rafmacalaba/datause-encoder-data huggingface dataset:
23 Notice that the GES rose sharply in Denmark in 1983.18 Instead, the results for France and Ixelaud are dramatic. As figures 9 and 10 show, expectations about future economic performance were very poor in these two countries. Moreover, those expectations consistently underestimated the performance of the economy during stabilization.

**Figure 8. France: GDP and General Economic Situation Forecasted at t-l. Source:** OECD National **Income Accounts and European Economy Supplemenlt** B.

<!-- Start of picture text --> l 9 10 12<br><!-- End of picture text --> 7 Notice that the forecast for 1984 was done in 1983. As the Danish stabilization was launched at the end of 1982, 1984 is the year we want to focus our attention on.
In brief our methodology consists of identifying many individual sources of data on perceptions of governance that we can assign to these six broad categories. We then use a statistical methodology known as an unobserved components model to construct aggregate indicators from these individual measures. These aggregate indicators are weighted averages of the underlying data, with weights reflecting the precision of the individual data sources. Crucially our methodology also generates margins of error for the estimates of governance for each country, which need to be taken into account when making comparisons of governance across countries and over time. We provide details on this approach in the remainder of this section.

# **2.1  Data and Methodology**

As in past years we rely on a large number of individual data sources that provide us with information on perceptions of governance. These data sources consist of surveys of firms and individuals, as well as the assessments of commercial risk rating agencies, non-governmental organizations, and a number of multilateral aid agencies. A full list of these sources is presented in Table 1. For the 2005 round of the data, we rely on a total of 276 individual variables measuring different dimensions of governance. These are taken from 31 different sources, produced by 25 different organizations. Appendices A and B provide a detailed description of each data source, and document how we have assigned individual questions from these data sources to our six aggregate indicators. Almost all of our data sources are available annually, and we use the data only the most recent year in our aggregate indicators. In a few cases, as noted in Appendix B, we use data lagged one or two years if current data are not available.<sup>2</sup> In this round of the governance indicators we have added one new data source, the United States State Department's Trafficking in People Report. It provides country narratives and numerical rankings of countries' policy efforts to prevent trafficking in

> 2 We never use lagged data when current data are available, in order to ensure that our indicators are as timely as possible.  In this respect our approach differs from Transparency International's Corruption Perceptions Index, which in each year uses data from the current and two previous years for some of its sources (GCS, WCY and PRC), although peculiarly it does not follow the same practice for three other annually-available sources (FHT, MIG, and WMO).  As a result the 2005 Corruption Perceptions Index is based on 2005 data from only 10 data sources.  In contrast our Control of Corruption Index is based on 19 data sources, all of which refer to 2005.

5
was the only country for which we had data for the pre-treatment period). It has no claim to be a representative or random sample. Different conclusions may be reached from other samples, and clearly a full, cross-country impact evaluation should be undertaken. Second and more importantly, it is possible that the PIP affected not only the treated export flows, but also untreated ones, through spillovers. Participating firms are the largest and more efficient of Senegal’s FFV sector. On one hand. this means that they are more susceptible than others to benefit from the program. Thus, what we obtain is an estimate of the average effect of the treatment on the treated (ATT), which may over-state the program’s potential effect on the whole population of producers. On the other hand, it may also mean that smaller firms, although left out of the program, can benefit from it through imitation of best practices and even unobserved assistance from larger firms. The argument is even more potent for untreated _products_ : when a firm gets PIP assistance for its FFV activities, it is quite possible (indeed, likely) that its other activities benefit as well from improved managerial practices; or _destination countries_ : export flows to non-EU destinations may benefit from the program for the same reason. In the presence of such unmeasured spillovers, the PIP’s impact would be underestimated by impact-evaluation methods. This is important to keep in mind, as public assistance (whether from local governments or donors) should be justified by market failures, like spillovers, rather than a positive rate of return to beneficiaries (which would simply create a market demand for assistance services without justifying use of public funds). Thus, impact evaluation of technical-assistance programs like the PIP is a double-edged sword and must be interpreted with caution.

The second conclusion that should be _avoided_ is that, either because the data are not sufficiently reliable or comprehensive or because of the caveats just discussed, rigorous impact evaluation should not be undertaken. The lack of rigorous impact evaluation undermines the credibility of claims about the program’s benefits made on the basis of case studies, because it is impossible to know whether they are representative or not. Indeed, this is the message conveyed by the 2006 assessment of the impact of EU TRA (te Velde et al. 2006). Worse, in a context where taxpayers are asking for acccountability and results in development aid, Paul Milgrom’s ‘unraveling principle’ applies: Rational taxpayers are likely to take all the news that is not told to be detrimental. In other words, the bad news that impact evaluation can possibly generate (as in the present case) is probably fully anticipated. The more the development community can provide rigorous evidence that at least some programs do make a difference; or that some components do; or that, when not, failure is part of useful experimentation and action is being taken to remedy the observed ineffectiveness, the more support there will be for development aid.

23
Table 5. Effect of a 1 Percentage Point Decrease in the Market Gini on Log GDP per capita

||Level of Economic Development:<br>1970 GDP per capita|Level of Economic Development:<br>2010 GDP per capita|
|---|---|---|
||(1)|(2)|
|Brunei|0.35|0.56|
|Cambodia|-0.18|0.13|
|Indonesia|-0.24|0.21|
|Laos|-0.25|0.16|
|Malaysia|-0.09|0.36|
|Philippines|-0.10|0.18|
|Singapore|0.08|0.56|
|Thailand|-0.10|0.31|
|Vietnam|-0.25|0.17|
|ASEAN Average|-0.09|0.29|
|ASEAN Median|-0.10|0.18|

Note: The table reports effects on log GDP per capita of a 1 percentage point decrease in the market Gini. For column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) with the values of countries' GDP per capita in 2010. Specifically, the estimated relationship for country _c_ and period _t_ is ln(GDP per capita)ct = 90*MarketGinict – 13*MarketGinict*ln(initial GDP per capita)c.

19
17 (p<0.07). However, in contrast to their findings, the first row indicates that, in the low salary regime, but not the high salary regime, pro-social types are more likely to join the public sector. In the low salary regime, subjects who send an additional 100 tokens to the Indonesian Red Cross (five percent of subjects’ initial endowment) are three percent more likely to choose the public sector.

Under both wage regimes, however, high ability workers are less likely to join the public sector, reflecting the earnings gap for high ability workers between the private sector and both public sector settings. Both salary levels (1400 token in the low salary treatment or 2200 tokens in the high salary treatment) are still lower than what most subjects earn from the private sector, consistent with many public sector positions around the world. Only 11 percent of our sample in the high wage treatment could earn more in the public sector (relative to their private sector outside option), while none of our sample could earn more in the Low wage treatment.

**Table 3:  Are public sector workers in high wage regimes less pro-social than those in low wage regimes?**

|Dependent Variable: Sec<br>|tor Choice(1 = Join<br>|Public Sector)<br>|
|---|---|---|
|Treatment|Pooled<br>I|Low + High Salary<br>II|
|Amount Sent in Dictator Game|0.180**<br>(0.07)|0.323***<br>(0.11)|
|Ability|-0.008***|-0.007|
|(Effort Exerted in Piece Rate)|(0.01)|(0.00)|
|Treatment (High salary = 1)||0.619*<br>(0.33)|
|Treatment X||-0.274*|
|Dictator Game||(0.15)|
|Treatment X||-0.005|
|Ability||(0.01)|
|Gender (D)||-0.117|
|(1 = Female)||(0.07)|
|Age (in years)||-0.011<br>(0.03)|
|Belief that Charity was Paid||-0.044|
|(5 = Complete Confidence)||(0.05)|
|Constant|0.316|0.309|
|Pseudo R-squared|0.058|0.102|
|Chi-squared|13.73|24.15|
|P-value|0.001|0.002|
|Observations|187|187|

Note: * p<0.1, ** p<0.05, *** p<0.01. Probit specification, standard errors in parentheses. Table reports marginal effects. All subjects from the University of Indonesia (non-public). The dictator game variable is divided by 1000 for presentational convenience.
Thus migration in this scenario results from two sources: first, we assume that a country is willing to exogenously increase migration to keep the migrant shares constant (forecasts); and second, we assume that migration is liberalized so that migrants can respond endogenously to changes in the real wages in the home and host economies (liberal). These two sources of migration may work in opposite directions. For instance, an economy with a growing population will increase the number of migrants to keep the share of migration in its total population constant; on the other hand, the same country may experience declining wages due to its rising population which will cause outward migration of its own population and return migration of its foreign population, thereby negating the exogenous increase in migration.<sup>12</sup>

# **4. S imulation Results**

In this section we examine the results of the scenario in terms of the projected demographic changes, changes in bilateral migration in response to the changes in wages resulting from the demographics and the impact of these on the EAP economy.

## **4.1 Demographic Changes and Real Wages**

Figures 2 and 3 show the yearly forecasted growth rates of skilled and unskilled labor forces obtained from the World Bank and United Nations. According to these data there is an unmistakable downward trend in skilled labor growth rate in all countries, with forecasted growth rates becoming negative or falling to almost zero in all of developed economies of Asia – Hong Kong SAR, China; Japan; Singapore; South Koreal and Taiwan, China – as well as in China and Thailand. The forecasted unskilled labor growth, on the other hand, does not decline as significantly as skilled labor, although it is negative or close to zero for the same seven economies –China; Hong Kong SAR, China; Japan; Singapore; Taiwan, China; Thailand; and South Korea.

> 12 The reader should therefore not interpret that these large decreases in migration or return migration are large flows of people going back to their home economies, instead they are migrants choosing not to migrate in the first place or choosing to migrate elsewhere instead.

16
The problem with these data on disbursement lags however is that initial disbursement projections are tentative and often revised over the life of the project. Thus, they are a kind of a “moving target” if we want to measure what actual disbursements delays are. In light of this, we instead measure disbursement delays relative to typical disbursement rates for projects approved in the same region, year, and sector, drawing from Kraay (2010). In particular, for each project we construct a typical disbursement profile based on the actual average annual disbursement rate for all projects in the same sector, approval year, and geographical region. For each year of the project, we then calculate the difference between the cumulative actual annual disbursement rate and the cumulative predicted annual disbursement rate. Finally, we take this cumulative disbursement lag over the first half of the project, and consider it as a potential leading indicator of project success ( _DISBLAGH1_ ).

Table 2 contains summary statistics on the main variables of interest. These are reported for two samples that are defined based on various data availability constraints. Our first sample begins in 1983 (i.e. with projects evaluated in 1983), and contains 6,253 subsequent project evaluations (after eliminating 548 observations with missing data on the relevant right-hand-side variables). In this sample, the outcome rating is a binary satisfactory/unsatisfactory, and we have data available on the indicated basic project characteristics as well as the ISR flags data. There are a further 810 project evaluations between 1972 and 1982 but we do not include them as the ISR flag data, as well as the preparation and supervision costs data, is not available from this earlier period. Our second sample consists of 3,887 evaluations performed since 1995. For this sample, we use the available six-point scale for the outcomes, and also a richer set of control variables including the disbursement lags and a limited set task manager variables, that we describe in more detail below.

# **4.  Results**

# _Country-Level Correlates of Project Performance_

We begin in Table 3 by documenting the role of country-level variables in explaining the heterogeneity in project outcomes. The left panel of Table 3 reports results for all World Bank projects, and the right panel for IDA projects separately. Within each panel, the first two columns report results for the full post-1983 sample, and the remaining two for the post-1995 sub-sample.<sup>7</sup> Throughout the

> 7 Throughout the paper we will report results from simple OLS regressions, even though the outcome variable is binary in the first sample, and discrete on a six-point scale in the second.  This is purely for pragmatic reasons. While an ordered multinomial choice model is in principle more appropriate for the second sample, when the number of categories is large, the value-added of recognizing explicitly the discrete nature of the dependent

13
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Dependent varia<br>|ble: logof GDPper c<br>|apita (in PPP)<br>||
|Persistence|0.781***|0.784***|0.726***|0.746***|
||(0.057)|(0.0563)|(0.0491)|(0.0392)|
|ln(exchange rate)|-0.064|-0.0622|-0.0553*|-0.0172|
||(0.0404)|(0.0392)|(0.0332)|(0.0355)|
|ln(schooling)|0.0178|0.0445|0.0104|-0.0266|
||(0.0503)|(0.0502)|(0.0463)|(0.0452)|
|ln(credit/GDP)|0.0743**|0.0542|0.0432*|0.0238|
||(0.0311)|(0.0304)|(0.221)|(0.0245)|
|ln(trade/GDP)|0.0824|0.0609|0.0916***|0.0968|
||(0.0502)|(0.0490)|(0.0359)|(0.0584)|
|ln(gov. cons.)|-0.262***|-0.259***|-0.215***|-0.127|
||(0.0442)|(0.0423)|(0.0359)|(0.0810)|
|ln(tele lines)|0.141***|0.129***|0.0769***|0.0816***|
||(0.0309)|(0.0297)|(0.0216)|(0.02161)|
|ln(inflation)|-0.0113|-0.0145|-0.00523|-0.0128|
||(0.0118)|(0.0110)|(0.00886)|(0.112)|
|∆ln(TOT change)|0.118***|0.123***|0.116***|0.110***|
||(0.0286)|(0.0277)|(0.0264)|(0.0339)|
|ln(bank crisis)|-0.0399|-0.0430|-0.0414|-0.0461*|
||(0.0317)|(0.0314)|(0.0259)|(0.0236)|
|∆ln(commodity|10.48***|11.11***|7.507***|6.963|
|prices)|(2.686)|(2.546)|(2.391)|(4.943)|
|ln(institutions)|-0.00265||0.00190|-0.00549|
||(0.0330)||(0.0247)|(0.0255)|
|Constant|2.502***|2.829***|3.202***|2.469***|
||(0.708)|(0.465)|(0.600)|(0.453)|
|Observations|464|502|464|464|
|No of countries|126|141|126|126|
|Method|Sys-GMM|Sys-GMM|Sys-GMM|FE|
|Specification|Baseline|w/o Polity2|Lags 1-3 as|baseline|
||||instruments|as FE|
|No of instruments|153|166|171||
|AB(1)|0.023|0.024|0.033||
|AB(2)|0.102|0.045|0.062||
|Sargan test|0.131|0.017|0.001||

Note: Based on Brueckner (2014). Standard errors in parentheses. ***,**, and * indicate statistical significance on the 1, 5, and 10 percent level, respectively. AB(1) and AB(2) is the _p_ -value of the Arellano and Bond test for first and second order autocorrelation, respectively. Sargan test reports _p_ -values.

Source: Moller and Wacker (2017).

In addition, for some countries and/or some periods, data on some of the infrastructure indicators were scanty and thus we used alternative measures. There is paucity of data on road density and electricity production for some WAEMU countries and/or for more recent periods. Given the data are organized into five-year periods, data for the most recent year is taken as representative for the period. For instance, if data on road density were available only for 2011 and 2014, the average of the two would be taken to represent road coverage in 2011-2016. Similarly, electricity production data were not available for a few countries and thus we used alternative measures (such as electricity consumption) as a proxy. It should be noted upfront that we are fully aware of the problems with the 8
## <u>Dynamics of rice price</u>

<!-- Start of picture text --> Banjarmasin Lampung Makassar Mataram<br>10211.08<br>2211.08<br>Menado Semarang Surabaya<br>10211.08<br>2211.08<br>mydate<br>Graphs by prov<br>2004m12005m12006m12007m12008m12009m12010m1<br>2004m12005m12006m12007m12008m12009m12010m1 2004m12005m12006m12007m12008m12009m12010m1 2004m12005m12006m12007m12008m12009m12010m1<br>rice<br><!-- End of picture text -->

# <u>Dynamics of maize price</u>

<!-- Start of picture text --> Banjarmasin Lampung Makassar Mataram<br>7080<br>1080<br>Menado Semarang Surabaya<br>7080<br>1080<br>mydate<br>Graphs by prov<br>2004m12005m12006m12007m12008m12009m12010m1<br>2004m12005m12006m12007m12008m12009m12010m1 2004m12005m12006m12007m12008m12009m12010m1 2004m12005m12006m12007m12008m12009m12010m1<br>maize<br><!-- End of picture text --> 36
further push to economic growth. Similar to the Baseline Scenario, the growth rate increases till 2021 and is slightly higher than the Baseline Scenario. The GDP growth rate peaks in 2021 <!-- Start of picture text --> 10 Real GDP Growth (%) 10 Real GDP Growth (%)<br>Baseline Scenario Efficiency Scenario<br>9 9<br>8 Plan Target 8 Plan Target<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>10 Real GDP Growth (%) 10 Real GDP Growth (%)<br>Participation Scenario Efficiency + Participation Scenario<br>9 9<br>8 Plan Target 8 Plan Target<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> under both scenarios in which the growth under the Efficiency Scenario outperforms the growth under Baseline Scenario by more than half a percentage points. Even though public investment is a small share of the aggregate investment, an increase in its efficiency has non‐ trivial impact on economic growth.

The efficiency of public investment is unchanged under the Participation Scenario. Instead, the aggregate participation rate rises driven by improvements in female labor force participation. The growth rate under Participation Scenario rises somewhat till 2020 and its behavior is very similar to the growth under the Baseline Scenario. However, the linear increase in the female labor force participation rate means that growth is higher compared to the Baseline Scenario. The 11 percentage point increase in female labor force participation spread across 5 years on average adds more than 1 percentage point to GDP growth each year. The 14
consensus about the sign (or shape) of the ETR-firm-size relationship.<sup>6</sup> Our evidence instead is largely consistent across countries, pointing to an inverse U-shaped pattern in the ETR-firm-size relationship.

Finally, we connect to the literature on tax capacity in LMICs. Corporate taxes are a larger source of revenue for LMICs than for high-income countries (Besley and Persson, 2013). Governments also rely on firms to collect other taxes (Kopczuk and Slemrod, 2006; Garriga and Tortarolo, 2022). Thus, an erosion of the corporate tax base can be costly, and a distortionary corporate tax may be particularly detrimental to growth in LMICs. We show that LMICs forgo a sizable share of revenue in tax benefits to the largest firms. Conversely, our simulations suggest that a minimum tax could raise tax revenue substantially.

# **2 Data and Methodology**

## **2.1 Data**

We use corporate tax records from 13 countries, listed in Table 1. Each dataset captures the universe of corporations filing tax returns in the country over a five to ten-year span. In our main analysis, we focus on the latest cross-section which occurs between 2015 and 2019, depending on the country. Administrative tax data contain precise information on firms’ taxable income, costs, and all tax exemptions which allows for a breakdown of the tax burden. Unincorporated firms are excluded, as their tax treatment differs across countries. Appendix B details each country’s tax system and how we deal with special tax regimes which apply in some countries. Although administrative tax data are increasingly used by researchers (Pomeranz and Vila-Belda, 2019; Mascagni et al., 2016), our study is the first to use micro tax data from a large number of countries.

The data include countries in Africa, Latin America, and Eastern Europe, and cover a wide range of income levels (from Ethiopia with a GDP per capita of 500 USD to Costa Rica with a GDP per capita of 10,000 USD) and population sizes (from Montenegro with 628,000 inhabitants to Mexico with 128 million inhabitants). The number of firms in each dataset correlates with a country’s size and income level, ranging from 2,800 firms in Senegal to 460,000 in Mexico. The

> 6In a majority of studies the data suggest that the ETR-firm-size relation is negative. However, other studies document positive, flat or non-linear relationships.

5