rafmacalaba/datause-encoder-data huggingface dataset:
issues were unrelated to the treatment, if they reflect underlying characteristics of the schools that, in turn, affect the impact of the treatment, then this would introduce some bias. More generally, the effect sizes estimated in this paper may not be replicable in the more remote areas of the country or in those prone to the issues that led to the attrition of the schools in Madang.

# <u>5. Cost effectiveness</u>

Benchmarking the impact of the Reader Booster Programme to other interventions helps assess the efficiency of the intervention and benefits of scaling up the intervention versus other types of interventions. The Abdul Latif Poverty Action Lab at the Massachusetts Institute of Technology compiles data on costs and impacts of several randomized impact evaluations in education. Table 8 presents the cost effectiveness of these programs measured as the impact on test scores in standard deviations per 100 USD cost. In their data, 2.278 standard deviations is the median impact per 100 dollars. The figures are not perfectly comparable. Tests differ in grade level and psychometric properties, but it provides a general range of cost effectiveness data with which to benchmark the Reading Booster Programme.

15
variable bias. The second challenge is potential reverse causality that, all else equal, countries that actively seek a green economy have high acceptance of carbon pricing and are more likely to have related policies implemented. The endogeneity problem may bias the OLS estimates and generate misleading results. Rafaty, Dolphin and Pretis (2020) develop a new database of coverage-weighted carbon prices and employ a generalized panel synthetic control estimator to estimate the average effect of carbon pricing on emissions in 39 (mostly European) countries from 1990-2016. They conclude that the introduction of carbon pricing reduced the growth rate of emissions by 1-2.5 percentage points on average in their sample.

Studies focusing on a particular country use a variety of techniques to deal with identification challenges. Floros and Vlachou (2005) examine the response of manufacturing industries (two-digit level of international standards for industrial classification) to a carbon tax in Greece by employing the two-stage translog cost function to time series data during the 1982–1998 period. They find that the carbon tax reduces CO 2 emissions in manufacturing industries in Greece, in part by reducing demand and in part by causing substitution of petroleum fuels with electricity and substitution of energy with capital. Martin et al. (2014) finds a strong negative impact of a carbon tax on energy intensity and electricity use in manufacturing firms in UK. Aghion et al. (2016) investigate, using patent data from 3,412 automobile firms and individuals between 1965 and 2005 across 80 patent offices, how a carbon tax helps reduce CO 2 emissions in the long run. They find that clean innovation is stimulated by increases in fuel prices, whereas dirty innovation is depressed. They also show strong path dependency, which locks economies into high levels of carbon emissions, even after introducing a mild carbon tax or R&D subsidies for clean technologies.

Much empirical work on the effect of introducing a carbon tax relies on the assumption that consumers or businesses respond to a carbon tax in the same fashion as they would to an equivalent change in the price of the product due to other causes, such as fluctuations in the world oil price or local demand or supply shocks. Some recent studies have found that this need not be the case. For example, Rivers and Schaufele (2015) find that the demand for gasoline in British Columbia is responsive to the introduction of a carbon tax, and indeed the responsiveness to the tax is larger than that of an equivalent fluctuation in gasoline prices due to market forces. Employing a quasiexperimental technique in Sweden to investigate the impacts of a carbon tax on CO 2 emissions, Andersson (2019) finds a significant causal effect of carbon taxes on emissions. It finds that the 6
**Figure 4 Per capita health expenditure (in current US$ and PPP$), 2004, and change in per capita health expenditure (PPP$), 1999-2004**

<!-- Start of picture text --> 3500 60<br>3000 50<br>2500<br>40<br>2000<br>30<br>1500<br>20<br>1000<br>500 10<br>0 0<br>Current US$ PPP$ % change 1999-2004, PPP$<br>Per capita expenditure<br>% change in expenditure<br>Kosovo Albania Serbia EU-12 Croatia EU EU-15<br>TFYR Slovenia<br>Bosnia-<br>Herzegovina Macedonia<br><!-- End of picture text --> Source: WDI database 2007 for current US$ figures and WHO HFA-DB 2007 for PPP$ figures for Albania, the FYR of Macedonia, Bosnia and Herzegovina, Croatia, Slovenia and the EU aggregates; World Bank estimates for Serbia and Kosovo Note: Countries are ordered by increasing magnitude of per capita expenditure in current US$; EU aggregates are population-weighted

# _<u>D1.2 The share of resources spent on health has not increased over the past decade</u>_

The expenditure patterns described in the previous sections have had a certain amount of durability. Despite the fact that real total health expenditures have increased substantially over time, there is evidence of spending containment. With the exception of Albania, the rate of increase of health expenditures has been lower than the rate of growth of the economy and, when expressed as a percentage of GDP, total health expenditure has, in fact, fallen slightly over time in the sub-region. The downward trend appears to have been equivalent to about 1 percentage point of GDP over the last decade. This is in contrast to the slightly increasing expenditures that have been observed in Slovenia and also elsewhere in the EU. Among the comparators, it is only in Croatia that health expenditures as a share of GDP have declined over the past decade (see Figure 5).

17
|||||(4.15)***|||(9.54)***||
|---|---|---|---|---|---|---|---|---|
|Mean number of room per hh per village||0.104<br>(1.85)*||||||-0.115<br>(3.87)***|
|Average proportion of hh member economically<br>active per village|-0.833<br>(1.99)**||||0.572<br>(1.98)**||||
|Proportion of electric oven per village||||-1.38<br>(3.59)**|||||
|Proportion of population under 15 per village||||||||2.66<br>(3.68)***|
|Constant|12.73|12.20|12.57|11.80|11.87|12.54|12.94|11.67|
||(60.53)***|(67.11)***|(270.05)***|(92.96)***|(84.3)***|(318.8)***|(115.97)***|(50.14)***|
|Observations|284|287|389|591|393|497|294|672|
|Number of cluster in HBS|9|3|9|15|6|10|5|16|
|Weight applied|YES|NO|NO|NO|YES|NO|NO|YES|
|Location effect modelled|NO|NO|NO|NO|NO|NO|NO|YES|
|**R-squared**|0.46|0.52|0.38|0.28|0.45|0.31|0.52|0.24|
|Absolute value of t statistics in parentheses<br>significant at 10%; ** significant at 5%; *** si|gnificant at 1%||||||||

56
of output, as domestic consumption and investment. ICIO tables, which have been developed combining national IO statistics with trade data, describe sale-purchase relationships between industries within and between economies as well as the uses in different final demand components (e.g. consumption, investment and government spending). In particular, an ICIO table specifies the country-sector pairs that provide intermediate inputs to a given industry and the country-sector pairs to which that industry sells its output - in the case of intermediate products - or the ultimate destination markets for final goods. In Appendix A we present the basic conceptual framework of ICIO models while in Section 2.1 we show how to load ICIO tables with the command `icio_load` .

# **2.1 Implementation: Loading ICIO tables using the** `icio_load` **command**

In order to use the `icio` command one needs to load a particular ICIO table by using the `icio_load` command. `icio_load` allows to work directly with the most popular ICIO tables - OECD TiVA database (OECD, 2018), World Input-Output Database (WIOD, Timmer et al. 2015), and Eora Global Supply Chain Database (Lenzen et al. 2013); in addition, any other user-provided ICIO table can be loaded.

## **2.1.1 Syntax**

The basic syntax for `icio_load` is

- `icio_load, [` _options_ `]`

where the main _options_ are:

- <u>`iciotable(`</u> _table_ _~~n~~ ame_ `[,` _usertable_ _~~o~~ ptions_ `])` , specifies the ICIO table to be used for the analysis; _default_ is `wiodn` , the last WIOD release available (release 2016, see below for more details on the available tables’ versions);

- <u>`year(`</u> _#_ `)` , sets the year to be used for the analysis; the _default_ is the last available year: 2014 for the WIOD tables ( `wiodn` ), 2015 for TiVA tables ( `tivan` ), 2015 for the Eora Global Supply Chain Database tables ( `eora` ). Not needed for userprovided tables;

- `info` , shows the data sources and the versions of the loadable ICIO tables.

## **2.1.2 Examples**

`icio_load` can be used for the following purposes: 4
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25 regime's social achievements. Unfortunately, attendance data are not available. In addition, enrollment masks the qualitative dimensions of educational systems. Although developing countries' enrollments have risen to levels close to developed countries, thelr achievement levels have consistently lagged behind.21 Also veiled by enrollment figures are the significant numbers of drop outs and repetitions.

Third, and certainly most preoccupying, educational data do not always display the same continuous improvements in the 1980s as health indicators do. For several developing countries, whether low or middle income, adjusting or non adjusting, enrollments have gone down in the 1980s, reversing a trend established over several decades. Of all regions, Africa, where enrollments were already lowest, has suffered most of this educational setback.

In the next pages, we review the data on enrollments at the three levels of education, with emphasis on primary education as the most important level, at which literacy is acquired. After reporting on the sources of the data and their reliability, we turn to a detailed analysis of them, with regional differentiation, covering the historical trends and practical problems raised in the previous paragraphs.

> 2 1International Association for the Evaluation of Educational Achievement (IEA), Science Achievement in Seventeen CountriesA Preliminary RAort, (Oxford/New York: Pergamon Press, 1988).
this question is the left-hand side, dependent, variable. For the experience variable, the _Enterprise Surveys_ contain information on whether businesses had to provide a bribe to complete any one of six different transactions: requests for an electrical connection, a water connection, telephone service, an import license, a construction-related permit, or an operating license.

With this perceptions dependent variable and direct experience independent variable, we specify an econometric equation of the form: (1) The dependent variable, _Yijk_ , is a binary variable that equals one if the firm ranked corruption as one of the top three obstacles and zero if it did not. The are two dummy variables, _Gijkb_ and _Gijknb_ , that represent firms that solicited a service or license and reported bribing or no bribing, respectively. These dummy variables of graft are the objective or experiential measure of corruption that is of interest here. The _Gijknb_ variable takes on a value of one when a firm solicited any of the services but no bribe was solicited or expected and zero otherwise. The _Gijkb_ variable takes on a value of one when a service was solicited and a bribe was asked for or expected and zero otherwise. The omitted category is firms that did not solicit services or licenses. In estimating Equation 1, we include a matrix **X** _ijk_ of control variables. It includes country and industry fixed effects to account for the unobserved parameters at the country and industry levels. It also includes firm size and age, and a binary variable indicating whether the firm has experienced arson, robbery or theft in the last year, as another control variable. We include the latter variable since corruption and crime are often symptoms of governance systems that are not functioning well. We expect that crime and corruption go hand in hand and our econometric specification would suffer from omitted variable bias had we not included some control for some measure of the quality of governance systems in the business environment. The term ( ε ) is an error term that is potentially heteroskedastic and that may be correlated across all firms within each country. Therefore, we calculate robust standard errors and allow for clustering by country.

Of interest are the signs of coefficients β _Gnb_ and β _Gb_ . If the true extent of corruption were common knowledge to all firm, then we could reasonably expect to see no difference in the way in which firms perceive corruption (when β _Gnb_ = 0 or β _Gb_ = 0), after controlling for observables, irrespective of whether they were bribed or not. In this situation, we would expect to see a very close relationship between corruption perceptions and its incidence. On the other hand, if the extent of corruption is
5 that the approval year of each the IMF’s arrangement programs corresponds exactly or is after the reference year of labor productivity growth. This means that the possibility of the level of productivity determining the implementation of reforms can be ignored.

Key findings are as follows. In developing countries, the macroeconomic structural reforms under consideration in this study (financial, fiscal, real sector, and trade reforms) significantly improve productivity at the firm level.<sup>6</sup> We also find that the pace of reforms matters since being a “strong reformer” is associated with a clear productivity dividend for firms. Interestingly, real sector reforms have the most sizable effects on firms’ productivity. In addition, we find that financially-included firms benefit less from financial reforms; financial access also strengthens the relationship between fiscal reforms and firms’ productivity; the effects of fiscal and trade reforms on labor productivity growth are hindered by distortions; and, furthermore, the evidence suggests that small firms benefit more from financial reforms relative to the larger ones. Finally, except for financial and trade reforms, all macro reforms considered are bilaterally complementary in improving firms’ productivity. The findings are robust to several sensitivity analyses, including alternative measures of productivity, alternative methodology, the inclusion of a variable accounting for the economic crisis conditions. Moreover, we take advantage of unsuccessful reforms and conduct a counterfactual experiment. The latter consists in estimating the impacts of reforms not met on labor productivity growth. The counterfactual experiment validates our findings in the sense that unsuccessful reforms tend to have a negative impact on productivity.

This paper brings at least two key contributions to the literature. It is the first paper to use IMF’s MONA data to construct new reform indexes. Constructing these indexes is one of the contributions of this paper, as the indexes can be used in other research on the impact of structural reforms in developing countries. Moreover, our reform indexes have the advantage of focusing on reforms successfully implemented, while the existing literature mostly uses a liberalization index as a proxy of structural reforms.<sup>7</sup> We use the performance criteria defined by the IMF review board to identify reforms met or met with minor delay. Second, to the best

> 6 The paper considers the impact of reforms under the IMF arrangement programs. Reforms implemented might raise productivity but are not considered in this paper.

> 7 See, for instance Arnold et al. (2015); Prati et al. (2013); Abiad and Mody (2005); Abiad, Detragiache and Tressel (2008).
nearly all respondents who left the region managed to escape poverty, while poverty reduction among non-migrants was more modest. These descriptive statistics reinforce the results reported in Beegle, De Weerdt, and Dercon (2011): individuals who moved did considerably better than those who decided to stay.

# _[Table 3 here]_

After moving, migrants remain linked to extended family members at home: 90 percent of the migrants report that they communicated with a non-migrant network member in the 12 months preceding the survey. Migrants who maintained some form of communication experienced an average consumption growth of 110 percent, while those who did not grew by 88 percent.<sup>16</sup> This difference is statistically significant at the 1 percent level. The severing of the most basic links does not seem to be associated with higher consumption growth; if anything, the reverse is true.

We use data from shock modules administered in 2004 and 2010. During both of these rounds, the panel respondents were asked to consider each year between the survey rounds and indicate whether a particular year was, in economic terms, 'Very good', 'Good', 'Normal', 'Bad', 'Very bad'. For each 'Very bad' response, the respondents were asked to provide the main reason for the hardship. We consider each 'Very bad' response as an economic shock. More than 60 percent of the panel respondents reported experiencing at least one such shock between 1994 and 2009.

Table 4 provides an overview of the shocks experienced. Most frequently reported economic shocks were death of a family member, serious illness and poor harvest due to bad weather.

# _[Table 4 here]_

The shock data were collected at the individual level – in particular for each person on the 2010 roster who also appears on the original 1991/94 rosters. Since our focus is to examine the role of shocks on household consumption, the data had to be

> 16 The mean consumption growth among those who maintained contact was 394,679 TZS and among those who severed links 286,991 TZS.

12
it expects that otherwise it would certainly be subject to an NTB. This effect would be magni fi ed if the country already exports this product, hence we interact this variable with the export indicator as well. Moreover, the decision to export a good prior to the UR may be independent of the subsequent MFN tariff changes so that the instrument is uncorrelated with the error term. Nonetheless, we will be able to test and verify the exogeneity of the export indicator and NTBs as instruments because we employ a GMM approach. The set of overidentifying restrictions that allow us to perform these tests arise from including other instruments such as world price changes between 1992 and 1994 which can help to predict if a good was exported in 1994 but are unlikely to depend on the changes in MFN tariffs that take place in subsequent years.

The variable that captures the political economy effect, ∆x, is likely to depend on the MFN tariffs since it involves the production/import ratio weighted by the import demand elasticity, all of which are functions of the EU’s domestic prices and hence its MFN tariffs. Therefore, we employ the levels of these variables before the MFN tariff is implemented, e.g. 1978 for xt−1 and 1992 for xt. We fi nd some evidence of endogeneity and therefore instrument ∆x with a measure of scale economies (Value added/number of fi rms) and its interaction with the average world price change in the industry between 1992 and 1994. We also test this set of instruments to ensure that they are orthogonal to the error.

Finally, the reciprocity term also poses a potential endogeneity problem in the form of reverse causation since the total tariff reduction by other WTO members in the UR will partially depend on the EU’s reductions. We instrument reciprocity by using the unilateral portion of the total tariff reductions that are eventually offered at the UR. More speci fi cally, the reductions between 1986 and 1992 were unilateral because they were undertaken by various countries, outside of GATT negotiations, which did not know if these reductions would be reciprocated at all. In fact it was not clear at the time if the round would even be completed. However, when the fi nal cuts were negotiated, between 1992 and 1994, the unilateral reductions undertaken from 1986 to 1992 were explicitly reciprocated because they had taken place after the official start date of the round (Finger et al. [1999]). Therefore, the unilateral liberalization by the other WTO members (between 1986 and 1992) is used as an instrument for their total liberalization (between 1986 and 1995).<sup>33</sup>

# 4.2 Data

In the appendix section A.5 we provide detailed de fi nitions and sources for each of the variables; their summary statistics are presented in table 7. Here we point out some salient characteristics of the

> 33 Limão (2003) provides a detailed argument for the use of this instrument for reciprocity.

21
Figure 1: Map of border areas with PSUs and regional cities <!-- Start of picture text --> Mali<br>Burkina Faso<br>Wa<br>Odienné<br>Guinea<br>Nzérékoré Bondoukou<br>Berekum<br>Danané Man Côte d'Ivoire<br>Sunyani<br>Abengourou<br>Liberia Ghana<br>Grand-Bassam<br>Atlantic Ocean<br><!-- End of picture text --> _Note_ : Black dots indicate the location of primary sample units (PSUs) for the 1986–8 surveys at the Cˆote d’Ivoire-Ghana border and for the 1992–4 surveys at the borders with Mali and Guinea. Grey diamonds indicate the location of regional cities (5,000 inhabitants or more) as of 1990; city names are reported only for cities over 30,000 inhabitants. The dashed line delineates the 75 km bandwidth around the boundaries.

We then characterize the PSUs of household surveys by five geographical variables: rainfall, elevation, distance to the nearest river, population density, distance to the closest regional city, and distance to the capital city (for more details on variable sources and construction, see supplemental appendix S2). The geographical locations of cities with 5,000 inhabitants or more in the years 1960 and 1990 15