rafmacalaba/datause-encoder-data huggingface dataset:
||**Appendix T**|**able 7: Household size at ea**|**ch follow-up and change fro**|**m baseline.**|
|---|---|---|---|---|
||**PAB**|**6 months**|**12 months**|**18 months**|
|||_(change since baseline/% change) _|_(change since baseline/% change) _|_(change since baseline/% change)_|
|**Household s**|**ize**||||
|Control|5.95|5.55<br>_(-0.40/-6.7%)_|5.61<br>_(-0.34/-5.7%)_|5.44<br>_(-0.51/-8.6%)_|
|Treatment|5.97|6.24<br>_(0.27/4.6%)_|6.28<br>_(0.31/5.2%)_|6.34<br>_(0.37/6.2%)_|
|**Members a**|**ged 0-5**||||
|Control|1.11|1.09<br>_(-0.03/-2.2%)_|1.09<br>_(-0.03/-2.4%)_|1.08<br>_(-0.03/-2.7%)_|
|Treatment|1.10|1.20<br>_(0.10/9.4%)_|1.22<br>_(0.12/11.1%)_|1.25<br>_(0.16/14.3%)_|
|**Members ag**|**ed 6-17**||||
|Control|1.76|1.47<br>_(-0.29/-16.7%)_|1.56<br>_(-0.20/-11.3%)_|1.50<br>_(-0.26/-15.0%)_|
|Treatment|1.72|1.95<br>_(0.23/13.2%)_|2.03<br>_(0.30/17.5%)_|2.05<br>_(0.33/18.9%)_|
|**Members ag**|**ed 18-59**||||
|Control|2.83|2.76<br>_(-0.06/-2.2%)_|2.73<br>_(-0.10/-3.5%)_|2.65<br>_(-0.18/-6.3%)_|
|Treatment|2.87|<br>2.79<br>_(-0.08/-2.7%)_|<br>2.74<br>_(-0.13/-4.5%)_|<br>2.76<br>_(-0.11/-3.8%)_|
|**Members ag**|**ed 60+**||||
|Control|0.25|0.23<br>_(-0.02/-7.3%)_|0.23<br>_(-0.02/-6.1%)_|0.21<br>_(-0.04/-16.6%)_|
|Treatment|0.28|0.30<br>_(0.02/7.1%)_|0.30<br>_(0.01/5.0%)_|0.28<br>_(-0.01/-1.8%)_|

_Notes: Cells show the average household size and its components per group for the matched sample; figures in parentheses show the nominal and percentage change since baseline._ 48
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**Table 2: Model Predictions vs. Official Estimates**

|**Nationalpoverty**|**2008**|**2009**|**2010**|
|---|---|---|---|
|Direct estimates (HBS)|0.061|0.069|0.092|
|Imputation using: 2008 model|**0.065**|**0.065**|0.076|
|Imputationusing: 2009model|**0.070**|**0.069**|**0.082 **|
|Imputation using: 2010 model|0.078|**0.077**|**0.092**|
|**Urbanpoverty**|**2008**|**2009**|**2010**|
|Direct estimates (HBS)|0.05|0.049|0.057|
|Imputation using: 2008 model|**0.051**|**0.048**|**0.053**|
|Imputationusing: 2009model|**0.051 **|**0.048**|**0.055**|
|Imputation using: 2010 model|**0.059**|**0.053**|**0.060**|
|**Ruralpoverty**|**2008**|**2009**|**2010**|
|Direct estimates (HBS)|0.075|0.096|0.136|
|Imputation using: 2008 model|**0.083**|**0.089**|0.105|
|Imputationusing: 2009model|**0.094 **|**0.096**|**0.117**|
|Imputation using: 2010 model<br>Source: Authors’ estimation using the HBS 200|0.103<br>8, 2009, and 2010 d|**0.108**<br>ata.|**0.134**|

# **4.3 Sampling and Balance Checks**

Sample selection was conducted to yield two comparable groups, and the samples were subsequently checked for balance. A total of 120 enumeration blocks in Belgrade were selected.

In the first stage, 60 enumeration blocks, which can be called Primary Sampling Units (PSUs), were drawn with probability proportional to size (PPS) for urban and rural areas separately. In the second stage, 10 households were randomly selected from each PSU and assigned to the F2F and CATI groups. Also, an additional 10 households were selected randomly from each PSU as replacement. The large number of candidates for replacement was due to the relatively high non-response rate observed in other surveys in Serbia.

As an extra measure of precaution, we checked for balance of the two randomly selected groups using the corresponding 2011 census data. Three household characteristics were checked for balance: (1) the average household size, (2) highest education level (percentage of households), and (3) home ownership tenure status (percentage of households). In the case that the groups were not balanced, the sampling would be redone until the distribution of household characteristics were statistically insignificant. Table 3 shows the F2F and CATI samples were balanced ex ante in terms of household size and highest education variables in the census.

Table 4 shows the number of non-responses and replacement during the F2F survey and the CATI survey. Note that the CATI survey has two stages – a baseline survey and a telephone interview. According to the table, this pilot experienced relatively high non-response rates for both F2F and CATI groups. The non-response rates in F2F and CATI (baseline survey) were 39.7 and 41.8 percent in urban areas, respectively, and 32.4 and 34 percent in rural areas, respectively (see table 4). These households with non-responses were replaced with households in the replacement list to make sure that both F2F and CATI groups would have a planned sample size of 600 households.

11
that retail firms are less likely to exit the market, perhaps a partial explanation for lower observed rates of exit in our sample are at least, in part, due to the inclusion of services.<sup>21</sup> Given the array of several variables that are included and the fact that this result holds generally, the factors for exit among service firms specifically is another topic that may benefit from more research.

# _Market-level factors_

We find mixed evidence for market-level factors as determinants of firm exit. Our proxy for competition in the market ( _comp)_ , the number of establishments in a firm’s industry and location, is negatively related to exit. While this may run counter to the argument that more competition from firms is likely to spurn creative destruction, it may also be an indication of several – necessarily incumbent – firms occupying markets that in themselves do not have high exit rates. In fact, after accounting for macro-level variables, our other market-level indicator for the productivity of competitors does have a positive and significant effect on firm exit. Put another way, firms facing more productive competitors are more likely to exit, all else equal, an indication that it is the quality of competitors and not the quantity which may indicate a greater force for firm survival, evidence that the quality of firm cohorts may have an effect on survival (Box (2008).<sup>22</sup>

# _Economy-level factors_

We find evidence of macro-level factors on the likelihood of firm exit. External shocks, as represented by GDP growth, have significant effects on the likelihood of exit, with the chances of firm exit decreasing with economic growth. We find limited results for the effect of GDP per capita, losing significance in our more conservative estimations, though the direction of this effect is negative, indicating that in richer countries firm exit is less likely. Trade openness is associated with a lower chance of firm exit, indicating perhaps that the benefits of foreign trade, both direct and indirect, may mitigate firm exit.

Regarding the regulatory environment, our measure using the Doing Business’s indicator on entry regulation is consistently and highly significant. A greater ease of entry, indicated by a higher score, is consistently related to a higher likelihood of exit. That is, in contrast to our measure for

> 21 As noted, most census based estimates are restricted to manufacturing firms.

> 22 This of course does not directly account for pricing power of firms in markets with few or no competitors.

23
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 12. Main sources of capital investment in infrastructure in Congo**

<!-- Start of picture text --> 3.5<br>resource-rich<br>Congo<br>countries<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT power transport WSS ICT power transport WSS<br>public ODA non-OECD PPI<br>% of GDP<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and others 2008.

Public investments are channeled almost entirely through the central government. Neither power nor water utilities report significant investments financed from own sources. Operating expenditure, on the other hand, is channeled almost entirely through state-owned enterprises (figure 13).

In recent years, on-budget public spending has increased substantially, most notably in investment in roads transport. After a sharp drop in 2006, budgetary spending on roads and power rose to unprecedented levels in 2008 (figure 14). The absolute value of road spending averaged $35 per capita per year, which is high compared with other Central African countries. The investment program consists mainly of rehabilitation, upgrading, and widening of the trunk network. However, budget estimates for 2009 were significantly lower than 2008 spending, which casts doubt on whether such a high level of public spending is sustainable.

29
Magnus Blomstrom and Ari Kokko, "Regional Integration and Foreign Direct Investment: A Conceptual Framework and Three Cases" (WPS 1750, April 1997) Eric Bond, "Using Tariff Indices to Evaluate Preferential Trading Arrangements: An Application to Chile" (WPS 1751, April 1997) Pier Carlo Padoan, "Technology Accumulation and Diffusion: Is There a Regional Dimension?" (WPS1781, June 1997) Won Chang and L. Alan Winters, "Regional Integration and the Prices of Imports: An Empirical Investigation" (WPS 1782, June 1997) Glenn Harrison, Thomas Rutherford and David Tarr, "Trade Policy Options for Chile: A Quantitative Evaluation" (WPS 1783, June 1997) Anthony Venables and Diego Puga, "Trading Arrangements and Industrial Development" (WPS 1787, June 1997) Maurice Schiff and L. Alan Winters, "Regional Integration as Diplomacy" (WPS1801, August 1997) Raquel Femrandez, "Returns to Regionalism: An Evaluation of Nontraditional Gains from Regional Trade Agreements" (WPS1816, August 1997)

# Planned future issues in this series include:

Sherry Stephenson, "Standards, Conformity Assessments and Developing Countries" Valeria De Bonis, "Regional Integration and Factor Income Taxation" and "Regional Integration and Commodity Tax Harmonization

# Other papers on regionalism produced by DECRG include:

Almed Galal and Bernard Hoekman (eds), Regional Partners in Global Markets: Limits and Possibilities of the Euro-Med Initiative. CEPR 1997.

Bernard Hoekman and Simeon Djankov, "Imports of Inputs, Foreign Investment and Reorientation of East European Trade," World Bank Economic Review (forthcoming) Bernard Hoekman and Simeon Djankov, "The EU's Mediterranean Free Trade Initiative," World Economy Bernard Hoekman and Simeon Djankov, "Effective Protection in Jordan and Egypt in the Transition to Free Trade with Europe," World Development.
Increasing students’ acquisition of cognitive skills can increase lifetime earnings, expand basic capacities, and potentially equalize a country’s distribution of income. Nonetheless, the many studies examining policy interventions aimed to improve schools find few that improve quality of schooling, measured by enrollment or test score outcomes. We measure the impact on dropout, repetition, and failure rates of Mexico’s five-year Quality Schools Program (PEC). PEC combines increased resources for schools with decentralization to allow school principals to make management decisions using the increased resources.

PEC has some similarity to public programs in many countries and U.S. states which give grants for local authorities to allocate (“school-based management”). Unlike some of these programs, PEC requires schools to design an improvement plan, involves parents in budget and planning decisions but does not let parents make personnel decisions, and PEC provides a short-term grant to schools and trains school principals while other programs do not. Since every PEC school receives these interventions as a package, we cannot distinguish their impact, but we can examine the impact of PEC as a whole.

We construct a panel of 74,700 schools and use two common non-experimental methods to create a control group and estimate program impact: regression analysis and propensity score matching. We compare the estimated impact of PEC on dropout, repetition, and failure rates using these two methods. The preferred estimator, difference-in-differences with matching, reveals that participation in PEC significantly decreases dropout rates by 0.24 percentage points, failure rates by 0.24 percentage points and repetition rates by 0.31 percentage points. The estimated impacts are slightly lower but remain statistically significant when participation in PEC is measured as receiving PEC grants for any one of the three school years covered by our study. PEC is found to have no significant impact on the dropout, repetition, and failure rates in indigenous schools.

This impact has moderate magnitude—it represents a decrease of 6 to 8 percent relative to the baseline mean levels of dropout, repetition, and failure. Given Mexico’s 26
_Table 2: Robustness PPML and small countries_

||(1)|(2)<br>PPML|(3)|(4)<br>Small|(5)<br>countries ex|(6)<br>cluded|
|---|---|---|---|---|---|---|
|||FDI|||ln(FDI)||
||Trade War|Covid-19|Invasion of<br>Ukraine|Trade War|Covid-19|Invasion of<br>Ukraine|
|**I(t>Event) x I(US) x ...**|||||||
|... x I(Same country)|1.172|1.389**|1.832***|0.613|0.888**|1.087***|
||(0.785)|(0.676)|(0.702)|(0.489)|(0.397)|(0.362)|
|... x I(Border)|0.562|1.084*|1.396**|0.264|0.923**|0.904**|
||(0.650)|(0.651)|(0.651)|(0.537)|(0.419)|(0.372)|
|... x ln(distance)|0.236|0.241|0.377|0.243|0.010|0.135|
||(0.346)|(0.319)|(0.342)|(0.201)|(0.142)|(0.125)|
|... x I(NATO)|-0.430|-0.370|-0.539|-0.066|-0.517**|-0.676***|
||(0.412)|(0.394)|(0.341)|(0.244)|(0.237)|(0.200)|
|... x I(Common language)|-0.299|-0.377**|-0.374**|-0.063|-0.159|-0.165*|
||(0.184)|(0.174)|(0.175)|(0.134)|(0.101)|(0.093)|
|**I(t>Event) x ...**|||||||
|... x I(Same country)|-0.453|-0.451|-0.724|-0.531|-0.632**|-0.775***|
||(0.600)|(0.524)|(0.514)|(0.386)|(0.305)|(0.281)|
|... x I(Border)|-0.246|-0.254|-0.540|-0.279|-0.325|-0.418**|
||(0.470)|(0.474)|(0.457)|(0.275)|(0.216)|(0.194)|
|... x ln(distance)|0.115|0.138|0.102|-0.318***|-0.108|-0.155**|
||(0.171)|(0.197)|(0.210)|(0.116)|(0.087)|(0.076)|
|... x I(Common language)|0.068|0.009|-0.112|0.129|0.133|0.057|
||(0.239)|(0.219)|(0.219)|(0.118)|(0.097)|(0.090)|
|Observations|7,622|10,006|12,678|15,996|21,930|27,864|
|R-squared||||0.861|0.860|0.859|

Note: The regression includes source country-time, host country-time, and source country-host country fixed effects. The sample covers bilateral US and EU investment. In columns (1) and (4), the sample covers the period from January 2015 to February 2020 (Trade War); in columns (2) and (5), from January 2015 to January 2022 (Covid-19); and in columns (3) and (6), from January 2015 to December 2023 (Invasion of Ukraine). Robust standard errors, clustered at the source-time level, are reported in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

# **5. Extensions to jobs**

This section expands the analysis to assess the impact of recent shocks on estimated jobs created directly through greenfield investment projects. Given the identified reshoring trend, we test for similar trends using information on the number jobs in the US associated 16
# **7. Growth Effects from Panel Regressions**

We re-examine the growth effect of subsidy reduction, just visited from single country studies, but this time using the same dataset used in the Panel study of Section 5. We distinguish between long term and shortterm policies of subsidy removal since in theory the two may have opposite effects. For this reason, we adopt a panel Autoregressive Distributive Lag (ARDL) with Error Correction (EC). We adopt this approach due to its ability to distinguish between the short run and the long run effects. Results, reported in Tables 4, show that subsidies do _not_ matter for any of our three samples (world, MENA and MENA oil group) either in the long-run or in the short-run. The single country forward projections from the CPAT tool also suggested no long run effects, consistent with our finding here from historical data. Our results further suggest no short-run effect while the CPAT tool did suggest some short-run dip in the rate of growth.<sup>11</sup> Between the systematic regression results based on actual historical data, and the CPAT simulation tool, the policy implication of our finding is that removing fuel subsidies do _not_ negatively impact growth. (We cannot say the same about Carbon Taxes since those are only simulation-based results.) With both the climate and the budgetary benefits of subsidy removal from single country simulation on one hand, and the absence of harm to long-run growth on the other, the benefits of such a policy are clear and need to be advocated on this basis, not only on the basis of well known adverse effects on efficiency, fiscal space, and distribution.<sup>12</sup>

# **8.  Conclusion**

Numerous findings of this paper have been summarized as we have presented them. Here we emphasize some of the points that are worth repeating for policy purposes. Among the highlights are that for the MENA

> 11 A technical note: The error correction coefficient is negative and significant, indicating co-integration among variables as expected. ARDL-EC accounts for this cointegration without the need to test for it. In all regressions, robust errors are reported. Using clustered error option did not significantly impact the general direction of the results, although in some instances the significance dropped slightly as would be expected under clustered error.

> 12 There is some suggestion, though with mixed evidence, that natural resource rich economies may experience depressed growth in the long run, as countries’ labor and capital migrate from high productivity to the lower productivity resource rich sector inducing a rentier economies. See Frankel (2010), Van der Ploeg, 2011. The direct and adverse effects of resources on the economy work through economic shocks (Van der Ploeg 2011), but Van der Ploeg and Poelhekke (2009) show that while countries with greater oil wealth may experience greater economic volatility, that does not necessarily result in slower growth.  The growth effects of the Dutch Disease, which leads to currency appreciation and a shift of labor and capital away from agriculture and manufacturing, are equally debated.

34
(equation in footnote 2, for n = 5 and n = 7 ). According to Figure 6 the behavior of the observed Brazilian TT collection closely corresponds to what theory predicts. If Albuquerque’s partial equilibrium approach had been pursued in this paper, roughly the same welfare losses would have been obtained.<sup>9</sup> In summary, though theory has argued that a non-neutral tax may be potentially highly distortionary, the existing empirical evidence has been unable to substantiate that claim.

# **7. SUMMARY AND POLICY IMPLICATIONS**

Despite the attractiveness and popularity of views claiming that a bank debit tax as a means of collecting government revenue is very costly for society, thus far empirical work has failed to provide support for that conjecture. The question is tackled in this paper by developing a multisector dynamic general equilibrium model that allows for the pyramidation of the nominal tax rate, the major drawback of a non-neutral tax. The model is used to examine the efficiency implications of a bank debit tax, understood here as a general transaction tax, from a quantitative standpoint; or equivalently, it is assumed that all transactions in the economy are channeled through the banking system and there subject to a bank debit tax. An important aspect of the model is that conventional (consumption, labor income and capital income) taxes can be evaluated and compared with non-neutral taxes on an equal footing.

The findings in this paper manifestly contrast with the existing literature. The results suggest that a transaction tax is not an especially distortionary tax. It introduces lower distortions than a capital earnings tax and, in some cases, depending on the growth engine, lower than a labor income tax. The model shows that a consumption tax is clearly the least distorting instrument. Though a fiscal policy that chooses a consumption tax instead of a transaction tax to raise the required additional revenue will reap the rewards of relatively large efficiency gains, no improvement in the rate of economic growth should be expected. So, the question is whether there is still room for transaction taxation when governments can use a consumption tax, which is the “(...) premier indirect tax from a technical point of view” (Harberger, 1990), instead. This study demonstrates that a government financing policy that temporarily uses transaction taxation and subsequently replaces it with an alternative conventional tax is superior in terms of welfare to any other financing policy that avoids transaction taxation and resorts to a conventional tax to raise the required revenue from the outset. This result depends on the government announcing the two-step tax reform from the outset and credibly committing to it. Finally, the benefits of scrapping transaction taxation as recommended by many analysts are assessed in the paper. The fact that the benefit of the policy hinges on how TT revenue is replaced is also shown. It is only in the case where these revenues are 9 The reason is that deadweight losses depend on the slope of the depicted relation. Notice that intercepts are zero.

24
In order to verify the robustness of our TFP results, we apply four different estimation strategies: OLS, fixed-effects as well as both approaches proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003). Output is measured in terms of value added, defined as revenues minus costs for material inputs. Table 5 reports the corresponding estimation results for  and .

Evidently, with regard to the labor and capital elasticities the five different estimation strategies lead to deviating results. To give one example, the input elasticities obtained from simple OLS estimation indicate that the Belarusian machine building industry produces using a rather labor intensive technology, while the Levinsohn and Petrin (2003) procedure suggests the opposite. Regarding the measurement of TFP, the residuals of the various production function estimates are of major interest. In contrast with the estimates of input elasticities, the TFP measures obtained form the four different procedures are very similar, with the correlation exceeding 0.95 in almost all cases. The only exception is the fixed effects estimator, which leads to a relatively poorly correlated TFP measure. However, in our application this latter estimator suffers from low within-firm variation leading to weakly identified input elasticities. For this reason, we use the TFP measures based on all four different estimation results in order to compare TFP between state-owned and non-state owned Belarusian machine building firms.

Table 5: Estimation results: Cobb-Douglas production function in machine building industries

|||Mo|del||
|---|---|---|---|---|
||OLS|FE|OP|LP|
|Labor|0.583***|0.263***|0.417***|0.314***|
||(0.044)|(0.064)|(0.097)|(0.088)|
|Capital|0.391***|0.158**|0.414***|0.569***|
||(0.038)|(0.065)|(0.144)|(0.171)|
|Returns to scale|0.974|0.421|0.834|0.883|
|Wald test|2.10|53.09***|1.19|1.21|
|Observations|914|914|759|914|

_Notes:_ Standard errors in parenthesis.*(*)[***] stands for significance at the 10%(5%)[1%] level. All regressions inlclude year fixed effects. OP indicates the Olley and Pakes (1996) approach. LP indicates the Levinsohn and Petrin (2003) approach, where material costs proxy for unobserved productivity shocks. The Wald test has constant returns to scale as the null hypothesis.

18