rafmacalaba/datause-encoder-data huggingface dataset:
6

- In the conventional approach, the only acceptable regional consumption tax is generally thought to be a simple single-stage (preferably retail) sales tax (RST) levied directly on final (resident) consumers, along with perhaps a few excise taxes (Martinez- Vazquez, Rider and Wallace 2008). The virtually worldwide replacement of single-stage sales taxes by multi-stage value-added taxes (VATs) in recent years (Bird and Gendron 2007) thus implies that regional governments have largely been squeezed out of this tax field also.

- Finally, in most countries considerable restrictions are imposed on the extent to which states can tax natural resources -- in part because of concerns with interjurisdictional fiscal equity (natural resources are never equally distributed across the national territory), in part because of conflicting ‘entitlement’ arguments, and in part for stabilization reasons.  Who gets how much and in what form from natural resource taxation is always and everywhere a highly controversial, political and context-dependent matter.  Although this issue is important in a number of countries it is too complex to be further discussed here.<sup>14</sup>

Unfortunately, the usual data sources on governmental finance seldom make clear how much tax ‘freedom’ subnational governments really have. In 2006, for instance, IMF (2008) shows that subnational taxes as a share of total taxes are roughly the same in Canada (52.9%) and China (51.8%). In fact, however, it can be argued that subnational tax power is stronger in Canada -- even in those provinces in which subnational governments themselves collect hardly any of their taxes – than it is in China.<sup>15</sup> Since this point is critical to understanding the key argument of the present paper, it is developed further in Section 2.2.

# **2.2. What Is a “Subnational” Tax?**

In principle, a “totally” subnational tax may be defined as one that satisfies five distinct conditions:

- (1) Subnational governments can decide whether to levy the tax or not.

- (2) They can also determine the precise base of the tax.

- (3) They can decide the tax rate.

- (4) They administer (assess, collect, enforce) the tax.

- (5) They get to keep all the revenue they collect.

In the real world, however, many so-called subnational taxes possess only one or two of these characteristics, with the result that the "ownership" of particular taxes may be unclear.<sup>16</sup>

> 14 For recent treatments of issues with respect to the assignment of natural resource revenues, see Brosio (2006), Collier and Hoeffler (2005) and Davis, Ossowski and Fedelino (2003). 15 Revenues from land assets and financing are substantial own financing sources for subnationall governments in China (Peterson and Kaganova 2010).  Non-tax revenues are not dealt with in this paper. 16 See OECD (1999) for a pioneering discussion of this problem and detailed evidence of the difficulty of classifying many taxes along these various dimensions even in developed countries; a recent update of this discussion may be found in Blochliger and Rabesona (2009).
In this paper, we rely on the same principles as in DM06 and turn our attention to the homogeneity and the comparability of the data. This induces a couple of methodological choices:

- In what follows, the term "source country" usually designates independent states. We distinguish 195 source countries: 191 UN member states, Holy See, Taiwan, Hong Kong, Macao and Palestinian Territories. We aggregate North and South Korea, West and East Germany and the Democratic Republic and the Republic of Yemen. We consider the same set of source countries in 1990 and 2000, although some of them had no legal existence in 1990 (before the secession of the Soviet block, former Yugoslavia, former Czechoslovakia and the German and Yemen reuni...cations) or became independent after January 1, 1990 (Eritrea, East-Timor, Namibia, Marshall Islands, Micronesia, Palau). In these cases, the 1990 estimated stock is obtained by multiplying the 1990 value for the pre-secession state by the 2000 country share in the stock of immigrants (the share is gender- and skill-speci...c).

- The set of receiving countries is restricted to OECD nations. We thus focus on the structure of South-North and North-North migration. Generally speaking, the skill level of immigrants in non-OECD countries is expected to be very low, except in a few countries such as South Africa (1.3 million immigrants in 2000), the six member states of the Gulf Cooperation Council (9.6 million immigrants in Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, Oman and Qatar), some Eastern Asian countries (4 million immigrants in Hong-Kong and Singapore only). According to their census and survey data, about 17.5 percent of adult immigrants are tertiary educated in these countries (17 percent in Bahrain, 17.2 percent in Saudi Arabia, 14 percent in Kuwait, 18.7 percent in South Africa). Considering that children constitute about 25 percent of the immigration stock, we estimate the number of educated workers at 1.9 million in these countries. The number of educated immigrants in the rest of the world lies between 1 and 4 million (if the average proportion of educated immigrants among adults lies between 2.5 and 10 percent). This implies that focusing on OECD countries, we should capture a large fraction of the world-wide educated migration (about 90 percent). Nevertheless, we are aware that by disregarding non-OECD immigration countries, we probably underestimate the brain drain for several developing countries (such as Egypt, Sudan, Jordan, Yemen, Pakistan or Bangladesh in the neighborhood of the Gulf states, Botswana, Lesotho, Namibia, Swaziland and Zimbabwe, etc.). Incorporating data collected from selected non-OECD countries could re...ne the data set. To allow comparisons between 1990 and 2000, we consider the same 30 receiving countries in 1990 and 2000. Consequently, Czechoslovakia, Hungary, Korea, Poland and Mexico are considered as receiving countries in 1990 despite the fact that they were not members of the OECD.

9
- 48 -

if = Foreign interest rate BA = Blocked account EXGS = "Exogenous" growth in the NISA = Uncovered interest parity parameter

# Notes:

1. The free (i.e. black) exchange rate follows P.P.P.

2. Uncovered interest parity parameter proxis for degree of capital mobility. *

- mobility. *

- 3. It is up to the policy maker to choose: NINSFD, e , e, , *, **i9,** id, a.

# Starting and Parameter Values Used in (Final Version of) the Simulation

- A) Initial Values (1990 Billions of L.E. or otherwise indicated):

M =17 Bd = 38 Bl = 51 (USS billion) TB = -7 (US$ billion) NISA = 7.5 (US$ billion) FA = 3 (US$ billion) y = 90.2 e*= 2.85 (L.E. per US$) e = 2.685 (L.E. per US$) = 30% per annum **id** = 16% per annum i= 7.8% per annum BA = 15 ga = 17.05 pa = 93.1

# B) Parameter Values:

e = 0.9 , for all t = 0.8 , for all t = 0.9 , for all t ig = is increased to 8% in FY91 and kept constant thereafter **id** = 25, 20, 15% in FY91-to-FY93, respectively e* = is increased to 3.60 L.E. per US$ in FY91, and kept in line with inflation thereafter n = 0.2 B = 0.2 a = 0.6
# **References**

- **Barwick, Panle Jia, Shengmao Cao, and Shanjun Li** , “Local Protectionism, Market Structure, and Social Welfare: China’s Automobile Market,” _American Economic Journal: Economic Policy_ , forthcoming.

- **Berry, Steven** , “Estimating Discrete Choice Models of Product Differentiation,” _RAND Journal of Economics_ , 1994, _25_ (2), 242–262.

- **, James Levinsohn, and Ariel Pakes** , “Automobile Prices in Market Equilibrium,” _Econometrica_ , 1995, _63_ (4), 841–890.

- **Busse, Meghan, Jorge Silva-Risso, and Florian Zettelmeyer** , “$1,000 Cash Back: The Pass-Through of Auto Manufacturer Promotions,” _American Economic Review_ , 02 2006, _96_ , 1253–1270.

- **Carley, Sanya, Rachel M. Krause, Bradley W. Lane, and John D. Graham** , “Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites,” _Transportation Research Part D: Transport and Environment_ , 2013, _18_ , 39–45.

- **Chetty, Raj, Adam Looney, and Kory Kroft** , “Salience and Taxation: Theory and Evidence,” _American Economic Review_ , September 2009, _99_ (4), 1145–77.

- **Corts, Kenneth** , “Building Out Alternative Fuel Retail Infrastructure: Government Fleet Spillovers in E85,” _Journal of Environmental Economics and Management_ , 2010, _59_ , 219–234.

- **Co ̧sar, A Kerem, Paul LE Grieco, Shengyu Li, and Felix Tintelnot** , “What drives home market advantage?,” _Journal of international economics_ , 2018, _110_ , 135–150.

- **Holland, Stephen, Erin Mansur, Nicholas Muller, and Andrew Yates** , “Are There Environmental Benefits from Driving Electric Vehicles? The Importance of Local Factors,” _American Economic Review_ , 2016, _106_ (12), 3700–3729.

- **Krutilla, Kerry and John D. Graham** , “Are Green Vehicles Worth the Extra Cost? The Case of Diesel-Electric Hybrid Technology for Urban Delivery Vehicles,” _Journal of Policy Analysis and Management_ , 2012, _31_ (3), 501–532.

- **Li, Shanjun, Jianwei Xing, Lin Yang, and Fan Zhang** , “Transportation and the environment in developing countries,” _Annual Review of Resource Economics_ , 2020, _12_ , 389– 409.

16
and Himanshu, 2005). This latter estimate was associated with the absolute number of people living in poverty remaining unchanged during the 1990s.<sup>1</sup> Another issue that commonly hinders the tracking of poverty over time is that consumption surveys are typically conducted only occasionally (particularly in developing countries), and poverty estimates are not available in the intervening years during which surveys have not been implemented. Yet another issue is that collecting, cleaning, and preparing data for analysis can be a protracted process that, at times, can span multiple years from the start of field work to the time when the data are ready for analysis. In all these cases, the challenge can be broadly regarded as one involving missing data: consumption data are available in one period but in the next period(s) are either not available, or are not comparable.

The topic of imputing missing consumption data from one survey to another (i.e., survey-tosurvey imputation) has received some attention in the statistics literature, but relatively little in the economics literature. With a handful of exceptions, the estimation framework utilized by most current economic studies that focus on poverty comparisons appears to be largely based on earlier work exploring the feasibility of survey-to-census imputation by Elbers, Lanjouw, and Lanjouw (2003). This survey-to-census imputation model provides a related, but not perfectly transferable, econometric model for survey-to-survey imputation.<sup>2</sup> It can be contrasted with the multiple imputation (MI) approach discussed in the statistics literature, which has grown rapidly since it was first introduced by Donald Rubin in the late 1970s (Rubin, 1978). Indeed, the widespread availability of a variety of missing data imputation procedures offered in most

> 1 See Deaton and Kozel (2005) for further discussion on this poverty debate in India. See also Christiaensen et al. (2012) and World Bank (2012a) for similar issues compromising the comparability of poverty estimates in Russia and Vietnam respectively.

> 2 Significant differences exist between survey-to-census imputation and survey-to-survey imputation methods. In particular, the former focuses on intratemporal (i.e., same point in time) imputation for producing poverty estimates at lower administrative levels than a survey would reasonably allow, while the latter focuses on intertemporal imputation for poverty estimates at more aggregated population groups. These differences clearly raise distinct econometric issues for each method. We will discuss the relevant studies in the next section on literature review.

3
**-8** -

# (3) The Short-cut Method

This estimation method is based on a simple formula: <!-- Start of picture text -->
*=  S(t*-,)<br><!-- End of picture text -->

where **_Y_** refers to the mean earnings of an individual with the subscripted educational level, _k_ is the higher educational level in the comparison, and AS represents the difference in years of schooling between k and the control group.

Although this method is very easy to use, it is by definition very inferior relative to, any of the other methods described above. The weakness of the method lies in the abstraction from the fact that age-earnings profiles are concave (see Figure 1), and that the discounting process (in estimating the true rate of return) is very sensitive to the values of the early ages entering the calculation. Hence we will not use this method in the empirical analysis that follows.

# **IH.** EMPIRCAL **FmDIGS**

The data used in this analysis come from a series of Household Surveys conducted in the region around 1989. For some countries the same survey was also conducted around 1980. Details on the nature of the surveys appear in Annex 1. Variable definitions appear in Annex 2.
||**Log**|**[Self-repor**|**ted Producti**|**vity (XOF/ha**|**)]-Food crop**|**s**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|Household head has Secondary|0.046|0.018|0.003|-0.005|-0.000|-0.007|-0.013|-0.013|-0.020|
|education|(0.030)|(0.019)|(0.015)|(0.013)|(0.012)|(0.012)|(0.011)|(0.011)|(0.016)|
|Household head has Tertiary education|0.000|0.000|0.000|-0.000|-0.000|-0.000|-0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household head disability|-0.002|0.001|-0.005|0.004|-0.007|-0.012|-0.022|-0.025*|0.013|
||(0.020)|(0.016)|(0.015)|(0.014)|(0.014)|(0.013)|(0.013)|(0.015)|(0.011)|
|Dependency ratio|0.016|0.050|0.042|-0.019|-0.056|-0.053|-0.063|0.019|0.042|
||(0.104)|(0.080)|(0.067)|(0.069)|(0.066)|(0.069)|(0.066)|(0.070)|(0.096)|
|Household Male Labor Use (Number)|0.068|0.024|0.063|0.006|-0.034|0.008|-0.001|-0.034|-0.050|
||(0.058)|(0.042)|(0.041)|(0.036)|(0.033)|(0.035)|(0.037)|(0.041)|(0.052)|
|Household Female Labor Use|0.023|-0.092|-0.183*|-0.002|-0.023|-0.007|-0.048|-0.116|-0.240*|
|(Number)|(0.148)|(0.130)|(0.110)|(0.107)|(0.095)|(0.102)|(0.101)|(0.121)|(0.144)|
|Household Child Labor Use (Number)|0.001|0.015|0.003|0.012|0.006|0.019|-0.011|-0.008|0.019|
||(0.021)|(0.018)|(0.016)|(0.013)|(0.016)|(0.018)|(0.017)|(0.022)|(0.016)|
|Hired Labor Use (Number)|0.001|-0.010|-0.004|-0.013|-0.002|-0.012|0.003|0.025|0.015|
||(0.024)|(0.023)|(0.024)|(0.027)|(0.024)|(0.024)|(0.023)|(0.021)|(0.026)|
|Number of Fields Managed by the|0.395|0.119|0.130|0.079|-0.122|-0.349|-0.296|-0.186|0.426|
|<br>household|(0.322)|(0.247)|(0.233)|(0.220)|(0.236)|(0.277)|(0.271)|(0.340)|(0.262)|
|Total number of crops produced|-0.280|-0.071|-0.147|-0.388*|-0.029|0.351|0.270|0.127|-0.333|
||(0.378)|(0.322)|(0.269)|(0.225)|(0.230)|(0.265)|(0.264)|(0.326)|(0.263)|
|Household used any purchased seed|-0.007|-0.014|0.033|0.031|0.029|0.005|-0.013|-0.014|-0.041|
||(0.033)|(0.034)|(0.028)|(0.023)|(0.018)|(0.019)|(0.018)|(0.022)|(0.029)|
|Plots that Use Chemical Fertilizer (%|-0.027|-0.016|-0.021|-0.008|-0.008|0.006|0.024*|0.009|0.018|
|<br>of total)|(0.018)|(0.015)|(0.013)|(0.013)|(0.012)|(0.016)|(0.014)|(0.016)|(0.022)|
|Plots that Use Pesticide (% of total)|0.007|0.038|0.008|0.016|0.007|0.012|-0.016|0.003|-0.044|
||(0.025)|(0.026)|(0.021)|(0.021)|(0.018)|(0.018)|(0.018)|(0.023)|(0.031)|
|Constant|1.171*|0.896|1.671***|1.467***|0.427|0.591|0.672|0.725|1.146*|
||(0.684)|(0.614)|(0.488)|(0.453)|(0.428)|(0.452)|(0.442)|(0.463)|(0.687)|
|Observations|4601|4601|4601|4601|4601|4601|4601|4601|4601|

43
**Table 2:** Extortion Predicts Higher Civic Engagement

||(1)<br>Civic Index|(2)<br>Protest|(3)<br>Community Mtg|(4)<br>Volunteer|(5)<br>Party|
|---|---|---|---|---|---|
|Extortion|0.94<sup>_∗∗_</sup>|0.42<sup>_∗_</sup>|0.26<sup>_∗_</sup>|0.25<sup>_∗_</sup>|0.17|
||(0.32)|(0.20)|(0.11)|(0.11)|(0.22)|
|Detained at Border|-0.10|-0.13|0.02|-0.03|-0.09|
||(0.32)|(0.19)|(0.12)|(0.11)|(0.20)|
|Round 3|-0.14|-0.56<sup>_∗∗∗_</sup>|0.28<sup>_∗∗_</sup>|0.15<sup>+</sup>|-0.14|
||(0.24)|(0.14)|(0.10)|(0.09)|(0.14)|
|Female|0.15|0.19|0.00|-0.05|-0.09|
||(0.50)|(0.26)|(0.17)|(0.18)|(0.26)|
|Age|0.02|0.00|0.01|0.01|-0.00|
||(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Indigenous|-0.21|0.01|-0.02|-0.22<sup>+</sup>|0.26|
||(0.32)|(0.18)|(0.12)|(0.12)|(0.19)|
|Years of Education|0.01|-0.01|0.01|0.01|-0.02|
||(0.03)|(0.02)|(0.01)|(0.01)|(0.02)|
|Tattoo|0.37|-0.07|0.16|0.20|0.05|
||(0.78)|(0.33)|(0.30)|(0.30)|(0.38)|
|Children in U.S.|-0.02|-0.18|0.13|0.01|0.23|
||(0.41)|(0.23)|(0.13)|(0.15)|(0.25)|
|Children in G.T.|-0.18|-0.19|0.02|-0.01|0.27|
||(0.32)|(0.21)|(0.12)|(0.11)|(0.22)|
|Assets Left in U.S.|-0.20|-0.10|0.03|-0.15|0.27|
||(0.39)|(0.27)|(0.15)|(0.13)|(0.29)|
|ln(Time in U.S.)|0.17<sup>_∗_</sup>|0.12<sup>_∗_</sup>|0.02|0.03|0.00|
||(0.09)|(0.05)|(0.03)|(0.03)|(0.05)|
|Employed|0.12|0.22|-0.08|-0.00|-0.12|
||(0.28)|(0.16)|(0.11)|(0.10)|(0.17)|
|Constant|10.67<sup>_∗∗∗_</sup>|3.58<sup>_∗∗∗_</sup>|3.60<sup>_∗∗∗_</sup>|3.69<sup>_∗∗∗_</sup>|2.73<sup>_∗∗_</sup>|
||(1.66)|(0.79)|(0.64)|(0.62)|(0.90)|
|Observations|513|516|513|515|513|

Standard errors in parentheses Robust standard errors clustered by respondent + _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

# **5.1 Mechanisms**

Here, we explore the two possible pathways discussed above through which the experience of extortion may affect political behavior: economic hardship and fear of crime. First, we test our expectation that extortion will cause both worse economic hardship and increased fear of further crime victimization. It is important to note that migrants who were extorted were no 18
If deportees are more likely to join the informal sector, formal firms can still be affected by, for example, facing higher competition from informal economic activity. In such scenario the effects on the formal businesses can be negative on firms’ behavior. Given that we do not observe the outcomes of informal firms, it is not possible to test if repatriation inflows affect their outcomes directly.<sup>10</sup> We can only test the existence of indirect effects on formal firms that face stronger competition from informal ones.

# **III Local Context: Repatriations to El Salvador**

Migration flows from El Salvador to North America have been high since the beginning of El Salvador’s civil in the early 1980s. Most migrants from El Salvador seek to reach and settle in the United States, where many of them have communities and networks they can rely upon to support their assimilation into the country (Contreras, 2019).

After the United States approved the Illegal Immigration Reform and Immigrant Responsibility Act of 1996 and strengthened immigration enforcement following the September 11 attacks of 2001, mass repatriations from the United States to El Salvador increased dramatically (see Figure 1). In fact, total deportations to El Salvador from the United States increased more than five times between 1995 and 2018. Considering that approximately 50 percent of the repatriations to El Salvador come from the Unites States (see Figure 2), the higher levels of enforcement in the United States were translated into large increases in the overall repatriation flows to El Salvador.

Although Salvadoran migration abroad has been constant since the second half of the twentieth century, the composition of migrants has changed over time. Most of the migrants who arrived in the United States in the 1980s and 1990s tended to be civil war refugees. The individuals who migrated during this period successfully settled in the United States, albeit their legal status has always been under question (Menj ́ıvar, 2000). Since the 2000s, the migrants are predominately

> 10The main household survey in El Salvador ( _Encuesta de Hogares y Prop ́ositos M ́ultiples_ , EHPM), contains information on whether individuals’ jobs are formal or informal. However, it is representative of only 55 municipalities.

7
practices are not shown in the table because we could not obtain useful data. This is because few non-participants understood our questions about production management.

After the training, the percentage of firms in the control group keeping records increased by only 6 percentage points whereas the increase was 36 points in the treatment group. Similarly, the adoption rates of the other two practices (i.e., keeping and analyzing records) increased much more in the treatment group than in the control group. These results indicate that the training had strong impacts on the adoption of the recommended practices. Another noteworthy point is that well over one third of the treatment group firms did not adopt the recommended practices. The variance of each adoption variable within the treatment group increased after the training because the variance of the dummy variable increases as the mean approaches 0.5.

The right side of Table 3 reports the data on annual sales revenue, value added, and gross profit by treatment status. Gross profit here is defined as sales revenue minus material cost and labor cost. Because the majority of firms did not keep records, we estimated these financial variables by asking each entrepreneur about the number of pieces sold and their prices by product type, material inputs and material prices, payments to subcontractors, and payments to workers and apprentices. We checked that the estimate of gross profit was consistent with the entrepreneur’s earnings, investment, living expenses, purchase of durable goods, and so on.<sup>10</sup> Written records, whenever available, were used deliberately, taking into account that each entrepreneur might have his own unique concept of costs and that his calculation might be incorrect.

The trend of declining profitability is visible in Table 3. Some respondents said definitely that this trend was set by the proliferation of competitors within the cluster, and that it was being worsened by massive imports of similar products from Asia and 13
Table 7. Macroeconomic and fiscal indicators during government debt booms

||World|Advanced<br>economies|EMDEs|
|---|---|---|---|
|**Macroeconomic outcomes**||||
|Output growth|2.2|1.6|2.4**|
|(Percent)|[0.3, 3.9]|[0.2, 2.8]|[0.4, 4.1]|
|Investment growth|1.4|0.9|2.0**|
|(Percent)|[-1.7, 4.4]|[-1.5, 2.1]|[-1.8, 6.3]|
|Consumption growth|2.2|1.4|2.8***|
|(Percent)|[0.4, 3.9]|[0.3, 2.7]|[0.4, 4.6]|
|**Debt sustainability**||||
|Primary balance, initial|0.4|1.2|0.0**|
|(Percent of GDP)|[-1.1, 2.3]|[-0.5, 2.7]|[-1.5, 2.1]|
|Primary balance, changes|-0.5|-0.6|-0.4|
|(Percentage points of GDP)|[-1.1, -0.1]|[-1.1, -0.1]|[-1.1, 0.0]|
|Sustainability gap, initial|1.7|1.7|1.7|
|(Percent of GDP)|[0.2, 4.4]|[0.4, 3.6]|[0.1, 4.8]|
|Sustainability gap, changes|-1.1|-0.9|-1.2|
|(Percentage points of GDP)|[-2.1, -0.4]|[-1.6, -0.4]|[-2.5, -0.4]|
|Interest rate-growth differential, initial|-2.5|-1.3|-4.2***|
|(Percentage points)|[-6.6, 0.1]|[-3.5, 0.9]|[-8.9, -0.3]|
|Interest rate-growth differential, changes|0.9|0.4|1.4***|
|(Percentage points)|[0.2, 2.2]|[0.0, 1.0]|[0.3, 3.6]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across all government debt booms in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. ** and *** denote that medians are statistically significantly different between advanced economies and EMDEs at the 5 percent and 1 percent levels, respectively.

27