rafmacalaba/datause-encoder-data huggingface dataset:
underreporting their income and receiving, on average, much more of their actual income as "envelope wages" or non-declared cash in addition to their official wage. This can lead to a spike in the minimum wage in the wage distribution and undermine the effectiveness of the minimum wage policy. Envelope wages remain an important problem in Central and Eastern European transition economies despite some improvements. Empirical research confirmed that the spike in the minimum wage is partially explained by the underreporting of income (tax evaders are overrepresented among minimum wage workers) in Hungary (Tonin, 2011), Latvia (Gavoille & Zasova, 2023), and Poland (Kiełczewska et al., 2021). Therefore, assessing tax compliance and underreporting of income, particularly among minimum wage workers, is essential for promoting effective social policy and designing effective minimum wage policies. It can help ensure that the benefits of social programs are targeted to those eligible and promote greater equity in the tax system. It can also inform policy design by identifying the factors contributing to tax evasion and developing measures to reduce them, promoting a more effective and equitable fiscal system.

**The document is organized as follows.** Section II explains the methodology and data sources used. Section III presents the main results of the study for Romania. Finally, Section IV concludes.

# **II. Methodology and Data**

**Several methods assess tax evasion in the literature, each with advantages and limitations. To obtain a more comprehensive understanding of the extent of tax evasion in Romania, it is essential to** . **understand which methods or combinations of these methods are more reliable**

**One method compares tax returns and tax audit data, although it is important to note that audits are not always conducted randomly** . Analyzing tax audit data can provide detailed information on noncompliance data. This information can be enriched by linking the audit data to population census data. However, this method is not always feasible, given the restrictions on access to audit data.

**Another standard method in the literature to estimate tax evasion is to use alternative sources such as income surveys, household budget surveys with consumption data, or discrepancies in economic statistics** . For instance, it is possible to compare the incomes reported in administrative tax records

5
confidence interval of the expected climate change impact. In principle, there are two ways to calculate confidence intervals: parametric and non-parametric. It is difficult to calculate the variance of the climate change impact parametrically in this model, because welfare is the product of three predictions.<sup>6</sup> Uncertainty estimates are provided by bootstrap methods by resampling and calculating the mean and 95% intervals of the climate change impacts.

# **3. Data and empirical specification**

The data this study relies on were part of a larger GEF project to study climate change impacts on agriculture (Dinar et al. 2006). The countries included were Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya, Niger, Senegal, South Africa and Zambia. (Zimbabwe had to be dropped from the livestock analysis because of turbulent conditions in that country during the survey.) They were selected as representative of the wide range of climate throughout Africa, and districts within each country were selected to provide a wide range of climatic variation. The original survey interviewed over 9000 farmers from the 11 countries. Within that sample, over 5000 were livestock farmers.

The data include information on livestock production and transactions, livestock product production and transactions, and relevant costs. The data indicate that the five major types of livestock in Africa are beef cattle, dairy cattle, goats, sheep and chickens. Other less frequently recorded animals include breeding bulls, pigs, oxen, camels, ducks, guinea fowl, horses, bees, and doves. The major livestock products sold were milk, meat, eggs, wool and leather. Others included butter, cheese, honey, skins and manure.

Climate data came from two sources: US Defense Department satellites and weather station observations. We relied on satellite temperature observations and interpolated precipitation observations from ground stations (see Kurukulasuriya and Mendelsohn 2006 for a detailed explanation). Soil data were obtained from the FAO digital soil map of the world CD ROM. The soil data were extrapolated to the district level using GIS (Geographical Information System). The data set reports 116 dominant soil types.

# **4. Empirical results**

Although there are many varieties of farm animal in Africa, we focused on the five primary ones that generated most of the income from livestock: beef cattle, dairy cattle, goats, sheep and chickens. Altogether these five animals generated 92% of the total revenue from livestock. We begin with an analysis of all farms. We then examine small and large farms separately.

> 6 See Seo et al. (2006a) for the parametric calculation of the confidence intervals.

10
# **1. Introduction**

The study is aimed at analyzing the nature, extent and causes of rural vulnerability in Serbia. While poverty and vulnerability are inextricably linked (Banerjee and Newman, 1994; Morduck, 1994; Kamanou and Morduck, 2002), the presence of risk and uncertainty—the fact that the level of future well-being is uncertain—distinguishes the concept of vulnerability from that of poverty. Results of worldwide consultations with the poor revealed that they regularly identify risk and uncertainty and their inability to effectively deal with them as crucial dimensions of their vulnerability (Narayan, et al., 2001; Kanbur and Squire, 1999; World Bank, 2001). Households are vulnerable to the risk of sudden (perhaps gradual) decline of their welfare positions to the extent they are unable to cope. As a result, there is a growing emphasis on a forward-looking concept of vulnerability in order to address the shortcoming characteristic of _ex post_ , often times one-off, poverty measures (World Bank, 2001). There is a general consensus that in the presence of risk and uncertainty, the traditional measures of poverty are inadequate to analyze household welfare and multifaceted risk management strategies. In this study, using panel Serbia Living Standard Survey (SLS) data in 2002 and 2003, we measure rural vulnerability in Serbia as a function of non-stochastic determinants of poverty, exposure to risk, and households’ ability to cope with shocks.

# **Background**

For a majority of Serbia’s population, the living standard at present is much lower than it was at the beginning of the transition from planned to market economy in the early 1990s, a result of ten years of political and economic adversity, internal conflict and international isolation. While this economic downturn affected all segments of the society, it has made conditions more sharply worsen particularly for the rural population and other disadvantaged groups such as internally displaced people (IDP) and refugees. Discussions with rural households suggest that the contemporary view of poverty in rural Serbia is not only as a situation of material deprivation, but also it is a demoralizing state of living that stems from comparing and contrasting the better past with the dire present and a not so bright future.

The Serbian economy averaged per capita growth rate of nearly 5 percent in the early 2000s (Table 1). Serbia implemented a highly effective stabilization program, which brought about important improvements in broader macroeconomic indicators including achieving single digit inflation, a stable exchange rate, and a considerable increase in foreign reserves. Substantial capital inflows from donors and other sources, and improvements in the management of public finances and financial policy also played an important role in establishing macroeconomic stability at the initial stage of the program. The combined effect of the macroeconomic stability and capital inflows has been a significant economic growth between 2000 and 2003.

1
income, including agricultural finances (Malapit et al. 2020), although men are often given more power over major budgetary decisions such as buying and selling land (Ashraf 2009). In agricultural households, women are expected to be generalists, making decisions over family matters, finances, and some elements of agriculture, while men are expected to be specialists in farming (Parks, Christie & Baganes 2015). A further relevant data point to contextualize our results is the existence of strong social norms in favor of consultative decisionmaking between spouses, although men still dominate agricultural decision-making in most households (Arugay et al. 2021).

# <u>3.2. Data Set</u>

The data set used in this paper comes from a detailed spousal survey carried out with farming couples in the Philippines from February to May of 2018. Part of the baseline survey of a larger impact evaluation on land reform, all couples interviewed were landowners, having received at least one agricultural parcel from CARP. Local enumerators interviewed a sample of 997 respondents in 570 agrarian reform beneficiary households, of which 422 were matched monogamous couples.<sup>3</sup> Only married respondents or respondents in common-law unions<sup>4</sup> were interviewed. Most of our sample was interviewed across six provinces of the southern island of Mindanao, while 72 households were interviewed in the Bicol Region of southeastern Luzon. As our sample was drawn strictly from former tenant farmers who had received land parcels at least 10 years prior to the survey, it skews older and poorer than the Philippines population overall (see Appendix Table 1). Thus, our results should be considered representative only of the population sampled. Most households in our sample (82.2%) lay below the 2018 national per capita food poverty income threshold of 7,337 pesos per month.

Spouses were interviewed simultaneously, with enumerators visiting households in pairs and administering questionnaires to male and female spouses in separate areas where they could not hear or influence each other’s responses. Men and women were administered identical questionnaires; however,

> 3 In 147 households only one spouse was available to be interviewed, while one household was polygamous. Our primary data analysis is limited to matched monogamous couples.

> 4 As divorce is illegal for Christians in the Philippines, cohabiting with a partner who is not the original spouse is considered a “common-law union”, which has the same legal connotations with regards to land and property as a formal marriage under Philippine law.

9
# **Table 8 Splitting fee income into trading income and other income**

The dependent variable in columns 1 to 3 is _Return on assets_ defined as profit before tax as a percentage of total assets of a bank. The dependent _ROA_ + _CAR_ variable in columns 4 to 6 is _Z score_ defined as where ROA is return on assets, CAR represents capital assets ratio and SROA _SROA_ stands for standard deviation of return on assets. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Trading income_ is the share of trading income in total operating income. _Non-trading, non-interest income_ is defined as the share of non-trading, non-interest income in total operating income. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We obtain bank level data from Bankscope and macroeconomic variables from World Development Indicators (WDI) of World Bank. We estimate all regressions with clustering at bank level. Standard errors are included in parentheses. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Return on Assets|Return on Assets|Return on Assets|Z score|Z score|Z score|
|Assets|0.000|0.000|-0.000|-0.246|0.169|0.606|
||(0.000)|(0.001)|(0.001)|(0.617)|(0.590)|(0.586)|
|Equity|0.054***|0.060***|0.052***|29.083**|28.913***|33.643***|
||(0.018)|(0.018)|(0.018)|(11.353)|(10.199)|(10.323)|
|Assets growth|0.011**|0.012**|0.010**|-0.257|-0.491|0.288|
||(0.005)|(0.005)|(0.005)|(3.769)<br>-|(3.731)|(3.759)|
|Overhead|-0.071|-0.081|-0.095|105.775***|-75.211***|-61.957***|
||(0.075)|(0.085)|(0.089)|(14.593)|(14.178)|(13.824)|
|Trading income|0.033**||0.038**|-24.656***||-32.982***|
||(0.015)||(0.017)|(6.922)||(6.233)|
|Non-trading, non-interest income||0.008|0.014||-21.314***|-25.390***|
|||(0.009)|(0.010)||(6.455)|(6.249)|
|Inflation|0.152***|0.162***|0.141***|34.704**|37.282**|55.043***|
||(0.049)|(0.051)|(0.051)|(16.957)|(14.802)|(17.590)|
|GDP growth|0.128***|0.132***|0.125***|17.526|16.737|23.194|
||(0.039)|(0.042)|(0.040)|(15.523)|(13.666)|(15.122)|
|GDP per capita|-0.001|-0.001|-0.001|0.637|0.234|0.513|

45
bring about a 0.10 percent reduction in the ocean transport cost.<sup>27</sup> The indexes of port infrastructure quality of _GCR_ and sea transportation of _WCY_ result in a bit smaller than the U.S. regression. Port efficiency by Blonigen and Wilson takes a bit less than one. Overall, the estimated coefficients on the port indicators for Japan are consistent with those in the U.S. regression, except for our port congestion index with a somewhat stronger impact on total transport costs.

Table 3 shows that _ad valorem_ trade costs are generally higher in Japan than in the U.S., except for imports from Indonesia and Australia. Due to the difference of the compositions of the imported commodity and modal aggregation in the data, we cannot directly compare the regressions between the U.S. and Japan. However, the comparison of the values of the explanatory variables in the regressions may give several possible explanations. For example, for the _ad valorem_ trade costs between the export and import of the pair of the United States and Japan in 2001-2006, their average difference is 1.65 in terms of natural logarithm. The air shipment ratio recorded 0.5118 for the import of Japan from the U.S., but only 0.2405 for that of the U.S. from Japan. This large gap should contribute about 0.4 (= (0.5118 – 0.2405) x 1.3533) to the difference in trade cost. In addition, the value / weight ratios in log are 4.845 and 4.486 for the U.S. and Japan, respectively<sup>28</sup> . As the elasticity of this ratio, after reflecting the interaction term, is around one, this factor would also contribute about 0.4 (= (4.845 – 4.486) x 1) to the difference. This observation suggests that about 0.8 (= 0.4 + 0.4), about the half of the difference in _ad valorem_ trade cost should be attributed to the difference in transportation modals and composition of imported commodities and their prices. The remaining difference, mainly coming from the difference in the parameters, may be probably due to the preference of Japanese importers to the speed and quality of the transportation, provided by liners, airs and container cargos, for the higher-priced goods.

> 27 The port congestion index is considered here as a real functioning variable, not a proxy of general infrastructure level. This prorating calculation is based on the following data: (i) the value of air shipments takes a 38 percent share in the total imports of Japan; and (ii) the _Ad valorem_ trade costs for air and ocean shipping are 3 percent and 5 percent, respectively, in U.S. imports data.

> 28  The measurement units are adjusted to yen per metric ton.

29
This is known as the non-parametric approach. However, this is highly demanding of the data, bringing about an exponential increase in the number of types with each circumstance variable that is added, and in practice creating an upward bias (Brunori et al. 2018).

In this paper, we use an approach proposed by Brunori et al. (2023) that efficiently trades off these downward and upward biases by using a conditional inference regression tree to partition the sample into circumstance types. A regression tree is an algorithm that splits the data into non-overlapping nodes based on a set of covariates (in this case circumstances) to predict an outcome out-of-sample (in this case consumption). At each branch of the tree, the algorithm will find the circumstance split associated with the largest difference in consumption. This is done using a set of statistical tests to prevent over-fitting: First, the algorithm finds the circumstances that are statistically significantly related to consumption (specifically, with a p-value less than .01 after a Bonferroni correction for multiple hypothesis testing), and then selects the one with the smallest p- value. Next, for that circumstance, it tests all possible divisions to find the one that creates the largest difference in consumption between groups. If the circumstance is continuous or ordered, the algorithm chooses a splitting point. If it is categorical, the algorithm separates values into two groups. This process is repeated until no more splits are statistically significant, or the maximum allowed “depth” has been reached.

Consider the following fictitious example using three circumstances: father’s education, mother’s education, and region of birth. In the first node, the algorithm splits the sample by whether the father has completed secondary education or not. Among those whose fathers have completed secondary, the algorithm decides that the most important factor determining differences in consumption is region, with higher consumption among those born in the Capital or Region 1. At this point, the branch ends because there are no more circumstance variables significantly predicting consumption with a p-value smaller than .01 in this subsample. The left side of the tree demonstrates that circumstances used in previous nodes can reappear – among those whose fathers did not complete secondary and whose mothers completed primary, the most important factor is whether the father completed primary. The “leaves” at the end of the tree represent the 6 types and their average daily consumption . This example is presented using a depth of 3, while in our estimation the maximum depth is set to 10.<sup>11</sup> CĈ A disadvantage of regression trees is that small changes to the data might change the splitting points detected and hence the level of IOp estimated. To circumvent this issue, we use random forests in our preferred specification. These are collections of hundreds

> 11 The parameters used in the estimation are detailed in the Model Appendix. As a default, we used the parameters used by a forthcoming IOp database led by LSE’s International Inequalities Institute.

7
|**Table 2: Confirming Randomization for Contro**|**l Vs Comm**<br><br>Fu|**unication Bloc**<br>ll Sample in B|**ks**<br><br>aseline|Sample|Interviewed a|t Follow-up|
|---|---|---|---|---|---|---|
||Control<br>Mean|Free Cost<br>Difference|Communication<br>Difference|Control<br>Mean|Free Cost <br>Difference|Communication<br>Difference|
|_Listing Variables_|||||||
|In commerce|0.46|0.0254|-0.0316|0.45|0.0424|-0.0648|
|In services|0.50|-0.0108|0.0199|0.51|-0.0112|0.0584|
|Has a sign outside|0.34|-0.0104|0.0102|0.35|-0.0244|0.0355|
|Num. Employees|0.99|0.0511|-0.0533|1.05|0.0766|-0.166*|
|Area (square meters)|43.8|7.293|1.886|41.7|7.735|7.765|
|_Baseline Variables_|||||||
|Owner is Female|0.37|-0.00542|0.0307|0.37|-0.0386|0.01000|
|Owner's Age|44.3|0.172|-0.175|44.3|-0.422|-0.558|
|Owner has primary or lower education|0.17|0.00315|0.00641|0.17|0.0171|0.00751|
|Owner has completed high school|0.42|-0.0319|-0.00110|0.42|-0.0356|0.0365|
|Owner is married|0.58|-0.00277|-0.0291|0.60|-0.0353|0.0103|
|Age of Business (Years)|7.82|0.309|0.150|8.18|-0.255|0.112|
|Hours owner works per week|54.1|-1.565|-1.244|54.6|-1.162|-2.767*|
|Total Number of Employees|1.14|0.154|0.201|1.19|0.200|0.239|
|Keeps Business Records|0.32|0.0574*|0.0728**|0.31|0.00181|0.0675|
|Annual Revenue (Reais)|48595|3,637|12,299|48411|6,002|21,644|
|Monthly Profits (Reais)|1797|159.3|824.3*|1879|107.5|1,264*|
|Owned Capital Stock (Reais)|46319|8,243|13,486|48746|12,875|31,059|
|Claims to have SIMPLES but no proof|0.15|0.0132|0.00256|0.15|-0.00697|-0.0189|
|Claims to be registered with JUCEMG|0.13|-0.00379|-0.00268|0.12|-0.0216|0.00295|
|Claims to have a CNPJ|0.23|-0.0164|-0.0108|0.24|-0.0757*|-0.0281|
|Claims to have an ALF|0.24|0.0419|0.0515|0.26|-0.000496|0.0504|
|Visited by municipal inspector in past year|0.33|-0.0539*|-0.0272|0.36|-0.0592|-0.0288|
|Visited by state inspector in past year|0.06|-0.00700|-0.0287*|0.06|-0.00870|-0.0374*|
|Visited by federal tax inspector in past year|0.03|0.00692|0.00380|0.03|0.00147|0.00595|
|Sample Size|689|328|331|436|192|198|
|Notes: *, **, and *** indicate statistically diff<br>randomization strata and clustering at the blo|erent from<br>ck level.|control mean|at the 10, 5 and 1% le|vels respecti|vely, after con|trolling for|

34
experience grew constantly for women over the two decades. Evaluated at 20 years, returns increased from 0.53 in 1980 to 0.61 in 1992 and 0.73 in 2002.<sup>3</sup> Changes in the distribution of labor market experience among full-time workers are depicted in Table 4. While Argentina experiences a gradual ageing of the population, the distribution of labor market experience remains roughly constant between 1980 and 2002. This fact may be due to longer periods of education.

# **3. Potential Explanations for Changes in Earnings Inequality**

There are several possible explanations for the rise in inequality that has taken place in Argentina in the 1990s, in particular the increase in the returns to education. Candidate explanations include institutional changes, such as changes in the minimum wage and union membership, trade-related changes in production, and skill-biased technological change (SBTC). We briefly consider whether institutional changes or trade may be responsible and then describe the evidence for SBTC in more detail.

## **_Institutional Changes_**

Some have argued that the increase in wage inequality observed in the 1980s in the United States was driven largely by dramatic declines in unionization rates and the real value of the minimum wage. (See, for example, Dinardo, Fortin, and Lemieux 1996.) Although effects of unions and the minimum wage on employment could go in either direction, it is generally expected that unions and the minimum wage reduce wage inequality among the employed by boosting the wages of those in the lower part of the wage distribution.

While data on unionization rates in Argentina is not collected consistently, estimates from household surveys show that union membership among non-agricultural salaried workers in Greater Buenos Aires increased only slightly from 45 percent in the beginning of the 1980s to 49 percent in 1990. Over the next decade, it fell to 42 percent in 2001 (Marshall 2005). The relationship between union membership, policies, and labor market outcomes is complex,

> 3 It is recognized that “potential labor market experience” measured as _age_ minus _years of education_ minus _school entry age_ most likely overstates actual labor market experience more for women than for men due to child bearing and the traditional division of labor in the family. This results in estimated returns which are biased downwards. See e.g. Blau and Kahn (1997).

5