiency in investment (figure 17). It also raises serious questions about the sector’s governance structure and the manner in which decisions were made with respect to capacity requirements and the allocation of the costs of reserve margins.

**Figure 18  Eskom’s capacity factor**

<!-- Start of picture text --> 70<br>65<br>60<br>55<br>50<br>45<br>75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05*<br>year<br>Generation Load Factor - %<br><!-- End of picture text -->

* refers to period Jan. 2004 – Mar. 2005.

Source: Eskom Annual Report (various years).

> 53 Wolak, F. 2005. “Market Design and Price Behavior in Restructured Electricity Markets: An International Comparison”.

33