rafmacalaba/datause-encoder-data huggingface dataset:
for the main taxes was introduced signaling trust in taxpayers (Okada, 2002). Usui (2002, p. 6) reports that in 1948 around 70 percent of the taxpayers who were required to fill-out the tax return had made filling mistakes or had not filled the returns at all. The percentage of tax delinquency exceeded 40 percent. After continues reforms tax compliance improved. Okada (2002) points out that at the tax agency made great efforts to provide the public with tax information via television, radio, internet and newspapers, distributing pamphlets and booklets and organizing every November a ―Know-Your-Tax‖ (p. 2) with round-table discussions and essay competitions for students about taxation. It is interesting to see how taxpayers are treated by the tax administration. If taxpayers make obvious errors they are informed and the result of the examination is explained to them, to make them understand the errors. Such procedures indicate that the tax administration in Japan tries to be fair to the taxpayers. Institutions and procedures taxpayers perceive as fair and efficient might have a positive effect on tax morale. Okada (2002) also gives a broad survey on training programs for tax administrators. The central organization is the National Tax College (NTC) including 12 branches nationwide, which work together with academics, among them, many university professors, giving lectures at the college. Such a structure shows that Japan, contrary to its Asian counterparts, invests substantial efforts into developing a highly trained tax administration. Furthermore, the salaries of the tax administrators according to Okada (2002) are slightly higher than for officials engaged in other government sections, which are paid at a level comparable to the private sector. This reduces the incentive for tax officials to demand bribes. Focusing on Asia, Torgler (2004) observes that Japan has relatively high tax morale values within the region.

Feld and Frey (2002a) using a data set of tax authorities‘ behavior in Switzerland (26 cantonal tax authorities), they find that tax authorities of cantons with more direct participation rights treat taxpayers more respectfully and are less suspicious if taxpayers report too low incomes than the authorities in cantons with less direct democracy. On the other hand, non-submissions of tax declarations attract heavier fines. Feld and Frey (2002b) continue using this framework and argue that tax morale is supported or even raised when tax officials treat taxpayers with respect and on the other hand is reduced when the administration considers taxpayers as individuals who have to be _forced_ to pay 22
13 "recovery plan", which sets out the steps to be taken to improve the condition of the species. Internal audits by the U.S. Department of the Interior estimate that the potential direct costs implied by the recovery plans of all listed species are about $4.6 billion.? Since the total available budget falls far short of this figure, all agencies with spending programs must make choices among projects.? During the 1980's, some members of Congress seemingly becaxne concerned that a disproportionate share of these limited conservation dollars were being used to preserve a small number of species. Apparently, there was sufficient interest in this issue to pass an Amendmen **t** o the Act in 1988 requiring FWS to prepare annual reports on the amount of federal and state spending broken down by species. The date. collected by EWS was first published for fiscal year 1989, and has subsequently been published for FY 1990 and FY 19913' Spending from these three years is the nwain object of study in this section. In the following paragraphs, we explain the nature of this data, how it was collected, and what types of spending are and are not included.

The 1988 Amendment specifically charged EWS with maldng a "good faith" effort to calculate all expenditures that were "reasonably identifiable" to an individual spcies. If spending cannot be broken down by species, then it is not included in the final total. Although the definition reasonably identifiable" may seem somewhat imprecise, in practice it seems to cover fairly broad classes of expenditures that are more or less operationally defined. Examples of expenditures usually included are habitat acquisitions designed primarily for a single species, captive breeding programs, operating expenses of wildlife preserves mostly dedicated to a single species, population censuses, and scientific study. Examples of expenditures that are typically not identifiable to a single species are salaries of

# =U.S. Fish **and** Wildlife Service (1990b), p.<sup>7</sup> .

**"Calculating** the total budget available for recovery projects is not straightforward. There are several sources of disretionary finds that can be used for **many** purposes in any year, with biodiversity preservation only one possibility. Any way that it is calculated, however, **the** budget is much less than $4.6 billion.

2<sup>3</sup> he relevant sources are U.S. Fish and Wildlife Service (1990a), **(1991) and (1992a).** We plan to update this dataset to include FY 1992 spending when **FWS** releases this information, but we do not anticipate major changes in our results.
11

# **Figure 3a. Latin American Inflation (1990-2000)**

<!-- Start of picture text --> 60 1000<br>40 100<br>20 10<br>0 1<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>Caribbean (LHS) Central America (LHS) South America (RHS)<br>Inflation (%)<br>Logarithmic Scale (%)<br><!-- End of picture text --> _Note: Weighted regional averages. Source: World Economic Outlook and IFS, IMF (2010)._

**Figure 3b. LAC Inflation, Selected Countries (1990-2000)**

(In Percentage) <!-- Start of picture text --> 836% 1482% 1356% 2655%<br>75<br>50<br>25<br>0<br>Argentina Brazil Chile Colombia Mexico Peru Venezuela<br>1990‐1992 1993‐1995 1996‐1999<br>Source: World Economic Outlook and International Financial Statistics,<br>IMF (2010).<br><!-- End of picture text --> <!-- Start of picture text --> Figure 4a. LAC Budget Balance, Selected Countries (1990-2000)<br>(Percent of GDP)<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>0<br>1990 1991 1992 1993 1994 1995 1 99 6 1997 1998 1999 2000<br>‐5<br>‐10<br>AR BR CL MX PE<br><!-- End of picture text --> _Note: Central government budget. Source: Economist Intelligence Unit (2010)._

**Figure 4b. LAC Public Debt, Selected Countries (1990-2000)** (Percent of GDP)

<!-- Start of picture text --> 75<br>50<br>25<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>AR BR CL MX PE<br><!-- End of picture text --> _Note: Central government debt. Source: Economist Intelligence Unit (2010)._
# **The Evolution of Services Trade Policy Since the Great Recession**

Ingo Borchert (1) Joscelyn Magdeleine (2) Juan A. Marchetti (2) Aaditya Mattoo (3)<sup>**1,2**</sup>

**JEL codes** : F13, F14, F23, L80, O24

**Keywords** : services trade policy, investment, STRI, trade restrictions, quantification

- _(1) University of Sussex_

- _(2) World Trade Organization_

- _(3) World Bank_

> 1 **Acknowledgments** : The authors would like to express their gratitude to all WTO and World Bank colleagues who assisted in the process of preparing this paper, especially Laura Baiker, Giulia Jonetzko and Fatima Anjum Quraishi. Cooperation by the OECD Secretariat, who made available data from the OECD Services Trade Restrictiveness Database (2016 vintage), which was used as source of information for 43 economies in the World Bank-WTO Services Trade Policy Database, is gratefully acknowledged. This paper is part of a World Bank research project on trade in services, supported in part by the Umbrella Trust Fund for Trade and Development.

> 2 **Disclaimer** : This is a working paper, and hence it represents research in progress.  The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.  They are not intended to represent the positions or opinions of the WTO or its Members and are without prejudice to Members' rights and obligations under the WTO. They also do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, the Executive Directors of the World Bank, the governments they represent, or any of the aforementioned individuals. Any errors are attributable to the authors.
25 <!-- Start of picture text -->
-<br><!-- End of picture text -->

T. t 6 Rural and urbani poverty in thie 1980s

||_Rural_<br>_population_<br>_as_<br>_percentage_|_Rural_<br>_poor_<br>_as_<br>_percentage_|_Infan_<br>_mortal_<br>_(per th_<br>_live bi_|_t_<br>_ity_<br>_ousand_<br>_rrhs)_|_Acces_<br>_safe_<br>_(percenca_<br>_popul_|_sto_<br>_water_<br>_ge_<br>_of_<br>_ation)_|
|---|---|---|---|---|---|---|
|_ionaidcotllriry_|_of_<br>_total_|_of_<br>_total_|_Rural_|_Urbao_|_Rural_|_Urban_|
|....:)-_S.3h113raiiAlrica_|||||||
|.U)ed'Ivoire|57|86|121|70|10|30|
||65|80|87|67|39|93|
||80|96|59|57|21|61|
|,Ais__.I_|||||||
|india|_77_|79|105|57|50|76|
|.ndoieesia|73|91|74|57|36|43|
|"..alavsia|62|80|||76|96|
|Philippines|60|67|55|42|54|49|
|<br>i!,ailand|70|80|43|28|66|56|
|_aJrn_<br>_A,!Pri_<br>_ca_|||||||
|ku.itemala|59|66|85|65|26|89|
||"xico 3|1<br>37|79|29|S1|79|
|Pa'.nama|50|59|28|22|63|100|
|i"ru|44|52|101|54|17|73|
|'e>nezuela|15|20|||80|80|

So:.arce: Reproduced from World Development 1990
the distribution of elicited WTP.

Of course, some of the patterns discussed earlier indicate that there likely _is_ error in individual choices; such as MSB, dominated choices (e.g., choosing not to buy a voucher that can be exchanged for cash), and implausible WTP distributions, such as a mass of never-switchers at both extremes of the MPL. Formally, the subject will weakly prefer option A if The terms _εijmt_ describe any disturbance to the preference for option _j_ in choice _t_ of MPL _m_ . Because of this term, the subject may sometimes choose option A even though _WTPi < vBt − vAt_ and option B even though _WTPi > vBt − vAt_ . This could lead to multiple switching as well as randomly switching “too early” or “too late”. Going forward, we will use the value difference term ∆ _t_ = _vAt − vBt_ . This is the inverse of the difference in values on the right hand side above, and so subjects will tend to choose option A when ∆ _t_ is large and option B when it is small (incl. negative).

Potential reasons for individual error, _εijmt_ , include inattention and incomprehension. For example, Yu et al. (2021) have shown in the case of risk preferences that greater attention reduces MSB in final MPL choices. Cason and Plott (2014) also provide evidence that unexpected behavior in a BDM occurs when subjects do not understand the payoff structure of the game created by the experiment, leading to behavior that looks irrational. Incomprehension may be a particular concern in populations with low literacy and numeracy. For example, Dave et al. (2010) have shown that subjects with a math ability score more than one standard deviation below the sample mean make inconsistent choices about four times more often than do other participants. On the other hand, Allcott and Kessler (2019) report only 2.4% inconsistent choices in a mail-in survey with US energy customers.

Note that Andersen et al. (2008) have argued that choices that look inconsistent simply indicate that the subject is indifferent between a range of options. Complete indifference should be a knife-edge case if the MPL is well constructed and choices are realized, but the subject may still care little about the offered payoffs compared to, for example, the time it takes to go through them carefully, and therefore choose not to incur the attentional 10
We test in column 3 whether these gendered survey impacts vary across seasons when the self-report indicator interacts with the gender and agricultural seasons indicators. The magnitude of the coefficient on this interaction term is non-negligible and, as expected, higher in the main rainy season (2.3 percentage points, or 5%) and harvesting season (3.7 percentage points, or 8%) relative to the short rainy season, suggesting that the differential child/proxy discrepancies in child labor statistics for girls vary across seasons. However, we do not have enough power to reject the equality of these interaction coefficients as shown by the p-values at the bottom of the table.

The panel at the right of Table 3 depicts results at the intensive margin of work. While in column 4 we observe meaningful overall child/proxy discrepancies in reporting of worked hours equal to 1.99hr, or 11% from the mean hours reported by the proxy, we do not observe statistically significant differential survey impacts for girls relative to boys (column 5), or differential gendered impacts that vary across seasons (column 6).

Turning our attention to the 7-day recall period in Table 4, column 2 shows that girls have a statistically significant 5.5 percentage points higher discrepancy in the participation of farm activities report compared to boys, or a 10.3% higher rate of participation than the mean participation obtained from the proxy. Likewise, column 3 shows that the higher child/proxy discrepancies in reporting for girls that that for boys increases in the harvesting season for 5.7 percentage points (10.7%), relative to the short rainy season. Moreover, as the 30-day recall results, the point estimates for the interaction of self-report indicator, child’s gender, and season indicator are somewhat larger in the harvesting season than in the short rainy season but it is not measured with statistical precision. Furthermore, by looking at the intensive margin of work for the 7-day recall period in columns 4-6 in Table 4, we observe the same patterns concerning the 30-day recall period. We do not observe meaningful differential self/proxy discrepancies in child labor reporting 19
# Policy Research Working Paper **Series**

|**Title**|**Author**|**Date**|Contact<br>**for paper**|
|---|---|---|---|
|WPS2085 Monitoring Banking Sector Fragility:<br>A Multivariate Logit Approach with<br>an Application to the 1996-97<br>Banking Crises|Asli DemirgOQ-Kunt<br>Enrica Detragiache|March 1999|K. Labrie<br>31001|
|WPS2086 Deregulating Technology Transfer<br>in Agriculture: Reform's Impact<br>on Turkey in the 1980s|David Gisselquist<br>CarlPray|March 1999|L. Tabada<br>36896|
|WPS2087 Male-Female Differences in Labor<br>Market Outcomes during the Early<br>Transition to Market: The Case of<br>Estonia and Slovenia|Peter F. Orazem<br>Milan vodopivec|March 1999|S. Faion<br>38009|
|WPS2088 Expropriation of Minority<br>Shareholders: Evidence from<br>East Asia|Stijn Claessens<br>Simeon Djankov<br>Joseph P. H. Fan<br>Larry H. P. Lang|March 1999|R. Vo<br>33722|
|WPS2089 Corporate Diversification in East Asia<br>The Role of Ultimate Ownership and<br>Group Affilication|: Stijn Claessens<br>Simeon Djankov<br>Joseph P. H. Fan<br>Larry H. P. Lang|March 1999|R. Vo<br>33722|
|WPS2090 Risks, Lessons Learned, and<br>Secondary Markets for Greenhouse<br>Gas Reductions|Donald F. Larson<br>Paul Parks|March 1999|P. Kokila<br>33716|
|WPS2091 Beyond Unequal Development:<br>An Overview|Andr6s Solimano|March 1999|A. Solimano<br>87671|
|WPS2092 What Does Aid to Africa Finance?|Shantayanan Devarajan<br>Andrew Sunil Rajkumar<br>Vinaya Swaroop|<br>March 1999|C. Bernardo<br>31148|
|WPS2093 Free Entry in Infrastructure|David Ehrhardt<br>Rebecca Burdon|March 1999|R. Bartolome<br>35703|
|WPS2094 What Triggers Market Jitters?<br>A Chronicle of the Asian Crisis|Graciela L. Kaminsky<br>Sergio L. Schmukler|April 1999|S. Holt<br>37859|
|WPS2095 Ten Years of Post-Socialist Lessons<br>for Policy Reform|Grzegorz W. Kolodko|April 1999|J. Prochnow-Walker<br>37466|
|WPS2096 Reforming the Urban Transport<br>Sector in the Rio de Janeiro<br>Metropolitan Region: A Case Study<br>In Concessions|Jorge M. Rebelo|April 1999|A. Turner<br>30933|
in addition to household size, the number of adults in the household, the education level of the household head and asset ownership levels can induce systematic differences between different measures of household consumption.

To test whether any potentially important heterogeneities are overlooked by our preferred specification, we allow the linear relationship between CPHS and NSS consumption to vary by these household characteristics. We consider six binary household level indicators: households with more than three adults, households with at least one member with a high level of education, household head with over primary levels of education, households with agriculture as the primary industry, Hindu households, and households that belong to schedule caste, schedule tribe or other backward classes. Each of these will be combined with the rural-urban indicator, such that four different linear relationship are estimated for each of these six cases.

Figure 19 plots the headcount poverty rate at the $1.90 line for each of the six specifications -- each accounting for a different choice of heterogeneity (labeled as the “heterogeneous” series). The “homogenous” series refers to our main specification that only accounts for heterogeneity between rural and urban India. All six specifications, each accounting for a different form of heterogeneity, produces similar levels and trends in headcount poverty than the estimates obtained with our preferred specification. The one outlier is the headcount estimate obtained for 2018 that accounts for heterogeneity in household head literacy.

We can further check the robustness of our imputation model of approach 1 by estimating poverty in 2004 and comparing it to the actual estimates for the year. This “back casting” exercise generates poverty figures for 2004 based on the estimated coefficients in Table 2 and imputing consumption for 2004 based on the NSS consumption round for the year. The back casted estimates can also help compare our approach to those of Newhouse and Vyas (2019) and Edochie, et al. (2022). As all three papers use the same training and validation dataset (NSS-2011 and 2004 respectively), these comparisons can reveal the accuracy of prediction across papers.

41
<!-- Start of picture text --> 0 1 2 3 4 5<br>Log of Distance from Frontier<br>1993 1998 2002<br>.8<br>.6<br>.4<br>.2<br>Kernel Density Distribution<br>0<br><!-- End of picture text --> Figure 3: Distance From Frontier (Source: INEGI, Aguascalientes) To conclude this section we want to discuss the importance of the liberalization process under NAFTA. During the 1990s Mexico underwent a process of deep integration with its the North-American economies. This process marked the completion of a liberalization already started during the second part of the 1980s. However, it is important to notice that, differently from the unilateral liberalization of the 1980s, the liberalization under NAFTA locked in Mexican policy makers much more than the previous reforms because of the credibility imposed by an agreement with a powerful counterpart. Therefore, when considering the scope of the liberalization under NAFTA we need to take into account also the importance of this credibility effect (Tomz 1997, Tornell and Esquivel 1995). In this perspective, NAFTA implied a deeper liberalisation than the drop in the average tariffs would suggest. would appear from just looking at the drop in the average tariff. Furthermore in figure 2 we show that the NAFTA tariffs<sup>12</sup> drop was not negligible, while the average tariff went down from about 16% in 1993 to less than 5% in 2002, the tariff peak was reduced from about 70% in 1993 to about 20% in 2002.

> 12Tariff rates for commodities produced in US or Canada.

8
on the interaction effects and coefficients of the other constraints. In the face of uncertainty about these effects, Hausmann, Rodrik, and Velasco recommend a practical approach based on removing the most binding constraint, with the “most binding constraint” defined as the one with the largest effect where issues of second-best effects are likely to be minimal.

## **2.4 This Study’s Contribution to the Literature**

In this paper, based on the HRV approach, we investigate the most binding constraint on the growth of firms, with the “most binding constraint” defined as the one with the largest estimated coefficient across all models and across regions and sectors. Compared with studies using the World Business Environment Survey data set, our paper uses a much larger sample. And while other studies use subjective firm responses as measures of the business environment at the firm level, we also include objective measures, in part to deal with endogeneity and in part to avoid measurement errors of perceptions at the country level.

In exploring the relationship between the business environment and firm growth, we go beyond distinguishing effects by firm size. We look closely at the effect of financial access variables—loan, credit constraint, sales credit, and external investment finance—on firm growth by firm size and age in different sectors and regions. We combine multiple financial access variables in a single regression in addition to evaluating the effect of each variable on employment growth controlled for firm size, age, and other characteristics. This allows an understanding of the impact of each dimension of finance on firm growth as firm characteristics change.

Moreover, our paper emphasizes which element of the business environment matters most for firms, especially for small firms. And it analyzes how different financial access variables affect the firm size distribution across regions and sectors.

# **3. Data**

In this paper we use a newly available firm-level data set from the World Bank Enterprise Surveys. The surveys cover more than 100,000 firms across more than 120 economies and six regions during 2006–10. We use a sample of 39,538 firms in 98 countries for which data are complete. The unit in the sample is the establishment; one firm may have more than one establishment. For simplicity, we use the term firms throughout the paper, though the analysis is based on establishment data.

Our outcome variable of interest is employment growth, measured by the number of permanent employees. Our policy interest is in understanding the determinants that are important to the longterm business operation and employment growth of firms.<sup>2</sup> Because there are no data on temporary

> 2 Like other researchers, we use employment growth rather than sales growth, for several reasons. Sales growth is more volatile and is also more prone to reporting and measurement biases, especially when survey respondents are reporting sales realized three years before. Moreover, for tax reasons, firms may not choose to report actual sales.

6