rafmacalaba/datause-encoder-data huggingface dataset:
68 increase the available quench zone and lead to higher hydrocarbon emissions; conversely, more compact shapes such as the hemispherical or bent roof chambers reduce heat loss, thus increasing maximum temperatures. This tends to increase the formation of NOx while reducing HC. Further, combustion chamber material and size and spark plug location can influence emissions. In general, because of its higher thermal conductivity, aluminum engine heads lead to lower combustion temperatures and therefore to lower NOx rates, but at the expense of increased HC emissions. Since the length of the flame path has a strong influence on engine detonation and therefore fuel octane requirement, larger combustion chambers which can lower HC emissions tend to be used only with lower compression ratios.

# Emission Control Technologies

Tighter emission standards have required more specific attention to the treatment of vehicle exhaust emissions. Commonly used technologies to control exhaust emissions include recirculation of exhaust gases, electronic control of engine performance, exhaust after-treatment devices, and advanced combustion techniques.

With the current state of the art, engine modification alone cannot reduce emissions to the same extent as with a three-way catalyst. Compared to a carburetted engine, an electronically controlled engine equipped with a 3-way catalyst can reduce CO emissions from a mean rate of 7.5 g per km to 1.5 g per km; HC emissions from _1.5_ g per km to 0.25 g per km; and NOx from 2.0 g per km to 0.25 g per km. Electronic fuel injection and ignition systems (EFI) without a catalytic converter are effective in reducing CO and HC emissions but have only a minor effect on NOx emissions [ECMT 1990].

# Exhaust Gas Recirculation (EGR)

Recirculating a portion of the exhaust gas back into the incoming air/fuel mixture is frequently used as a technique for lowering NOx. The dilution of the incoming charge reduces peak cycle temperature by slowing flame speed and absorbing some heat of combustion.

Charge dilution of homogeneous-charge engines by excess air and/or by exhaust gas recirculation (EGR) has been used for many years. The use of excess air alone results in relatively small NOx reductions, in the order of 35-40%. When EGR is incorporated, substantially higher NOx reductions have been demonstrated. Excessive dilution, however, can result in increased HC emissions, driveability problems or fuel economy losses.

Fuel consumption can be modified when EGR is utilized. Brake specific fuel consumption and exhaust temperature decrease with increasing EGR because dilution with EGR decreases pumping work and heat transfer, and increases the ratio of specific heats of the burned gases. Improvements in mixture preparation, induction systems, and ignition systems can increase dilution tolerance. The latest technique for improving dilution tolerance is to increase the burn rate or flame speed of the air-fuel charge. Dilution can then be increased until the burn rate
that includes a detailed module on migration which is administered if the household reports having one or more members overseas. Importantly, the migration module (called the Survey on Overseas Filipinos) includes questions on migration history that allows a researcher to track migration episodes up to 5 years in the past. Such a module turns out to be crucial for identifying households that had migrants in specific shock-exposed locations _prior to_ the shock, because location after the shock could be endogenous and therefore introduce bias in estimation. A key implication for surveys of migrant-sending households in countries where migrants tend to only go to one or a few main destinations is to collect information not only on which country the migrant is in, but also the city or region to provide more scope for identifying local shocks.

# _Remittance responses to conditions in migrant origin areas_

An important potential benefit from international migration is that remittances may serve as insurance, rising in the wake of negative shocks in migrants’ home countries. Rural households in many developing countries are highly exposed to weather risk, experiencing storms, flooding, and droughts with great frequency. Households therefore should benefit greatly from access to formal and informal insurance that alleviates their most important sources of weather risk. Potential benefits include the ability to maintain nutritional, health, and educational investments, to adopt new production technologies, and to start new entrepreneurial activities that weather risk made previously unattractive. A large literature has examined the mechanisms through which households cope with risk in developing countries, but until recently the insurance role of remittances has not been investigated.

Yang and Choi (2007) and Yang (2008a) explore whether migrant remittances serve as insurance in the wake of negative weather shocks. This is a mechanism for coping with shocks ex post on which previous micro-level studies have not focused. At the international level, it is commonly posited that remittance flows from overseas buffer economic shocks in the migrants’ home countries (for example, Ratha 2003), but there have been relatively few empirical tests of this claim with micro-level household data. Mishra (2005) examines remittances in 13 Caribbean countries from 1980 to 2002 and finds that every 1 percent decrease in GDP is associated with a 3 percent increase in remittances two years later. Related research on the role of internal 12
# Quintile 1: Comparison of Tax Regimes to the Baseline

|Quintile1|2013|2014|2015|2016|2017|2018|2019|2020|
|---|---|---|---|---|---|---|---|---|
|Baseline|0|0|0|0|0|0|0|0|
|Sales Tax Increase|-0.09|-0.26|-0.12|0.11|0.01|-0.25|0.12|0.63|
|Corporate Tax Increase|-1.07|-0.88|-1.22|-0.60|-0.66|-0.21|-0.12|0.98|
|Tariff Increase|0.12|0.03|-0.03|0.16|-0.09|-0.08|-0.04|0.55|

Quintile 1: Comparison of tax regimes to the baseline <!-- Start of picture text --> 1<br>0.5<br>0<br>2013 2014 2015 2016 2017 2018 2019 2020<br>-0.5<br>-1<br>-1.5<br>Baseline Sales Tax Increase<br>Corporate Tax Increase Tariff Increase<br><!-- End of picture text --> Quintile 2: Comparison of Tax Regimes to the Baseline

|Quintile2|2013|2014|2015|2016|2017<br>2018|2019|2020|
|---|---|---|---|---|---|---|---|
|Baseline|0|0|0|0|0<br>0|0|0|
|Sales Tax Increase|0.06|-0.13|-0.07|0.17|0.05<br>-0.26|0.17|0.80|
|Corporate Tax Increase|<br>-0.78|-0.75|-1.30|-0.58|-0.63<br>-0.10|0.00|1.36|
|Tariff Increase|0.10|0.03|-0.02|0.20|-0.10<br>-0.07|-0.03|0.68|
|0<br>0.5<br>1|Quintile 2:|Compari|son of tax|regimes to|the baseline|||
|-05|2013<br>2014|2015|2016|2017<br>2|018<br>2019<br>2020|||
|-1.5<br>-1<br>.|Base|line||Sales Tax I|ncrease|||
||Corp|orate Tax I|ncrease|Tariff Incre|ase|||

35
Further, we control for whether the general characteristics<sup>12</sup> of the two sets were significantly different from those of other observations: Set A compared with all other consumption poor; set B compared with all other consumption non-poor (see Appendix); Set A compared to Set B; and finally Set A compared to the consumption non-poor. This cross-comparison substantially validates our hypothesis. The general characteristics of Set A are significantly superior (higher education levels, greater access to services, more valuable assets, etc.) to those of other consumption poor, whereas they closely resemble those of the consumption non-poor if we exclude Set B.

Set A observations also show a clear regional pattern (Table 1). In both survey rounds, about 70 percent of Set A households were located in the South (Gaza, Inhambane and the Maputo area); this concentration is not consistent with population distribution, as these regions account for only 25 percent of the national population (see Map 1 in Appendix). This suggests that, in the South, the interaction between location-specific PLs and predicted consumption might significantly affect the calculation of poverty figures.

**Table 1: Set A and Set B Observations and Shares (IAF 2002/03-IOF 2008/09)**

|||**200**|**2/03**|||**200**|**8/09**||
|---|---|---|---|---|---|---|---|---|
|13provinces|**Set**|**A**|**Set**|**B**|**Set**|**A**|**Set**|**B**|
||**Obs.**|**%**|**Obs.**|**%**|**Obs.**|**%**|**Obs.**|**%**|
|Niassa & Cabo Delgado-rural|5|1|231|24|2|0.1|128|13|
|Niassa & Cabo Delgado-urban|59|10|33|6|39|9|18|3|
|Nampula-rural|1|0.1|117|22|8|2|232|23|
|Nampula-urban|29|13|12|6|56|12|35|6|
|Sofala & Zambezia-rural|10|1|310|30|17|2|291|19|
|Sofala & Zambezia-urban|87|17|11|2|168|25|27|3|
|Manica & Tete-rural|19|2|228|25|21|2|233|22|
|Manica & Tete-urbana|134|20|25|4|95|21|22|4|
|Gaza & Inhambane-rural|142|14|89|9|126|19|64|7|
|Gaza & Inhambane-urban|171|31|9|2|151|27|12|2|
|Maputo Province-rural|84|29|3|1|53|43|1|1|
|Maputo Province-urban|261|48|0|0|332|57|1|0.1|
|Maputo City|381|41|0|0|313|35|0|0|
|Total|**1383**|**15**|**1068**|**12**|**1381**|**12**|**1064**|**9**|

**Set A:** Consumption poor/ Wealth index non-poor; **Set B:** Consumption non-poor/ Wealth index non-poor;

Regarding Set B, i.e. the consumption non-poor/wealth poor set, around 80 percent of observations are concentrated in Central and Northern rural areas in both surveys (see Table 1). The general

> 12 These are: the non-food share of household consumption, the head-of-household’s total years of education, the highest level of education attained by any household member, the household’s total asset ownership, access to a protected water source, electricity and sanitation, housing conditions and dietary variety.

7
below are robust to using higher percentiles of ordinary ...rms’DVAR as ...lters.

In summary, we focus on a subset of single-industry processing exporters that have their <u>IMP</u><sup>betweenthetwocuto¤s:</sup> EXP<sup>bounded</sup> where DV AR(25)<sup>OT= 1 �</sup> � EXPIMP �OT(25)<sup>isthe25percentileoftheDVARofordinaryexportersin</sup> the same industry.<sup>26</sup> Table 1 summarizes the main issues, assumptions and solutions of our approach to constructing the DVAR at the ...rm, industry and aggregate levels.<sup>27</sup>

# 3.1 Movement of the Industry and Aggregate DVAR of Processing Exports

The ...nal data set is an unbalanced panel of 17,903 observations for 8,459 single-industry processing exporters over 8 years (2000-2007).<sup>28</sup> Our sample covers a balanced panel of 15 industries throughout the sample period. An advantage of using the micro approach is that we can construct random samples drawn from the ...rm sample and compute bootstrapped standard errors for our estimates of the aggregate DVAR. Figure 1 shows our benchmark

> 26Table A8 in the appendix reports ~~�~~ EXPIMP �OT(25)<sup>byindustry-year.Wewillcheckthesensitivityofour</sup> regression results by including both excessive importers and exporters in the sample below.

> 27Sometimes, ...rms have incentives to stock up imported materials when the international prices of commodities are low, particularly in those industries that use a lot of commodities, such as iron, copper and crude oil, as inputs. Thus, imports may not be fully used to produce goods in the same period. For these ...rms, the calculation of the DVAR based on (4) may not be accurate. However, there is no easy way to resolve the issue of inventory management. As we will show in the next section, all ...rm observations with negative DVA are no longer negative once we use (8) to restrict our sample. This suggests that inventory management does not appear to drive our results.

> 28Our sample covers both types of processing trade in China – pure assembly (PA) and import-andassembly (IA). While we will check the robustness of our regression results below by repeating the analysis separately for the two regimes, it is important to point out that IA accounts for a much larger share of processing, in terms of the volume as well as the number of exporters, compared to PA. In our regression sample, over 90% of the observations belong to IA, in which exporters take control and hold ownership over the imported materials. We show in Figure A5 in the Appendix that even at its peak in 2000, PA never accounts for more than 30% of total processing exports, and continuously declined to less than 20% by 2007.

12
Table 5: **Sector shares of total value added exports – World Trade Direct and indirect exports through forward linkages**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.055|0.053|0.046|0.045|0.042|0.041|
|Processed Foods|0.015|0.021|0.020|0.019|0.020|0.019|
|Energy Extraction|0.092|0.058|0.061|0.060|0.093|0.118|
|Minerals nec|0.015|0.013|0.009|0.008|0.013|0.016|
|Beverages and Tobacco Products|0.005|0.005|0.006|0.006|0.005|0.005|
|Textiles|0.017|0.018|0.019|0.020|0.016|0.014|
|Wearing Apparel|0.012|0.012|0.011|0.011|0.008|0.007|
|Leather Products|0.006|0.007|0.006|0.006|0.005|0.004|
|Wood Products|0.011|0.011|0.011|0.011|0.011|0.010|
|Paper Products, Publishing|0.027|0.027|0.025|0.024|0.021|0.019|
|Chemical, Rubber, Plastic Products|0.058|0.075|0.067|0.069|0.072|0.069|
|Mineral Products nec|0.009|0.011|0.015|0.015|0.010|0.010|
|Ferrous Metals|0.020|0.023|0.021|0.018|0.020|0.020|
|Metals nec|0.012|0.012|0.014|0.013|0.014|0.016|
|Metal Products|0.022|0.022|0.022|0.022|0.025|0.024|
|Transport Equipment|0.044|0.043|0.044|0.048|0.048|0.045|
|Machinery and Equipment nec|0.131|0.147|0.148|0.146|0.128|0.119|
|Manufactures nec|0.013|0.014|0.014|0.017|0.012|0.012|
|Electricity, Gas, Water|0.024|0.027|0.024|0.031|0.028|0.029|
|Construction|0.007|0.010|0.011|0.010|0.012|0.012|
|Trade and Transport Services|0.207|0.191|0.181|0.173|0.145|0.139|
|Other Commercial Services|0.177|0.162|0.196|0.197|0.223|0.223|
|Public Services|0.013|0.033|0.030|0.029|0.027|0.024|
|Dwellings|0.006|0.006|0.003|0.003|0.003|0.003|
|Total|1.000|1.000|1.000|1.000|1.000|1.000|

12
# **6. Robustness Checks**

We probe the robustness of our results against some alternative scenarios and threats to identification. First, as we combine pre-and post-COVID-19 data, one may suspect that this can increase the treatment effects associated with the number of confirmed cases and deaths. We thus apply a more conservative approach by restricting our sample to the COVID-19 period, JanuaryApril 2020, and exploit the intensive margin of variation in the spread of the pandemic only. Second, our main results could be driven by a few influential countries and regions. To gauge whether a few countries and regions influence our results, we, therefore, split our sample by continent and estimate disaggregate treatment effects. As different regions have varying levels of access to the internet and use different languages in their Google search queries, such disaggregation can inform whether our results hold for several regions and different languages. We estimate our main empirical specifications for countries in the four main continents in our sample: Africa, the Americas, Asia, and Europe.

The results in Table B.7 (in Appendix B) show similar effects of a weekly increase in confirmed cases and deaths. The sizes of the coefficients are comparable with those based on the larger sample. The impacts of the pandemic on some services are even more visible in this smaller sample and conservative approach. For example, demand for delivery services appears to have significantly increased because of the pandemic in this smaller sample. Overall, this exercise confirms that our results are not affected by specific periods and sampling. This is not surprising as much of the variation in the intensive margin arises from the COVID-19 period.

The disaggregated results generally confirm that most of our results hold across all the four continents covered (see Table B.8 in Appendix B). As expected, there exist some differences in the size of the effects across continents. The impact of the pandemic on consumer demand in Africa, for instance, is smaller compared to other regions of the world. This is owing, presumably, to the lowest confirmed cases and deaths that the continent has registered and the relatively lax movement restrictions that African governments implemented compared to other regions. The most notable and statistically significant effect of the pandemic is on hotel and restaurant services, which is higher in Latin American and Asian countries. In comparison, the effect of the pandemic on the airline industry is much higher in Europe than the rest of the world. These are likely to be driven by the differences in government responses, including social-distancing, travel restrictions, and lockdowns, across countries and regions. Overall, the evidence suggests that the patterns of 25
# **Table A.17. Meta-analysis of Imputation Methods, Between-Year Imputations, Marginal Effects from Logit Regressions**

|b/se|
|---|
|Imputation method – normal distribution of error terms<br>-0.030<br>(0.02)|
|Poverty line is consistent<br>0.724**<br>(0.28)|
|Base year is NPS 2020/21<br>0.694**<br>(0.27)|
|Type of questionnaire – Treatment Arm 2<br>0.028<br>(0.05)|
|Type of questionnaire – Treatment Arm 3<br>-0.020<br>(0.05)|
|Inflation source – quarterly WB CPI<br>-0.063<br>(0.05)|
|<br>Inflation source – monthly WB CPI<br>-0.069<br>(0.05)|
|Inflation source – annual IMF CPI<br>-0.031<br>(0.06)|
|<br>Number of observations<br>480<br>**Note:*** p < 0.1, ** p < 0.05, *** p < 0.01. The estimation results are obtained from logit regressions. The dependent<br>variable is a binary variable that indicates whether the predicted poverty rate is within the 95% confidence interval of<br>the true poverty rate. The robust standard errors are clustered at the model level. The reference groups are empirical<br>distribution of the error terms for the “imputation methods,” inconsistent poverty line for “the type of the poverty|

**Note:** * p < 0.1, ** p < 0.05, *** p < 0.01. The estimation results are obtained from logit regressions. The dependent variable is a binary variable that indicates whether the predicted poverty rate is within the 95% confidence interval of the true poverty rate. The robust standard errors are clustered at the model level. The reference groups are empirical distribution of the error terms for the “imputation methods,” inconsistent poverty line for “the type of the poverty line,” NPS 2019/20 for the “base year,” TA 1 for the “type of the questionnaire”, annual WB disaggregated CPI for the “inflation source.” The consistent poverty line is defined as poverty line measured at the base year (i.e., the 2020/21 poverty line used for the imputations from 2020/21 to TAs and the 2019/20 poverty line used for the imputations from 2019/20 to TAs).

61
without any type of microfinance program. This breakdown is used in the so-called pipeline method, which estimates program effect by exploiting the variation in timing of program implementation, using an eligible comparison group that has not yet received the treatment. Thus, the control villages were PRIME target villages where no intervention had occurred by the time the InM survey was conducted in 2008–09. We use a simple pipeline method to estimate the program effect of PRIME, as follows: where ATT (average treatment effect of treated) = differences of outcomes between program participants of treated village (T) and non-participants of control villages (C) adjusted by the program participation ratio (  ) in treated villages.

Note that equation (7) can be estimated by OLS where treatment dummy ( _T_ ) is assumed exogenous, as follows: This equation assumes the observational equivalence (both household and village-wise) of households that participated and those who did not, except that one group is treated and the other is not. In equation (8),  estimates the average treatment effect of the treated (ATT).

An obvious shortcoming of the pipeline method is that the timing of program placement is not necessarily exogenous but may depend on area characteristics.<sup>22</sup> One way to resolve this non-random program placement or participation is to estimate the program effect using the propensity score matching (PSM) technique. PSM requires that PRIME participants match with “otherwise identical” non-participants, and quantifies any difference

> 22 Since PKSF’s POs introduced PRIME initially in areas where they already had regular microfinance programs, PRIME placement is not randomly given.

29
labor more slowly than the agricultural sector in the initial stages, until the nonagricultural sector grows to be a very large share of the economy (see Timmer, 2008 for a numerical example). Obviously, a more rapid demographic transition, by slowing the growth of the labor force, would reduce the lag time, all other things being equal.

In the modern theory and practice of economic development, one of the key issues in economic thought has been the relationship of structural transformation and poverty reduction. Initially, this question was not even considered, but then Simon Kuznets and others began to analyze the linkages, at first empirically and then theoretically (Kuznets, 1966). Kuznets and others noted that in some countries, the process of structural transformation was accompanied by increasing inequality. In this case, although a transformation was taking place which provided the basis for sustained growth, it was not leading to much poverty reduction because too many of the gains went to the upper quintiles of the income distribution. The analysis of this point in the literature of economic development also has a long history, and it links up the structure of economic growth (output transformation), the distribution of growth (income inequality and inclusive growth) and levels of deprivation (absolute poverty on multiple dimensions). Ultimately, the failure to find strong panel evidence of a Kuznets relationship (especially the downward sloping part of the curve) led economists to move away from viewing an increase in poverty or inequality as in some way a mechanical or automatic consequence of broad-based growth or structural transformation. On the contrary, broad-based, growth and structural transformation came to be seen as necessary for poverty reduction and vice versa (World Bank 1990, 2001, 2005).

6
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