rafmacalaba/datause-encoder-data huggingface dataset:
signi...cant, some of the coe¢cients on the control variables lose signi...cance. This suggests that the e¤ects of the control variables are not immediate but take some time to work themselves through, and that at the same time there is relatively limited autocorrelation in the control variables.

Column (4) reports a control experiment. We assigned the average date of reform to the three countries (Poland, Romania and the Czech Republic) that did not undertake a reform over the sample period and checked whether evidence of a credit acceleration around the average reform time emerges for non-reform countries. The result con...rms that the accelerations in ...nancial intermediation in the reform countries do not re‡ect a global trend in the region.

# 4.2 Cross-Sectional Approach

The analysis thus far has relied on identi...cation through di¤erences in the timing of reform, with the reform being treated as a zero-one event. As an alternative strategy we use two continuous measures of payment system development: (1) the number of credit transfers per inhabitant; (2) the number of all non-cash payments per inhabitant (including direct debits and cheques). The idea is that the number of credit transfers (and to some extent all non-cash payments) directly re‡ect the system’s use.<sup>16</sup> Yearly data are reported in the ECB Blue book for the period 1996-2001 for all countries in our sample except Bulgaria. Descriptive statistics reported in Table 1 show a huge variability in the use of non-cash payment instruments across countries which, in our sample, is unlikely to be attributable fully to variations in levels of economic development or culture. For instance, the number of credit transfers per inhabitant in 1996 is 30 in the Czech Republic and 4 in Hungary.

To capture the variation in the measures of payment systems development is mostly in the cross-section, we use an ordinary least squares estimator including year ...xed e¤ects but excluding country ...xed e¤ects to identify the e¤ect of payment systems development on credit.

Our speci...cation controls for real income per capita and the full set of determinants of ...nancial development used before (all lagged one year) and as listed in Table 2. The estimation results reported in Table 4 con...rm our previous conclusions. An increase in the number of credit transfers (non-cash payments) is associated with a statistically signi...cant increase in credit. This e¤ect is again economically signi...cant. A one standard deviation increase in the number of credit transfers per inhabitant is associated with 6.4 percentage points rise in the annual credit to GDP ratio.

16Credit transfers are e¤ected directly through the main interbank payment systems, while direct debits and cheques are ...rst netted in ancillary systems before ...nal settlement occurs in the main system.

12
In Table A.6, we re-compute input tariffs using only manufacturing industries, which allows us to avoid assigning tariff rates of zero to non-traded goods, but requires us to assume that only manufacturing inputs are used in production. The results for input tariffs are substantially larger in magnitude, but qualitatively similar, to the baseline results.

Next, we remove the two industries (the blending of spirits and the production of wine) for which the tariff reforms did not fit the pattern shown in Figure 1. In Table A.7, we show that the results are robust to excluding these two industries. Finally, as discussed in Section 4, we weight our baseline results by the total number of firms in a state-industry group over all years. Table A.8 shows that most of the results are similar when we place an equal weight on each state-industry.

Table A.1: Productivity Decompositions and Policy Changes: Winsorized Productivity

||Aggregate|Average|Reallocation|
|---|---|---|---|
||(1)|(2)|(3)|
|Final Goods Tariff|-.053<br>(.025)<sup>_∗∗_</sup>|-.047<br>(.028)|-.007<br>(.013)|
|Input Tariff|-.549<br>(.099)<sup>_∗∗∗_</sup>|-.546<br>(.109)<sup>_∗∗∗_</sup>|-.003<br>(.053)|
|FDI Reform|.030<br>(.011)<sup>_∗∗∗_</sup>|.049<br>(.013)<sup>_∗∗∗_</sup>|-.019<br>(.008)<sup>_∗∗_</sup>|
|Delicensed|-.008|.002|-.010|
||(.016)|(.016)|(.009)|
|Obs.|17074|17074|17074|
|_R_<sup>2</sup>|.119|.11|.016|

Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share-weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level. The top and bottom 1% of TFP have been winsorized.

53
# **S.3 Geo-referencing Roads**

To geo-reference roads, we used the information on the variable “section”, and geo-coded by hand the start and end point of each road segment.<sup>38</sup> Table S.12 shows the distribution of work activities across countries.

Table S.12: Countries with Geo-referenced Roads

||Freq.|Percent|Cum.|
|---|---|---|---|
|Bangladesh|158|16.79|16.79|
|Ethiopia|61|6.48|23.27|
|Ghana|237|25.19|48.46|
|India|77|8.18|56.64|
|Philippines|96|10.2|66.84|
|Thailand|139|14.77|81.62|
|Uganda|173|18.38|100|
|Total|941|100||

_Source_ : ROCKS data.

Across these seven countries, we were able to geo-locate 941 road work activities; in 84% of these cases we could locate both the start and end point of the road project, in 12% of the cases only the start point (some of the projects are bypasses so only one location is provided), and in 3% of cases we only located the end point. We then computed the average population density, precipitation, ruggedness, distance to the nearest city of more than 100,000 people, and distance to the nearest port as defined in Table S.13.

To extract average population density, ruggedness and precipitation, we connected the start and end location of a road segment with a straight line and drew a 30km buffer around the line segment. For the road work activities for which we only had one point, we drew a 30km buffer around the location. We resampled the precipitation data at a cell size of 0.05 x 0.05 degrees as the original 0.5 degree resolution was too coarse for our polygon size to compute average cell level statistics. We also computed the shortest geodesic distance of any point on the straight line road segment to the nearest city of more than 100,000 people and to the nearest port. Similar to before, if we only had the start or end location, we compute the distance between the start or end location and the nearest city of more than 100,000 people and the nearest port.<sup>39</sup> Table S.14 provides the summary statistics.

> 38If there was ambiguity about the location, we further used the length variable to narrow choices.

> 39Alternatively, we used the 30km buffer to compute the distances to the nearest city and the nearest port.

17
14 in Mauritius, Hungary, China, Indonesia, the Philippines, and Singapore. It is often not clear in these descriptions to what extent rural employees or the rural self-employed are covered. There is certainly coverage for at least some rural workers in Zambia, Cameroon, Morocco, Israel, Hungary, Portugal, Yugoslavia, Sri Lanka, and China. 5 Mesa-Lago (1986) points out that in Latin America, "coverage tendr to be positively correlated with income, the degree of skill at work and the power of pressure groups." Similarly, in the rest of the developing world it is the urban elites who receive the most protection under social security while needier groups are not covered. 6 This becomes a matter of particular concern to the extent that social security is paid for out of general revenues or will later become a burden upon the central - government. In these cases arguably rare outside of Latin America - socal security systems may constitute a mechanism for a perverse redistribution from the worse- to the better-off.

# The financial size of social security systems

While it is difficult to determine the extent of social security coverage in the population, information on financial flows is accessible for most countries. Tables 5 through 8 present information collected by the International Labour Office for its triennial survey, Cost of Social Security. 7 Table 5, in particular, presents the size of social security finances relative to the economy as a whole as measured by Gross Domestic
Figure 2 **Fertilizer Subsidy and Consumption in Pakistan** <!-- Start of picture text --> .''..c-- ::f f...S -. .-. .f .f:. f... .... S- :-f-Y::.-.......S-.- ... :i: :f;::: ':f'S :f:f..:''<br>41200<br><!-- End of picture text -->

> Note: Feztihzc consmton is measured in td_snd ol mcnc tos

The shortfall between water charges and expenditure on irrgation also represents a major subsidy The active market for tubewell water shows that fanrners are willing _to pay_ more for water than the current price. Thiis underpnicing has led to inefficient overuse of water, deterioration in the system resulting from the squeeze on O&M expenditure, and massive rent seeking, with most of the rents going to large fiarmers and irrgation officials.

Electricity is also a big hidden subsidy. It is provided at a concessional rate for operating electric tubeweUls. Electricity thefis are also considerable. However, as the current power crisis in Pakdistan shows, this subsidy is of dubious benefit to farmers. Rural areas can be without power fior up to 12 hours per dayr. Capital expenditure on electricity generation has been crowsded out by the low rate of cost recovery.

# lnadeguate Expenditure

Public expenditure has been insufficient in other areas. Naturl resource degradation arising from waterlogging and salinit,y has not been sulfficiently addressed. To the extent that these problems are due to inladequate drainage (a public good), then the govrnment can play an increased role At the same time, the government has provided funds for the development and maintenance of prnvate tubewells, even though most of the benefits of tubewells are privately appropriated (althoug there may be some public benefit of tubewells arising from lowering of water tables).

Particularly serious iS the neglect of operaions and maintenance expenditure on the irrigation system, which has consequently deteriorated (Table 12). The shortfal vanes by province, and is as high as 37 percent in Sind, and stems fran low water rates and inadequate assessent and coilection of charges B In addition, revenue does not go directly to the Provincial Irrigation Departmns, which creates poor collection incentivres.

13Mor,eoier. levd reqird n tindlude public tubewens, the assumption tbat heewould be privatie. But privaizton of grudwaeubees is slowsrta planned, and the 0& ne:eds oflhs tuibewels has been conieable.

27
17 trade as a long-term activity among the riparians, our theory suggests that trade is determined outside of the model and is uncorrelated with the error term of the equation. Therefore, we can use trade as an independent variable in our single model estimates.

# **RESULTS**

We start with the principal component analysis followed by some general trends and distribution patterns. We then move to reporting on the econometric results.

# **Principal Component Analysis Results**

The results of the principal component analysis are presented in Table 1. Several dependent and independent variables were created, using (1) the entire dataset, (2) observations with a treaty only, and (3) observations with a water allocation treaty only. The eigenvectors of the first principal components, used in the creation of the principal component variables, are presented in the table.<sup>xx</sup> These eigenvectors explain between 0.84 and 0.89 percent of the standardized variance among the variables. The _Water scarcity intercept_ and _Water scarcity slope_ variables are used in our econometric estimates to measure water scarcity at the river level.

# **General Descriptive Results**

There are 13 geographical configurations representing the set of 226 rivers (based on Dinar S. 2007).<sup>xxi</sup> The 226 bilateral rivers in the dataset were categorized, for the purpose of our analysis, into three geography types: through-border, border-creator, and others, comprising 44, 7, and 49 percent of the observations, respectively (Table 2).

Simple analyses of treaty distribution provide compelling results. As Table 3 indicates, the number of treaties per river varies between 1 and 10. Records of treaty signature dates provide very useful information regarding the distribution of the treaties over time. Table 4 reveals that between 25 and 75 percent of the treaties in the dataset were signed between 1850-1950 and 1951-2002, respectively. Using our notion of treaty as a measure of cooperation, these results suggest that more cooperation is apparent in recent years.<sup>xxii</sup> The treaty content descriptive analysis, vis-à-vis water allocation, hydropower, pollution control, flood protection, and general issues (such as statements reconfirming the good intent of the riparians, setting a basin committee, etc...) also provides useful lessons. While treaties could address a multiple set of issues, we find that 67 percent of the treaties are single-issue ones. Table 5 presents
to direct spillovers in stock selection, new investors then select similar strategies to their experienced peers. In turn, social interactions promote the adoption of high volatility strategies even when new investors have no inherent preference for volatility.

Another alternative explanation for our findings is that individuals accurately observe peer outcomes but decide to ignore negative signals; we refer to this behavior as _selective information neglect_ . For instance, individuals might overestimate their ability to replicate peers’ successful trades while avoiding peers’ mistakes.<sup>10</sup> According to our survey, informal conversations about stock trading are more common in courses where peer outcomes are good; 93% of students report investment conversations with classmates when peer returns are positive, compared with 79% in courses with negative peer returns. While the survey evidence does not directly rule out selective information neglect, it suggests that the exchange of investment ideas seems stronger when some students have experienced positive outcomes.

Our work is closely related to Han, et al. (2020). The authors are the first to model communication bias among individual investors to explain why active strategies (e.g., those with more personal involvement and with more variance) dominate passive investments. Using a classroom enviroment, we present novel empirical evidence consistent with the view that selective communication plays a key role in financial decisions.<sup>11</sup> Our evidence suggests that positively-selected information about the value of active trading leads to enhanced stock market entry among uninformed individuals.

Our analysis contributes to the empirical literature that studies the effects of social interactions on investment (e.g., Hong, et al., 2004; Hong, et al., 2005; Ivkovic & Weisbenner, 2007; Brown, et al., 2008; Li, 2015; Hvide & Ostberg, 2015; Ouimet & Tate; 2020). Most of this literature focuses on identifying peer effects—whether information or behavior is transmitted—and on the strength of social contagion. We add

> 10 Confirmation bias can also lead to similar behavior. For example, individuals enter the class thinking that trading stocks is a good idea and they internalize only good experiences from their classmates while ignoring the bad. 11 Other papers have used classroom settings to identify peer effects. Shue (2013) and Lerner & Malmendier (2013) use the random assignment of students into sections of Harvard’s Master of Business Administration program to study how professional networks affect managerial decisions and how the interactions of students with successful and unsuccessful entrepreneurs affect new entrepreneurial activity.

7
significant impacts. A second is what type of statistical inference is appropriate given the sample size. Third, the sample may be too small to be representative of large firms in developing countries. We discuss each concern in turn and the steps we took to address them.

<u>Significance of results:</u> Even though we have only 20 experimental plants across 17 firms, we obtain statistically significant results. There are five reasons for this. First, these are large plants with about 80 looms and about 130 employees each, so that idiosyncratic shocks – like machine breakdowns or worker illness – tend to average out. Second, the data were collected directly from the machine logs, so have very little (if any) measurement error. Third, the firms are homogenous in terms of size, product, region and technology, so that time dummies control for most external shocks. Fourth, we collected weekly data, which provides high-frequency observations over the course of the treatment and the use of these repeated measures can dramatically reduce the sample size needed to detect a given treatment effect (McKenzie, 2010b). Finally, the intervention was intensive, leading to large treatment effects – for example, the point estimate for the reduction in quality defects was over 50%.

<u>Statistical inference: A second concern is over using statistical tests which rely on</u> asymptotic arguments in the N dimension to justify the normal approximation. We use three alternatives to address this concern. First, we use firm-clustered bootstrap standard errors (Cameron et al, 2008). Second, we implement permutation procedures (for both the Intent to Treat (ITT) and Instrumental Variables estimators) that have exact finite sample size and so do not rely upon asymptotic approximations. Third, we exploit our large T sample to implement procedures that rely upon asymptotic approximations along the time dimension (with a fixed N).

_Permutation Tests:_ Permutation tests use the fact that order statistics are sufficient and complete statistics to derive critical values for test procedures. We first implement this for the null hypothesis of no treatment effect against the two sided alternative for the ITT parameter. This calculates the ITT coefficient for every possible combination of 11 treatment firms out of our 17 total firms (we run this at the firm level to allow for firm-level correlations in errors). Once this is calculated for the 12,376 possible treatment assignments (17 choose 11), the 2.5% and 97.5% confidence intervals are calculated as the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the treatment impact. A treatment effect outside these bounds can be said to be significant at the 5% level. Permutation tests for the IV estimator are more complex, involving implementing a procedure based on Greevy et al. (2004) and Andrews and Marmer (2008) (see Appendix B).

11
|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|Guedes et al.<br>(2022)|Brazil,<br>GBD 2019|General<br>population|sodiumconsumption (excessive =<br>>3g/day)|hypertension(intermediate risk factor),<br>CVD,chronic kidneydisease|Mortality, DALYs|
|Nilson, da Silva,<br>and Jaime (2020)|Brazil||Saltconsumption|CVD, mediated by effect of salt<br>consumption on systolicblood<br>pressure|Direct costs<br>(hospitalization for CVD<br>causes)|
|Nilson et al.<br>(2020)<br>Studies estimating|Brazil<br>cost associated with|Adults 30+ years<br>other individual dietar|Sodiumconsumption<br> yrisk factors|High blood pressure|Mortality and costs-of-<br>illness (inpatient &<br>outpatient care and<br>medications)|
|Liu et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|diet high in red meat(beef, pork,<br>lamb and goat, and all processed<br>meats - notpoultry,fish or eggs)|ischemic heart disease,diabetes, and<br>colorectal cancer (3 leading diseases<br>attributable to diet high in red meat)|Mortality, DALYs|
|Rocha et al.<br>(2023)|Brazil|Adults 25+ y|diet rich in processed meats(daily<br>consumption of cured, smoked,<br>salted meats)|ischemic heart disease,diabetes,<br>colorectal cancer|Mortality, DALYs<br>+ costs of hospitalizations<br>and outpatient<br>procedures|
|Zhuo et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|Diet low infiber|NCDs|Mortality, DALYs|
|Alcaraz et al.<br>(2023)|Argentina, Brazil,<br>El Salvador, and<br>Trinidad and<br>Tobago|Children/<br>adolescents,<br>adults|SSBconsumption|Effects on healthvia BMIand<br>independent effects ondiabetesand<br>CVD|Mortality, DALYs, direct<br>medical costs|
|Pradhananga et<br>al. (2022)|Nepal +<br>comparator<br>countries<sup>†</sup>, GBD<br>2010-2019|General<br>population|suboptimal breastfeeding, diet high<br>in SSB, diet high in trans-fatty acids|Moderate/severe acute wasting<br>high BMI(BMI>20-25 in adults 20+y,<br>IOTF cutoffs for obesity in children<br><20)|Mortality, DALYs|
|Li et al. (2021)|China, GBD 2017|General<br>population|diet high in SSB|Ischemic heart disease (IHD),T2DM|Mortality, DALYs|
|Basto-Abreu et al.<br>(2020)|Mexico|Adults 20-59 y|Beverages and snacks intake<br>(kcal/day)|Weight change (kg),obesity|Direct health care and<br>indirect costs (premature<br>deaths, work<br>absenteeism,others)|
|Javanbakht et al.<br>(2018)|Islamic Republic<br>of Iran, micro-<br>simulation<br>modelingstudy|Adults|Dairy foods consumption|CVD,T2DM|annual per capita health<br>care costs|

21