rafmacalaba/datause-encoder-data huggingface dataset:
use of third-party information, behavioral design) can generate a larger impact overall.

# 4.3 Other Compliance Outcomes

As the experiment had a strong impact on the targeted compliance outcomes, notably income tax ling and payment, it is appropriate to consider also spillovers on other compliance outcomes. Indeed, taxpayers might perceive the intervention as a general increase in enforcement, and improve compliance also with other tax ling and payment obligations. Alternatively, they might perceive the intervention as pertaining to income tax compliance only, and increase compliance with the targeted tax but compensate for lost income by reducing compliance with another obligation towards the government. To shed light at this, we use rich administrative data on rm's ling of informative declarations, sales tax declarations, sales tax payment, deregistration, and switches to the simplied regime<sup>29</sup> , all of these representing compliance outcomes which were not directly targeted by the intervention. We also consider income tax compliance in 2013, which may be positively or negatively aected by the intervention (targeting non-lers for 2014), as a non-negligible share of rms le (or pay) their taxes with substantial delay (see Brockmeyer and Hernandez (2016) for details), and 80% of non-lers for 2014 were also non-lers in 2013. This is to our knowledge the rst study to examine the impact of enforcement on such a large variety of compliance measures.

Table 6 shows the treatment impact on the above-mentioned outcomes, for experiment 1 in Panel A and experiment 2 in Panel B. Columns 1-3 in Panel A show that treated rms were marginally more likely to le an information report (D151) about their supplier, and signicantly more likely to le an information report about their client.<sup>30</sup> With a 4.8 p.p. increase over a control group average of 12.8%, the eect is twice as large for the information treatment as for the baseline treatment, and the two coecients are statistically distinguishable at the 5% condence level. The observed impact can be explained by the fact that, for rms ling an income tax declaration, ling an informative declaration generates only a small hustle cost, but no monetary cost, and prevents a possible ne for non-compliance with the reporting obligation. Consistent with this, 53% of non-lers that led a third-party report have also led their own income tax declaration. As the emails with additional third-party information made the existence of third-party reports salient and provided information

> 29Firms can opt into simplied income or sales tax regimes if they meet certain criteria, such as expenditure or employment thresholds. To do this, they must submit a modication form to the tax authority.

> 30We do not consider other information reports, such as the D150 and D153 reports, as those cannot be led by rms.

20
shares (U.S. dollar value) traded in the day, and _Rt_ in the Amihud ratio is the daily (percentage) price change during the day.<sup>13</sup> To construct these measures we use a total of 21,900 weekly observations based on 105,000 original daily observations, covering seven emerging markets: Argentina (7 stocks), Brazil (8), Indonesia (7), South Korea (5), Mexico (12), Russia (5), and Thailand (8).<sup>14</sup> As crisis dating is to some extent arbitrary, we employ two different criteria to define crisis periods in order to verify the robustness of our findings. We define _SM crises_ based on the local stock market index: SM crises begin when the stock market index starts a decline of at least five consecutive weeks that reach a cumulative drop in excess of 25%, and end on the first date after which the index grows for at least four consecutive weeks. Alternatively, we use an exchange rate market pressure (EMP) index of daily changes in interest rates and exchange rates (referred to as _EMP crises)_ . EMP crises begin when the volatility of EMP (its 15-day rolling standard deviation) exceeds a threshold level (set equal to the mean EMP volatility plus one standard deviation), and ends on the first date after which the EMP volatility stays below the threshold level for at

> 13 Volume is made missing (not zero) on days when the stock market is closed. If the stock is not traded on a certain day, the price is also made missing (to avoid carrying over the price of the previous day and miscomputing returns) as is the Amihud ratio; volume is zero on those days. Since we work only with liquid stocks, non-trading days are infrequent.

14 The stocks used for each country are the following. Argentina: Banco Frances del Rio de la Plata, Grupo Financiero Galicia S.A., Irsa-Inversiones y Representaciones S.A., Petrobras Energia Participaciones, Siderar S.A., Telecom Argentina, and Transportadora de Gas Sur S.A. Brazil: Aracruz Celulosa S.A., Banco Bradesco S.A., Companhia Brasileira de Distribuicao Grupo Pao de Acucar, Companhia Siderurgica Nacional, Companhia Vale do Rio Doce, Electrobras (Centrais Eletricas Brasileiras S.A.), and Unibanco (Uniao de Bancos Brasileiros S.A.). Indonesia: Astra International, Gudang Garam, Hanjaya Mandala Sampoerna, Indah Kiat Pulp & Paper Corp, Indofood Sukses Makmur, Indosat TBK, and Telekomunikasi Indonesia. Mexico: Alfa S.A., Cemex S.A., Empresas ICA Sociedad Controladora S.A., Fomento Economico Mexicano, Grupo Industrial Maseca, Grupo Televisa S.A., Holcim Apasco S.A., Kimberly Clark de Mexico S.A., Savia S.A., Telefonos de Mexico, Vitro S.A., and Wal-Mart de Mexico S.A. South Korea: Hyundai Engineering & Construction Company, Korea Electric Power Corporation, Pohang Iron & Steel Company, Samsung Electronics Company, and SK Telecom. Russia: Mosenergo, Oil Company LUKOIL, Rostelecom, Tatneft, and Unified Energy System. Thailand: Bangkok Bank, Bank of Ayudhya, Kasikornbank, Krung Thai Bank Public Company, Thai Military Bank Public Company, Thanachart Capital Public Company, True Corp, and Siam Commercial Bank.

7
in the same product. The same is not true for competition within a destination, since the average number of firms exporting to a given destination increases overtime.

The dataset of Tanzanian firms includes exports of a wide range of products. For this reason it is useful to look at the variation in export prices. The first row of Table 5 reports some statistics on the price of each firm-product-destination combination by year. The standard deviation of the logarithm of prices is very high (2.4), indicating that – not surprisingly - variation in export prices is considerable. The variation in the price of prices by firm-product pairs across destinations is also high, which can already be a preliminary sign of price differentiation across destinations. Furthermore, prices for a product-destination pair vary considerably across firms, which indicate heterogeneity across firms. This first statistical description of the data predicts that results may be in line with models assuming variable mark-ups, i.e. the variation in prices across firms indicates that firms charge different mark-ups. Table 5 also predicts that results should be in line with quality competition models, because the variation across destinations indicates that firms charge different prices (and maybe sell different qualities) in different destinations.

The products exported are classified according to the Rauch Classification<sup>20</sup> (1999): (1) homogeneous goods are defined as products whose price is set on organized exchanges; (2) differentiated goods are products whose price is not set on organized exchanges and which lack a reference price because of their intrinsic features; (3) while goods not traded on organized exchanges that possess a benchmark price are defined as reference goods<sup>21</sup> . The dummy variable for product differentiation equals one if the product falls into category (2) and zero otherwise. Of the 3542 products exported over the sample period, more than 60 percent are differentiated products (as per column 2 of Table 6). Nevertheless, a look at the number of total shipments indicates that products are almost evenly divided between differentiated and homogeneous goods.

# **5. Heterogeneity across Firms and Export Destinations**

This section studies the variation in prices across firms and across destinations with the aim of investigating if the Tanzanian data shows evidence of quality or price competition. The analysis draws from the study undertaken by Manova and Zhang (2012). As these authors highlight, causal interpretation of coefficients is not always possible. In fact, some of the specifications would likely be affected by reverse causality issues. Moreover both dependent and independent variables in some specification might be correlated with firms’ characteristics that cannot be controlled for (a part from including fixed effects).

> 20 The Rauch Classification is a measure of horizontal differentiation, not vertical. An example of horizontal differentiation is the ice-cream, which is produced in different tastes, but one is not necessarily better than the other. 21 Javorcik, B. and Narciso, G. (2008).

11
# **Crony Capitalism in Ukraine: Relationship between Political Connectedness and Firms’ Performance**

Oleksii Balabushko<sup>a</sup> , Oleksandra Betliy<sup>b</sup> , Veronika Movchan<sup>b</sup> , Ruslan Piontkivsky<sup>a</sup> , and Mykola Ryzhenkov<sup>b*</sup> a. World Bank, b. Institute for Economic Research and Policy Consulting JEL Codes: D72, P26 Keywords: Crony Capitalism, State Capture, Ukraine

* We thank Satu Kahkonen, Adrian Fozzard, Maurizio Bussolo, David Bernstein, Jana Kunicova, Mark Schiffbauer, and Joel Turkowitz for their helpful inputs and comments. The work was prepared under the Ukraine Governance Reform Program financed by the UK Department for International Development (UKAid) with dissemination supported under the Good Governance Fund.
Lagakos, D. and M. Waught, 2013. “Selection, Agriculture, and Cross-Country Productivity Differences”, _American Economic Review_ . 103 (2): p. 948-980 <mark>Ledesma, R.D., and P. Valero-Mora, 2007. "Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis."</mark> _<mark>Practical assessment, research & evaluation</mark>_ <mark>12.2: 1-11.</mark>

- Leight, J., P. Glewwe, and A. Park, 2015. “The Impact of Early Childhood Shocks on the Evolution of Cognitive and Non-Cognitive skills”, _Gansu Survey of Children and Families Papers_ . Paper 51

- Lipnevich, A. A., C. MacCann, and R.D. Roberts, 2013. “Assessing noncognitive constructs in education: A review of traditional and innovative approaches. In D. Sklofske and V. Schwean (eds.), _Oxford Handbook of Psychological Assessment of Children and Adolescents_ . Cambridge, MA: Oxford University Press.

Manski, C. F., 2004. “Measuring Expectations”, _Econometrica_ , 72 (5): 1329–76.

- McKenzie, D., 2012. “Beyond baseline and follow-up: The case for more T in experiments”, _Journal of Development Economics_ , 99(2): 210-221.

Murnane, R. J., J. B. Willett, and F. Levy, 1995. “The growing importance of cognitive skills in wage determination.” _Review of Economics and Statistics_ , 77 (2), 251-266.

- Pierre, G., ML. Sanchez Puerta, A. Valerio, and T. Rajadel, 2014. “STEP Skills Measurement Surveys - Innovative Tools for Assessing Skills." _<mark>Social protection and labor discussion paper</mark>_ <mark>1421. World Bank Group, Washington, DC.</mark>

- Primi, R., C. Zanon, D. Santos, F. De Fruyt, and O. P. John, 2016. “Anchoring Vignettes Can They Make Adolescent Self-Reports of Social- Emotional Skills More Reliable, Discriminant, and Criterion-Valid?” _European Journal of Psychological Assessment,_ <mark>32: 39-51</mark>

- Pritchett, L. and A. Beatty, 2015 “Slow down, you’re going too fast: Matching curricula to student skill levels” _International Journal of Educational Development: 40C : 276-288._

- Radloff, L.S., 1977. “The CES-D Scale: A Self-Report Depression Scale for Research in the General Population”, _Applied Psychological Measurement_ 1(3): 385-401.

- Rammstedt, B., C.J. Kemper and I. Borg, 2013. “ Correcting Big Five Personality Measurements for Acquiescence: An 18-Country Cross-Cultural Study”, _European Journal of Personality_ , 27(1): 71-81.

37
# _Calibrating the Model to Thai Data_

According to the Bank of Thailand, total government domestic debt not held by the central bank at the end of December 1996, was roughly 41.7 billion baht or 0.9 percent of GDP. We set bo = 0.009.17 The Bank of Thailand estimated that the foreign debt of the public sector in December 1996 was roughly equal to 430.2 billion baht. According to the IFS, net foreign assets of the Thai central bank equaled 988.6 billion baht in December 1996. This suggests that the level of net foreign assets of the consolidated public sector was equal to 558.4 billion baht or _-_ 12.1 percent of 1996 GDP. Thus, we set bo fo = -0.112. Using data from the Bank of Thailand we estimate that net foreign debt of the private sector at the end of 1996 was roughly 38.4 percent of GDP, so we set do = -0.384.'1 In Section 6 we argue that a rough estimate of the fiscal cost of the banking bailout to be 30 percent of GDP, so we set 0 = 0.3. The parameter I was chosen to match the decline in consolidated government assets that occurred in the period leading up to the Thai crisis in July 1997. We estimate that during this period (until the end of June 1997), net assets of the consolidated public sector fell to 7.5 percent of GDP. We set 1 = -0.075.

According to the IMF World Economic Outlook, the ratio of government revenue to GDP in Thailand was 18.9 percent in 1996. As in the Korean case, we subtracted an additional 26.5 percent from output to ensure that the consumption to output ratio in the model equaled the 1996 value of that ratio in the data, roughly 0.5.

"<sup>7</sup> Bank of Thailand data was obtained from their World Wide Web site at http://www.bot.or.th/.

'<sup>5</sup> This figure was obtained by taking a standard measure of total gross external debt, subtracting public sector external debt, and then subtracting the foreign assets of the banking system. As in the Korean case, these are the only private sector foreign assets for which information is generally available.

19
declines over the simulation period. On the spending side, the share of consumption declines more than that of investments, while the share of government transfers to households increases somewhat.

**Table 6. Base: GDP shares by sector for selected years (%)**

||**2008**|**2012**|**2016**|**2020**|
|---|---|---|---|---|
|Agriculture|11.4|10.3|9.8|9.4|
|Industry|21.4|24.3|23.6|23.2|
|Industry -manufacturing|13.2|16.4|15.4|14.9|
|Industry -other|8.2|7.9|8.2|8.3|
|Services|67.2|65.4|66.6|67.4|
|Non-government services|54.7|53.3|53.6|53.6|
|Government services|12.5|12.1|13.0|13.8|
|Total|100.0|100.0|100.0|100.0|

**Table 7. Base: Government budget for selected years (% of GDP)**

|||**2008**|**2012**|**2016**|**2020**|
|---|---|---|---|---|---|
|**Receipts**|**Direct taxes**|11.5|10.2|10.6|11.0|
||**Import tariffs**|1.8|1.7|1.7|1.7|
||**Other indirect taxes**|16.6|14.7|15.3|16.0|
||**Private transfers**|1.4|1.4|1.4|1.4|
||**Foreign transfers**|2.7|3.7|2.9|2.2|
||**Factor incomes**|1.7|1.6|1.7|1.8|
||**Domestic borrowing**|3.7|2.1|2.1|2.1|
||**Foreign borrowing**|0.3|1.2|1.2|1.2|
||**Total**|39.6|36.6|37.0|37.4|
|**Spending**|**Consumption**|20.4|18.7|18.7|18.3|
||**Fixed investment**|3.0|2.8|2.8|2.7|
||**Private transfers**|14.8|14.1|14.5|15.3|
||**Foreign transfers**|0.4|0.4|0.4|0.4|
||**Domestic interest payments**|0.6|0.1|0.1|0.2|
||**Foreign interest payments**|0.4|0.5|0.5|0.6|
||**Total**|39.6|36.6|37.0|37.4|

Total household incomes increase at a slightly lower rate than GDP, primarily because of a slowdown in remittance growth. However, given negative population growth, per-capita household income and real consumption both increase at a n average rate of 4.2 percent per year, permitting a significant decrease in the national poverty rate, which has fallen to 9.5 by the end of simulation period according to the MAMS poverty module (Figure 10).

Regarding inequality, MAMS produces a picture of slowly increasing Gini coefficient (see Figure 11). Changes in inequality are generated by the evolution of per-capita consumption growth across households; the fact that it is higher for the three households with the highest initial within-group inequality drives up aggregate inequality up in our base scenario. In addition, the growth in consumption by household group is not favoring farmers and entrepreneurs, who have rather low per-capita consumption levels in 2012 (see Figure 12).

20
Despite declining poverty rates and rising living standards, the Philippines continues to struggle with food insecurity and undernutrition. Around 65 percent of individuals across the country struggle with chronic food insecurity. This breaks down to 39 percent of individuals experiencing mild food insecurity, 17 percent of individuals experiencing moderate food insecurity, and 8 percent of individuals experiencing severe food insecurity (IPC, n.d.). Further, according to Mbuya et al. (2021), there has been almost no reduction in the prevalence of undernutrition for 30 years. Nearly 30 percent of children younger than five suffer from stunting, with some regions suffering from rates greater than 40 percent. Micronutrient deficiency is also prevalent, including anemia and vitamin A deficiency (Mbuya et al., 2021).

Undernutrition and food insecurity tend to most affect vulnerable peoples, including landless poor households, indigenous peoples, and individuals engaged in irregular income livelihoods (IPC, n.d.). Additionally, many individuals living in the Philippines are subject to regular and severe climate shocks, including flood, landslides, drought, and typhoons, all of which cause compounding stresses on ensuring food and nutrition security for households. The Philippines’ rapid growth and urbanization further threatens the tenuous food security which exists. As the Philippines continues to grow, good nutrition will be imperative for ensuring continued economic prosperity. Addressing undernutrition will increase human capital and will improve the lives of individuals throughout the nation.

# _2.2 National Nutrition Survey_

As we are interested in household nutrition and intra-household consumption, our primary analysis is based on the National Nutrition Survey (NNS) of the Philippines. The NNS is a nationwide survey on nutritional status, diet, and other factors related to noncommunicable disease. The NNS data are nationally representative within each cross-section collected (Patalen et al., 2020).

The NNS in 2013 is a unique data set, due to the availability and richness of the individual-level consumption data. Despite the value of food consumption and/or dietary data, there are very few examples of nationally representative individual-level dietary surveys outside North America and Europe. There are significant challenges to collecting such data (Coates et al., 2017). And, as a result, there are relatively few individual-level data sets which analyze consumption for all those 7
Financial Regulation and Government Revenue Table 2: Regulation and Safe Assets: Quarterly Variation (Million Real Birr)

||(1)|(2)|
|---|---|---|
|Variable|NBE Bills|Safe Assets|
||(Real Birr)|(Real Birr)|
||Pre-Policy||
|Quarter 2|0.1|13.56|
||(120.9)|(102.1)|
|Quarter 3|13.44|72.27|
||(110.1)|(82.79)|
|Quarter 4|38.41|72.81|
||(109.4)|(84.50)|
||Post-Policy||
|Quarter 5|438.4***|517.0***|
||(86.86)|(78.39)|
|Quarter 6|528.8***|627.1***|
||(86.43)|(83.19)|
|Quarter 7|640.9***|519.6***|
||(87.63)|(80.38)|
|Quarter 8|722.1***|361.7***|
||(89.03)|(70.95)|
|Quarter 9|775.2***|485.5***|
||(89.77)|(70.64)|
|Quarter 10|807.8***|540.9***|
||(90.90)|(74.51)|
|Quarter 11|882.6***|602.3***|
||(93.36)|(70.02)|
|Quarter 12|961.8***|671.1***|
||(96.78)|(73.13)|
|Quarter 13|1,017***|613.9***|
||(98.51)|(71.19)|
|Bank FE|Yes|Yes|
|Observations|538|538|
|Adjusted _R_<sup>2</sup>|0.791|0.945|
|Mean Dep. Var.|501.9|1261|
|SD Dep. Var.|616.6|1062|

_Note:_ This table reports OLS estimates. The unit of observation is bank level and bank fixed effects are included. Robust standard errors are in parentheses. NBE Bills is the amount of bills issued by the NBE in million birr, and it is a continuous variable. Safe Assets aggregate at bank level the following assets: liquid cash, government bonds, NBE bills, bank-to-bank deposits, and voluntary reserves at the NBE. Their mean and standard deviation are reported in the final two rows. In columns (1) and (2), we regress the quantity of NBE bills and Safe assets, respectively, over a series of quarterly dummies, which fully describe the sample. These variables catch the average evolution of the accumulation of NBE bills and safe assets across the quarters. It is clear that as the policy is introduced in April 2011, which falls in Quarter 5, both NBE bills and safe assets start to markedly increase. As Figure 1 shows, this increase is close to being one-to-one. All quantities are in million of real birr, at their 2010 value. The adjusted _R_<sup>2</sup> of these regressions is also reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

11
(5) or (2). On the contrary, when there is a sample selection problem, the point of reference to compare with (12) would be the Heckman selection model.

# **5.3 Multiple imputation via switching regression**

The aim now is to propose different imputation mechanisms to compare their performance with the ICA method and its variations. The following imputation mechanism was first proposed by van Buuren, Boshuizen and Knook (1999) and it has been chosen because it fits very well with datasets with a large amount of missing values in many variables, such as IC datasets. See also Schafer (1999) for a tutorial on multiple imputation, and Schafer (1997) and Gelman, King and Liu (1998) for applications.

The basic idea is to create a small number of data copies, each of which has the missing values suitably imputed. Each imputed dataset is then analyzed independently. Estimates of the parameters of interest are properly averaged across the data copies, while standard errors are computed according to _‘Rubin rules’_ , see Rubin (1987). In particular, this multiple imputation mechanism is accomplished in the following steps:

1. Specify the posterior predictive density of incomplete data as _p(JMIS|X,s)_ given that the nonresponse mechanism is _p( s | J, IC, C, D)_ and the complete data model is _p_ (J _, IC, C, D_ ), where _X_ is the set of covariates used in the imputation mechanism and _s_ is the pattern of missing values. The posterior predictive density is generally given by

where the standard procedure to impute missing data consists of first, drawing a value of  * from _p_ (  | _X_ , _s_ ) and second, drawing a value _JMis*_ from _p_ ( _J MIS_ | _X_ , _s_ ,    *) . 2. The next step is to draw imputations from this density to produce _m_ complete datasets. Here we follow van Buuren et al. (1999) and we produce m=5 datasets.

3. Estimate equation (1) _m_ times.

4. Pool the _m_ results.

This imputation mechanism involves choosing the form of the linear model and the predictor variables. In particular, we use a linear regression of each _JMIS_ = _Y, L, M and K_ on a set X of predictor variables, where the set of predictor variables is given by _X_ = _Y, K, L, M,_ and _D_ . Note that each _J_ is used as a predictor variable and as an imputed variable in (10), while _D_ are used only as predictor variables.

# **5.4 Sample selection correction (I): Heckman on complete case**

If the pattern of missing values is endogenously determined (it is correlated with output ( _logY_ ) in equation (4)), thereby giving rise to a self-selection problem, the ICA method may lead to inconsistent estimates of parameters of (1). In these cases one has to implement the Heckman (1976) or Heckit method to correct for self-selection, since OLS applied either to the complete case or to the sample with replacement is inconsistent. In particular, the Heckman model over the complete case is given by 52