rafmacalaba/datause-encoder-data huggingface dataset:
(World Bank 2020b). By the same token, many people are outside the formal social benefit and tax system, and would not benefit from tax deferments and cuts, or from higher regular social benefits.

**_Macro-financial vulnerabilities in energy exporters._** During the oil price plunge of 2014-16, energy exporters with highly concentrated export and revenues bases, weak fiscal positions, and fixed exchange rates witnessed considerably steeper growth slowdowns. In today’s context, these effects are likely to be more pronounced since there has been limited progress in export diversification, and fiscal positions are weaker than they were before the 2014-16 oil price plunge.

In 2014-16, growth in energy exporters with a higher degree of economic diversification (for example, Bahrain, Ghana, Malaysia, Qatar), and a floating exchange rate regime (for example, Albania, Russia), recovered more quickly from the fall in oil prices than in those with low diversification and fixed exchange rates. Fiscal balances also fared better in energy-exporting EMDEs with more flexible exchange rate regimes, in part because real exchange rate depreciation mitigated revenue declines and spurred needed adjustment within the private sector. Growth remained stronger in energy exporters with larger foreign reserves and low historical inflation volatility (Grigoli, Herman, and Swiston 2017; World Bank 2016a). The need for fiscal adjustment was greater in energy-exporting EMDEs that lacked the necessary buffers (Husain et al. 2015; World Bank 2015b). Energy-exporting EMDEs with higher reliance on oil-related revenues faced a more pronounced deterioration in fiscal balances than in those economies that managed to diversify government revenue away from oil before 2014.

Energy exporters remain highly reliant on commodity exports and have more precarious fiscal positions (Figure 9). In 2019, the energy sector continued to account for 12 percent of government revenues in the average energy-exporting EMDE. Government debt in energy-exporting EMDEs had risen to 50 percent of GDP in 2019 from 27 percent of GDP in 2013, and the fiscal balance has turned from near-balance in 2013 to a deficit of 2.7 percent of GDP in 2019 (IMF 2017a; World Bank 2017a). As a result, even after the public health crisis subsides, the need to shore up public finances is likely to weigh on their recovery.

18
2017.

Define _R_ 0 as the level of reported sales prior to the introduction of VAT rebates and _R_ 1 as the level of reported sales after the introduction of VAT rebates. Define _E_ 0 and _E_ 1 analogously, so that ∆ _E_ is the increase in electronic sales triggered by the VAT rebates. For simplicity, we assume for now that ∆ _E_ = _−_ ∆ _C_ , so the VAT rebates lead consumers to switch from paying in cash to paying by card, but do not affect overall consumption. We are interested in whether _R_ 1 _> R_ 0. Given the above-mentioned audit rule, firms have to report _R_ 1 _≥ E_ 0 + ∆ _E_ after the introduction of VAT rebates. So firms’ reporting behavior will change if _Ro < E_ 0 + ∆ _E_ , that is, if the consumer response ∆ _E/E_ 0 to the VAT rebates is sufficiently large and the share of true sales reported to the government prior to the reform, _R_ 0 _/S_ , is sufficiently low.<sup>2</sup>

# **3 Background and Data**

This section describes the relevant aspects of Uruguay’s tax system, the policy variation generated by the financial inclusion reforms and the data we use.

## **3.1 Tax System**

Firms in Uruguay are liable for an annual corporate income tax (CIT) at 25 percent and remit a monthly VAT. The VAT is levied at a standard rate of 22 percent, with a reduced rate of 10 percent for necessity goods such as basic food products. Large firms which are part of the large taxpayer office called CEDE ( _Control Especial de Empresas_ ) file and pay the VAT monthly. All other firms (henceforth called non-CEDE firms) file the VAT annually, but report output VAT, input VAT and net VAT for each month in their annual VAT declaration.<sup>3</sup> In 2015, there were 4099 CEDE firms and 60,640 non-CEDE firms registered.

Credit and debit card companies in Uruguay report all card transactions of their client firms (i.e. firms using their POS) to the tax authority. The tax authority uses the card transaction reports to cross-check taxpayers’ self-assessment declarations, and to strengthen the credibility of enforcement among taxpayers with discrepancies between self-reported and third-party reported income. B ́ergolo et al. (2018) show that firms in Uruguay perceive the audit probability over a three-year period to be 40 percent, although the true audit probability

> 2Our discussion focuses on revenue reporting, as any change in compliance in our setting should be driven by a change in reported sales. Since there is no evidence for a change in reported sales in response to the VAT rebates, there is no reason for reported costs to change. We thus do not consider cost adjustments.

> 3In Appendix Section A.1, we discuss why firms in simplified tax regimes should not be affected by the VAT rebates we study.

6
the first stage of NSP in 2003 - 2006.<sup>8</sup> This evaluation enabled randomization of NSP across 500 villages spanning 10 rural districts that were sufficiently large to allow for a control group in addition to villages mobilized by the program; contained no villages that were previously mobilized by NSP; and possessed security conditions that would safeguard the well-being of enumerators involved in the administration of household surveys, per human subjects protocols.

Although not a random sample of districts in Afghanistan, these 10 districts are representative of the country’s geographic, ethnic, and economic diversity and cover the western, central highlands, northern, north-eastern, and eastern regions of the country. Using the 2007–08 National Risk and Vulnerability Assessment (NRVA), it is possible to identify any differences between households sampled for the study and a randomly-selected stratified sample of the population of rural Afghanistan. Although there is no significant difference in the age of respondents or income, evaluation villages are more likely to be engaged in production activities related to agriculture, have slightly worse access to medical services and better access to electricity.<sup>9</sup> The magnitude of these differences, however, is quite small.

Importantly for this study, security conditions in the 10 districts are generally representative of those across Afghanistan, with the exception of the south. As shown in Figure 1, the rate of security incidents between January 2006 (a year and a half before the start of the study) and February 2010 (two years and a half after the start of study) are similar for the area around the evaluation villages and for all of Afghanistan excluding the south. Among the 10 districts, two districts in the eastern province of Nangarhar have significantly higher levels of violence and thus provide a basis for inference over the effects of NSP on the reduction of violence in already insecure regions. The other eight districts represent ‘marginal’ areas which may be at risk of increased violence. Collectively, the sample thus provides for the estimation of effects for areas which have already succumbed to the insurgency and for areas which are vulnerable.

> 8 Our evaluation assesses the effects of this bundled development treatment - both the creation of the elected gender balanced local institution and the allocation of funds - on an array of outcomes ranging from security to women’s rights (Beath, Christia, Enikolopov 2012), economic wellbeing and access to services, governance, and state building. Preliminary results for this analysis in the form of a report are available in Beath et. al. (2010) with additional academic papers presently in progress. Results on security were not considered separately in the report since at that time the data on security incidents was not available.

> 9 The differences are likely to be driven by the fact that the villages that are located closer to big cities and provincial centers received NSP between 2003 and 2007, i.e. before the start of the impact evaluation and, are thus, excluded from the analysis.

9
SYS-REVIEW/HI-LMIC outcome measures are different (for example, the time intervals varied); (2) the insurance schemes were different, as outlined in table 2; and (3) the estimations of the impact depended on the functional form or the estimation method and the unit of measure, such as the period in which data were measured.

The unit of measure can shape the results, especially with regard to health expenditures (Das, Hammer, Sánchez-Paramo 2011). It can also dictate different estimation methods. For example, OOP expenditures as a share of income can be modeled through probit, whereas OOP expenditures may be modeled using linear estimation methods. In addition, when magnitudes are reported, they should be understood within the context of the study; magnitudes have limited generalisability outside of a study, even for the same insurance scheme within the same region. Thus, only trends are reported.

# **<<A>>Identification Issues**

Although low enrollment fees should have attracted universal adoption, in most cases, enrollment rates were low. Low enrollment may induce selection effects, and selection into insurance may ultimately affect the outcome. One way that selection may affect outcome is through adverse selection: ill individuals select themselves into insurance at premium levels, which individuals in good health find the premium too costly given their expectation of their health care needs (Rothschild and Stiglitz 1976).<sup>2</sup> The pool of the insured may be sicker than the pool of the uninsured. The expectation of becoming healthy influences the adoption of

> 2 Adverse selection is one of the reasons mandatory insurance is prescribed in many instances. Voluntary enrollment increases the possibility of adverse selection, which is one of the reasons that enrollment fees must be low and the cost of the program must be subsidized.

9
reviewed in this study is presented in the appendix.

# **II MIGRANT CHILDREN**

This section outlines the evidence concerning the impact of migration on migrant children. It reviews areas such as education, health, and early childhood development, and it highlights the critical role of immigration policies.

# **II.A Education outcomes**

Existing work in this area documents lower educational performance and attainment of immigrant children relative to native children. These differences have long-term consequences for immigrants’ economic success, including career choices and wages. Documented drivers of these disparities include higher socioeconomic vulnerability, parental decisions, stereotypes held by teachers, school sorting, and immigration enforcement.

_Immigrants exhibit lower performance relative to hosts_ . Studies consistently indicate that immigrant children tend to perform worse than their native counterparts in terms of school GPA and standardized test scores. The performance gap is more pronounced among boys, whereas girls tend to perform relatively better (B ̈ohlmark, 2008).

Early economic research tested whether the age of arrival plays a crucial role in immigrant children’s education outcomes. The premise was that the age of arrival correlates with a stage of children’s physical and cognitive development in which younger immigrants adapt and learn faster due to the brain’s plasticity at early ages. These studies showed a noticeable decline in performance among children who arrive after a certain age threshold (typically around 7–9 years old); this decline is particularly worse on verbal tests (Bleakley and Chin, 2010). Specifically, B ̈ohlmark (2008) finds a strong negative impact of immigrating after age nine on immigrant children’s GPA in Sweden, and Cahan _et al._ (2001) show that starting at the arrival age of seven, there was a monotonic decrease in performance for immigrant children in Israel between 1952 and 1970, with worse results on verbal tests than on mathematical ones. Similarly, Cortes (2006) shows that the test score gap between immigrant children and second-generation children narrows according to how long the former have been residing in the United States.

Moreover, research on this topic underscores the importance of the critical period for language 5
- The effects by country since 1995 show that, of the 19 countries, 12 show negative impacts of the China effect; Singapore and Taiwan (China) are the most significantly affected. (This is consistent with the fact that the NIEs are moving production to China rather than exporting.)

|**Table 6 Is China Displacing the Exports of Other East Asian Economies?**|
|---|

|_Statistic_|_All products_|_Nonindustrial products_|_Industrial products_|
|---|---|---|---|
|Export supply effect (_diflnimpnonCHN_)|1.176***|1.090***|1.194***|
||[43.70]|[28.91]|[38.67]|
|China export effect (_diflnimpCHN_)|−0.208***|0.102|−0.307***|
||[2.94]|[1.43]|[3.55]|
|Observations|1338229|299056|1039173|
|R-squared|0.35|0.34|0.35|

_Source:_ Freund 2006.

_Note:_ The regressions include export, year, and 4-digit product-fixed effects. The estimates thus rely entirely on cross-market variation in Chinese import penetration in a given product. Robust t-statistics are shown in brackets. For the terms in parentheses, see equation (e) in endnote 18.

*** significant at 1 percent.

<!-- Start of picture text --> Figure 21 Who Is Displaced and Where?<br>a. Where is the displacement b. Which countries are being displaced? a<br>Viet nam<br>Developing countries**<br>Thailand<br>other OECD<br>Taiwan, China<br>EU** Singapore<br>Philippines<br>Japan***<br>Malaysia<br>North America*** Korea<br>-1 -0.5 0 0.5 1 Indonesia<br>estimation coefficient -0.6 -0.4 -0.2 0 0.2 0.4<br>change in export growt h<br><!-- End of picture text --> _Sources:_ Freund 2006; calculations of the author.

a. This is the coefficient on diflnimpCHN in equation (e) in endnote 18 estimated by country for 1995–2004. b. OECD = Organisation for Economic Co-operation and Development.

** significant at 5 percent; *** significant at 1 percent. For example, a coefficient of −0.5 implies that, for a product with a Chinese market share of 10 percent and Chinese export growth of 20 percent, the export growth in East Asia would be reduced by 1 percentage point. Observations: 1,338,229. R-squared: 0.35.

**_China’s competition is mainly in high-wage export products._** Estimations for each 2-digit category separately for the first period and the second period (see above) reveal that 39 of 67 categories (about 60 percent) show negative impacts of Chinese exports in the second period. Of these, 16 are significant (see figure 22). Many of these products that are negatively affected by Chinese export growth tend to be hightechnology products, including electronics, communications equipment, and machinery. The tradeweighted average PRODY for the products in which China is displacing East Asia tends to be high; the trade-weighted average PRODY of this group of products is US$12,205, above the average PRODY of US$11,001 for East Asia.

21
It follows that the standard error of the mean difference for a single price change can be estimated as Similarly, if we wish to estimate the standard error of the mean difference between _CS_ with Cobb-Douglas preferences and _LV_ we can derive the estimation of the standard error as follows Since the estimator of ∆ _CS,LV_ uses two basic estimators ( _μCS_ and _μLV_ ), the delta method can be used to estimate the standard error (see Rao (1973)).

_Single price change_ . We are now in a position to test statistically the means difference between welfare estimation methods at the societal level. To illustrate these tests, we use data from the 1987/88 _Nationwide Food Consumption Survey_ , which is conducted by the United States Department of Agriculture. We simulate price increases between 0 and 100% of _Meats_ and test means differences between _PV_ , _EV_ , _CS_ and _LV_ in turn using 95% confidence intervals. As shown in Figure 15, differences across welfare estimation methods are visible starting from price increases in between 10% and 20% while confidence intervals remain rather narrow until price increases of 100%. For example, with a price increase of 85%, the mean difference between _PV_ and _LV_ is around 8 percentage points but the confidence interval around that difference is a fraction of a percentage point at the 95% confidence level. In other words, confidence intervals do not really put into question the remarkable difference across welfare estimation methods already very evident beyond price increases of 10-20%.

_Multiple price changes_ . Of course, price changes may be more complex than one simple price increase and may include increases as well as decreases in prices. Using the same set of data, assume that the price reform concerns two goods with a decrease of 45% in the price of _Fruits and vegetables_ and an increase between 0% and 100% in the price of _Meats_ . As shown in Figure 16, two main lessons can be drawn. First, the judgment on the impact of the price reform on welfare can depend on the selected measure of welfare change. For instance, for an increase of 60% in the price of _Meats_ , only the _PV_ measurement indicates an improvement in welfare. The second lesson concerns the gray area for which one cannot make any judgment about the nature of the impact. For instance, for the range [83%, 90%] of increase in the price of _Meats_ , the impact measured by _PV_ is not significantly different from 0. In essence, statistical inference can help in determining whether the impact of one welfare measure is significantly different from zero but beyond price increases of 10-20% confidence intervals are too narrow to overlap across measures. Hence, all the empirical results reported for individual welfare effects and related to differences between welfare measures largely apply to mean social welfare.

43
<!-- Start of picture text --> M<br>∑ wuagr k ⋅ weightk<br>w ~ uagr = k = 1 M ⋅ ( 1 + g uagr ) (42)<br>∑ weightk<br>k = 1<br>K<br>*<br>∑ wuagr k ⋅ nweightk<br>~ k = 1<br>w =<br>uagr K<br>∑ nweightk<br>k = 1<br>∑ K exp ⎛ ( α uagr + Δα uagr ) + X uagr * β ˆ uagr + uw ∧ uagr ⎞ ⋅ nweightk (43)<br>k = 1 ⎜⎝ ⎟⎠ k<br>=<br>K<br>∑ nweightk<br>k = 1<br>L<br>∑ wunagr l ⋅ weightl<br>w ~ unagr = l = M + 1 L ⋅( 1 + gunagr ) (44)<br>∑ weightl<br>l = M + 1<br>L<br>*<br>∑ wuagr l ⋅ nweightl<br>~ l = K + 1<br>w =<br>unagr L<br>∑ nweightl<br>l = K + 1<br>L ˆ ∧ (45)<br>∑ exp ⎛ ( α uagr + Δα uagr ) + X unagr β unagr + uwunagr ⎞ ⋅ nweightl<br>l = K + 1 ⎜⎝ ⎟⎠ l<br>=<br>L<br>∑ nweightl<br>l = K + 1<br><!-- End of picture text --> ∧ The unexplained wage _uwunagr_ for those who enter non-agriculture is calculated by taking the unexplained wage from agriculture and multiplying it by the ratio of the standard deviations of the residuals in the non-agricultural and agricultural sectors, respectively. For non-remunerated household members moving into non-agricultural sectors we simulate a residual.

In addition to labor income, we consider transfer and capital income as reported in the PNAD. Transfer income is scaled up or down with the GDP per capita growth rate and capital income with the change in the rental rate from the CGE model. The sum of all 25
TSle 3 shows that, compared to those in wage employment, the self-employed re on average *.z years older, they have completed about ane year less of 0hool, 4 and they earn about the same amount per hour but work eight hours more pec week. Workers in OUAs are about one year older and have completed a year and a half less schooling; they earn 1.25 Intis less per hour and work three hours more per week than workers in Lima.

Table 3 **_AGE,_** S(HXLDM, EARNflM, AND H1AS W)REI _BY T=A.&ON_ AND SIP,

||**Age**in years|Years<br>of schol<br>completed|Hourly<br>earnin8s<br>In<br>June<br>1985 Intis|Hours<br>worked<br>per week|
|---|---|---|---|---|
|Ietropolitan LIM|||||
|1Wge<br>ezployees|33.6 (12.3)|9.2 (3.4)|6.71 (7.8)|48.43 (14.8)|
|Self-etloyed|39.8 (13.3)|8.4 (3.9)|6.37 (6.5)|50.70 (21.4)|
|AUlLimwrkers|35.9 (13.0)|8.9 (3.6)|6.59 (7.4)|49.25 (17.5)|
|Other urban areas|||||
|Wage efployees|33.7 (12.6|) 7.5 (3.5)|4.95 (5.4)|49.51<br>(16.4)|
|Self-eaployed|40.1 (13.3|) 7.2 (3.8)|5.75 (7.3)|50.58<br>(20.4)|
|AU.OU<br>(oUwkers|36.8 (13.3|) 7.4 (3.7)|5.34 (6.4)|49.98<br>(18.5)|
|All urban<br>areas<br>of Per|u||||
|VWa eaployees|33.6 (12.4|) 8.5 (3.6)|5.98 (7.0)|48.87<br>(15.5)|
|Self-eiployed|40.0<br>(13.3|)<br>7.8 (3.9)|6.04<br>(7.0)|50.58<br>(20.8)|
|All urbar<br>workers|36.3<br>(13.2|)<br>8.2 (3.7)|6.01<br>(7.0)|49.59<br>(17.9)|

Note: Means (standard deviaticns in parentheses).

Table 4 shows the number and percentage of workers in each of the samples who finished their education at any particular level and type of school. ;-nly 1.5 percent of urban male workers (13 and 21 individuals, respectively, in the Lima and OUA samples) never went to school at all. About two-thirds (1,477 individuals) and nearly one-fifth (413 individuals) continued their education past the primary and secondary levels, 9
of the 54th month, than with the actual wealth distribution. The effect on sales over the course of the adoption process, on the other hand, is rather small. With an equal asset distribution, simulated sales never exceed predicted actual ones by more than 7 percent. Moreover, when we focus on differences between simulated and predicted actual sales of more than 3%, simulated sales never lead predicted actual ones by more than five months.

According to Figure 6, a similar picture emerges for the dynamics of inequality. While the inverted U contracts by about 20% toward the origin, the change in the general pattern of inequality as measured by the Gini can hardly be judged economically significant.

A second set of simulations investigates two extreme scenarios. The first one assumes that each household in the sample holds only the smallest observed wealth, that is each house is assumed to be worth Rs. 20,000. The second one, in contrast, assumes that each household in the sample holds the highest observed wealth, that is each house is assumed to be worth Rs. 500,000. The results for this set of simulations together with the predicted actual values are set out in Figures 7 and 8. We thus consider situations in which all households are either tightly credit-constrained or virtually do not face a credit constraint at all. The mean income and inequality paths for the first simulation very closely follow the respective paths generated from the actual asset data, which suggests that the observed income pattern accompanying the introduction of the new technology closely resembles a situation in which all households are substantially credit-constrained.

The results for the second simulation, where the credit constraint is released for the entire sample, are more striking. The dotted lines in Figures 7 and 8 suggest that with uniform asset wealth the adoption process is completed in just five months. As a consequence, the village enjoys a substantially higher mean income for about two years by which adoptions in the simulated data lead predicted actual ones. This result suggests that a community in which households face virtually no credit constraints is able to move up the technology ladder much faster than the one investigated by this study. Similarly, only a minor spike remains of the observed pronounced inverted U shape of inequality.

28