rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3. Distributional effects**

In order to identify distributional effects, we run IV regressions on split samples disaggregated by the household head’s initial level of education, main source of income and land ownership.

**Education.** Table 5 suggests that the positive effect of refugees is mostly driven by households whose heads did not complete primary school level in the base year of the study period (Panel A). However, the point estimate is not precisely estimated when observed heterogeneity (column 3) and household fixed effects (column 4) are controlled for. Moreover, a t-test shows that the difference between the coefficients is not statistically significant. Education level of the household head therefore does not seem to matter for the effects of refugee presence on household welfare.

Table 5: Second stage: Effects of refugee presence on household welfare disaggregated by initial education level, with - <u>analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of welfare|(consumption a|ggregate per a|dult equivalent)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Panel A<br>|(Didn’t compl<br>|ete primary sch<br>|ooling)<br>|
|Log of refugees (50km)|0.09736*|0.09596*|0.12497|0.11212|
||(0.05594)|(0.05540)|(0.07754)|(0.06946)|
|Observations|3,224|3,224|2,616|2,614|
|Kleibergen-Paap rk Wald F|12.54|12.55|8.298|8.177|
|Root MSE|0.544|0.543|0.509|0.372|
|<br>|Panel<br>|B (Completed<br>|primary schoo<br>|ling)<br>|
|Log of refugees (50km)|0.02190|0.01969|0.00903|0.03060|
||(0.04250)|(0.04211)|(0.04793)|(0.04568)|
|Observations|2,015|2,014|1,489|1,488|
|Kleibergen-Paap rk Wald F|14.45|14.45|12.47|12.24|
|Root MSE|0.546|0.540|0.476|0.340|
|T-stat (_comparing corresponding coeffs in Panels A & B_)|0.9633|0.9829|1.0641|0.8292|
|_t_0.05,∞|1.96|1.96|1.96|1.96|
|_t_0.1,∞|1.645|1.645|1.645|1.645|
|**_Included controls_**|||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education,<br>occupation, other income sources, land ownership)|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|

_Apart from HH size, the more endogenous controls added are as at the base year of the study. In Regression (4) where household fixed effects replace the cluster fixed effects, all the time invariant variables drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1._

**Occupation.** For the effect of refugee presence on welfare of households disaggregated by main source of income in the base year, Table 6 depicts an increase in welfare by about 8% for households within 50km of the closest refugee settlements and whose initial main source of income is subsistence

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- 42 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

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Lynch, K (1981) _A theory of good city form_ . MIT Press, Cambridge, MA

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seek inpatient care) and focus on only eight tobacco-related diseases. Third, the previous simulations centered on cigarette consumption, while the medical costs are calculated for all tobacco products. Finally, the simulations are not able to model individual behavioral responses to increases in tobacco prices and do not consider smoking histories; they only rely on differences in the share of households consuming cigarettes by decile and the different price elasticities.

The health benefits of reducing tobacco consumption presented in table 6 accrue only from complete abstinence, which would be reflected in a reduction in smoking prevalence and number of smokers. However, the elasticities estimated here include both the effect on smoking prevalence and the effect on cigarettes smoked per day among continuing smokers. The epidemiological evidence indicates that continuing smokers do not experience tangible health gains in terms of avoiding diseases or gains in life years. To address this concern, the literature has generally used only the effect on smoking prevalence, which is broadly half the total price elasticity according to the literature (IARC 2011). Even if one reduced the price responses by half, the estimates are significantly larger than the direct impacts in consumption.

# **6. Conclusion**

Despite the obvious positive health impacts of tobacco taxation, an argument raised against taxation is that poor households bear much of the burden of the increased prices because they spend more on tobacco. This note summarizes estimates of the distributional impact of increases in the prices of cigarettes based on a partial equilibrium approach using the HIES 2016/17.

The note highlights that tobacco consumption is widespread across households in Bangladesh. In 2016, approximately 65 percent of Bangladeshi households had consumed some type of tobacco product during the month before the interview; 42 percent had consumed smoked tobacco; and 45 percent had consumed smokeless tobacco products. About 29 percent of households consumed cigarettes. The poorest 10 percent (households in extreme poverty) were less likely to consume cigarettes (19 percent).

There is little difference in the budget shares spent on tobacco across the consumption distribution. About 3 percent of total household consumption was allocated to tobacco products. Focusing on cigarettes, the budget share is similar across the consumption distribution (1.3 percent on average). The similarity of consumption patterns is important in assessing the distributional impacts of a potential tax reform.

Overall, this note shows that an increase in the price of cigarettes has small consumption impacts and does not significantly change the poverty rate or consumption inequality. These results stem from the fact that poor households are slightly less likely to consume cigarettes and that cigarette budget shares are relatively even across the consumption distribution.

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good track records, and who were therefore approached by branch officers about becoming an agent. As such, less than 0.1% of applications to become an agent have been rejected to this point. There were thus too few rejected applicants (less than ten) to permit meaningful analysis differentiating the characteristics of those who became agents from those who did not.<sup>16</sup>

# **3. Data and the Estimation Approach**

Our data come from 190 FINCA DRC agents who began operations at some point from 2012 to 2015. We rely on monthly transactions data for the dependent variables in the regressions that follow. Although we have some data on other transfers to FINCA accounts, those transactions occurred too infrequently to permit meaningful analysis. Similar to Jack and Suri (2013, 2014), we therefore focus on cash-in, cash-out transactions in our analysis.

As explanatory variables, we rely on the personal/business characteristics of the agents and the socio-economic characteristics of the agents’ locations. Personal and business characteristics are taken from the applications that agents submitted to FINCA. Our data are from agents located in and around Kinshasa since that was the focus of FINCA DRC in the initial phases of their agent rollout. Agents are located in four major business districts– Funa, Lukunga, Tshangu and Mont Amba –which are further subdivided into 24 so-called municipalities.<sup>17</sup> In our regressions, we rely on data from agents operating in 23 of those municipalities. These municipalities vary greatly in

> 16 In principle, it might be possible to distinguish the characteristics of agents who ceased operations from those that continued throughout our period of study to assess the viability of the agent model in different contexts. But according to FINCA agent network representatives, hardly any agents quit the business in the first few years of developing the network, precisely because those agents were picked based on ongoing business relationships with branch officers.  We acknowledge, however, that 14% of our sample is comprised of agents that are missing operational data for some or all of the last five months covered by our study. We cannot say with certainty whether they quit or took a break from agent work, which sometimes happened due to liquidity issues, or whether data were missing for some other reason.

> 17 About 10 percent of our transactions data come from agents located outside Kinshasa. We were unable to create reliable measures of income and commercial/financial development for those locations, many of which were larger than a municipality, and thus we have excluded them from the regressions that include municipal-level variables that follow.

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Learning from Developing Country Power Market Experiences: The Case of Philippines Table 8‐3 Detail of wholesale market governance indicators for Philippines

|**Level**|**Philippines score**|
|---|---|
|**Market Governance**|**72%**|
|**Decision Making Autonomy**|**71%**|
|Is there an Independent System Operator (ISO), or Independent Transmission System<br>Operator(ITSO)?|0|
|Does the system operator hold ultimate authority on real‐time system operation (i.e.<br>above market operator instructions orgenerator self‐schedules)?|1|
|Does the board of the market (or system) operator allow one class vetoes (e.g. allowing a<br>class of market participants to veto rule changes sought by other classes, thus producing<br>deadlocks and impede changes to the system operator and itsprocedures)?|NAV|
|Is regulatory backstop allowed, whereby the regulator (or minister) can review, revoke or<br>modifydecisions taken bythe board of the market(or system)operator?|NAV|
|Is the market (or system) operator legally independent from market participants and<br>government?|1|
|Can the market(or system)operator decisions be appealed?|1|
|Are there regulatory requirements for the board members of the market (or system)<br>operator to be independent from market participants and the government to prevent<br>conflicts of interest?|0|
|Is there a formally established mechanism for dispute resolution among market<br>participants?|1|
|Does the board of the market(or system)operator have real decision‐makingauthority?|1|
|**Transparency**|**88%**|
|Are operationprocedurespubliclyavailable?|1|
|Is bid datapubliclyavailable?|1|
|Are marketpricespubliclyavailable?|1|
|Are dispatchquantities and schedulespubliclyavailable?|1|
|Is settlement informationpubliclyavailable?|0|
|Are the grid code and all the operating procedures (including market clearing processes)<br>publiclyavailable to all interestedparties?|1|
|Are there well‐defined boundaries on acceptable market outcomes?|1|
|Is the form of regulatoryintervention clarified in advance?|1|
|**Accountability and Monitoring**|**57%**|
|Is there an external overseeingor monitoringentity?|0|
|Can the external monitoring entity access information from system and market operation,<br>includingcommerciallysensitive and confidential information?|NAV|
|Does the external monitoringentityreport to the board of directors of the operator?|0|
|Does the external monitoringentityreport to the regulator?|0|
|Does the system operator report regularlyto the regulator,andpolicymakers?|1|
|Was sufficient time (> 3 years) allowed for fully phasing in the operation of the wholesale<br>power market?|1|
|During the transition to a wholesale power market were any measures taken to reduce<br>marketpower in thegeneration segment?|1|
|Was any transition mechanism put in place to ensure a minimum degree revenue certainty<br>forgenerators and acceptable degree ofprice risks for retailers?|1|

47
``` Drm2 = + 0.679*Drm2_1 – 0.016*Cia_1 – 0.115*dumm2008 (SE) (0.0836) (0.00678) (0.0455) Drgdp = - 0.00456*DINFL_1 – 0.0222*Dinterestrt_1 + 0.459*Drm2_2 (SE) (0.000871) (0.00565) (0.0603)
- 0.0167*Cia_1 – 0.0604*dumm1986 + 0.127*dumm2013
(0.00429) (0.0273) (0.027) DINFL = + 32.5*Drgdp_1 – 0.377*DINFL_1 + 15.4*Drm2_2 + 2.19*Cia_1 (SE) (10.3) (0.106) (7.05) (0.476) + 7.19*dumm1986 + 7.17*dumm2008 (3.29) (3.26) Dinterestrt = + 2.68*Drm2_1 – 4.21*Drgdp_1 + 0.0293*DINFL_1 + 0.551*DDISC_1 (SE) (1.57) (2.27) (0.0173) (0.093)
- 3.9*Drm2_2 + 0.184*Dinterestrt_2 – 0.497*Cia_1
(1.44) (0.111) (0.109)
- 5.41*dumm1992 – 2.68*dumm2009 + 0.991*dumm2010 + 2.82*dumm2013
(0.931) (0.649) (0.575) (0.653) ``` The residuals diagnostics of the parsimonious system indicate that the individual equations, in addition to the whole system’s residuals, are free from serial correlation and hetroskedasticity. Residuals of all individual equations are also normally distributed, with the exception of the interest rate equation. This influenced the normality of the system’s residuals that reject the null hypothesis of the normality test. Moreover, the visual inspection of the residuals diagnostic graphs showing the plots of actual and fitted values, cross plot of actual and fitted values, and scaled residuals show close fits, and the residuals aren’t seen to exhibit a pattern; despite the presence of several outliers. (see graph 8). The visual inspection of the residual correlograms, residual density, residual histogram, and residual distribution reconfirm the proposition of absence of serial correlation with distributions close to the normal bell shape.

Moreover, the parsimonious system is remarkably stable. Visual inspection of the Chow one step test and the break point Chow test at a 1 percent significance level. Also, the roots companion matrix lies within unity circle. Accordingly, the model converges in the long term. In addition to each of the four individual equations, a one step residuals test shows remarkable stability within 29
_Figure 4: Effects of BRI on the trade pattern (% Change)_ Effects on the Trade Patttern <!-- Start of picture text --> Wood and mineral prods<br>Vegetable, fruits, nuts, crops<br>Textile, Leather and Apparel<br>Processed food<br>Other Mnfcs (paper, metals, mnfcs nec)<br>Motor, parts and Transport<br>Meat Products<br>Machinery, parts and electronics<br>Livestock<br>Grains, Seeds and Fibers<br>Extraction<br>Chemical, ferrous metals, rubber, plastics<br>0.0 0.2 0.4 0.6 0.8 1.0 1.2<br>Direct effect Indirect effect<br><!-- End of picture text --> Note: (i) The impact is estimated using the sector specific average time sensitivity characteristics irrespective of the regional production and trade structure. (ii) Impact is calculated using ∆lnX��� �100 ∗�β�t� � �β� t̃� ��∗∆GisTime�� with the average change in time to trade from the upper‐bound scenario (17.61 hours) iii. Complementarity between improved infrastructure, corridor management and trade facilitation reform In this section, we study two alternative cases for reductions in trading times after BRI are presented: (i) improvements in infrastructure combined with reductions in border delays, and (ii) improvements in infrastructure along economic corridors. In the first case, time improvements after BRI are computed in the GIS analysis assuming that border delays are reduced 50 percent across countries. In the second case, we assume that improvements in corridor management and decreases in congestion take place after BRI interventions along the corridors. To take this into account, we assume that improvements in the rail links after the BRI intervention apply along the economic corridors (for the whole rail instead of the segment that is new or improved).

25
villages did not differ much, and conclude that the diffusion of new agricultural technology does not necessarily lead to a high level of efficiency. This would require other interventions such as technical training for farmers.

Among the stochastic frontier models, Ekanayake and Jayasuriya (1987) drew a distinction between rice farms depending on their location at the ‘head’ or ‘tail’ of a major irrigation canal in the Mahaweli Development Project in Sri Lanka. Farmers at the ‘head’ of the canal had access to irrigation water throughout the year, while access to water was more limited for farmers at the ‘tail’. Comparing estimates from deterministic and stochastic frontier models, the authors find that farmers at the ‘tail’ experienced a high level of inefficiency in both models, but that the farmers at the ‘head’ show inefficiency only in the deterministic model. The latter result is attributed to pitfalls of the deterministic model, which does not allow the separation of random ‘noise’ from deviations arising from technical inefficiency. The authors conclude that inefficiency of farmers at the ‘tail’ is strongly correlated with water shortages, and that the stochastic frontier model is more suitable than deterministic procedures for measuring technical inefficiency.

Ali and Flinn (1989) started out by emphasizing that the traditional production function approach may not be appropriate if farms face different prices and have different factor endowments. Using farmlevel data for Basmati rice producers in two Punjabi villages in Pakistan, one with better market access and lower transport cost than the other, they estimated farm-specific inefficiency via a profit frontier approach. They also identified sources of inefficiencies in order to design programs for increasing profitability. Among the factors included in the model were water constraints, mainly due to electric breakdowns, tubewell breakdowns, and unscheduled closure of canals. The authors find that water constraints cause profit losses, and recommend the establishment of workshops in the rural areas to help reduce down time due to pump breakdown through maintenance and timely repair.

Sherlund _et al._ (2002) used stochastic frontier modeling to examine the influence of omitted variables, such as environmental production conditions, on the measurement of technical efficiency. They use panel data for smallholder rice plots under rainfed conditions in Côte d’Ivoire, and apply two specifications for the model—one with and one without environmental variables. These variables comprise total rainfall and number of rainy days, and others such as soil and topographic characteristics. The findings show that properly accounting for heterogeneous environmental production conditions leads to much lower levels of estimated technical inefficiency, and more intuitive and precise estimates of its sources. The authors conclude that a bias in efficiency estimates from omitted variables may distort decisions on whether scarce resources should be spent on developing improved technologies or helping farmers to make better use of existing technologies.

27
3B, we see that with increasing mobility, differences in work stoppages between the top 20 and bottom 20 became larger, with the bottom 20 being more likely to stop working. Similar results can be seen in the regressions in Appendix Table 1. The coefficient on quintile 5 (relative to quintile 1, the omitted category) is negative and significant in periods of higher mobility (omitted), while this effect disappears when mobility is lower.

Figure 3. Work stoppages across country-rounds are negatively correlated with mobility. Poorer workers were more likely to stop working during times of high mobility.

- A. Mobility and work stoppages

- B. Mobility and Top 20 to Bottom 20 ratio in work stoppages

Note: Each point represents a country-round. The mobility index in an average of the percentage change in mobility to retail and recreation locations, transit stations, parks, and workplaces. The average of the mobility index is calculated for each countryround. The regression lines exclude outliers (i.e., PNG in Panel A and Vietnam in Panel B). See footnotes 17 and 18 for further details.

Source: HFPS, Google mobility reports Below, countries are categorized into groups to shed light on different experiences across countries in the region.

Mongolia and Myanmar – Between April and May 2020, Mongolia and Myanmar experienced significant economic shocks and a drop in mobility due to precautionary containment measures that were put in place, in addition to the decline in global demand, particularly from China (World Bank 2021a). Round 1 of both the Mongolia and Myanmar high-frequency surveys took place after this shock during a period when mobility was relatively high (Figure 1b). At this time, poorer workers in both countries were less likely to resume work, leading to higher work stoppages (Figure 4). In Mongolia, poorer workers were significantly more likely to be out of work in May-June 2020, with workers in the bottom quintile being twice as likely to have stopped working as those at the top. A similar downward gradient in work stoppages across quintiles was also visible in Myanmar between May and September 2020. However, as stringent mobility restrictions were reinstated in both countries late in 2020 due to a sudden increase in locally transmitted cases, differences across the distribution dissipated. As sectors with high participation among workers in the top 20 such as retail and previously less affected sectors such as transportation, finance, real estate, professional activities suffered contractions, the pandemic’s impact on employment became more widespread across the distribution.

12
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