rafmacalaba/datause-encoder-data huggingface dataset:
fore provides information on the food security status within households. Furthermore, as is evident from the Table 2, the incidence of poverty was higher among non-CTA adopters (86.55%) than CTA adopters (77.685%). In addition, both the depth and severity of poverty were also higher (60.65% and 53.02%) among non-adopters than the adopters (51.74% and 45.64%). All three poverty measures indicate that poverty was more prevalent and severe among non-adopters compared to adopters. The result reported in the fifth column of Table 2 shows that there is a significant difference between all trade and household characteristics of adopters and non-adopters. The mean differences in per capita income and expenditure of adopters and non-adopters indicates that adopters of CTA are better off than the non-adopters. However, the differences in observed mean outcomes between adopters and non-adopters cannot be attributed entirely to CTA adoption due to the problem of self-selection and non-compliance (Heckman and Vytlacil, 2005; Imbens and Angrist, 1994). The impact of the adoption of CTA on trade and household outcomes is discussed in the next section

# **4.2 The impact on trade outcomes**

Table 3 reports estimates of Equation 3 for the two trade outcomes. The dependent variables are continuous and log transformed while the independent variable of interest is binary, indicating that the CTA adoption would increase the turnover of SSCBT (first panel) and in turn would increase their profit (second panel). Column 1 reports unconditional LATE estimate. This gives descriptive relationship between the CTA adoption and trade outcomes. Column 2 controls for Socio-Demographic characteristics that can influence both the turnover and the profit of SSCBT, Column 3 controls for borders fixed effects and column 4 add the type of product sold fixed effects and finally, the column 5 controls for provinces fixed effects. This allowa us to control non-parametrically for all potentially omitted variables that can vary across borders, type of products and province levels.

All Columns of Table 3 show a positive and statistically significant effect of CTA adoption on trade outcomes, both the daily turnover and the daily profit. The most conservative estimates in Column 5 restrict all variation to within-type of product sold, within borders and within-province. Taking it as the benchmark specification, we find that trading in a cluster leads to a 21% increase in the daily turnover and 31% increase in the daily profit of SSCBT. Given that the mean level of these variables are 181,992.5 CDF (Congolese francs) and 16,881.9 CDF respectively as reported in Table 1, the effects of CTA adoption are sizable. Importantly, CTA adoption not only has an impact on the daily turnover but also shape the daily profit of traders which represents the share of trade activities in the SSCBT’s total income. This suggests that, SSCBT’s shift to CTA 14
55 Table A3: Households further away from the metropolitan centers are more engaged in agriculture, have less assets and irrigated land, are less likely to receive remittances from abroad, but as likely or more likely to receive domestic remittances and disaster relief, albeit at much smaller amounts than those closer to the metropolitan centers

|Distance|Ratio of household emp|loyees engaged in|asset h|oldings|||suppo|rt systems|||
|---|---|---|---|---|---|---|---|---|---|---|
|from 500k<br>city (hrs)|||||||||||
|averages|wage earner|farmer|share of|log(value|Share|average|share|average|share|average|
||||land<br>irrigated|of<br>durable<br>and fixed<br>assets)|households<br>receiving<br>disaster<br>relief|disaster<br>relief<br>received<sup>1)</sup>|households<br>receiving<br>domestic<br>remittances|domestic<br>remittances<br>received<sup>1)</sup>|households<br>with<br>remittances<br>from abroad|remittances<br>received<br>from<br>abroad<sup>1)</sup>|
|< 1 hour|0.51|0.19|0.91|9.73|0.05|2038|0.83|3513|0.13|19303|
|1‐5 hours|0.28|0.53|0.87|9.14|0.08|1013|0.88|2073|0.05|14284|
|5‐10 hours|0.25|0.59|0.72|9.08|0.09|1252|0.79|1741|0.05|16729|
|> 10 hours|0.30|0.50|0.72|9.05|0.12|634|0.85|1626|0.06|12475|
|Total|0.31|0.48|0.82|9.21|0.08|1096|0.85|2172|0.07|16017|

1) average amounts among those who received disaster relief or remittances
**Table 5 Povert** **<mark>y</mark> and Distributional Effects under the BaU and Canal Scenarios**

|WelfareIndicator|Observed<br>(2003)|BaU<br>2014|BaU<br>2020|Canal –<br>BaU<br>(2014)|Canal –<br>BaU<br>(2020)|
|---|---|---|---|---|---|
|Average Real||||||
|Income|2,490|3,219|3,725|6|243|
|Poverty Headcount|Ratio (%)|||||
|Extreme|16.6|12.9|13.3|-0.07|0.0|
|Moderate|36.8|28.3|27.5|-0.08|-0.3|
|Poverty Gap (%)||||||
|Extreme|6.4|5.2|5.4|0.0|0.10|
|Moderate|15.2|11.9|12.0|0.0|0.06|
|Gini Coefficient|56.8|57.7|59.8|0.0|0.7|

* Notes: Authors‘ own estimation using data from LSMS 2003 and the results from the CGE-micosimulation model for Panama. The extreme and moderate poverty lines are equal to 533 Balboas and 953 Balboas, respectively, which correspond to the official poverty lines used by the government of Panama.

Labor reallocation, between agricultural and non-agricultural sectors and, within the latter, between formal and informal activities also plays a relevant role in reshaping income distribution. In the BaU scenario, the process of urbanization mentioned in section 2 continues during the period 2003-2020 with the share of workers in nonagricultural activities increasing from 78.5 percent in 2003 to 82.7 percent in 2014 and finally reaching 84.4 in 2020 (Figure 4). Movement of unskilled workers out of agricultural activities creates pressure for job creation in the non-farm, mainly urban, segment of the economy. If not enough formal jobs are created informality increases and urbanization can thus be followed by a higher incidence of poverty in the non-farm urban centers. This is what may happen in Panama between 2003 and 2014: although overall poverty is reducing, by increasing informality, the process of urbanization reduces average incomes and increases poverty among non-farm informal households. This trend is reversed during period 2014 – 2020 when informality is reduced from 40 percent to 36 percent despite the continuous rural to urban migration of unskilled workers.

The welfare effects discussed so far are those that would take place between 2003 and 2020 in the BaU scenario. Consider now the Canal scenario. Model simulations show that the welfare differentials between the BaU and the Canal scenarios are negligible during the construction phase and rather small during the the first 6 years of the operation phase.

The moderate real income gain of 6 Balboas on average (Table 5) during the construction phase is explained by a rise in wages and a reallocation of workers out of agricultural sectors and into formal activities. As a consequence of the Canal construction, almost a thousand workers move out of agricultural sectors, and more than three thousand abandon informal occupations. All the movers enjoy considerable welfare gains; however, due to the small size of this group the overall distributional effect is negligible as demonstrated by no change in the Gini coefficient and the flat growth 18
beach) and the real income gains due to the public good provision also reduce the volume of taxed goods (as with a tax on labor supply).

Much attention has been focused on the very special cases in which the public good and the taxed private goods are ordinary independents, or are compensated independents (see, for example, Wildasin 1984). As we have shown, the MMCF can be used in the first case, and the MCF in the second. However, it seems desirable to specify a more general specification which allows for any income and substitution effects that may exist, and to choose either the MCF or the MMCF, and then to deal with any required adjustments associated with spending or revenue effects.

Many studies try to assume away substitution and income effects of public good provision by invoking separability between the public good and taxed private goods. However, assuming separability (as in Ballard and Fullerton 1992, p126 and many other studies) is not sufficient to ensure that changes in public good provision have no impact on consumption of taxed goods. While separability places restrictions on compensated substitution effects, it need not eliminate them (Deaton and Muellbauer, 1980, p128). No matter how strong the assumptions imposed on net substitution parameters, the need to include the income effects of public good provision on the demand for taxed goods highlighted by the<sup>χ</sup> Iπ' term in equation (11) means that the impacts of public good provision on the volume of goods subject to tax cannot be ignored under the uncompensated approach.

Examination of equations (6), (7) and (11) also raises questions about the widelyaccepted assignment (see Ballard 1990; Dahlby 2008) of compensated measures of the marginal cost of funds to the case of differential tax changes with constant revenue, and uncompensated measures to the cost of public funds raised for the provision of public goods. Equations (6) and (11) show clearly that either the compensated or the uncompensated MCF can be used to measure the cost of raising public funds for the provision of public goods.

The comparison of (6) with (7) and (11) makes clear that the real distinction between measures for the impact of public good provision on welfare is the choice of measurement approach, rather than of “thought experiment”. Equation (6) measures the welfare impact of public good provision in terms of the compensation to the private sector from outside the economy needed to leave the private sector indifferent to a marginal change in provision of public goods. Equations (7) and (11) measure the impact in terms of the domestic private sector’s 18
the change in monthly profits from one quarter to the next was less than -90 percent or greater than 962 percent, corresponding to the 1<sup>st</sup> and 99<sup>th</sup> percentiles of the distribution of changes in profits. The estimated nominal return on the treatment is 5.9 percent per month after this trimming.

Inflation in Sri Lanka was low over the period studied, with point-to-point inflation of 4.0 percent between March 2005 and March 2006. The second row of Table 2 examines the treatment effect on real profits, where the monthly Sri Lanka Consumers’ Price Index was used to deflate nominal profits.<sup>6</sup> After trimming based on extreme changes in profits, we see that the real monthly increase in profits was 5.7 percent of the treatment amount.

# _Impact on Labor Hours_

This treatment effect represents the increase in real monthly profits from our treatment. The treatment increased capital stock. However, labor hours may have also have been affected if firm owners adjusted their own labor hours, or if the resulting rise in profits caused owners to substitute towards leisure. We therefore empirically investigate the impact of the treatment on labor hours by estimating equation (1), using own labor hours and family worker labor hours as the dependent variables.<sup>7</sup> The treatment is found to have a positive and significant impact on own labor hours: a 10,000 rupee treatment increases own weekly hours by 3.4 hours (t=3.23, p=0.001). There is a small and insignificant negative impact on family labor hours (a -0.7 hour per week effect, p=0.467).

Recall that business profits include the earnings of the firm owner. Hence the increase in real profits from the treatment reflects both the return to the additional capital, and the return to the owner of the additional labor hours worked. To isolate the impact of capital, we need to subtract the implicit wage earned by the firm owner. We estimate the marginal return to own labor by using the baseline data to regress profits on capital stock (exclusive of land and buildings), age of the owner, owner’s education, a dummy for the

> 6 Source: Sri Lanka Department of Census and Statistics,

> <u>http://www.statistics.gov.lk/price/slcpi/slcpi_monthly.htm</u> [accessed February 17, 2007].

> 7 Recall that none of the firms had paid non-family workers in the baseline, and less than 5 percent of firms had non-family workers at any time during the panel survey.

12
in 2016<sup>11</sup> ), the vocational education sector is relatively small as well with only one vocational high schools and 9 academic high schools. In the broader Baoshan Municipality where Tengchong County belongs, only 28 percent of high school enrollment is in vocational schools.

The stakeholder analysis includes both quantitative and qualitative measures and has a broad-based representation. The study team visited an academic school with Grades 6–12, a vocational high school, and a high school targeting minority students. The student surveys collect information on student demographics, academic achievement, career expectations, their attitudes and their parents’ attitudes toward vocational education. The parent survey includes questions on family income, parents’ educational level, career expectations for their children, and their attitudes toward vocational education. The interviews with the educational administrators, principals, and teachers aim to identify the current status and the existing problems of vocational education, while the interviews with the students and parents focus on further exploring their attitudes toward vocation education. Our analysis of the responses from the surveys and the interviews shows the following results.

Most parents and students in Tengchong County preferred pursuing higher education to receiving vocational education, either at the secondary or tertiary level. As shown in Figure 4.6, only 17 percent of the middle school students and 23 percent of the parents surveyed are willing to choose vocational high schools, while only 34 percent of the high school students are inclined to attend vocational colleges. Meanwhile, more than 80 percent of the surveyed middle school students and high school students believe that middle school or high school diplomas alone would be insufficient to prepare them for the labor market (Figure 4.7).

Figure 4.6 Survey results of the inclination for educational choices: middle school students, parents of middle school students, and high school students <!-- Start of picture text --> Middle school students<br>2%<br>Attend college after attending<br>academic high school<br>17%<br>Work after attending academic<br>high school<br>18%<br>65% Work after attending vocational<br>high school<br>It does not matter<br>Parents of middle school students<br><!-- End of picture text --> <!-- Start of picture text --> 3%<br>Attend college after attending<br>academic high school<br>9%<br>Attend vocational high school<br>23%<br>Work after graduation<br>65%<br>It does not matter<br><!-- End of picture text --> 50
statements of affected firms to determine their cash outflows and inflows, as in Gatchev, Pulvino, and Tarhan, (2010) and Dasgupta, Noe, and Wang (2011). We find that the number of potential miracles is further reduced. Of the 6 potential miracles identified in the macro data, only in the case of Argentina, Malaysia, and Thailand, do we find a recovery in corporate sales before a recovery in short-term borrowing. In Indonesia, we find both sales and short-term credit recover at the same time where as in Mexico and Russia we find that while credit begins to recovers, sales still hasn’t recovered, suggesting that these 3 countries are not Phoenix Miracles. Of the 3 non-miracles identified in the macro data, the 2 crisis episodes in Turkey remain non-miracles at the micro level, with corporate sales and short-term credit not only moving together, but also not having recovered. However, Korea appears as a Phoenix miracle in the micro data with sales recovering before a recovery in credit markets.

Third, we find no evidence that the US is a Phoenix Miracle. At the macro level, we find that GDP and Bank Credit (defined as the aggregate amount of assets held by banks excluding vault cash) decline in 2008 but begin to recover simultaneously in 2009. At the firm level, we find that both Sales and Debt in Current Liabilities (aggregated across publicly traded firms in Compustat) recover simultaneously, suggesting that output recovery was not credit-less.

We then focus our analysis at the firm-level to understand the microeconomic foundations of what drives the recovery process in the emerging markets and the US. While the mechanism outlined in Calvo et al. (2006b) suggests that recovery is driven by restoring the stock of short term assets out of self-generated cash flows and discontinuing investments, we find that the recovery is not credit-less and that on average firms substitute short-term credit with long-term external finance either through long-term borrowing or capital issuance. Thus, these potential miracles are miracles in a very restricted sense, if at all, in that firms are not relying on short-term financing but relying on long-term financing. Thus, we find little support for the mechanism in Calvo et al (2006b) that firms finance themselves primarily out of cash saved from cutting investments following a crisis.<sup>6</sup>

> 6 This is broadly consistent with a few of the studies (e.g. Bleakley and Cowan, 2008; 2010) that have examined firm investment patterns in a small sample of crisis episodes to conclude that the pattern of recovery across firms is not correlated with the mechanism suggested by Calvo et al. (2006b). However, there are inherent inconsistencies in these studies with findings that firms dependent on short-term financing engage in both involuntary restructuring at

5
amount of health care received by individual _i_ . For many service indicators, this will simply be a dichotomous variable, taking the value 1 if the individual has used the service in question. For a population _S_ , ranked by socioeconomic status, the concentration curve for _h_ is _CCh(p)_ , where _p_ ∈(0,1) is the fractional rank, or proportion of the population below a certain socioeconomic level. The degree of inequality, measured by the concentration index, _CIh_ , is then defined as: For individual level data this can be calculated as where μ is the mean level of the health indicator, _h_ , and _Ri=i/n_ is the fractional rank of the _i_ th person. Alternatively, the concentration index can simply be calculated in terms of the covariance between _h_ and the rank: This points at a convenient way of estimating the concentration index from micro data (Jenkins 1988; Kakwani, Wagstaff, and van Doorslaer 1997) as the coefficient β in the regression As discussed by Wagstaff and Watanabe (2003), the same approach can be used to test for differences in the concentration index under different ranking variables. Specifically, the difference between two concentration indices _CIh1_ and _CIh2_ , where the respective concentration index is calculated on the basis of different ranking variables ( _Ri1_ and _Ri2_ ) can be computed by means of the regression where ∆ _Ri_ = _Ri1_ - _Ri2_ captures the re-ranking that results from changing the measure of SES, and σ<sup>2</sup> ∆ _R_<sup>is the variance of the difference in rank. Here β</sup> _diff_<sup>is an estimate of</sup><sup>_CI_</sup> _h1_<sup>-</sup>

> 8 The ordinal nature of the ranking variable results in autocorrelation. This can be taken into account by using the Newey-West estimator (Newey and West 1994) to provides reliable estimates of the standard error of the concentration index. Alternatively, the standard error of the concentration index can be calculated using a formula (Kakwani, Wagstaff, and van Doorslaer 1997).

4
<!-- Start of picture text --> Weekly unemployment rate in India<br>Percent<br>35<br>3°asas Lom<br>20<br>is<br>10<br>oO<br>se se Ss se Ss SS se Ss Ss<br>ad x 8 4 e $ v x ¥& % Sx Xv oo<br>— Urban Rural<br><!-- End of picture text --> <!-- Start of picture text --> Google Mobility Report for India<br>Deviation from baseline in Percent<br>40 35<br>20<br>aoe,<br>° 3°asas<br>Vi NAS‘N 20<br>-20 y \<br>|\<br>\ is<br>-40 H<br>H 10<br>-60 iy<br>-80 vyFo Messen oennnereeceos nny — nee . oO<br>“109<br>a-Mar 8-Mar 15-Mar 22-Mar 29-Mar 5-Apr 12-Apr 19-Apr 26-Apr 3-May 10-May ad<br>------- Retail and Recreation |©—— Workplaces —— Residential<br><!-- End of picture text -->
underreporting is thus 9.3%, close to the estimate of Bendek & Lelkes (2011) for Hungary (9%-13%), an estimate of Kiełczewska et al. (2021) for Poland (6%), and the estimate of Paulus (2015) for Estonia (12%). Thus, Romania is not an outlier in the regional context.

**We use the ratio of median imputed tax income ("reported income") to median survey income ("true income") to measure tax (non)compliance.**<sup>**15**</sup> The more significant the difference between the median incomes, the higher the non-compliance with tax laws. This approach assumes that the survey income represents the "true" income earned, while the imputed tax income represents the income that individuals or companies report to the tax authorities. Using this ratio to measure tax compliance/noncompliance has several advantages. First, it provides a straightforward and easy-tocalculate measure that can be used to compare tax compliance across different groups or regions. Second, it is less affected by outliers or extreme values than other measures of income, such as the mean or total income. However, this approach also has some limitations. For example, it assumes that the survey data accurately captures the "true" income earned, which may not always be true. Additionally, the ratio may not capture all non-compliance with tax laws, such as underreporting deductions. We do not use the ratio between mean imputed tax income and mean survey income as the measure of tax (non) compliance because, as discussed above, the mean value of income in imputed data is driven upwards by top incomes not represented in the survey data.

**(measured at median income) seen in transport (32% gap), construction (26% gap), and accommodation and food services (18% gap)** . We report the tax (non)compliance across the sectors of the economy in Table 5. Because the EU-SILC sample size imposes a constraint on the analysis and estimates for smaller sectors of the economy may not be reliable, we discuss only outcomes for branches employing at least 5% of employees in the EUSILC data. Our results show that tax compliance is lower than 100% in all sectors of the economy, and the median imputed tax income is lower than the median survey income, indicating underreporting of income. The lowest tax compliance was observed

> 15 The ratio is expressed as: imputed tax income / survey income.

24