rafmacalaba/datause-encoder-data huggingface dataset:
farmers in Kagera have expanded into new crops, such as cabbages, tomatoes, green peppers and vanilla, while others have diversified into non-farm activities, such as Nile perch fishing, mining, trading and so on (De Weerdt, 2010). These sectoral shifts have coincided with substantial migration within and outside the region (Beegle, De Weerdt and Dercon, 2011).

Overall, the Kagera population is of diverse ethnic make-up with Haya and Nyambo tribes dominating in the north, and Subi, Sukuma, Zinza and Hangaza in the south. Projections based on the 2002 census put the population of Kagera at a little over 2.5 million people (URT, 2006b). Under half of the population was aged 0–14 years and around 5 percent were over 65 years old. Kagera is known for being one of the early epi-centers of HIV/AIDS with the first cases having been detected at Ndolage Hospital in 1983. Kwesigabo et al. (2005) studied trends in prevalence rates and note their steady decline over the years. While urban Bukoba recorded a peak of 24 percent prevalence in 1987, other districts had figures well below that. The Tanzanian Commission for AIDS (TACAIDS), the National Bureau of Statistics (NBS) and ORC Macro (2005) put the region wide prevalence in 2004 at 3.7 percent of individuals aged 15–49, well below the national average of 7 percent. Finally, in the early nineties the region hosted several hundreds of thousands of refugees from Rwanda and Burundi. The economic impact of this has been described by Baez (2007) and Maystadt (2010).

The baseline Kagera Health and Development Survey (KHDS) consists of 915 households, sampled to be representative of Kagera and interviewed up to four times from autumn 1991 to January 1994 and then twice more in 2004 and 2010. A more formal comparison of the baseline KHDS data with the Tanzania’s national 1991/2 Household Budget Survey, shows that across a range of demographic and socio-economic indicators, there is only minimal difference between the Kagera sample and the rest of rural Tanzania (results available upon request). The region also appeared to mirror the rest of the country in terms of growth and poverty reduction: real GDP growth was just under 4 percent per year between 1993 and 2002, while poverty in Kagera is estimated to have fallen from 31 to 29 percent between 1992 and 2001 (Demombynes and Hoogeveen, 2007). The challenges of 8
# **Further Reading**

APEC (Asia-Pacific Economic Co-operation) (2002). _Measuring the Impact of APEC Trade Facilitation on APEC Economies: A CGE Analysis_ . APEC Economic Committee, Bangkok.

Asian Development Bank (2004). “Joint Border Processing at Regional BorderCrossings.” Working Paper No. 2. (http://www.adb.org/Documents/Events/2003/CCC/Second_Mtg/wp_02.pdf) Australian Department of Foreign Affairs and Trade and Chinese Ministry of Foreign Trade and Economic Cooperation (2001). “Paperless Trading: Benefits to APEC.” Commonwealth of Australia. (www.dfat.gov.au/publications/paperless/paperless_trading.pdf) Bagai, Shweta, Richard Newfarmer, and John S. Wilson (2004). “Trade Facilitation: Ways WTO Disciplines Could Promote Development.” Trade Note 15. Washington, D.C.: World Bank.

Carruthers, R., J. Bajpai, and D. Hummels (2003).Trade and Logistics: an East Asian Perspective. In _East Asia Integrates: A Trade Policy Agenda for Shared Growth_ , eds. K. Krumm, and H. Kharas. Washington, DC: World Bank.

Clark, Ximena, David Dollar, Alejandro Micco (2004). “Port Efficiency, Maritime Transport Costs and Bilateral Trade.” Working Paper 10353. Washington, D.C.: National Bureau of Economic Research.

Cudmore, Edgar and John Whalley (2004). “Border Delays and Trade Liberalization.” University of Western Ontario and NBEER Peking University CESinfo. (http://www.nber.org/books/ease14/cudmore-whalley6-11-04.pdf) Fink, Carsten, Aaditya Mattoo, and Ileana Cristina Neagu. (2002). “Trade in International Maritime Services: How Much Does Policy Matter?” _World Bank Economic Review_ 16(1): 81–108.

——— . (2002). “Assessing the Role of Communication Costs in International Trade.” World Bank Working Paper 2929. Washington, DC.: World Bank.

Freund, Caroline and Diana Weinhold (2000). “On the Effect of the Internet on International Trade.” International Finance Discussion Papers 693. Board of Governors of the Federal Reserve System.

Hummels, David L. (2001). “Time as a Trade Barrier.” (mimeo). Department of Economics. Purdue University, Lafayette, Ind.

27
<!-- Start of picture text --> Effect of Uy on béy (Low Income Group)<br>Le<br>¥<br>0.73<br>0.72<br>0.7<br>12 0.4 0.6 0.8 7<br>a.69<br>0.69<br>se<br><!-- End of picture text --> <!-- Start of picture text --> Effect of u, ony (Low Income Group)<br>y<br>350 a<br>300 J \<br>250 J \<br>200 /<br>150 ‘<br>/<br>100<br>500 a<br>ae Wy<br>0.2 0.4 0.6 0.8 1<br><!-- End of picture text --> <!-- Start of picture text --> Effect of uy on U (Low Income Group)<br>Effect of (1-,) on h (Low Income Group)<br>U h<br>Pa 1000<br>tL A B00<br>6 ff 600<br>g / 400<br>/ u, 200<br>/ 0.2 0.4 0.6 0.8 1 1-1)<br>O.2 o.4 o.6 0.8 1<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 1. Behaviour of large and small firms<br>(around periods of tight money)<br>0<br>-3<br>-6<br>-9<br>-12<br>Large Small<br>-15<br>Sales Inventories Short-term Debt<br><!-- End of picture text --> Source: Kudlyak and Sánchez (2017). Note: The figure presents the difference between the minimum value of the detrended series in an interval of 12 quarters following an episode of tight money and the peak value of the series. Tight money periods are: Q2 1966, Q4 1968, Q2 1974, Q3 1978, Q4 1979, Q4 1988 and Q2 1994 based on the historical record analysed in Romer and Romer (1989). Small firms are defined as those at or below the 30th percentile of assets and large firms as those above the 30th percentile.

59
allowances<sup>7</sup> and was a net buyer of allowances within the EU. The cement and iron industries are vulnerable to carbon leakage as both are tradable industries and cannot pass through increased energy costs into product prices without incurring a loss of market share. All together, the three (power, cement, iron and steel) sectors account for 86.76 percent of carbon emissions in the EU and constitute 86.98 percent of total demand and 85.33 percent of total supply of carbon allowances.

We match firm financial data from the AMADEUS database maintained by Bureau van Dijk with emission transaction records reported in the Community Independent Transaction Log (CITL) run by the European Commission. We then use participant and nonparticipant firms of similar sizes within the same industry category to construct the control and treatment groups. Using a fixed effects specification, we estimate the impact of carbon trading, as well as the initial allocation of allowances on firm competitiveness. Our analysis differs from previous research in two ways. First, unlike the study of Abrell et al. (2011) that uses firms from different (non-ETS) industries as the counterfactual, we compare performance of regulated and unregulated firms within the same industry. In doing so, we avoid potential bias by omitted variables characterizing time-variant differences among industries. Second, our study covers more industries, more countries and a longer period of the trading program than other studies. Therefore, our paper provides additional evidence on the impacts of the carbon regulation.

The results from this study suggest that the EU ETS had different impacts across sectors. Our results show that the program may have resulted in higher material costs of the power industry on average by about 5 percent during 2005-2007 (Phase 1) and 8 percent during 20082009 (Phase 2). Since power generation requires fuel input, rising material costs may suggest that firms choose to substitute low-cost coal with more expensive fuel such as natural gas to mitigate emissions. In addition, the turnover of the power companies on average increased by 30 percent in Phase 2. This could imply that fossil-fuel power generation companies have passed through the carbon price to ratepayers, resulting in higher electricity prices and higher revenue. On the other hand, the trading scheme seems to have had no statistically significant impacts on any of the three variables in the cement and iron and steel sectors. The differences between ETS

> 7 These numbers are determined using the sample of 10 countries used in this study due to the need to identify power plants from the combustion sector.

4
_Credibility Theory_

# <u>Description of step</u>

This step is similar to the credibility step in calculating probable yields. However, instead of smoothing average yields within a district, weighted average historical loss costs are smoothed within the state.

First, the Product Base Rate (PBR) is defined as the average capped loss cost for that district. For district GJ8, using the capped loss cost history of Table 12, this is calculated as . Product base rates are displayed for all insurance units in the Gujarat in column [3] of Table 13.

Table 13. Cotton in Gujarat: calculation of base pure rate

|**District**|**District Area**<br>**Sown 2007**<br>**(ha)**|**Product**<br>**Base**<br>**Rate**<br>**(PBR)**|**Sample**<br>**variance of**<br>**historical**<br>**capped loss**<br>**costs**|**Weighted**<br>**average PBR**<br>**across state**|**Ratemaking**<br>**Credibility**<br>**Factor,**|**Base Pure**<br>**Rate**<br> <br> <br>|
|---|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|GJ1|190,999|2.5%|0.0%|7.6%|67%|4.2%|
|GJ2|198,051|8.2%|1.5%|7.6%|67%|8.0%|
|GJ3|37,195|4.5%|0.2%|7.6%|67%|5.6%|
|GJ4|186,303|2.6%|0.1%|7.6%|67%|4.3%|
|GJ5|160,974|1.2%|0.0%|7.6%|67%|3.3%|
|GJ6|343,553|10.3%|2.0%|7.6%|67%|9.4%|
|GJ7|21,024|1.2%|0.1%|7.6%|67%|3.3%|
|GJ8|184,944|14.7%|3.1%|7.6%|67%|12.4%|
|GJ9|55,857|6.9%|0.5%|7.6%|67%|7.2%|
|GJ10|56,054|4.8%|0.3%|7.6%|67%|5.7%|
|GJ11|94,726|2.1%|0.0%|7.6%|67%|3.9%|
|GJ12|281,750|21.6%|6.9%|7.6%|67%|16.9%|
|GJ13|98,831|5.4%|0.6%|7.6%|67%|6.1%|
|GJ14|448,126|3.0%|0.2%|7.6%|67%|4.6%|
|Unwe|ighted average:|6.3%|1.12%||||
|S|ample variance:|0.338%|||||

Second, calculate the Empirical Bayes Credibility Factor for ratemaking at the crop/state level using the capped loss cost histories of Table 12. For cotton in Gujarat the credibility factor of 67% is calculated as follows: Let denote the weighted average capped loss cost for district in year . These are tabulated in columns [3] to [12] of Table 12;

- , since there are fourteen districts in the state;

- , since all products have ten years of loss cost history to be used;

- , the product base rate is given in column [3] of Table 13;

- (see the bottom of column [3] of Table 13);

- (see the bottom of column [3] of Table 13);

21
transfers to households. They proceed by modelling labor supply conditional on labor market participation. The resulting parameters are then used to simulate the counterfactual of what individuals' labor market effort, and hence incomes, would have been in the absence of the Sri Lankan food-stamp scheme.

Cox and Jimenez turn the issue on its head and look at how the behavioral responses of some key nonbeneficiaries may also have bearing on a policy's net impact. Their concern is with the responsiveness of interhousehold charity to the introduction of public redistributive programs. Cox and Jimenez econometrically estimate the determinants of net transfers received in the absence of government intervention using household level data for the Philippines, and use the predicted parameters to simulate the likely private transfer response to the introduction of unemployment benefits, lump-sum transfers to those below the poverty line and retirement benefits.

As I have noted, simulation techniques are often used, here and elsewhere, to draw out behavioral implications from empirical models. It is worth noting that simulations may often require additional assumptions, which may in turn be important in drawing policy conclusions. To illustrate the point let us examine more closely the interesting findings of Cox and Jimenez. The unemployment insurance simulation-which indicates a dramatic displacement effect of 74 percent of private transfers-assumes that unemployed male household heads are provided with cash transfers equal to one-half their imputed eamings, and that this causes donors to treat these households as if their head is now employed. As the authors point out, the bulk (over 90%) of the displacement is driven by the shutting off of the "household head not employed" dummy variable in their econometric model. The state of being unemployed is a strong attractor of transfers independently of income.

The analysis provides us with an upper and lower bound estimate of private transfer displacement, and flags how critically the results depend on an assumption that is itself difficult to test. Given our limited knowledge of how donors view unemployed people receiving unemployment 17
<!-- Start of picture text --> Uganda<br>°8 ] x<br>oO<br>a “|<br>to)2.ze eigS<br>5 eB<br>38 9<br>« 3= ~o 3<br>Uy<br>° ] el<br>1950 1960 1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ————_ log-GDP per capita<br><!-- End of picture text -->

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<!-- Start of picture text --> 3 Nicaragua<br>3 3<br>ce i<br>3 PY<br>ES sg<br>a”Q ofS&<br>8 ! &<br>228 2“%<br>g } 8<br>| ©<br>Il [Ss<br>° \ Ne<br>1960 1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ————_ log-GDP per capita<br><!-- End of picture text -->

<!-- Start of picture text --> 2 Nicaragua<br>88 1 G<br>- | oO<br>| oer5<br>x g 'I BS=<br>ue ! 8 3<br>3 I x<br>S$9 I z<br>338 ge<br>“8 \ =£<br>ge<br>I ag<br>o o<br>1960 1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ——— Investment inflow<br><!-- End of picture text -->

<!-- Start of picture text --> 2 Sierra Leone<br>So<br>8=} ! N<br>So<br>ao<br>ag :<br>aQS g 28oe.g<br>88 8<br>$3 59<br>aa2= nN z<br>~<br>3 o<br>° I ©<br>1960 1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ————_ log-GDP per capita<br><!-- End of picture text --> <!-- Start of picture text --> 2 Sierra Leone<br>3 =<br>8s 3562<br>x Ni r=)<br>EE ge<br>ae oae<br>228$3 a6cE 2<br>3 ae<br>o LN !! a o =<br>1960 1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ———_ Investment inflow<br><!-- End of picture text --> <!-- Start of picture text --> 3 Cambodia<br>8 | 9<br>& I x<br>8 i<br>8 sg<br>aFa3 8 ! i “a=&<br>as .<br>272 |! 22<br>2 | of<br>8 i<br>|<br>I<br>° I ro)<br>1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees ————_ log-GDP per capita<br><!-- End of picture text -->

<!-- Start of picture text --> ° Cambodia<br>88 | eldw<br>& | B<br>8 1i} cy<br>«23 |1 2 S3<br>°38 1 s<br>8s “o£<br>28= f =<br>3 =<br>“8 we£<br>8 | “8=<br>I &3<br>o o<br>1970 1980 1990 2000 2010<br>year<br>——— Stock of refugees © ———— Investment inflow<br><!-- End of picture text -->
The estimated change in poverty is conservative with respect to the contraction of the Afghan economy between 2021 and 2023. The political crisis in Afghanistan on August 15, 2021, caused a sizeable economic contraction, with GDP per capita dropping 23 percent between 2020 and 2021. The reduction in economic activity in the aftermath of the political crisis was caused by lower aggregate demand fueled by the cessation of aid flows, which sustained a large part of the service sector, and supply constraints have forced the scale-down of business operations. Economic contraction presented different sectoral dynamics, with the service and industry sectors contracting by 32.7 and 14.1 percent, respectively. The political crisis also hurt agricultural production, which fell by 3 percent during the year. Since then, the economy has experienced a slow recovery in agriculture and stabilization in the service and industry sectors.

Reconciling poverty estimates with macroeconomic trends requires understanding the combined effects of a reduction of conflict and overall economic stabilization after the regime change, the significant dependence of households on agriculture, and their usage of coping mechanisms. Prior to 2021, high conflict in rural areas and a constant threat of attacks in cities depressed overall economic output. With the regime change, conflict is at a historical minimum, which has helped stabilize economic activity in what appears to be a new equilibrium. Welfare monitoring shows that households mobilized extra labor by women and youth to cope with the economic instability of post-August 2021. Mobilization of additional labor has allowed households to maintain consumption levels, even with a lower GDP.

The lack of data makes it impossible to measure poverty trends between 2021 and April/June 2023. While no data were collected in R1 and R2 of the AWMS survey to directly assess how monetary poverty evolved during the first two years of the interim Taliban administration, emerging trends in self-reported welfare and food security make it reasonable to expect that poverty increased in the immediate aftermath and declined progressively afterward to the current level.

The rest of the paper continues as follows: Section 2 expands on the SWIFT-plus methodology, the imputation model, and its internal and external validity; Section 3 presents the poverty imputation result, an overview of welfare trends in the country since the regime change, and how they support poverty evolution trends.

Data We implement poverty imputation on the AWMS R3 using a SWIFT-plus module trained using 2019–20 IE-LFS data, the survey contains information for 18,139 households and has the official consumption vectors used for estimating the official poverty rates in the country. It was conducted between October 2019 and September 2020, matching the year’s four seasons and being representative at the quarterly and urban/rural levels. Households are considered poor if their expenditures are insufficient to satisfy 3
**Table 5.2. Impact of treatment on PqTRs in treated schools**

||**2016-17/ 2017-**<br>**18**|**2017-18/ 2018-**<br>**19**|**2018-19/ 2019-**<br>**20**|
|---|---|---|---|
|Control|111.067 ***|108.804 ***|106.133 ***|
||(1.066)|(1.284)|(0.860)|
|Time|5.231 ***|3.585*|1.845|
||(1.789)|(2.058)|(1.508)|
|Treatment|3.193**|1.433|2.392|
||(1.393)|(1.535)|(1.549)|
|DiD (Treatment and|-46.187 ***|-40.099 ***|-37.431 ***|
|Time)|(2.036)|(2.263)|(2.085)|
|Control (N)|415|299|470|
|Treatment(N)|912|714|330|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01_

**Table 5.7. Robustness check: Impact of PqTR reduction on repetition rate (PqTR 80)**

||**Overall**|**Lower Primary**|
|---|---|---|
|Control in 2018|0.260***|0.272***|
||(0.007)|(0.008)|
|Time (year=2019)|-0.009|-0.010|
||(0.010)|(0.011)|
|Lagged Treatment|0.012<br>(0.009)|0.016<br>(0.010)|
|Lagged Treatment (DiD)|-0.034***<br>(0.012)|-0.040***<br>(0.014)|
|Control (N)|216|216|
|Treatment (N)|424|424|
|r2|0.03|0.03|

_Data Source: EMIS 2018 and 2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 Compares rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._ 27
# **Table 6(c): Subjective welfare regressions for Tanzania**

||(1)<br>|<br>Pre-vi<br>|(2)<br>gnette||(3)<br>|Post-vi<br>|(4)<br>gnette||
|---|---|---|---|---|---|---|---|---|
||Ordered<br>|probit|CHOP<br>|IT|Ordered pr<br>|obit|CHOP<br>|IT|
||Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|
|_Household Characteristics_|||||||||
|log per capita consumption|0.301***|0.101|0.421***|0.099|0.386***|0.110|0.498***|0.095|
|log household size|0.432*|0.234|0.385*|0.201|0.207|0.206|0.507***|0.192|
|share of women|-0.231|0.338|0.361|0.306|0.232|0.307|0.384|0.288|
|share of children under age 6|-0.175|0.582|-0.558|0.512|-0.182|0.590|-1.030**|0.484|
|share of children between 6<br>and 15|-0.412|0.605|-0.186|0.437|0.138|0.472|-0.603|0.412|
|share of members 55 and older|0.413|0.553|0.000|0.475|0.565|0.548|0.199|0.452|
|log land size (in acres)|0.193**|0.087|0.161**|0.075|0.158**|0.079|0.169**|0.071|
|_Household Head Characteristics_|||||||||
|household head female|-0.324|0.262|-0.369|0.237|-0.671***|0.249|-0.568**|0.226|
|household head age|-0.009|0.008|-0.003|0.009|-0.012*|0.007|-0.011|0.009|
|household head years of<br>education|0.046|0.050|0.064*|0.038|0.046|0.038|0.041|0.037|
|_Respondent Characteristics_<br>respondent female|-0.399**|<br>0.166|<br>-0.314*|0.180|-0.538***|<br>0.170|<br>-0.456***|0.173|
|respondent age|0.009|0.008|0.005|0.009|0.008|0.007|0.004|0.008|
|respondentyears of education|0.052|0.055|0.026|0.038|0.044|0.036|0.034|0.036|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1 31
# **(b) Sources and actual health impacts**

|**Air pollutants**|**Health impacts or diseases**|
|---|---|
|Sulphur dioxide<br>andnitrous oxides|Asthma; chronic obstructive pulmonary disease (COPD); cardiac<br>disease;ischemic stroke|
|Transportation|Asthma, COPD, lung cancer, cardiac arrhythmias; acute myocardial<br>infarction, congestiveheartfailure,ischemic stroke|
|Particular matter<br>(PM10, PM2.5)|Cardiovascular and respiratory diseases, as well as of lung cancer|
|Polycyclic Aromatic<br>Hydrocarbons (PAHs)|Adverse effects on liver, kidney, and testes; may impair the<br>reproductive system|
|Volatile<br>Organic<br>Compounds (VOCs)|Impaired lung functions, impaired memory, respiratory symptoms,<br>effects to theliverandkidneys, probable carcinogen|
|Heavy metals<br>(Lead, Arsenic,<br>Cadmium, Nickel,<br>Selenium, Mercury<br>Chromium, etc.|Damages the nervous systems, kidneys; cardiovascular disease,<br>carcinogens (lung, bladder, kidney, and skin cancers), impacts on<br>respiratory and immune systems|

Source: WHO (2006).

Figure 2 illustrates how air pollution either ambient air pollution or household (indoor) air pollution affects human health. Incomplete combustion of fossil fuels (e.g. coal) in power generation, petroleum products (e.g., gasoline, diesel) in transportation and solid fuels (e.g., fuel wood, charcoal, agricultural residues) in household cooking emits substantial quantities of harmful air pollutants and contaminants including small particulate matter (PM), carbon monoxide (CO), methane (CH4), carcinogenic polyaromatic hydrocarbons (PAH), volatile organic compounds (VOC), benzene and formaldehyde and sulfur and mercury (Smith et al., 2013). All these pollutants are harmful to human health in varying ways and degrees. Among these pollutants, particulate matter poses the greatest risks to human health. Large particulate matters (with diameter 10 μm and higher), such as dust or pollen, get lodged in the nasal cavity, upper airways or thoracic cavity. Smaller particles (with diameter 2.5 μm or smaller) can reach deeper into the smaller airways of the body and deposit on the alveoli – the tiny sacs in the lungs where oxygen exchanges with carbon dioxide in the blood (WHO, 2016b).

6