rafmacalaba/datause-encoder-data huggingface dataset:
**Table A11. Net savings (savings‐dissavings) as share of disposable income**

|||2014||||201|5|||2016||||201|7||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|1|2|3|4|1|2|3|4|1|2|3|4|
|||||||Place|of resid|ence|||||||||
|Minsk|18|20|19|28|29|27|10|22|17|23|22|22|17|21|20|19|
|Large cities|13|18|16|19|12|19|12|16|8|14|14|13|9|16|13|16|
|Small cities|14|21|17|18|12|18|12|14|5|12|9|8|9|15|12|12|
|Rural area|7|14|14|12|3|13|15|13|9|13|13|14|11|14|15|18|
||||||||Age||||||||||
|Child|11|20|19|19|11|19|12|16|10|17|14|14|10|17|15|17|
|working age|13|21|18|21|14|22|12|18|11|18|16|16|13|19|16|18|
|Retiree|12|11|13|14|12|13|11|14|6|9|11|10|9|12|12|13|
|||||Employ|ment|status f|or work|ing age|popul|ation|||||||
|Employed|17|23|20|22|17|23|16|20|13|20|19|19|16|20|18|20|
|Partly employed|9|27|5|1|3|19|9|13|1|20|10|11|9|20|6|21|
|Nonemployed|8|15|17|20|11|18|7|11|7|10|13|11|5|16|15|13|
|||||Educ|ation l|evel for|workin|g age p|opulat|ion|||||||
|high education|17|18|16|23|17|22|7|21|15|22|16|19|17|19|13|17|
|secondary|||||||||||||||||
|specialized|14|25|18|20|14|23|10|17|9|17|17|15|10|17|16|16|
|Other|11|19|18|20|12|21|17|15|10|15|16|16|12|20|18|20|
||||||Ty|pe of h|ouseho|ld (HH)|||||||||
|HH without|||||||||||||||||
|children|12|14|12|16|11|16|9|14|9|13|13|13|11|14|12|14|
|Retiree HH|9|6|10|10|5|7|8|9|3|4|7|7|3|6|7|8|
|HH with children|13|22|21|21|15|22|15|19|10|17|16|16|12|19|17|19|
|1 child|15|25|23|22|18|23|20|21|8|16|17|17|13|20|18|21|
|2 children<br>3 and more|13|20|19|21|16|20|5|15|14|18|14|14|12|20|15|17|
|children|3|16|14|15|‐15|17|19|13|9|23|17|13|5|12|17|11|
|singleparent|0|8|4|3|‐1|2|8|6|‐5|2|‐2|3|0|4|‐1|7|

Source: Authors’ estimates based on HBS data.

38
other studies of smoking in Albania. The results of the Population-based Survey show that 38 percent of adult males and 18 percent of females were current smokers in the capital Tirana in 2002 (Shapo _et al_ . 2003). IMF (2006) reports that, according to 2002 LSMS survey, 31 percent of Albanian men smoked. Because of a small proportion of female smokers in the sample our further analysis will focus only on working males 2560 years of age.

The incidence of smoking is similar among urban and rural male population in Albania. The prevalence of current smokers is close to 50 percent among the leasteducated males and declines for better-educated men. Only one-quarter of men with a university degree smoke. The propensity to smoke varies for men by religion. The highest proportion of smokers (36 percent) is among Albanian Muslims, including Bektashi. Albanian Christians smoke at a rate of about 30 percent.

The left panel of Figure 1 graphs the results of non-parametric regression (running-line least-squares) for the proportion of smokers by age. The incidence of smoking is increasing with age of the respondents. Less than 20 percent of 25 years-old men classified themselves as current smokers. The proportion of smokers increases sharply for older age groups peaking at about 35 percent for men 38 years old and older.

The right panel of Figure 1 shows the relation between monthly log-wages and age for smokers and non-smokers. On average, non-smokers earned about 26,300 Lek (3.27 in logs) per months relative to 24,290 Lek per month (3.19 in logs) for smokers (8.3 percent difference). Both groups have an inverted U-shaped age-wage profile. Wages of younger males are increasing and reach maximum at age of 35 and then decline for the older workers. Wages of non-smokers are higher than wages of smokers for all age groups with the largest gap in wages observed for workers 35 to 50 years of age.

# **4. Results**

In order to estimate the effect of smoking and the counterfactual as described in Section 3, the dependent variables in the regression estimates are the logs of monthly wages for smokers and those who never smoked, and the binary indicator capturing whether a respondent is a current smoker or has never smoked. Table 1 shows the descriptive 12
**Figure 2.2. Dynamic association of PIQ-D with PIMI and proxy variables**

_Sources:_ Authors’ estimates; Dabla-Norris et al. (2012); International Country Risk Guide (ICRG); World Bank’s Worldwide Governance Indicators (WGI) and Country Policy and Institutional Assessment (CPIA). _Note:_ Coefficients from 10-year rolling regressions of PIQ-D on the covariates. Dashed lines indicate 90-percent confidence bands.

12
private sector from this perspective may also help. If private sector participation can bring benefits, governments need to address public perceptions to increase trust and reduce uncertainty around private sector involvement in public service provision.

# **3. Infrastructure Service Delivery Triad through a Behavioral Lens**

In this section, we will take a closer look at the three actors in the Infrastructure Service Delivery Triad - the policy maker, service provider and the consumer and explore how behavioral regularities are likely to influence many of the decision-making challenges they face in their respective roles. In this section, we focus our attention primarily on experimental evidence from the water sector to highlight the main findings. We recognize that some of these studies are not statistically wellpowered, some of them are either lab based or lab in the field experiments (Harrison and List, 2004) and are of smaller scale with limited replications. However, they provide important insights which are useful for the sector.

# **3.1 Policy Makers and the Investor Side**

The analysis will start with the beginning of the service delivery chain from the policy maker’s perspective. In the realm of infrastructure, policy makers typically have the primary role in making decisions on: (1) what projects to invest in from across several alternatives and how to finance them, (2) setting up the contractual arrangements to build, maintain and manage the infrastructure, and (3) regulating the market, particularly when services are provided privately at large.

# **3.1.1 Infrastructure Prioritization and Financing**

Policy makers have a key role in choosing which infrastructure projects should be prioritized (World Bank (2003)). The role of the policy maker is to identify the populations’ needs, balance often conflicting interests and implement solutions, subject to the available resources. As a point of departure, we start with the traditional view of the government as a ‘social welfare maximizer’, affected by behavioral constraints. First, policy makers start by deciding what infrastructure projects are selected from among a number of alternatives. Apart from the obvious resource constraints posed by fiscal space, policy makers are influenced by a myriad of behavioral factors - imperfect optimization, bounded self-control and nonstandard preferences- in making 12
One, Two, and Three 4

**Table S3.** Oster Test

|(1)|(2)|
|---|---|
|�<br>**_βc, β_(****_δ, Rmax_)**<br>�|**_δ|β_=0**|
|�<br>0_._023_,_ 0_._026<br>�|-12.00|

_Source_ : Author’s postestimation analysis based on table 1. _Note_ : Column 1 reports the identified set of elasticity estimates. _βc_ is the elasticity estimate obtained after adding all controls (column 4, table 1 while _β_ ( _δ, Rmax_ ) is computed at _δ_ = 1 and _Rmax_ = 0 _._ 9402. Column 2 reports the coefficient of proportionality that is required to explain away the treatment effect after adding the unobservables in a hypothetical regression.

**Table S4.** Effect over different program regimes

||(1)|(2)|
|---|---|---|
|_sinh_<sup>_−_1</sup>(_ed,t_)|2.342<sup>_∗∗_</sup>||
||[1.021]||
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=0||5.556<sup>_∗∗∗_</sup><br>[2.032]|
|_sinh_<sup>_−_1</sup>(_ed,t_)<br>��<br>_NREGAd,t_=1||1.724|
|||[1.063]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . Column 1 shows the wage elasticity as reported in column 4 of table 1. Column 2 reports separate elasticities for SGRY and NREGA regimes. All other controls are the same as in table 1 in the main article. Regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district-level and robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
dierence-in-dierence estimation of the payment response to the withholding rate increase, and nally examine the mechanisms of the withholding impact.

# 6.1 First Stage and Impact on Card Use

As discussed in section 3.3.2, a reform in August 2011 revised the sales tax withholding rate schedule. Panel A in Figure VII shows that the reform lead to a large increase (approxiately a doubling) of the average withholding rate, as intended. This also conrms the high compliance of card companies with the government-assigned withholding rates. The graph displays small jumps every semester, when the withholding rates are revised by the tax authorities and communicated to the withholding agents. The graph also displays a gradual decrease in the withholding rate after the initial postreform jump, suggesting that a number of rms obtained a lowering of their withholding rate, probably by making a revision request to the tax authorities.<sup>45</sup> To understand the extent of deviation between the assigned and realized withholding rate, we predict each rm's withholding rate based on its past tax returns and the withholding rate schedule in Table II. As panel B shows, the predicted rate tracks the realized rate closely, for rms for which we can observe both rates, but the realized withholding rate is always slightly higher. This is consistent with the fact that rms can request a lowering of the withholding rate from the tax authorities, for instance if they have losses for several consecutive months.

Panel C and D investigate the possibility that the reform may have discouraged rms from ling their sales declaration, or using their credit/debit card machine, which would have limited information reporting and withholding by card companies. Both the number of sales tax declarations and of credit/debit card reports (D153 informative declaration by card processing companies) led with the tax authorities is steadily increasing and smooth at reform time. Similarly, the share of sales tax declarations that can be matched with at least one credit/debit card report and vice-versa does not display a discontinuity at reform time. In fact, the share of credit/debit card reportees that le sales tax was decreasing prior to the reform (driven by an increase in the number of credit/debit card reportees), but then stagnated after the reform. The possibility of reclaiming the tax withheld may have increased withholdee's propensity to le sales tax. Overall, we conclude that the reform did not reduce tax ling and credit/debit card marchine usage on the extensive margin.

A remaining concern is that the reform may have decreased the intensity of card marchine usage. However, as panels E and F show for rms using a card machine (i.e. reported by at least one credit/debit card company), neither the sum of card sales over total sales nor the average

> 45There is only a weak behavioral response to the withholding rate notches in reported value-added and the share of local sales, suggesting that few rms, if any, can manipulate the withholding rates by manipulating the relevant line items on their sales tax declaration.

26
27 growth-growth regressions are consistent with the small positive and nearly always insignificant, effects in the level-level regressions reported here<sup>4</sup> ".

Table 9: Regressions of level of GDP per worker on level of physical and human capital per worker, various years.

||B-L educa|tion data||N-S-D edu|cation data||
|---|---|---|---|---|---|---|
|Year:|1965|1975|1985|1965|1975|1985|
|Physical<br>capital|.601<br>(15.7)|.598<br>(14.6)|.612<br>(14.88)|.619<br>(16.55)|.626<br>(15.68)|.625<br>(15.28)|
|Educational<br>capital|.085<br>(1.68)|.089<br>(1.49)|.136<br>(1.97)|.032<br>(.658)|.064<br>(.966)|.114<br>(1.36)|
|N|89|96|96|79|79|79|
|R-Squared|.867|.888|.909|.885|.900|.917|

Notes: Absolute values of t-statistics in parenthesis. PWT5 data for GDP per worker and King-Levine physical capital data used for all regressions.

A result from the "new" growth regressions, which do not rely on neoclassical production functions is that the level of education matters for subsequent growth or that there is a threshold effect which depends on the level of education (Joyanovich. Lach and Lavy, 1992, Benhabib and Spiegel, 1994, Azariadis and Drazen, 1990). A level on growth effect is typically rationalized in terms of spillover effects that make the creation (or utilization) of human capital better or easier if there is more of it. With the present data, if the initial level of education is added to the specification the mildly negative impact of the education accumulation persists.

While an empirical finding in cross-national data that the level of education affects growth rates has some interest, it probably raises more puzzles than answers. First, one would think that the spillover effects of knowledge would be _in addition to,_ rather than _instead of_ the usual productivity effects. That is,

**41** If one includes the growth of the level of the years of schooling (as opposed to the growth of the log of schooling or of educaional capital as I define it) then the coefficients are not negative but are small and insignificant, as in the level on level regressions. It is not clear what to mnake of this finding as the only rationale ever presented for a production fuinotion specification of the type necessary to include the growth of the level of schooling in a growth regression is the circular one that is the production function assumption that is necessary to justify that specification.
married women for the 2018 wave. In one-third of the sample, all men aged 15–49 were also eligible to be interviewed. For consistency, we also restrict the sample of men to married men. The DHS surveys provide geo-referenced information on the centroids of village/household clusters.<sup>8</sup> _Conflict data._ We use data from the Armed Conflict Location and Event Data Project (ACLED). ACLED collects data about political violence and protests from secondary sources on the date, location (GPS coordinates), actors involved, event type, and number of fatalities.<sup>9</sup> We aggregate attacks in five-year intervals to merge them with the DHS data. Therefore, we use attacks from 2008 to 2012 for the 2013 DHS wave and attacks from 2013 to 2017 for the 2018 wave.

For the analysis of jihadist attacks, we restrict the data to attacks from 2008 to 2018 in which at least one of the actors involved belonged to Boko Haram or Islamic State. The ACLED data register 2,359 violent events between 2008 and 2017 in which at least one of the actors involved belonged to Boko Haram. Forty-seven percent of events are related to battles, 30.4 percent to violence against civilians, and 17.5 percent to explosions and remote violence, while 5 percent are classified as “strategic developments.” During this period, the ACLED data register 28,001 deaths that resulted from attacks involving Boko Haram. A large percentage of all events occur in the Northeast region (Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe). For instance, 71 percent of events take place in Borno, 9.2 percent in Yobe, and 8.1 percent in Adamawa (these statistics are not displayed in any table).

We use these data to build variables that count the number of Boko Haram attacks

> attacks nor distant Fulani attacks. Results are available upon request.

> 8In order to ensure that respondent confidentiality is maintained, the GPS latitude/longitude points are randomly displaced. Urban clusters are displaced from 0 to 2 km while rural clusters are displaced from 0 to 5 km, with an additional 1 percent displaced from 0 to 10 km (https://dhsprogram.com/Methodology/GPS-Data.cfm). Given that the displacement of household clusters is orthogonal the number of distant attacks, it does not cause inflated results. It may, however, introduce measurement error in our key explanatory variables which can yield to attenuation bias—i.e., conservative point estimates.

> 9There are other data sources for violent events—for example, the Uppsala Conflict Data Project. For the purposes of this study, however, ACLED has the advantage of collecting data on violent events perpetrated by communal militias (specifically, Fulani) that other sources do not. Further, in our study interval, the ACLED data set has better coverage than that of the Uppsala Conflict Data Project, in terms of both number of events recorded and geographic coverage (Clionadh and Roudabeh, 2019).

15
There cannot be any doubt that growth has picked up in the post-reform period. The trend rate of growth in India’s NDP per capita in the period 1958-1991 was 1.63% (with a robust standard error of 0.06%) while it was and 4.28% (0.18%) in the period 1992-2006 (Table 1).<sup>29</sup> Similarly, the rate of growth of private consumption per capita from the national accounts also increased 1.21% per annum in the first period to 3.13% in the second. The acceleration in the survey-based per capita consumption growth—though less than that in mean income or consumption from the national accounts—is also significant, from 0.68% per annum in the pre1991 period to 1.33% in the post-1991 period. Looking at the composition of output by sectors, the highest growth rates in the period after 1991 has been in the tertiary sector (primarily services and trade), followed closely by the secondary (manufacturing) sector, while agriculture has continued to lag. Compared to the pre-reform period, the sector that gained the most was services, while agricultural growth rates showed little or no improvement (Chaudhuri and Ravallion, 2006).The main long-run structural shift in India’s economy has been out of agriculture into services, and this continued after 1991.

Turning to the poverty measures, Figure 6 gives the headcount index and squared poverty gap for both urban and rural sectors. There was neither a trend increase nor decrease in rural poverty until about 1970, when a trend decrease emerged; sustained, though uneven, progress against poverty had clearly emerged in India prior to the economic reforms starting in the early 1990s. Co-movement is strong between the urban and rural measures and there is a clear indication of a declining absolute difference between the poverty measures for urban and rural areas after about 1970.<sup>30</sup> Indeed, the urban SPG overtakes the rural index by the end of the period. In common with other developing countries (Ravallion et al., 2007) poverty has been urbanizing over time, with a rising share of the poor living in urban areas. In the early 1950s, only about 15% of India’s poor lived in urban areas, but this had risen to about 28% in 2005-6. However, given that more than 70% of the population is still in rural areas, the rural sector

> 29 These are based on regressions of log NDP per capita on time. Here and elsewhere we follow Boyce (1986) in estimating the two growth rates as parameters of a single regression, constrained to assure that the predicted values were equal in 1992 (to avoid an implausible discontinuity).

> 30 The regression coefficient of rural H minus urban H on time after 1970 is -0.231% points per year (t-ratio=-4.617); for SPG it is -0.062 (t=-9.545).

16
the self-employed tend to face lower contribution rates than salaried workers in the public and private sectors.

Many categories of labor force participants are exempt from paying contributions. The state budget transfers money to the health insurance funds to cover certain vulnerable groups, such as the disabled and war veterans. The unemployed and pensioners either have insurance contributions paid on their behalf by the unemployment and pension funds or are covered by budgetary transfers.

**<u>Table 2 Health insurance contribution rates</u>**

||**Year**<br>**started**|**Salaried worker**<br>**(Employer:**<br>**Employee share)**|**Self-employed**|**Non-active and**<br>**contribution-exempt**<br>**groups**|
|---|---|---|---|---|
|Albania|1995|3.4% payroll<br>(1.7:1.7)|3-7% of statutory<br>minimum wage,<br>depending on<br>urbanicity|Central budget|
|Bosnia and Herzego|vina||||
|-Federation|1997|17 % payroll<br>(4:13)|15% of cadastre<br>revenue|Central budget, pensions and<br>unemployment funds|
|-Republika|1999|15 %|15% of cadastre|Central budget, pensions and|
|Srpska||(0:15)|revenue|unemployment funds|
|<br>Macedonia, FYR|1991|<br>9.2% payroll<br>(9.2; 0)|9.2% of income|<br>Central budget|
|Montenegro|1993|<br>15% payroll<br>(7.5:7.5)|13.5% of main<br>wage|Pensioners: 19% of net<br>pension.<br>Unemployed: 9.7% of<br>unemployment benefit, but in<br>practice 7.5%|
|||||Others: central budget|
|Serbia|1992|15.92% payroll<br>(7.95:7.97)|14.4% of net<br>wage<br>Farmers: 4% of<br>property tax|<br>Pensioners 12.3% of net<br>pensions<br>Others:<br>central<br>budget, equal to 15.95% of<br>average wages.|
|Croatia|1993|15% payroll<br>(15%:0)|18% of income<br>Farmers: 15% of<br>income if in VAT<br>system, or 7.5%<br>of income<br>estimated based<br>on land ownership|18% of gross pension and<br>other benefits plus central<br>budget and county budgets|
|Slovenia|1993|13.25% payroll|<br>13.25% of<br>income|Central budget.|

Source: Langenbrunner _et al._ (forthcoming)

# **<u>C2. Challenges ahead</u>**

In all countries of the sub-region (with the exception of Kosovo), it is the health insurance fund that pools revenues for health, regardless of whether the source of those revenues are payroll taxation or general taxation (i.e. central budget transfers). The flow of revenues from these primary revenue streams to the health insurance fund has been unreliable, and together with escalating expenditures, has resulted in repeated annual deficits and chronic arrears (see section 10
Figures 1(a) and 1(c) strikingly illustrate the potential impact of climate change and sea level rise on species in salinity tolerance groups 1 and 2. For the two species in group 1 (tolerance range 0-2 ppt), stable habitat occupies a large swath of the eastern region (approximately 15,363 sq. km) in 2012 but practically disappears from that area by 2050 in the A2 (most change) scenario. The potential stakes are also high for the 25 species in group 2, which comprise 30% of all fish species consumed in the Sundarbans region. In 2012, almost the entire eastern part of the Sundarbans region is stable habitat for group 2. In the A2 scenario for 2050, however, maximum salinity has moved beyond the tolerance range of group 2 in broad north-south swaths at the eastern and western margins of the eastern part. By implication, poor communities in these swaths might face significant drops in fish supply by 2050.

# **4.  The Impact of Salinization on Fish Habitats**

We generalize the previous illustration using the digital salinity maps provided by Dasgupta, et al. (2015). For each of the 12 species salinity tolerance groups, we assign 1 to a pixel in the map for 2012 that satisfies the stable habitat criterion (pixel salinity range falls within the species tolerance range) and 0 otherwise. We add across 101,600 pixels to determine total stable habitat by salinity tolerance group in 2012.<sup>9</sup> We perform the same operations for all 27 salinity scenarios in 2050; calculate percent changes from 2012 to 2050 for each scenario and salinity tolerance group; and tabulate the results in Tables 2 and 3.

Table 2 displays all the results, ordered by local subsidence level, IPCC AR4 scenario and global circulation model. We include summary information in Tables 3 and 4 to aid interpretation.

> 9 We use pixels for numerical convenience, although pixel numbers are readily translated to areas.  In our mapping system, one pixel has an area of 0.327 sq. km.  This is equivalent to a square cell with side length of 571.54 meters.

10