rafmacalaba/datause-encoder-data huggingface dataset:
measurement issues will be based mainly on a simple income tax model where the relevant population is assumed to be socially homogeneous with respect to non-income attributes. This implies that tax liability will be the same for income units with the same level of income. The model further assumes that both tax and after-tax income are increasing function of pre-tax income<sup>12</sup> (Lambert 2001). The results derived from this simple model will then be generalized to the case of social heterogeneity.

Table 2.2: Alternative Tax Options for Australia

|Expenditure Group|Option A|Option B|
|---|---|---|
|Clothing and Footwear|0.30|0.30|
|Furniture and Appliances|0.30|0.30|
|Motor Vehicles and Parts|0.40|0.30|
|Recreation Items|0.20|0.30|
|Miscellaneous|0.20|0.30|
|House-Building Payments|0.40.|0.30|

Source: Creedy (2001)

# **Measurement**

The burden associated with a progressive tax is disproportionately distributed in favor of the worse off while exerting an equalizing effect on the distribution of the living standard. Measures of progressivity can therefore be developed either from the disproportionality effect or from the equalizing effect of the tax under consideration. In the case of income tax, assuming a homogeneous population with respect to tax-relevant attributes such as marital status or family size, progressivity can be established by plotting the ratio of the amount paid in tax to income against income. If this is an increasing function, the tax is progressive. It is a decreasing function of income for a regressive tax<sup>13</sup> . The ratio is constant for a proportional tax. Lambert (2001) explains

> 12 Analytically, this model can be represented by a tax function t(x) with the following properties: 0 ≤ _t_ ( _x_ ) < _x_ ; 0 ≤ _t_ ′ ( _x_ ) < 1, where x represents pre-tax income. 13 One can also measure the degree of tax progression along income scale by considering the so-called measures of local or structural progression.  One such measure compares the marginal and average tax rates.  For a tax schedule to be progressive, it is necessary and sufficient to have the marginal rate greater than the average rate for all income levels (Lambert 2001).

11
most forceful characterizations of links between climate change and water variability came out of a 2008 Technical Report of the Intergovernmental Panel on Climate Change, claiming that increased precipitation intensity and variability will increase flooding and drought in many areas, which will affect food stability, water quality, as well as exacerbate many forms of water pollution (Bates et al. 2008: 3-4). Such environmental changes could also aggravate political tensions, increasing the vulnerability of certain regions, and present substantial challenges to water infrastructure and services (Vörösmarty et al. 2000: 287; Kabat et al. 2002: vii; IPCC 2007: 49). Climate change may have indirect negative effects that can challenge the legitimacy of governments, undermine individual and collective economic livelihood, and affect human health (Barnett 2003:9). Regions comprised of developing countries that are more reliant on climate sensitive resources may lack the capacity to adapt to climate change impacts, making them more vulnerable to these changes and may fare worse to these changes (Barnett and Adger 2007: 648). Moreover, according to Buhaug, Gleditsch, and Thiesen (2008: 6), unexpected and sudden climate induced events (such as floods and droughts) are expected to constitute a large threat to human security and the prospects of sustained peace.

The transboundary nature of rivers and shared bodies of water further complicates scenarios of climatic and environmental change as inter-state tensions may rise as water variability increases. Evidence suggests that the likelihood of political tensions is related to the interaction between variability or rates of change within an international river basin and the institutional capacity to absorb that change (Wolf, Stahl, and Macomber 2003; Stahl 2005). In this context, climate change may further act as a ̳threat multiplier‘ exacerbating existing economic, social, and political problems (CNA 2007: 43) and even contributing to waves of eco-refugees as people attempt to escape ecological devastation (Reuveny 2007).

Given the links between climate change, water variability, economic development, and political tensions, the role of institutions in assuaging potential inter-state conflicts and associated economic and ecological problems seems paramount (Brochmann and Hensel 2009; Salehyan 2008: 317). Such institutions, in the form of international water treaties, may confer a certain level of resilience allowing the participating basin riparians to deal with the perturbations brought upon by environmental change (Adger et al. 2005). However, given the uncertainty that is implicit in climatic change and the consequent effects of water variability, even basins that are governed by international water treaties may be susceptible to the effects of environmental change—mainly, if these institutions were not originally designed to deal with these complex environmental changes 4
# **S2 Detailed Country Results**

## **S2.1 Using Harmonized Household Data**

Table S2.1 Trends in Overall Inequality

||||||**Level**|||||
|---|---|---|---|---|---|---|---|---|---|
|||Gini||V|arianc|e|log|(p90/p|10)|
|Country|1995|2002|2015|1995|2002|2015|1995|2002|2015|
|Argentina|34.76|39.16|33.32|0.40|0.52|0.41|1.61|1.84|1.66|
|Bolivia|49.43|52.28|40.74|1.02|1.35|0.74|2.59|2.92|2.14|
|Brazil|49.37|48.14|43.91|0.78|0.72|0.54|2.26|2.15|1.70|
|Chile|44.86|40.98|38.01|0.61|0.52|0.44|1.98|1.80|1.57|
|Colombia|n.a.|47.86|40.19|n.a.|0.78|0.57|n.a.|2.34|1.89|
|Costa Rica|36.59|38.72|42.12|0.43|0.49|0.61|1.62|1.75|2.01|
|Ecuador|n.a.|43.81|33.99|n.a.|0.67|0.46|n.a.|2.08|1.65|
|Guatemala|n.a.|49.83|41.32|n.a.|1.02|0.71|n.a.|2.61|2.06|
|Honduras|43.48|46.93|44.87|0.67|0.87|0.86|2.17|2.42|2.35|
|Mexico|44.61|43.26|40.85|0.74|0.71|0.62|2.18|2.12|1.94|
|Nicaragua|45.74|39.69|39.41|0.73|0.52|0.51|2.14|1.84|1.79|
|Panama|41.77|44.89|37.96|0.59|0.80|0.56|1.99|2.34|1.84|
|Peru|44.03|45.71|39.47|0.81|0.86|0.61|2.24|2.37|1.97|
|Paraguay|44.75|45.51|40.09|0.78|0.86|0.63|2.20|2.35|2.03|
|El Salvador|38.20|42.29|36.42|0.50|0.75|0.52|1.86|2.12|1.75|
|Uruguay|38.64|41.38|33.64|0.49|0.55|0.37|1.80|1.90|1.52|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Wages are defined as real hourly income (using 2005 purchasing power parity conversion rates) in the main occupation. The sample was restricted to individuals ages 18 to 65 years who were employees or self-employed. The 1st and 99th percentiles of the country-year wage distributions were trimmed.

52
Randriamamonjy (2002) and Orr, Mwale and Saiti‐Chitsonga (2009). The seasonal gap measure is calculated as the difference between the highest and lowest seasonal factor: In statistical terms, this is a range. Although the estimates of the 12 seasonal factors themselves are unbiased, the range is a nonlinear function of these seasonal factors and so will potentially be biased. Bias arises if large irregular movements confound identification of the peak and trough months. This is more likely when samples are short. This bias would not occur if it is known a priori that the seasonal peak is in month _a_ , say, and the trough is in month _b_ since the difference _sa_  _sb_ is the unbiased difference between two unbiased statistics.

Monte Carlo results indicate that the dummy variable procedures to characterizing seasonality perform poorly for samples up to 20 years with irregular variations. They frequently suggest the existence of seasonality which is in effect absent in the data generating process (Table 1, columns 1‐2).<sup>7</sup> With no seasonality genuinely present and using 10 years of data, the average estimated seasonal gap is 10 percent using the dummy variables approach, while in effect it is zero.<sup>8</sup> In addition, these spurious seasonal factors purport to explain an average of 9.2 percent of the sample price variability (R<sup>2</sup> =0.0925). A more extended set of simulations, reported in Gilbert, Christiaensen and Kaminski (2015) indicates that large biases also persist in cases in which the seasonality is present, but not well‐defined. Series of 10 to 15 years of food price data are common in African samples. The series used here has 19 years of data.

This weakness of the common dummy variable approach to characterizing seasonality can be mitigated by using a more parsimonious, but more restrictive functional approach to characterizing seasonality, such as the trigonometric functions. Ghysels and Osborn (2001) provide a general discussion of trigonometric representations of seasonality. Here, a pure

> 7 The experiments estimate seasonal factors from data where no seasonality is present employing 5, 10, 20 and 40 years of data. They are based on 10,000 simulations.

> 8 The bias and R2 for each of the simulations are very similar when using the moving average deviation approach to estimate the seasonal gaps (equation 1).

8
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|modelexpensiveandunaffordableforpoorerhouseholdsinurbanareas(Aranet.al,(forthcoming)).|
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|decided to leavethe laborforceandthe majority ofthesewomen (71 percent) reported leavingthe laborforce<br>“due to child care responsibilities” (World Bank (2015)).<br>option. More specifically, the policyscenarios considered included: (i) an investmentgrant only (Scenario 1),<br>(ii)anoperationalgrant(withorwithoutapricecap(Scenarios2Aand2B),(iii)throughavoucherscheme|
|---|
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on status of the firm (legal entities or individual enterprises) and rayon (tax district). Based on discussions with the tax authority and the implementing partner on expected response rates, we estimated that we needed to contact 2,000 firms to have 1,500 firms attend the training sessions. Since we expected the intervention to be more effective on legal entities which are usually bigger firms than individual enterprises, we over-sampled legal entities to achieve a relative proportion of 75 percent of legal entities to 25 percent of individual enterprises in the study population. We randomly allocated the 2,004 firms into three groups: 802 into Group A, 400 into Group B and 802 into Group C.<sup>11</sup> We conducted the randomization by computer using STATA and stratified using legal status, sector of activity and rayon (tax district). Figure 1 illustrates the experimental design and the sampling strategy.

# **3.2 Program Implementation**

The training programs and logistical support were delivered by a Dushanbe-based firm with the support of the tax authority from October to December 2014. Firms were invited to attend a general training on taxation through telephone calls by trained operators.<sup>12</sup> Although we assigned firms randomly into the treatment and control groups before the trainings (to facilitate scheduling into the different training sessions), all firms received invitations following exactly the same script (see Appendix A for the text of the script). Out of the 2,004 firms in the study database, 1,722 (86 percent) received telephone calls and 1,498 (75 percent of the full sample and 87 percent of those invited) attended training. These response rates were balanced across the three treatment groups.

Since the invitations to all firms followed the same protocol, each firm had to decide

> 11 More firms were allocated to Groups A and C than to Group B in order to increase our statistical power to measure the impact of e-filing on firms adopting it since we expected the Group A treatment to have the greatest impact on e-filing adoption.

> 12 The tax authority periodically organizes events and trainings for firms, so this program was not unusual.

11
# 3.2.6 Demographic variables

The first of the demographic variables is _human capital_ (education). We rely on the log human capital index, _lhc_ , from the PWT 10.0, which linearly interpolates data on educational attainment from Barro and Lee (2013) and assumes a certain rate of return for primary, secondary, and tertiary education. Human capital is widely assumed to be a key driver of cross-country income differences (Mankiw, Romer, and Weil 1992; Hanushek and Woessmann 2015), but its approximation through attainment levels is problematic (Hanushek and Woessmann 2012). Given the lack of a better measure available across a wide range of countries for many decades, we rely on this standard measure.

To measure _inequality_ , we take the Gini coefficient of market incomes, _gini_mkt_ , from the Standardized World Income Inequality Database (Solt 2019). Following the literature (Brueckner and Lederman 2018; Scholl and Klasen 2019), we do not log-transform this variable. As discussed in those studies, there are several approaches to address apparent endogeneity concerns for this variable, and several arguments suggest various functional forms (changes versus levels and quadratic and interaction terms). After testing several functional forms, we opted to include this variable in first differences in our equation (see the next subsection).

To gauge potential demographic dividends, an extension of our model also includes the _employment rate_ , _emprate_ , as the fraction of the population that is employed, emp/pop from the PWT 10.0. Employment enters the production function directly and is thus considered a “proximate” and not “ultimate” driver of growth. Yet, some developing countries, including Bangladesh, have experienced drastic demographic changes. It is hence informative to understand the implications for growth and partially factor out this effect to understand whether it impacts other variables (for example, FDI or infrastructure).

Since demography can influence economic growth through various channels (Vandenbrouke 2021), we additionally experimented with other demographic variables, such as the share of urban population in the total population and population density (both from the WDI), but we did not obtain meaningful regression results. Their inclusion also does not considerably alter our key results.

# 3.3 Estimation

We follow the standard approach in the empirical growth literature (Araujo et al. 2016; Moller and Wacker 2017; Brueckner and Lederman 2018) and estimate equation (1) as nonoverlapping five-year averages in levels, which gives us: lnyct = θlnyc,t−1 + β1x1,ct + ⋯+ βkxk,ct + δt + αc + εct. (2)

-11-
Figure 3: Energy Efficiency Penetration: Aggregate Impacts, 2015-2035 <!-- Start of picture text --> Armenia Georgia<br>A. Real GDP and CO2 Emissions<br>0 0<br>-0.5<br>-1<br>-5<br>-1.5<br>-2<br>-2.5 -10<br>-3<br>-3.5<br>-15<br>-4<br>-4.5<br>-5 -20<br>-2 -1.5 -1 -0.5 0 0.5 1 1.5 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5<br>% Change in Real GDP from baseline % Change in Real GDP from baseline<br>B. Welfare<br>1.4 1.4<br>1.2 1.2<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>2015 2020 2025 2030 2035 2015 2020 2025 2030 2035<br>C. Sectoral CO2 Abatement and Leakage<br>0 400<br>350<br>-50 300<br>250<br>-100<br>200<br>150<br>-150<br>100<br>-200 50<br>0<br>-250 -50<br>0 100 200 300 400 500 600 700 0 200 400 600 800 1000 1200 1400 1600<br>Abatement by Discrete Technologies (kTCO2) Abatement by Discrete Technologies (kTCO2)<br>Annual rate of increase in μ : 1% 2% 3% 5% 7% 10% Bottom-Up Analysis<br> emissions from baseline% Change in CO2 emissions from baseline% Change in CO2<br>% Change in Welfare from Baseline % Change in Welfare from Baseline<br>)Abatement by Rest of Sector (kTCO2 )Abatement by Rest of Sector (kTCO2<br><!-- End of picture text --> 20
19 fact. Strictly, Korea began SHI only in 1963, but it was a largely out-of-pocket system before then, and coding it a tax-financed system before 1963 would likely be a bigger error. Mexico and Turkey began setting up a SHI system in the 1940s, but universal coverage has yet to be achieved in either country; nonetheless, both countries manifestly aspire to be fully-fledged SHI systems. In group (ii), there are no coding ambiguities. Nor are there any in group (iii)—all are countries that had a fledgling SHI system in place but abandoned it in favor of universal taxfinanced health care. In group (iv), the only ambiguity is Poland, which reverted to SHI, having had a fledgling SHI system before World War II, but levies a dedicated income tax on everyone to finance it rather than an earnings-related payroll tax on formal sector workers. The results presented below are based on Poland being coded a SHI country from 1998 onwards; the results are similar if Poland is coded a tax-financed country throughout the period.

# _4.2 Outcome variables_

The dependent variables include total health expenditure (THE) per capita, potential years of life lost (PYLL) for nine ‘amenable’ causes of death, employment as a share of the population of working age, and the share of people employed who are paid a salary or a wage.

THE per capita is converted at purchasing power parities (PPP) and expressed in real terms using the country’s GDP deflator. PYLL indicate the number of years of life (up to the age of 70) that are lost as the result of a death. They are calculated by summing up deaths occurring at each age and multiplying this by the number of remaining years to live up to the selected age limit of 70. To improve comparability over time and across countries, the OECD Health Data PYLL calculations are age-standardized to a reference population. The nine ‘amenable’ causes selected for the analysis below are the nine causes that appear both in the OECD Health Data and
|**Table A4: Population (in mil**<br>|**lions) recla**<br>|**ssified moving fro**<br>|**m per capita to roo**<br>|**t N rul**<br>|**e-$5.50**<br>|
|---|---|---|---|---|---|
|Region|Not poor<br>under<br>both<br>rules|Poor by pc rule,<br>RECLASSIFIED<br>as not poor|Not poor by pc<br>rule,<br>RECLASSIFIED<br>as poor|Poor<br>under<br>both<br>rules|Population|
|(1)|(2)|(3)|(4)|(5)|(6)|
|Middle East & North Africa|166|20|12|131|329|
|South Asia|307|92|71|1286|1757|
|Sub-Saharan Africa|116|36|28|866|1045|
|**World**|**2747**|**195**|**195**|**2729**|**5865**|
|Other High Income|1002|0|5|13|1020|
|Europe and Central Asia|412|9|17|53|491|
|Latin America & Caribbean|425|13|24|122|583|
|East Asia & Pacific|318|25|38|259|640|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table is an extension of Table A3 above, so these estimates are also for the 2017 reference year. These</u> results are based on the full sample of 162 countries covered in the paper.

48
The predicted probabilities of receiving credit also indicate that length of relationship is a significant determinant of credit access for indigenous-African SMEs. Across average firm size classes and across countries increases in length of relationship with supplier raise the chances of receiving supplier credit for indigenous-African firms. A micro-enterprise in Zambia, which has a ten year established relationship with its supplier (one standard deviation higher than the mean) has a 9% chance of receiving credit, an increase of 28%. The probability of receiving supplier credit for networked SMEs, on the other hand, is unaffected by length of relationship with supplier, as we noted above.

**Table 9: Probability of Receiving Supplier Credit for Indigenous-African Firms**

**(Evaluated at various levels of enterprise size and length of supplier relationship)**

||_Zambia_|_Kenya_|_Zimbabwe_|
|---|---|---|---|
|**Micro firm, 4 years**|0.07|0.18|0.32|
|**relation with supplier**||||
|**Micro firm, 10 years**|0.09|0.23|0.38|
|**relation**||||
|**Small firm, 4 years**|0.14|0.30|0.47|
|**relation with supplier**||||
|**Small firm, 10 years**|0.18|0.36|0.53|
|**relation with supplier**||||

**Table 10: Probability of Receiving Supplier credit for Networked SMEs**

**(Evaluated at various size classes)**

||_Zambia_|_Kenya_|_Zimbabwe_|
|---|---|---|---|
|**Micro firm**|0.29|0.36|0.85|
|**Small firm**|0.35|0.43|0.89|

31
||Full<br>acquisition<br>cost|Transportation<br>cost reduced<br>by 50%|Transportation<br>costs reduced<br>by 75%|Fertilizer<br>available<br>in the<br>village|
|---|---|---|---|---|
|Cereal-root crop|0.39|0.57|0.69|0.78|
|Tree crop|0.64|0.75|0.77|0.80|
|Root Crop|0.60|0.64|0.65|0.68|

_These results are gotten from a simulation of fertilizer profitability with different transportation cost._ We see a large difference in the distribution of plots on which nitrogen application is profitable when risk aversion versus risk neutrality is assumed. While nitrogen application (at full acquisition costs) is profitable for less than 20% of maize plots in the former (for the C-RCFS) this increases to almost 40% for the latter (See table 7 vs. table 6). Similarly reducing transportation costs by 75% increases the number of plots for which nitrogen application is profitable to almost 70% in the C-RCFS compared to about 45% when risk aversion is assumed. With risk neutrality, these transport cost reductions increases the number of plots on which nitrogen application is profitable from just under 60% (in the TCFS) to closer to 80%. With these large variations in profitability when risk neutrality is assumed versus risk aversion, it is important to see if farmers observed behavior is more in line with one assumption more than another.

# **4.5 Fertilizer profitability and observed use rates**

Next, we compare actual observed fertilizer use rates on maize plots in Nigeria with the expected profit maximizing levels. Following Sheahan et al. (2013) we use the estimates from the production function to derive the amount of nitrogen that should be applied for the marginal value cost ratio to be equal to 2 (for a risk averse farmer) and 1 (for a risk neutral famer) given the full acquisition price of fertilizer and the market price.

Comparing table 8 and table 6 we see that the percentage of plots on which nitrogen is actually applied in the TCFS (35%) is almost identical to the percentage of plots for which the application of nitrogen is considered profitable for a risk averse farmer (i.e. MVCR>2); which is 34%. Over 70% of these households for which nitrogen application is profitable actually use some of the input. However, for the C-RCFS, while fertilizer use is only profitable in expectation for about 16% of plots, we have over 65% of maize plots using some fertilizer. This likely indicates that 24