rafmacalaba/datause-encoder-data huggingface dataset:
# **Regression Table 1(c)** : _Long-term relation between crises and Gini_

Dependent <u>variable: Gini coefficient (series of Table 3a)</u>

||Equation:|1.|K|1.|L|1.|M|1.N||1.|O|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant||-95.8|**9.2|-100.8|**7.6|-105.6|**8.0|-90.0|**5.2|-88.1|**3.0|
|Inflation||||||||||||
|Institutions (Economic f|reedom)|||||||||||
|Sub-Saharan Africa||18.4|**9.5|19.6|**8.4|20.6|**9.0|17.8|**5.8|17.9|**3.4|
|Latin America||13.7|**13.4|12.8|**10.0|12.9|**9.0|12.1|**6.9|11.2|**4.0|
|GNI per cap (PPP, log)||15.9|**12.5|16.6|**10.7|17.2|**11.0|15.4|**7.5|15.1|**4.3|
|End of crisis dummy||||1.8|1.2|||||||
|Date||||||||||||
|Policy||-1.9|*2.2|||||||||
|Policy x End of crisis du|mmy|7.7|**6.1|5.1|*2.2|7.1|**4.1|4.2|*2.8|2.2|0.6|
|_Which policy_||_GUAR_||_GUAR_||_GUAR_||_FORB1_||_PDRP_||
|_Countries excluded_||_Trans_||_Trans_||_Trans_||_Trans_||_Trans_||
|R-squared / NOBS||0.997|9|0.995|9|0.993|9|0.986|9|0.964|9|
|Adjusted R-squared||0.992||0.986||0.985||973||0.928||
|S.E. of regression||1.0||1.3||1.4||1.8||3.0||
|Loglikelihood||-7.6||-10.2||-11.9||-14.6||-19.0||

** and * indicate significance at the 1% and 5% levels, respectively.

The dependent variable is the “long before” and “long after” Gini coefficients reported in Table 3(a) Inflation rate is the mean of CPI inflation for the 1990s (Source _International Financial Statistics_ ) Institutional variable is the Heritage Foundation index of overall economic freedom – average of 1995-99 figures subtracted from 6. GNI per capita at PPP is from _World Development Indicators_ . Policy variables are as defined in Honohan and Klingebiel (2003). GUAR takes the value 1 if there was blanket government guarantee of deposits; FORBA indicates capital forbearance; PDRP indicates existence of a debtor relief program. Countries excluded: all transition economies.

# **Regression Table 2** : _Short-term crisis-Gini links_ Dependent variable: Change in Gini between just before and just after onset of crisis <u>(series of Table 3b)</u>

|Equ|ation:|2.A||2.B||2.C||2.|D|
|---|---|---|---|---|---|---|---|---|---|
|||Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant||0.97|1.1|-0.07|0.0|1.62|0.7|-1.06|0.2|
|Africa||-8.17|**4.6|-7.13|**3.2|-8.27|**4.3|-8.10|**4.3|
|Latin America||||1.55|0.8|||||
|Institutions (Corrupt)<br>Initial Gini||||||-0.134|0.3|0.045|0.4|
|_Countries excluded_||_Non_|_e_|_Non_|_e_||_None_|_Non_|_e_|
|R-squared / NOBS||0.676|12|0.698|12|0.679|12|0.683|12|
|Adjusted R-squared||0.644||0.631||0.608||0.613||
|S.E. of regression||2.7||2.7||2.8||2.8||
|Loglikelihood||-27.8||-27.3||-27.7||-27.6||

** and * indicate significance at the 1% and 5% levels, respectively.

23
access to compound-level piped water and sewer services between 2014 and 2018 (World Bank, 2019).

The model to finance the last mile connection costs relied on the assumption that property owners would repay their loans and pay the monthly service fees. During the course of the program, however, it became clear that that the utility faced critical nonpayment issues. In 2016, 40% of connected property owners had yet to make a single service payment or loan installment even though the average property owner had already been connected for 1.3 years. Moreover, the average share of bills and loans paid fell from above 65% in 2014 to below 50% in 2018 (Figure 1).<sup>14</sup> Service quality deteriorated substantially over this time period, including a city-wide rationing program that started in early 2017. Based on data collected from a panel survey of 587 households from one of the settlements, the proportion of compounds that received water from their water point in the week prior to the survey fell from 95% to 40% between 2014 and 2018.<sup>15</sup>

# **3.2 Compounds and Residents**

Characteristics of property owners and tenants living in one such informal settlement, Kayole Soweto, are presented in Table 1 for owners and Table 2 for tenants.<sup>16</sup> In Kayole Soweto, almost all property owners are sole owners of their compounds with self-reported sales’ values of about US$ 20,000 on average. Property owners are 51 years old, are 60% male, 54% have completed secondary education or higher, and make US$ 551 in income per month on average from all sources.

Property owners rent out space in 86% of the compounds and make the compound their principal residence in about half. On average, the rental compounds have 7.7 distinct rental rooms, and generate US$ 115 per month in total rent per compound, or US$ 83 in income after deducting expenses. Approximately three quarters of tenants have written or verbal month-to-month rental agreements and paid a deposit in 47% of the cases. Eighty percent of individual tenancy rents fall between US$ 15 and US$ 35 per month and averages US$ 25. A tenant makes US$ 137 on average in income per month which translates into an average total compound renter income of US$ 1,306.

The primary source of water in the informal settlement is piped water. The utility connects a single piped water and sewer line that serves the entire compound which is considered a single customer. The property owner is responsible for paying the water and

> 14The program target was an 80% collection rate, which is consistent with the Kenyan regulator guidelines. 15We use data from a listing exercise that we conducted in 2014 and combine this with an updated listing exercise conducted by the utility in 2018 which is described in further detail in Section 7.

> 16Based on a survey of property owners and a random sample of their tenants conducted in Kayole Soweto in 2015/2016.

8
V.2 Prices <!-- Start of picture text --> $0.90<br>S0.80<br>$0.70<br>$0.60<br>0$0.50<br>=$0.40 National I g<br>$0.30<br>$0.20<br>_____________________ $0.10 4--,<br>1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Figure 14: National Direct Call Charges (US$/3minutes, End of Year). Note: Tariffs and exchange **rates** are for end of year. Tariffs were increased in local currency in 1999. source: Pnrc data for Malawi is from 1996 amendnents to e 1994 Teleconmunicutions act and MTL's webpage (www.mtl.malawi.net).

Although tariff re-balancing was not identified as a goal in the 1998 Communications Policy Statement, MPTC re-balanced international and domestic tariffs in September 1999. Prices were increased for local and long-distance calls (see Figure 14) and decreased for international calls (see Figure 15). The nominal price for a three-minute local call was increased from 1.2 Kwacha (US$0.03 at end1998) to 4.8 Kwacha (US$0.10 at end1999) and the nominal price for a long distance call was increased from 12 Kwacha (US$0.27 at end-1998) to 24 Kwacha (US$0.52 at end-1999). However, prices for local and long-distance calls were set in Kwacha and since they were not adjusted after 1999, rapid inflation in Malawi (relative to inflation in the US) has resulted in a drop in tariffs in real terms. By 2000, they had reached levels in US$ similar to the levels that they had been at before re-balancing. Prices for longdistance and local calls were adjusted downwards by an additional 6.25 percent in 2002.

At the same time that call prices were adjusted, connection charges were increased and the difference in fees between business and residential customers was eliminated. For residential customers connection charges were increased from by 72 percent, from K700 to K1200 and for business customers they were increased by 20 percent. The monthly rental charge, which had already been re-balanced in the early 90s, was also raised by 20 percent to Kwacha 100 (US$2.15 at end-1999). Once again, inflation quickly eroded these increases. In fact, by the end of 2001 the monthly subscription rate was lower in real terms than it had been at the end of 1998 (see Table 4 and Table 10).

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including different demands for fossil fuels. In all scenarios, we maintained government revenue neutral to the carbon tax, which means that only the government revenues (including carbon tax revenue) in excess of that in the baseline is recycled to the economy.

Under Scenario 1 all households that supply labor to formal as well as informal sectors get benefits of lump-sum transfer from carbon tax revenue recycling. This is because households can not be differentiated as formal and informal households. Similarly, under Scenario 4, all commodities, irrespective of their origins (formal firms, informal firms, imports) get VAT reduction through the carbon tax revenues. This is because it is not possible to distinguish a commodity in the market based on its supply source (formal firms, informal firms, imports). The swapping of VAT with carbon tax reduces tax evasion because the carbon tax is applied upstream and is already embedded in the price of the commodity when it is transacted in the market.

# **5 Findings from the Model Simulations**

In this section, we first present and discuss the impacts of the policies on some of the main economy-wide indicators, followed by a discussion of formal and informal sector trade-offs. At the end of the section, we discuss CO2 mitigation impacts under alternative scenarios.

# **5.1 Impacts on the overall economy**

Figure 4 presents changes in GDP and welfare under alternative scenarios. GDP is composed of final consumption plus exports minus imports, given that investment is fixed in this static model.

Under Scenario 1, the carbon tax causes a loss in both GDP and welfare in the model (Figure 4). This result is an expected result reported in most CGE analyses of a carbon tax.<sup>11</sup> The carbon tax still causes GDP and welfare loss even if the tax revenue is used to cut production tax rates of the formal firms. However, if the carbon tax revenues are recycled to cut production tax rates of non-fossil fuels industries, the revenue recycling effects offset the GDP loss. If all carbon tax revenues are used to cut production tax rates of non-energy goods (Scenario 2e), GDP and welfare will increase. Some existing studies (e.g., Jorgenson et al. 2015; McKibbin et al. 2015)

> 11 Please see Timilsina (2018) for economic impacts of carbon tax reported by existing studies.

19
difference. Individual studies designed mainly for the purpose of evaluating sanitation infrastructure are likely to find larger effects due to the fact that technologies were newly introduced and closely monitored. Large positive health impacts of water and sanitation in the short term are likely to weaken over time in the absence of proper maintenance and due to general infrastructure deterioration. In contrast, our estimates are not intervention based and do hence – as well as other cross-sectional studies – estimate more medium to long-term effects of sanitation. Moreover, interventional studies can better control for the actual use of infrastructure whereas our study should rather be seen as a measure of the impact of the access to certain water and sanitation technologies. Infrastructure that is reported in the DHS surveys by households may or may not be used by all family members or all of the time.

Third, epidemiological studies often analyze the health impact of village interventions, where entire villages switch from poor to improved sanitation coverage. In contrast, in our main specification we compare the differences in child health of households with to households without access to improved sanitation. If positive health externalities of improved sanitation exist, as our estimates in Table 5 imply, epidemiology studies include those positive externalities whereas this is not taken up by a study at the household level. Our cluster level estimates in Table 5, do indeed show effects of sanitation more closely in magnitude to the once estimated in previous studies.

Last, in a cross-sectional study based on data sets not specifically designed to estimate the impact of water and sanitation infrastructure on child morbidity, misclassification of infrastructure is more likely than in epidemiological studies, which will mechanically bias the estimated coefficients towards zero. While it is hard to quantify the exact magnitude of this effect, the main reason for choosing the simplest possible coding rules, i.e. dividing water and sanitation access into three broad classes, was to keep the likelihood of miscoding as small as possible. While one may argue whether or not one type of latrine is better than another one, it is hard to argue that latrines are on average not better than having no toilet at all. Thus, while there clearly is some degree of miscoding in the data, we argue that the fraction of the total variance generated by this kind of error is likely small relative to the true variation observed in the underlying variables.

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**Table 2.** Economic security and work landscape pre- and during COVID-19 with balanced panels

||**Balanced pan**|**el in waves 1**|**and 3**<br>**p-**|**Balanced pan**|**el in waves 1**|**and 2**<br>**p-**|**Balanced pa**|**nel in waves 2**|**and 3**<br>**p-**|
|---|---|---|---|---|---|---|---|---|---|
||**Wave 1**<br>**2018**<br>**(n=1,117)**|**Wave 3**<br>**2021**<br>**(n=1,117)**|**value**|**Wave 1**<br>**2018**<br>**(n=346)**|**Wave 2**<br>**2020**<br>**(n=346)**|**value**|**Wave 2**<br>**2020**<br>**(n=214)**|**Wave 3**<br>**2021**<br>**(n=214)**|**value**|
|**Total income**<br>**(USD/mo)**|123.1 (145.7)|117.6 (93.2)|0.28|120.2 (98.9)|88.1 (98.0)|<0.01|92.5 (113.4)|125.4 (109.1)|<0.01|
|**Sex work**<br>**income***<br>**(USD/mo)**|121.6 (146.4)|119.0 (78.6)|0.66|113.9 (91.7)|79.5 (55.4)|<0.01|84.2 (60.9)|127.1 (83.7)|<0.01|
|**Clients/week**|8.6 (7.0)|6.2 (5.5)|<0.01|8.4 (6.7)|4.8 (3.2)|<0.01|5.0 (3.4)|6.3 (5.9)|<0.01|
|**Food**<br>**insecurity**|14.8|26.1|<0.01|13.6|45.4|<0.01|43.0|24.3|<0.01|
|**Place of**<br>**work****||||||||||
|Street|6.7|12.4||||||||
|Bar|40.9|34.7||||N/|A|||
|Brothel|33.1|28.0||||||||
|Other|19.3|24.9|<0.01|||||||
|**Mean**<br>**price/client**|6.6 (9.6)|6.2 (5.5)|0.27|||||||
|Missing data: T|otal income (Wa|ve 1 & 3 bala|nced pan|el, n=21, Wave|1 & 2 balanc|ed panel|, n=11) sex wo|rk income (W|ave 1|
Lastly, Figures 3 and 5 underscore that reporting has tended to be less dependable for the most recent years. While there is significant interest in the latest data, it is also more prone to discrepancies as recent data are continuously updated in the UN Comtrade systems.

# **Discrepancies increase with product disaggregation.**

Discrepancies in merchandise trade statistics tend to intensify with greater levels of product detail, suggesting misclassification or misinvoicing. We support these findings by comparing differences in successful mirroring between highest and lowest level of product granularity within HS and SITC nomenclatures. We find that an increase of product granularity significantly decreases the number of successfully mirrored records and the portion of trade value that can be verified (Figure 6). This pattern was consistently observed across the various nomenclatures considered in this study. Two of them are highlighted in Figure 6 and seem to suggest less reliable recording of higher-resolution data. Consider, for instance, percentage of dual recording for H0 nomenclature in 2017. At aggregate level, 56% of records demonstrate dual recording. However, when zooming into a more granular 6-digit level, this figure drops to 39%.

Typically, some countries will be impacted by product disaggregation to a larger extent. Figure 7 shows the average annual per country reduction in successful mirroring that occurs in disaggregated data for the selected nomenclatures. In the H0 nomenclature for 2017, the rate of successfully mirrored records at the most detailed level fell by an average of 37% compared to the aggregated level. Similarly, the share of verifiable trade value decreased by an average of 15% at the most detailed level. Consequently, countries displaying larger reductions in verifiable data at finer levels of product detail are likely to see more substantial distortions in their export and import structures.

A common belief is that dual recording discrepancies at broader categorizations often stem from errors in attributing trading partners. In contrast, discrepancies at finer levels typically indicate challenges in correct product categorization. This divergence in matched transaction percentages across product granularity levels is crucial to note since many applications use detailed data. Although high-resolution data is coveted for its detailed insights in empirical research and policy formulation, it is also the most vulnerable to issues such as misclassification and mis-invoicing.

25
25

**Table 4: Effect of Treatments on Consumption, Repeated HH Observations**

|**Outcome Variable: Average of August and**<br>**September 2013 and 2014 Water Consumption,**<br>**cubic meters, m**<sup>**3**</sup>|_I_|_II_|
|---|---|---|
|**2014 Consumption Dummy**<br>**(SE)**|-0.13<br>(0.45)|-0.13<br>(0.45)|
|**Treatment 1: Neighborhood Comparison**<br>**(SE)**|0.10<br>(0.98)|0.10<br>(0.98)|
|**Treatment 2: Municipality Comparison**<br>**(SE)**|-0.20<br>(0.83)|-0.20<br>(0.83)|
|**Treatment 3: Planning Postcard**<br>**(SE)**|0.21<br>(0.93)|0.21<br>(0.93)|
|**2014 Consumption Dummy x Treatment 1**|-1.34*|-1.35*|
|**(SE)**|(0.81)|(0.81)|
|**2014 Consumption Dummy x Treatment 2**|-0.97|-0.97|
|**(SE)**|(0.70)|(0.70)|
|**2014 Consumption Dummy x Treatment 3**|-1.28*|-1.28*|
|**(SE)**|(0.67)|(0.67)|
|**Constant**|26.54***|26.67***|
|**(SE)**|(0.57)|(0.58)|
|**Monthly Fixed Effects**||X|
|**Observations**|20,939|20,939|
|**R-squared**|0.00|0.00|
|_Notes:_<br>_1. Numbers in parentheses are standard errors_<br>_2. Stars indicate statistical significance: *=p<0.10, **=_|_p<0.05, ***=p<0.01_||
Table 2: Study sample and treatment assignment by block and phase

||Start date|||%|recruits|assigned to|:|
|---|---|---|---|---|---|---|---|
|Phase|(MM/YY)|Block (slum)|Sample|Therapy|Cash|Therapy|Neither|
|||||||& cash||
|1|12/10|Red Light|100|25.0%|25.0%|25.0%|25.0%|
||06/11|Red Light|219|26.9%|25.1%|24.2%|23.7%|
|2|06/11|Central Monrovia|179|31.8%|19.0%|31.8%|17.3%|
||03/12|Clara Town|175|28.6%|27.4%|22.9%|21.1%|
|3|02/12|Logan Town|86|26.7%|29.1%|19.8%|24.4%|
||02/12|New Kru Town|240|26.3%|26.7%|23.8%|23.3%|
|All|||999|27.7%|25.1%|24.9%|22.2%|

motivated violence, such as crime or election thuggery. The simplest model treats crime as an occupational choice between legal and illegal work.<sup>21</sup> We then consider how such a model can capture more expressive and reactionary forms of anti-social behavior. We develop the formal model in Appendix C and outline the structure and results here.

The most unusual element here, of course, is CBT. NEPI’s approach and the psychological theory underlying the therapy suggests it could influence criminal occupational choice in three main ways. First, improved emotional regulation, planning, and related noncognitive skills can be modeled as a type of human capital that enters the individual production function. Alternatively, therapy’s effect on self control could be modeled as a change in time preferences, including time-inconsistency. Finally, we model a change in values as a change in intrinsic preferences over criminal occupations or other antisocial acts.<sup>22</sup> Of course, the therapy is a multifaceted treatment that likely operates through a number of other mechanisms: changed peers, family reunification, skills of conflict resolution, reduced drug abuse, and so forth. We focus on (and had pre-specified) self control skills, time preferences, and antiviolent values because these mechanisms were most in line with NEPI’s design principles and theories, as well as the psychological theory and evidence cited above.

**Setup** Suppose people can allocate their time between leisure _l_ , legal work _L_<sup>_b_</sup> such as petty business or labor, and illegal occupations _L_<sup>_c_</sup> such as crime, mercenary work, or election thuggery. We refer to these simply as “business” and “crime”.

We assume crime uses labor alone and pays a wage _w_ , which may be uncertain. This resembles the returns we observe to illegal work of the type available to our population in

> 21It is rooted in models of occupational choice with capital infusions and adapted to illicit behavior, as in Blattman and Annan (2015), in the tradition of many economic crime models (Draca and Machin, 2015).

> 22Typically models treat such preferences as fixed, or ignore them. We outline how exogenous changes in noncognitive abilities or preferences affect the comparative statics in an otherwise standard model.

11
# Appendix I.A.16 Historical Demographic Data, Hungary 1869-1970

|Year|1869|1880|1890|1900|1910|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|20.75|19.00|14.97|13.54|11.77|
|Population aged 20 to 59/<br>population over 60 years old|9.67|7.90|7.02|6.15|5.67|
|Percentage of population<br>over60yearsold|5.01|6.20|6.81|7.58|8.18|
|Percentage of population<br>under 15 years old|37.02|35.08|36.61|35.60|35.56|
|Women over 60 years old/<br>Men over 60 years old|0.90|1.01|1.06|1.03|1.03|
|**Year**|1920|1930|1941|1949|1970|
|Population aged 15 to 64/<br>population over 65 years old|11.52|10.49|9.60|8.99|5.88|
|Population aged 20 to 59/<br>population over 60 years old|5.55|5.45|5.03|4.72|3.11|
|Percentage of population<br>over 60 years old|8.97|9.75|10.69|11.65|17.04|
|Percentage of population<br>under 15 years old|30.61|27.55|25.98|24.88|21.10|
|Women over 60 years old/||||||
|<br>Menover60yearsold|1.06|1.07|1.14|1.27|1.33|

Ratios calculated using data found in Mitchell, B. R. 1990 "Intemational Historical Statistics: Europe 1750-1988".