rafmacalaba/datause-encoder-data huggingface dataset:
many factors can be controlled for which are similar in both parts of the country, e.g., a common language, similar education systems and a shared cultural and political history after the Second World War and prior to the separation. As a consequence, an East-West comparison has a methodological advantage as compared to cross-country studies. Thus, it is possible to observe how taxpayers adapt to a new legal environment that provides signals to them how they should behave and how others behave regarding taxation. As the Iron Curtain fell, former GDR citizens became exposed to the West German system including the social welfare state, the tax system and the whole set of formal and informal rules. The question remains how fast individuals may change their social norms. In the focus countries, people gradually internalized norms and values they were forced to respect for years before the reunification. Feld et al. (2008) were interested in exploring these social learning effects on tax morale over time. The unification process provides for a unique opportunity to study this research question. East German citizens faced different legal systems before and after unification. While deterrence was generally high in the old GDR, black markets and the shadow economy were ubiquitous. Thus, each GDR citizen could observe that their fellow citizens engaged in the shadow economy. After unification, East German taxpayers were confronted with new laws and new deterrence levels which have only been slowly increased across time. East Germans could also realize the tax morale of the West German taxpayers and adjust their behavior accordingly. So far, the literature on tax compliance has failed to explore social learning, herding behavior or convergence processes in general. This paper addresses this shortcoming by exploring the unification process in Germany using three World Values Survey/European Values Survey waves between 1990 and 1999. German unification is interesting for the analysis of tax morale over time and the exploration of a social learning process. As mentioned, many factors can be controlled (e.g., a common language, similar education systems and a shared cultural and political history prior to the separation after the Second World War). Feld et al.‘s (2008) findings indicate that tax morale converged after unification in Germany. While tax morale was significantly different in East compared to West Germany in 1990, both regions do not differ significantly in their tax morale levels in 1999. This result holds whether descriptive analysis, non-parametric tests or a differentiated multivariate analysis is conducted.

31
**Table 11: Location characteristics and the probability of living in poverty**

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent variable:|Households being<br>trapped inpoverty|Households being<br>trapped inpoverty|Households being<br>trapped inpoverty|
|Estimation|probit|probit|Probit|
|Sample|All households|Agro households<br>(crop production > 0)|Non-agro households<br>(crop production = 0)|
|ln(agropotential,cash crop|-0.101***|-0.104***|-0.103**|
||(0.014)|(0.015)|(0.047)|
|ln(travel time to city)|0.227***|0.157***|0.189***|
||(0.018)|(0.021)|(0.048)|
|Constant|yes|yes|Yes|
|Observations|9520|8660|860|

Note: 1. Robust standard errors in parentheses 2. * significant at 10%; ** significant at 5%; *** significant at 1% 43. As living in locations with good agriculture potential reduces the probability of being poor, transport improvements in these areas are win wins in terms of growth and poverty reduction objectives. In locations with poor agronomic potential, transport investments may reduce poverty incidence but come with a trade-off with national economic growth.

# **Complementary policies to reduce domestic transport prices**

44. The analysis of this study shows that rural routes have higher transport costs – while quality of feeder roads are important, lack of demand along these routes ends up inhibiting competition. While opening up international competition in the trucking services industry will reduce transport costs in urban markets (with higher trading volumes), it is unlikely that international operators will deploy their existing fleets to serve rural markets. Why? First, road quality on many segments is so bad that existing fleets will not be able to pass along these routes. Second, and more important, is the low level of trade volume in most rural areas – it would be unprofitable for large trucks to go and collect small volumes and often return with empty loads – they would have to charge high prices to cover fixed capital investments.

45. So what is the alternative? It may be useful to consider options for promoting appropriate Intermediate Means of Transport (IMTs) for connecting rural areas to market towns. Related work by the World Bank’s transport department suggests that IMTs offer considerable opportunities in relation to the short distance travel and transport demands of rural communities with regard to efficiencies in terms of cost and time savings. IMTs are seen as important in reducing travel and transport burden and increasing overall rural mobility in terms of reduced effort and time, longer distances and high carrying potential since IMTs:

- Have a higher load capacity compared to head loading, which carries only a maximum of only 30 kg.

- Have a higher speed and range compared to head-loading saving on time and energy in transporting of people and goods compared to walking and head loading.

- Do not require very expensive infrastructure like motorways, and tarred roads used by motorized vehicles. Depending on the size of an IMT, well maintained access roads, tracks and footpaths are enough.

- Ensure that they can be easily maintained locally and at affordable cost.

- 20 -
driving range. The first five columns are from OLS while the last three are from IV. The last column is our preferred specification and the results are used in the policy analysis below.

From column (1) to (5), we successively add more controls to examine the effects of potential confounding factors on our key parameters of interest. Column (1) include city-model fixed effects to control for city-specific but time-invariant consumer preference or demand shocks for each model. All the coefficient estimates have intuitive signs and are statistically significant. Bearing in mind that these estimates may not be consistent, the results suggest that: (1) a subsidy of ¥10,000 increases EV sales by 7.2% ; (2) an increase of 1,000 charging ports increases EV sales by 3.6%; (3) the exempt from driving restriction increases EV sales by over 50%; and (4) the green plate policy increases EV sales by nearly 30%.<sup>33</sup> Column (2) adds time (year-quarter) fixed effects to control for national time-varying demand shocks. Including time fixed effects meaningfully changes the coefficient estimates for the number of charging ports, green plate policy, and driving range, suggesting the time-varying unobservables as important confounders for these three variables of interest.

Column (3) adds city-year fixed effects to control for city-specific demand shocks that vary from year to year. There are two significant changes on the coefficient estimates. First, the estimate on the number of charging ports become statistically significant and the magnitude stays roughly the same in all the remaining specifications. The key variation for identifying the effect of charging infrastructure in column (3) is within-city quarter-to-quarter variation in the number of charging stations within a year. Second, the coefficient estimate on the driving restriction exempt policy decreases and becomes statistically insignificant for the remaining specifications. By removing the year-to-year variation in the policy, the identification relies solely on the withincity within-year changes in the policy status. That is, the identification is based on the limited cases where the policy was adopted mid-year in the city. There is limited quarter-to-quarter variation within a year in policy adoption as suggested by Appendix Figure A1. The result suggests that the policy does not significantly incentivize consumers to switch from gasoline vehicles to EVs. This could be due to the weak enforcement of the policy or consumers’ adaptive behavior in their travel decisions. Driving restriction only applies to peak hours within city center and consumers could change their travel time or even travel mode (e.g., using public transit) without having to resort to buying EVs.

As discussed in Section 3.1, one of the key identification challenges is the time-varying and

> 33The percentage impact of a policy dummy on EV sales can be consistently estimated by 100*[ _exp_ (ˆ _β −_ � _var_ ( _β_<sup>ˆ</sup> ) _/_ 2) _−_ 1].

20
- 26 -

that parameter could be specified as a positive function of u, say 6e/6u-e(u), e'>O. This consideration may become important in the Chilean case as a high level of capacity utilization may start to generate inflationary pressures that will be underestimated if predicted with parameters corresponding to a macro regime with more slack. (Pressures to change the wage indexation coefficient could also be observed if inflation accelerate, though for the current levels of inflation in Chile these pressures may not be very serious yet).

Finally, it is worth to mention that the coefficient that measures the impact of changes in the rate of devaluation on inflation (0. 731) is much higher than the coefficient of the output gap (a feature appearing also in other empirical studies on inflation in Chile, Corbo, 1985 and Jadresic 19S5). Therefore, it is important to be aware that the exchange rate policy is bound to have a quantitatively significant effect on the rate of inflation in Chile.

# 4. Policy Exercises

With the model parameterized we are in conditions to carry out some policy exercises for Chile. As discussed at some length, the model flavour is that the qualitative effects of different policy instruments are regimedependent; therefore we have to make some assessment on the dominant growth regime under which the policy exercises take place. As Table 1 in the first section shows the Chilean economy in 1988 operated with little excess capacity, and less so in 1989, getting close to what we have termed as a capacity constrained-growth regime. Then the exercises are carried out, mainly under this regime. Furthermore, for the sake of completeness the demand constrained and inflationary regimes are also explored.
to be mindful of fraud schemes. Households with limited access to formal credits may thus miss the chance of acquiring anti-fraud information, and are unable to recognize the fraud schemes when facing fraudsters.

To address this concern, we add in the model two measures on household information acquisition. The first measure captures the intensity of information acquisition based on CHFS’s question about the intensity with which the respondent follows economic/financial news from the media. Five options are provided: from ‘extremely intensive’ to ‘never paying attention’ (corresponding value from 1 to 5). Households that intensively follow economic/financial news would obtain greater amount of anti-fraud information. The second measure is total household expenditure on internet, magazine, newspaper and other information sources. Households with a higher amount of such expenditure are more likely to acquire anti-fraud information.

Another concern is that the relationship between credit constraints and fraud victimization may be driven by the omission of financial literacy. On the one hand, a higher level of financial literacy is associated with a higher likelihood of obtaining bank loans (Lyons et al., 2019; Xu et al., 2019) and a lower likelihood of engaging in high-cost borrowing and using informal financial services (Disney and Gathergood, 2013; Lusardi and Scheresberg, 2013). A higher level of financial literacy also helps people better understand financial products and make better financial decisions about taking advantage of formal financial services. Financial literacy thus should relieve credit constraints. On the other hand, financial literacy should help households distinguish between fraud schemes and high returns from legitimate investment projects, and thus lower the chance of fraud victimization.

To address this concern of omitted variable bias, following the previous literature (Van Rooij, 2011; Lusardi and Mitchell, 2008), we add two measures for financial literacy in the model. CHFS has a series of questions designed to measure respondents’ financial literacy.<sup>16</sup> The first measure is a dummy variable that equals to 1 if the respondent

> 16 The first question regarding financial literacy is: assume the annual interest rate of the bank is 4 percent, if you deposit 100 yuan in the bank for one year, how much do you have one year later? The second question is: suppose the annual interest rate of the bank is 5 percent, and the inflation rate is 3 percent, after saving 100 yuan in your bank account for one year, can you buy more or less than last year?

22
environment quality, respectively) interacted with pre- and post-intervention literacy rates and average years of schooling in order to estimate accurate impacts from water quality reforms.

Finally, for robustness checks a description of matching and outcome imputations between surveys is presented in the annex.

# **<u>Econometric Framework and Identification</u>**

Using empirical design from a randomized experiment framework helps us to create homogenous groups in the observed and unobserved characteristics at the initial stage of the analysis. Consequently, this reduces bias and allows the greatest reliability and validity of statistical estimates of the treatment effect. To test for theoretical prediction, this paper estimates the differences between the control and treatment group, with a simplest model without the interaction between household assets and the diffused media component (treatment group). This follows the functional form: where _score_ is is the test score<sup>9</sup> for individual _i_ in household _h._ The variable _Treatmentih_ is a dummy variable in which 1 indicates that the group received the second component. The coefficient, ��, is of interest. This coefficient assesses the average treatment effect on three different dependent variables: _Communication developmen_ t, _Motor development_ and _social-emotional development_<sup>10</sup> . For interpretation purposes, we standardized the dependent variables coefficients to have a mean of zero and standard deviation of one.

> 9 A cognitive assessment provides information about a child’s intellectual strengths and weaknesses, as well as insight into her overall cognitive potential. The test gives general information about a child’s abilities compared to others her age in several areas. The tests are intended to be a predictor of how well and in what ways a child will learn new information. The tests used are based on the ASQ‐third edition test to assess cognitive development of children. It consists of three sections (communications, motor and social skills) where each section has 11 to 14 questions used to aggregate them in a single index for each cognitive component. Each index is then standardized in its distribution to allow comparability.

> 10 Cognitive development refers to the progressive and continuous growth of perception, memory, imagination, conception, judgment, and reason. Cognition also involves the mental activities of comprehending information and the process of acquiring, organizing, remembering, and using knowledge (Owens, 2008; Nicolosi, Harryman, and Kresheck, 1989). Communication skills relate to the ability of children below 2 to 4 years old to process language effectively. Motor skills correspond to children development of movements. Motor skills can be hindered by the presence or incidence of disease in

16
Mundlak, Y. and Larson, D. 1992. On the transmission of world agricultural prices. World Bank Economic Review. 6(3):399-422.

Nicaragua, Instituto Nacional De Estadsticas Y Censos. 2001. Encuesta Nacional De Hogares Sobre Medicin De Nivel De Vida. Managua: Instituto Nacional De Estadsticas Y Censos.

Pakistan, Government of, Federal Bureau of Statistics.1999. Pakistan Integrated Household Survey, Round 3 (1998-99). Islamabad: Government of Pakistan, Federal Bureau of Statistics.

Rashid, S. 2002. Dynamics of Agricultural Wage and Rice Price in Bangladesh: a Reexamination. MSSD Discussion Paper No. 44, Washington DC: International Food Policy Research Institute.

Ravallion, M. 1990. “Welfare changes of food price changes under induced wage responses: theory and evidence for Bangladesh.” Oxford Economic Papers, 42, 574–85. Ravallion, M. 1992. Poverty Comparisons: A Guide to Concepts and Methods. LSMS Working Paper No. 88, Washington DC: World Bank.

Ravallion, M. and Datt, G. 2002. Why has economic growth been more pro-poor in some states of India than others? _Journal of Development Economics_ 68:381-400.

Ravallion, M. and Lokshin, M. 2005. “Winners and Losers from Trade Reform in Morocco” Mimeo, World Bank.

Ravallion, M. and van der Walle, D. 1991. “The impact on poverty of food pricing reforms: a welfare analysis for Indonesia.” Journal of Policy Modeling 13(2):281-99.

Ravallion, M. and van der Walle, D. 2008. _Land in Transition: Reform and Poverty_ _Reduction in Rural Vietnam._ New York and Washington DC: Palgrave Macmillan and the World Bank.

24
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We now turn to the dynamics of off-farm employment and investigate the stability of these jobs. Table 4 shows the rates of people who entered and exited off-farm employment and who were never or always employed over time. The share of people who are continuously off-farm employed is quite low across countries. It ranges from 16 percent in Ethiopia, 31 percent in Uganda, 32 percent in Nigeria and Tanzania, to 33 percent in Malawi. This implies that off-farm employment is characterized by unstable jobs, which might hamper its potential for contributing to economic development. A large share of the population switches employment status over time; 20 percent in Nigeria, 28 percent in Ethiopia, 29 percent in Uganda, 31 percent in Tanzania, and 35 percent in Malawi, corresponding to a cross-country weighted overall share of 25 percent. The total number of people that either entered or exited off-farm employment between 2010 and 2016 in these five countries amount up to about 39 million. This is a huge demographic shift that can only be revealed by relying on panel surveys that track individuals over time. Entry rates are higher than exit rates in almost all countries; e.g. Malawi (22 percent versus 13 percent), Nigeria (11 percent versus 9 percent), Tanzania (19 percent versus 12 percent) and Uganda (15 percent versus 14 percent). Only in Ethiopia exit rates are higher than entry rates (18 percent versus 10 percent). Overall, the stability of off-farm employment is higher in urban areas than in rural areas, with higher rates of continued employment and lower rates of entry and exit. This is associated with the higher prevalence of full-year/full-time employment in urban areas.

# _[Table 4 here]_

The gender gap also plays a major role in the dynamics of off-farm employment. Women are less likely to continue employment than men in most countries, which is linked to the lower quality of jobs in which women participate. The gender gap stands at 7 pp in Ethiopia, 14 pp in Uganda, 15 pp in Tanzania, and 21 pp in Malawi. Nigeria is an exception where women are slightly more likely to continue employment (33 percent) than men (31 percent). This is probably due to the relatively high stability of (female owned) NFEs (Nagler and Naudé, 2017). The gender gap is consistently higher in urban areas (ranging from 1 pp in Nigeria to 33 pp in Malawi) than in rural areas (ranging from 7 pp in Ethiopia to 18 pp in Malawi). Entry rates are higher for women than for men in Malawi and Tanzania, while exit rates are higher for women in Malawi and Uganda.

Those who are never off-farm employed or exit employment are usually on-farm employed (Table A **3** in the Appendix). The share of people that did not participate in off-farm employment in the 15
sri lanka country risk profile 9

# 2 sri lanka country risk profile

## 2.1 Risk to assets

Typically, risk assessments incorporate information on the _hazards_ (the natural occurrence of destructive events such as severe winds, surges, floods, and earthquakes); _exposure_ (the value of natural and built assets that might face a destructive event); and _vulnerability_ (the expected consequences to exposed assets when a destructive event occurs) of the targeted area. Together, these three dimensions describe average annual asset losses in the area of interest.

Table 1 on the following page displays expected annual asset losses to precipitation flooding, by district. These values, based on the SSBN model, are long-term averages of the or _total replacement cost of household income-generating assets_ damaged destroyed by precipitation flooding each year [1].<sup>1</sup> Figure 2 maps total asset risk, expressed at left in US$ and at right as a percentage of district-level aggregated household expenditures (AHE). According to this model, asset risk in Sri Lanka is highly concentrated in greater Colombo (over 50% of the total; left panel), but other districts throughout the country also face significant losses, when expressed in percentage of local AHE.<sup>2</sup> Significant differences across districts are explained by variations in the hazard, and of each of the Most asset losses exposure, vulnerability part country. notably, are heavily concentrated in greater Colombo and surrounding districts. This is to be expected, as it reflects the high concentration of valuable assets in and around the capital (elevated exposure). Differences in hazards are secondary but still important:

> 1 The total repair and replacement value of assets damaged or destroyed by floods in each district is a direct output of the SSBN/Fathom model [1], but this analysis is particularly interested in disaster impacts on the assets, consumption, and welfare of _households_ . One issue faced here is the difference between the Gross Domestic Income (GDI) derived from national accounts and the aggregate household expenditures (AHE) calculated from household surveys (in this case, HIES). As is well known, the latter tend to report lower expenditures than national accounts [3]. In this analysis, we work based on the AHE. Therefore, the expected losses in Table 1 have been reduced by a scale factor, equal to total household consumption over nominal regional productivity (cf. Tab. 5), and express average annual damage or destruction to assets used by Sri Lankan households to generate income, by district. Asset losses in Jaffna, Kilinochchi, and Matara are omitted due to their exclusion from available SSBN/Fathom flood maps.

> 2 SSBN is a global model subject to limited availability and variable quality data. Therefore, its precision and accuracy can be low in some places, and we would seek ideally to replace these inputs with highresolution local models.