rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7. Share of Household Expenditure on Transportation** **<u>by Income and Commute Mode of Principal Earner</u>**

||**Walk**|**Train**|**Bus**|**MTW**|**Car**|
|---|---|---|---|---|---|
|<5k|12.5%|16.8%|19.4%|28.5%|NA|
|5-7.5k|8.6%|9.3%|9.9%|19.8%|NA|
|7.5-10k|7.8%|8.3%|8.7%|16.0%|NA|
|10-20k|7.6%|9.0%|8.4%|14.4%|20.0%|
|>20k|7.8%|6.8%|5.8%|11.6%|14.2%|

Source: Baker et al. (2005)

**Table 8.  Mean Monthly Household Expenditure (Rs.) on Transportation and its** **<u>Share in Income, by Income Group</u>**

||**<5k**|**5k-7.5k**|**7.5k-10k**|**10k-20k**|**>20k**|**HH Avg**|
|---|---|---|---|---|---|---|
|Bus|151|195|221|286|275|210|
|Rail|89|124|165|227|296|152|
|Taxi|91|121|165|287|397|169|
|School Bus|3|5|13|50|59|18|
|Fuel|59|160|200|589|1545|301|
|Bicycle Repair|1|2|2|2|7|2|
|Vehicle Repair|6|31|39|96|300|54|
|Transportation<br>expenditure (fare & fuel<br>only)|393|605|764|1439|2572|850|
|Total transportation<br>expenditure (incl.<br>maintenance)|400|638|805|1537|2879|906|
|Share in Income (fare &<br>fuel only)|15.7%|9.7%|8.7%|9.6%|10.3%|10.0%|
|Share in Income (total<br>transportation<br>expenditure incl.<br>maintenance)|16.0%|10.2%|9.2%|10.2%|11.5%|10.7%|

Source: Authors’ calculations Table 8 shows mean total household expenditure on transport, by category of expenditure. Average household expenditure on rail increases with income; as does average expenditures on buses—until the highest income category, when it decreases slightly. The percent of income spent on public transport is, however, highest for the lowest income group.

The figures in Table 8 foreshadow some results regarding the incidence of transit subsidies. As long as the transit subsidy is a constant percentage of the fare for all income groups, the subsidy in Rs. will increase with household expenditure on transit. Hence, transit subsidies will increase with income for rail and also for bus (up to the 11
a relatively more preferred country _k ∈/ j_ , _τEC ,k_ , is to lower the price index _PEC_ ( _z_ ), i.e. the price index of _all_ varieties’ exports of good _z_ to the EC. For instance, _k_ could denote the group of ACP countries, all of which enjoy the same preferential advantage as compared to ordinary GSP countries. One of the latter would then constitute a _j_ country. Since _pEC ,j_ remains unchanged (by construction) and because ( _σ −_ 1) _>_ 0, Equation (4) predicts that a decline in _PEC_ ( _z_ ) depresses exports from country _j_ to the EC relative to that of another destination (here the US). This is the trade diverting effect to the detriment of the less preferred country _j_ .<sup>7</sup> Hence, a testable hypothesis of the model is whether the preferential tariff rate accorded to country _k_ , e.g. the ACP group, is negatively related to the less preferred country’s exports, conditional on relevant control variables that will affect the GSP country’s exports to the EC and to the US.

Equation (5) shows that in order to isolate this effect market entry conditions for GSP countries to both the EC and the US need to be accounted for, alongside proxies for the price indices of product _z_ in the EC and the US and relative transport costs and expenditure shares. In order to address all of those theoretical channels, I include in the empirical specification the EU’s MFN as well as preferential ACP tariff rates to capture the first term on the right of Equation (5), the individual GSP country’s EU tariff rates correspond to the second term, the US MFN and preferential Mexico rates to capture the third term, and the individual GSP country’s US tariff rates for the fourth term. In addition to serving as a proxy for unobserved price indices, a commodity’s MFN tariff is also likely to depend on the existence of a preferential rate, as Karacaovali and Limão (2008) find, thus omitting them would lead to inconsistent estimates. Further I assume that the fifth and sixth term—relative transport costs and expenditure shares—can be

> 7In general equilibrium, tariff changes will affect goods prices if the elasticity of supply is less than infinite. Recent evidence for this to be case is furnished by Broda et al. (2007) (on 15 non-WTO countries) and Romalis (2007) (on elastic exports to the US). While those price changes are an important part of welfare analysis, here I abstract from terms of trade effects because the primary focus is on identifying trade diversion. At any rate, the negative demand shock to GSP countries would causes the prices of their exports to fall so that the terms of trade effect, if anything, works in the same direction as does trade diversion.

8
Three features of Latvia innovation activity merit particular attention. First, most of the LVL 142 million innovation spending in Latvia in 2001 took the form of acquiring new machinery and equipment, primarily from outside Latvia. Intramural research (R&D conducted inside the firm) was a relatively minor source of innovation. What little intramural research did take place was concentrated in only one or two sectors. Similarly, extramural research (R&D done outside the firm by R&D institutes working under contract to an enterprise) was also quite small and concentrated in only one or two sectors. Finally, Latvian enterprises do not appear to be particularly active in acquiring external knowledge either in the form of licensing technology or purchasing patents from abroad for use in Latvia.

To a certain extent, this is not surprising given the fact that Latvia’s industrial structure tends to be concentrated in traditional sectors. The in-house R&D intensity in the US food and wood industries, for example, is much lower than in the electronics industry. The situation is similar in Latvia: the share of firms conducting R&D in-house is almost twice as high in “Knowledge Sectors” (53%) as in non-knowledge sectors.<sup>31</sup> However, by developing the absorption capacities of the traditional sectors, promoting technology upgrading in Latvian enterprises, and cultivating closer links between Latvian R&D institutes and Latvian enterprises, Latvia would still appear to have considerable scope for increasing domestic demand for the R&D output of Latvian scientific institutions.

**Figure 22 Embodied Technology Flows in the United States**

<!-- Start of picture text --> Real Estate & Business Services<br>Wholesale & Retail trade<br>Finance & Insurance<br>Mining<br>Agriculture, forestry & fishing<br>Construction<br>Wood, cork and furniture<br>Social & personal services<br>Food, beverage & tobacco<br>Paper, print & publishing<br>Textiles, footwear&leather<br>Electricity, gas & water<br>Ferrous metals<br>Transport&Storage<br>Communication<br>Fabricated metals<br>Non-ferrous metals<br>Petroleum refining<br>Other transport<br>Other manufacturing<br>Stone, clay&glass<br>Rubber&plastic products<br>Other non-electrical machinery<br>Electrical machinery<br>Shipbuilding<br>Motor vehicles<br>Chemicals<br>Instruments<br>Computers&office machinery<br>Communications&semiconducto<br>Aerospace<br>0 2 4 6 8 10 12 14 16 18 20<br>Direct R&D Intensity Acquired Technology Intensity<br><!-- End of picture text --> _Source_ : OECD, 1998

> 31  For details, see FIAS Report, p. 48.

36
**Effects of the Patient Information Intervention.** Returning to Table 3, we see that the effects of patient information on testing and treatment outcomes are notably different. Counter to the aim of the intervention, we find a 6 percentage point decrease in the incidence of malaria testing, significant at the one percent level. This result is mainly driven by the 4 percentage point reduction in the share of patients tested with an RDT only (significant at the 5 percent level). Table 4 shows that the patient information treatment had no discernible effect on treatment allocation, despite the reduction in malaria testing.

A possible mechanism for this result is that (at least some) patients distrusted RDTs or arrived at the clinic with a strong ex-ante preference for a specific treatment. Reduced testing without a change in treatment allocation could have arisen because marginal patients who “opted out” of malaria testing were those who had very strong preferences regarding treatment and, therefore, took steps to ensure providers dispensed their preferred prescription regardless of the diagnostic outcome. This hypothesis is consistent with evidence from our companion paper, which shows that patients can successfully exert pressure on providers to prescribe antimalarials, worsening the match between treatment and underlying illness (Lopez et al., 2022). In the next section, we first discuss the effect of our interventions on patient knowledge and satisfaction and then explore this idea further.

# **4.4 Impacts on Patient Knowledge and Satisfaction**

Figure 4 uses data from the home survey to assess whether the extended training improved patients’ knowledge of their malaria testing outcome. Before carrying out the home RDT test, enumerators asked patients about the likely outcome of their test (on a scale from 1, definitely negative, to 5, definitely positive). The figure shows average patient scores conditional on the patient’s actual home RDT result for both the basic and extended training groups. Patients treated at extended-training clinics were better able to predict their test result, with those who tested negative reporting lower scores (relative to their peers in the basic training group) and those who tested positive reporting higher scores. For a formal test, we run a difference-in-difference regression paralleling that in column 5 of Table A6. The coefficient on the interaction term is 0.516, with a p-value of 0.097.

Since patients at extended-training clinics got higher-quality care _and_ became better informed about their malaria status, it is natural to ask whether they recognize and reward this quality of care improvement. The home survey included a series of questions to measure patients’ satisfaction with testing at the clinic, medicines prescribed at the clinic, and overall care. Specifically, patients were asked about the extent to which they agreed with a series of statements, such as “the doctor/nurse should have done additional medical testing before prescribing treatment”, “the doctor/nurse based the treatment decision strongly on the result 17
# Applying Tax Policy Models in Country<sup>Economic Work</sup> Bangladesh, China, and India

by Henrik Dahl and Pradeep Mitra

# Table of Contents

|Summar|y|1|
|---|---|---|
|I.|Introduction|5|
|II.|Bangladesh|7|
||Background<br> <sup>R</sup>|7<br>7|
||Raising One Percent of Indirect<sup>Tax evenue</sup><br>|11|
||Underlying Assumptions of the Model||
||The Agenda<br>|14<br>|
||Step 1: A Summary Measure<br> <br> <br>|14<br>1|
||Step 2:<br>Shifting Assumptions:<br>Three Cases<br>|5<br>|
||Step 3: The Ranking of Sectors<br>|16<br>|
||Step 4: Sensitiv. y of Rankings<sup>to Shifting</sup><br> <br>|19<br>|
||Distributionai<br>Emphasis and Disaggregation<br>|19<br>|
||Implication for Tax Policy Analysis|21|
|III.|China|22|
||The Problem|22<br>|
||Institutional<br>Structure<br>|22<br>|
||VAT:<br>Simplification<br>or Unification?<br>|25<br>|
||Tax Reform and Price Reform|28|
|IV.|India|29|
||The Problem|29<br>|
||The Agenda<br> <br>|32<br>|
||Step 1: Required Tax Change:<br>Disaggregated Model<br> <br>|32<br>|
||Step 2: Responsiveness<br>to Factor Prices:<br>Disaggregated<br>Model|34<br>|
||Step 3: The Effect on Factor<sup>Prices:</sup><br>Aggregated Model<br>|34<br>|
||Step 4: Required Correction to<sup>the Disaggregated</sup>|34|
||Model||
||Summary|35|
|V.|Implementing Tax Policy Models|35|
||Selection of Model Structure|36<br>|
||Compilation<br>of a Raw Data Base|36<br>|
||Model Implementation|37<br>|
||Generation of a Consistent Data<sup>Base</sup>|38<br>|
||Model Calibration|39<br>|
||Experimentation<br>with the Model|39|
||Overall Assessment|40|
|VI.|Conclusions|42|
|Refer|ences|46|
# **Figure 10. Access to Health and Education**

Public spending per capita on health and education is lower for countries with high poverty rate.

- a. Poverty Rate vs Expenditure on Public Health b. Poverty Rate vs Expenditure on Public

Education <!-- Start of picture text --> 4.0 7<br>3.5 6<br>3.0<br> 5<br>2.5<br> 4<br>2.0<br> 3<br>1.5<br> 2<br>1.0<br> 1<br>0.5<br>0.0 -<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Poverty headcount ratio at $3.20 a day Poverty headcount ratio at $3.20 a day<br>(2011 PPP), percent of population (2011 PPP), percent of population<br>d. Poverty Rate vs Illiteracy Rate<br>c. Poverty Rate vs Coverage of Doctors<br> 8 90<br> 7 80<br> 70<br> 6<br> 60<br> 5<br> 50<br> 4<br> 40<br> 3<br> 30<br> 2 20<br> 1 10<br> - -<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Poverty headcount ratio at $3.20 a day Poverty headcount ratio at $3.20 a day<br>(2011 PPP), percent of population (2011 PPP), percent of population<br>Government expenditure on<br>education (PPP$ millions) in logs<br>Public health expenditure per capita (PPP current international $) in logs<br>ages 15 and above<br>Physicians, per 1,000 people<br>Illiteracy rate, percent of people<br><!-- End of picture text --> _Source_ : Authors’ calculation based on data from World Bank.

_Note_ : Data are for latest observation available including 142 countries from the World Bank’s POVCALNET database. Poverty headcount ratio uses the poverty measure of $3.20 per day in 2011 purchasing power parity (PPP) terms.

30
**Figure 8:** Distribution of the means of the CC (red) and ML (blue) in the test sample for 100 simulations

<!-- Start of picture text --> Millet<br>900 950 1000<br>Rice<br>2000 2500 3000<br>Cowpea<br>250 300 350 400<br>0.04<br>0.02<br>0.00<br>0.002<br>0.000<br>0.04<br>0.02<br>0.00<br><!-- End of picture text --> <!-- Start of picture text --> Sorghum<br>950 1000 1050 1100 1150 1200<br>0.020<br>0.010<br>0.000<br><!-- End of picture text --> <!-- Start of picture text --> Maize<br>1500 1600 1700 1800 1900<br>0.010<br>0.000<br><!-- End of picture text --> <!-- Start of picture text --> Groundnut<br>1700 1800 1900 2000 2100 2200<br>0.012<br>0.006<br>0.000<br><!-- End of picture text --> 33
<!-- Start of picture text --> y) oy R<br>U al — “1<br>oF<br>Be .<br>j oh (ge. // ,<br>ail a<br>: VA af il<br>{ Pe5 (i:BSsal ee Wh I<br>4 nili = 82 hn‘soeoe i aLBon<br>. Sa a pe Sagem ee :<br>“ | ) pe - - 7 1<br>J ee a ate See<br>y pad is oon rs)<br>4 mi if z a oe \ Pie Se 1 i}<br>fake aN al ees ce ee _ |<br>.74Ia<br>=ra F<br>Harare ' NY‘= .<br>_ Shuttle Bus Routes<br>——— Kombi Minibus Routes he<br>Percentage Access aS EN<br>| High : 70 N ><br>a<br>s<br>Low :0 ORE. 5 10<br>Kilometers .<br>Esri, HERE, Garmin, © OpenStreetMap contributors,.and the GIS user community<br><!-- End of picture text -->

<!-- Start of picture text --> »,<br>fe<br>: °<br><!-- End of picture text -->
on both variables are jointly statistically at the 10% level, suggesting that “larger” sectors generate more outward FDI. This can be the outcome of a higher number of producers, higher average firm-scale, or more internal finance. The inclusion of these measures of sector-specific activity has little impact on the coefficient of the triple interaction term, which remains large, negative, and statistically significant at the 1% level.<sup>36</sup> Hence, the credit channel effect of the global financial crisis that we have highlighted throughout this paper does not appear to be a simple proxy for sectorspecific output shocks.

and intensive margins of outward FDI. In column (5), the dependent variable is the number of FDI projects while in column (6) the dependent variable is the average value of the FDI projects.<sup>37</sup> In both columns, the coefficient on the triple interaction term is negative and statistically significant. However, its magnitude and statistical significance is much larger in column (6), suggesting that MNEs have responded to the lack of access to external finance mainly by reducing the size of their foreign projects.

# **5 Conclusion**

the decline in the availability of external finance. This result complements those of the trade literature on the effects of credit conditions on international trade and highlights the role played by source

> databases.html. on past cash flows.

> 36Without the inclusion of these two variables and keeping the sample constant, the coefficient on the triple interaction term is almost the same as in column (4).

> 37It is important to note that modelling the average size of the cross-border investments conditional on positive FDI does not result in a sample selection issue. Given that we are interested in how the global financial crisis influences average project size when FDI occurs, the observations for which outward FDI is positive form an appropriate subsample of the population of interest (Wooldridge, 2010)

19
precautions and damage-averting behavior, and health effects from 821 farmers, randomly selected.<sup>3</sup> The survey, by design, focused on major pesticide intensive crops such as Boro (winter rice), potato, bean, eggplant, cabbage, sugarcane and mango. To provide greater depth, we also interviewed 68 randomly-selected farmers who currently use Integrated Pest Management (IPM). The sample was also geographically stratified among 11 districts of Bangladesh as: Bogra, Chapainawabganj, Rajshahi, and Rangpur districts in the Rajshahi division (Northwest); Chittagong and Comilla in the Chittagong division (East); Jessore in the Khulna division (West); and Kishoreganj, Munshiganj, Narsingdi, and Mymensingh in the Dhaka division (North - see Figure 2). Table 1 displays the regional distribution of farmers in our sample.

**Table 1: Regional Distribution of Survey Respondents**

|**District**|**No. of Farmers**|**Percent**|
|---|---|---|
|Bogra|39|4.8|
|Chapainawabganj|8|1.0|
|Chittagong|76|9.3|
|Comilla|80|9.7|
|Jessore|195|23.8|
|Kishoreganj|41|5.0|
|Munshiganj|27|3.3|
|Narsingdi|86|10.5|
|Rajshahi|158|19.2|
|Rangpur|36|4.4|
|Mymensingh|75|9.1|
|Total|821|100.0|

To minimize reporting bias, the survey was implemented under the agreement that the team would not reveal the identity of the farms surveyed or the respondents who participated.

> 3 The survey was designed and supervised by the World Bank team, and conducted by the Development Policy Group in Bangladesh.

5
realized some returns, and possibly also disinvested in response to adverse shocks and other factors.

# _B. Data_

The study uses two rounds of household data: a baseline survey was collected in 2010 before the project was implemented and a follow-up survey was collected in 2013, 18 months after households received the last transfers. In September 2010, a PMT (baseline) questionnaire was administered to all households in the pilot villages. The information collected is limited to the variables necessary to calculate the PMT formula.<sup>13</sup> In November 2013, approximately 18 months after the end of the program, 2,000 households were sampled to participate in a comprehensive follow-up survey. In each project village, 20 beneficiaries and 20 nonbeneficiaries were randomly sampled based on the baseline PMT data. The follow-up survey includes all the variables in the 2010 baseline questionnaire, and additional modules on investments (durables, local credit, household enterprises, and agriculture), education, health and consumption.

As further discussed below, the quasi-experimental empirical strategy employed to evaluate the impact of the cash transfer program on productive investments is largely driven by the structure of the data. Notably, most investment variables were collected only in the follow-up survey.

# _C. Empirical identification strategy_

We estimate the effect of cash transfers on household �’s investments and other outcomes of interest ��. Specifically, we consider a range of outcomes including livestock (stock at the follow-up survey in 2013, stock 12 months before, consumption and sales); housing quality (house material, access to water, toilets, etc.); durable goods (number and value of durable goods); engagement in household enterprises (HEs) (number of HEs, revenues, charges and profits, value of equipment); and agricultural investments (surface cultivated, quantity produced and yields, input spending, type of crops). These outcomes cover a wide range of potential investments. We also consider the participation in _tontines_ (number of _tontines_ , amount received, and usage).

> 13 Several food security variables were collected in 2010 only for the subsample of households in Tahoua. These variables are analyzed as a robustness check.

11
<!-- Start of picture text --> $6,000 215%<br>5,007<br> $5,000<br>183.0% 165%<br> $4,000<br>115%<br> $3,000<br>65%<br>1,874<br> $2,000 1,228<br>1,235<br>37.4%<br> $1,000 23.6% 638 634 28.2% 15%<br>-1.5%<br>-13.8%<br> $- -35%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text --> If we would allow wages to depreciate due to the displacement of workers between sectors in our simultaneous convergence model, the growth would be 22.1 percent instead of 23.6 percent. When adding non-tradable production to the full gender convergence, GDP would equal to USD 5.89 billion. With such addition, women’s potential would be maximized in the Liberian economy as their contribution to GDP would rise to 41.4 percent while the value added to the labor market would be 52.8 percent.

# **6 Conclusions**

The role of gender equality is a subset of a broader body of scholarship on the relationship between intergroup inequality and growth (Kalecki, 1954). Perhaps most significant in this body of research is the finding of a two-way causality between gender equality and economic growth outcomes. If gender equality is found to be a stimulus to growth, then policies and interventions that promote greater gender equality can contribute to a virtuous cycle of growth.

In this study, we showed that the current contribution of women to the Liberian economy is undervalued, creating an economic cost to not only women individually but also to society at large. We adapted a macrosimulation model to construct a model of the gender dividend, which quantifies the current economic contributions of women in Liberia. We then identified the potential economic contribution that women would be able to make if there were equality of opportunity and capability by gender across education, fertility, and labor market factors that have been identified as determinants of the gender dividend and inputs to economic growth and productivity. We also incorporated into the production estimate the contributions that are made from undertaking housework and domestic chores.

We created a series of estimates by varying the parametrization of the model. We showed that the current contribution of women – using data as recently as 2020 – represented 26.8 percent of GDP and 39 percent of the aggregate production of labor. The contribution of women to the modern sector was solely 31.3 percent while the contribution of women to the traditional sector was 46.8 percent. If we account for the value of home-based non-tradable output, GDP would be 19.3 percent higher, reaching USD 4.83 billion and with women contributing 33.4 percent of the output.

23
# **D. G External Validation using Parliamentary Elites in Latin American Surveys**

Table D.8. Presidential Candidates with Divergent Ideological Classifications

|**Candidate**|**Election Year**|**Our Classifcation**|**Avg. Parliamentarians’ Perception**|
|---|---|---|---|
|GERMAN VARGAS|2010|Center|Right|
|GERMAN VARGAS|2018|Center|Right|
|JUAN MANUEL SANTOS|2014|Center|Right|
|MARTHA LUCIA RAMIREZ|2014|Center|Right|
|NOEMI SANIN|2002|Center|Right|
|NOEMI SANIN|2010|Center|Right|

_Note:_ This table shows 5 candidates who have run in 8 presidential elections, with ideological classifications based on Fergusson et al. (2020) that differ from how legislators, on average, perceive these candidates’ ideologies when asked to classify them on a 1-10 left-right spectrum.

84