rafmacalaba/datause-encoder-data huggingface dataset:
oil price based fiscal rule, moving towards more exchange rate flexibility, and improving the process of selecting, implementing and monitoring public investment projects) are exactly in the areas where reform was needed. But there are clear dangers of slippage: non-oil deficits have been above safe levels, in particular if off budget commitments and arrears are taken into account, and have been increasing. And finally, Nigeria’s poor record on public investment performance makes it clear that reform of the public investment process, including anti-corruption measures, should remain at the top of the policy agenda. Only if the reform process is brought back on track and maintained in the years to come, is there a chance that Nigeria’s oil wealth will turn from a curse into a blessing.

# **References**

Aghion, Ph., Ph. Bacchetta and R. Ranciere (2006), Ëxchange Rate Volatility and Productivity Growth: the Role of Financial Development", NBER WP 12117 Budina, N. and S. van Wijnbergen (2006), “The Dutch Disease revisited: Low Growth in Oil Rich Countries”, mimeo, World Bank Corden, M. and J. P.Neary (1984), "Booming Sector and Deindustrialization in a Small Open Economy", Economic Journal, vol.92 Hausman, R. and R. Rigobon (2002), Än Alternative Interpretation of the Resource Curse: Theory and "Policy Implications", NBER WP 9424

- IMF(2005), Nigeria: 2005 Article IV Consultation – Staff report; Staff Supplement; and Public Information Notice on the Executive Board Discussion, IMF country report No. 05/302.

- Lane, Phillip R. and Aaron Tornell, “Power, Growth and the Voracity Effect,” _Journal of Economic Growth_ , Vol. 1(2), 1995

Manzano and Rigobon (2001), Resource Curse or Debt Overhang?, NBER Working Paper 8390.

Pinto (1991), “Black markets for foreign exchange, real exchange rates and inflation”, Journal of International Economics 30, 121-135, North Holland.

Bank Economic Review, Vol. 3, No. 3: 321-338.

Pinto (1990), “Nigeria during and after the oil boom: a policy comparison with Indonesia”, The World Bank Economic Review, Vol. 1, No. 3, pp. 419-445.

Wijnbergen, S. van, "Inflation, Employment and the Dutch Disease in Oil Exporting Countries: A Short-Run Disequilibrium Analysis", Quarterly Journal of Economics, May 1984 25
|AIC<br>266683.4|265230.7<br>192269.3|190755.5|197779.2<br>197003.1|
|---|---|---|---|

Notes: each column shows an OLS model predicting a dependent variable with or without controls for age, gender and employment status. Tax index is the sum of the tax morale and tax fine variables.

# **Manipulation checks**

We run models to assess whether the treatments affect responses to the manipulation check questions. The questions were phrased as follows: _What type of corruption does the [comptroller name here] investigate? (Government / Private sector / Not sure)_

# And,

_After taking this survey, do you feel like you have opportunities to tell the government how to spend money? (Yes / No )_ Since these questions were designed to make sure that the treatments would affect them, failure to reject the null hypothesis in these models would lead us to question the effectiveness of the treatment. As Table 5 shows, the treatments are effective in moving respondents’ answers to the manipulation check questions. However, there appears to be some spillover between treatments: the bottom-up treatment also appears to move the top-down manipulation check and vice versa, though only to about half the degree that the “correct” treatment affects the manipulation checks. We also implement a robustness check in Table 6 based on the manipulation check results.

Table 5: Manipulation check models

||**Top-down manip check**|**Bottom-up manip**<br>**check**|
|---|---|---|
|**(Intercept)**|0.462***|0.480***|
||(0.003)|(0.003)|
|**Top down/Control**|0.064***|0.055***|
||(0.005)|(0.005)|
|**Bottom up/Control**|0.031***|0.091***|
||(0.005)|(0.005)|
|**N**|65433|65434|
|**p**|0.0|0.0|
|**Log-likelihood**|-47390.0|-47224.7|
|**AIC**|94788.1|94457.5|

Notes: Each column shows an OLS model predicting the manipulation check variables relating to the top down treatment and the bottom up treatment. The relevant treatment has a large effect on the correct manipulation check in each case.

13
||||domestic<br>sales|
|---|---|---|---|
|TRD‐<br>11|1<br>1<br>,<br>,<br>,<br>,<br>_x_<br>_c_<br>_ds_<br>_c t_<br>_c t_<br>_c_<br>_e_<br>_c t_<br>_c t_<br>_c_<br>_QE_<br>_PE_<br>_QD_<br>_PDS_<br><br><br><br><br><br><br><br><br><br>|_c CE_<br>_CD_<br>_t_<br>_T_<br><br><br>|Export‐<br>domestic<br>sales ratio|
|TRD‐<br>12|,<br>,<br>,<br>,<br>,<br>,<br>_c t_<br>_c t_<br>_c t_<br>_c t_<br>_c t_<br>_c t_<br>_PX_<br>_QX_<br>_PDS_<br>_QD_<br>_PE_<br>_QE_<br><br><br><br><br>|_c_<br>_C_<br>_t_<br>_T_<br><br>|Producer<br>output<br>price|
|TRD‐<br>13|���,��<br>������,��⋅����,������,��⋅����,������,��<br>��∈�<br>⋅����,��|�∈��<br>�∈�|Trade and<br>transport<br>margin<br>demands|
||_t_<br>_TRDGDP_<br>|||
|TRD‐<br>14|00<br>00<br>00<br>00<br>,<br>,<br>_c_<br>_c t_<br>_c_<br>_c t_<br>_c C_<br>_c C_<br>_t_<br>_EXR_<br>_pwe_<br>_QE_<br>_EXR_<br>_pwm_<br>_QM_<br>_RGDPMP_<br><br><br><br><br><br><br><br><br>|_t_<br>_T_<br>|Real trade‐<br>GDP ratio|
|TRD‐<br>15|00<br>,<br>, ,<br>00<br>,<br>,<br>, ,<br>00<br>,<br>, ,<br>00<br>,<br>,<br>00<br>00<br>00<br>00<br>,<br>,<br>_t_<br>_c h_<br>_c h t_<br>_c C_<br>_h H_<br>_c f_<br>_f c_<br>_i f t_<br>_c C_<br>_i INS_<br>_f_<br>_FCAP_<br>_c dstk_<br>_c i t_<br>_c C_<br>_i INS_<br>_c gov_<br>_c t_<br>_c C_<br>_c_<br>_c t_<br>_c_<br>_c t_<br>_c C_<br>_c C_<br>_RGDPMP_<br>_PQD_<br>_QH_<br>_PQD_<br>_capcomp_<br>_DKINS_<br>_PQD_<br>_qdstk_<br>_PQD_<br>_QG_<br>_EXR_<br>_pwe_<br>_QE_<br>_EXR_<br>_pwm_<br>_QM_<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|_t_<br>_T_<br>|Real GDP<br>at market<br>prices|

# **A.2.3. Current payments by domestic institutions**

This equation block explains payments that are part of the current accounts of domestic institutions, i.e. current incomes and spending for households, the government and enterprises. In the model and its database, it is necessary to include at least one household, and, in practice, models applied to countries in this world invariably have a government. Enterprises are optional. Even though the model and the database can handle multiple representative households, this mathematical statement assumes for simplicity that there is only one household. The sets for institutions distinguish between _INSD_ (all domestic institutions), _INSDNG_ (all non‐government domestic institutions, i.e. households and enterprises), and _H_ (households, which may include “non‐profit institutions in service of households”). Enterprises differ from households in that they do not consume.

On the income side, the shares of domestic institutions in factor incomes, _SHIF_ , are defined on the basis of their stock (or endowment) shares (INS1); the stocks ( _QFINS_ ) are defined ‐43‐
We also asked about 6 hypothetical forms of political violence, including whether they discuss protesting with friends or making trouble or conflict with the authorities (See Appendix D). We did not measure perceived social category directly.

An index of all three composites shows that all treatments decreased the self-reported acceptability of violent behaviors in the short run. Cash had little impact on self-reported attitudes in the long run, but therapy plus cash led to a .18 standard deviation decline. We cannot reject that the effects of therapy only and therapy plus cash are equal. The overall effect is driven by attitudes toward criminality and political violence.

As with self control, we divide the 29 questions into two indexes by high and low emphasis in the curriculum. Table 7 reports the ITT estimates after a year. The effect of cash and therapy is at least as low for the low emphasis components.

# **6.6 Other outcomes of interest**

NEPI’s therapy targeted other behaviors, especially ones they thought could contribute to antisocial behavior, including substance abuse or harmful peers. These possible mechanisms and also important outcomes. Table 8 reports impacts on all these other measures.

**Prosocial behavior** We measured various prosocial behaviors in the long run surveys (including group memberships, group and community leadership, contributions to local public goods, and trust in others). In contrast to the steep reductions in antisocial behaviors, we see no evidence that therapy or cash led to more positive social engagement.

**Mental health** We asked about two disorders we deemed relevant. We measured five symptoms of post-traumatic stress using existing Liberian instruments.<sup>46</sup> We also measured neuroticism, the tendency to experience emotional instability or anxiety, assessed with 8 questions from the NEO-5 factor personality inventory (Costa and McCrae, 1997). After one year, the therapy plus cash group reported 0.17 standard deviations lower post traumatic stress and 0.15 standard deviations lower neuroticism (though both are not significant).

**Self efficacy and esteem** The therapy did not treat these traits directly, and we have no theory to suppose a direct effect, but both could improve with self control and image change, and both could reinforce reductions in antisocial behaviors.<sup>47</sup> We asked eight questions from a standard locus of control questionnaire, which aims to measure the extent to which 46We used the five symptoms with the highest factor loadings in surveys of ex-combatant mental health by Blattman and Annan (2015).

> 47Negative self image has been linked with many aspects of negative behavior and counterproductive or extreme risk-seeking behavior (Coopersmith, 1967).

28
Panel c. Kakwani Coefficient and Redistributive effect <!-- Start of picture text --> 0.70<br>0.014<br>0.60 Kakwani Marginal contribution to redistributive effect<br>0.012<br>0.50<br>0.010<br>0.40 0.008<br>0.30 0.006<br>0.20 0.004<br>0.10 0.002<br>0.00 0.000<br>-0.10 -0.002<br>-0.20 -0.004<br>Indirect taxes Direct taxes and contributions<br>None<br>Kakwani<br>Redistributive effect<br>VAT PIT<br>unequalizing <-------> equalizing<br>Regressive<--------------------> Progressive<br>Excises alcohol Excises tobacco Carbon taxes PIT interest<br>employed) employer) Disease Fund<br>Oil derivates direct effect Oil derivates indirect effect Cont.Gral Sickness & maternity (employee & employer) Cont.Gral Sickness & maternity (self Cont.Health (employee & employer) Cont.Health (self employed) Cont.Unemployment (Employee and Contributions Work Injury Occup<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

Indirect subsidies (gas and electricity)

**The distributional impact of indirect subsidies on gas and electricity becomes evident when considering their progressivity, size, and effects on poverty and inequality.** Electricity subsidies, although less progressive when compared to most direct transfers, stand out due to their larger scale. In contrast, gas subsidies are less progressive and relatively minor in magnitude. While displaying a progressive nature, family transfers are limited in size, as depicted in Figure 8a.

**Indirect subsidies play a role in poverty reduction but contribute to small inequality reductions. However, the influence of gas subsidies in this regard is minimal.** Indirect subsidies, which reach lowincome households below the poverty line, reduce poverty, as illustrated in Figure 8b. When viewed collectively, the group of indirect subsidies contributes to a progressive impact on the overall distribution. This is reflected in a slight reduction of the Gini coefficient, indicating a move towards greater equality. Figure 8c provides insights into this, showing that the direct effects of electricity subsidies follow a progressive trend, whereas those of gas subsidies are regressive.

**In essence, the comprehensive analysis of these subsidy programs underscores the trade-off between their progressivity, size, and effects on poverty and inequality.** While larger subsidies like electricity may be less progressive than direct transfers, they still have a considerable impact due to their scale. On the other hand, the contribution of gas subsidies to poverty reduction and overall distributional improvement is relatively limited.

29
# 3. Data and Methodology

The data used in this paper are obtained from three rounds of Moroccan household budget surveys ( _Enquête Nationale sur les Niveaux de Vie des Ménages_ ) held in 2000, 2006 and 2013. Household expenditures (per capita) are used as the main welfare indicator throughout the analysis. Since reliable income data are difficult to obtain, we use consumption as a measure of well-being (Deaton and Zaidi, 2002). Indeed, informal sectors are an important part of the workforce in these countries, despite difficulties involved in quantifying such income. To compare different years, consumption is deflated using the local CPI and further converted into 2011 international dollars, using the PPP conversion factor obtained from the World Development Indicators website.<sup>5</sup> The methodology used throughout the paper is the “relative distribution” method, a fully nonparametric statistical framework that enables comparison of the entire consumption distribution at two different points in time (Handcock and Morris, 1998, 1999).<sup>6</sup> Despite its name, the relative distribution method produces an _absolute_ measure of polarization. More formally,<sup>7</sup> let<sup>_Y_</sup> 0<sup>be the welfare variable for the reference year (2000) and</sup><sup>_Y_the welfare</sup> variable for the comparison year (2013). The relative distribution is defined as the ratio of the density of the comparison year to the density of the reference year evaluated at the relative data _r_<sup>8</sup> :  1 where _f_  and<sup>_f_</sup> 0  denote the density functions of _Y_ and _Y_ 0 , respectively, and<sup>_y_</sup> _r_  _F_ 0  _r_ is the quantile function of<sup>_Y_</sup> 0<sup>. When no changes occur between the two distributions,</sup> _g_  _r_ has a uniform distribution; a value of _g_  _r_ higher (lower) than 1 means that the share of households in

> 5 Accessible at: <u>https://data.worldbank.org/.</u>

> 6 The relative distribution is a well-established approach to distributional analysis. At present, it has been employed by Alderson et al. (2005), Massari et al. (2009a, b), Alderson and Doran (2011, 2013), Borraz et al. (2013), Clementi and Schettino (2015), Nissanov and Pittau (2016), Petrarca and Ricciuti (2016), Clementi et al. (2017, 2018, 2019a, 2019b), and Nissanov (2017).

> 7 Here we limit ourselves to illustrating the basic concepts behind the use of the relative distribution method. Interested readers are referred to Handcock and Morris (1998, 1999) for a more detailed explication.

> 8 For a formal definition of “relative data”, see _infra_ .

6
of the curve, we find a group of Latin American and Caribbean (LAC) countries with high inequality and average female participation rates, and another group of Euro-Mediterranean countries with similar participation rates but much lower inequality. Other groups of countries are more spread out along the curve, as is the case for sub-Saharan African (SSA) countries.

Secondly, we test the U-shaped hypothesis at the micro level; notably, we expect that FLFP can both have a pro-equality or pro-inequality impact when regressed on household income inequality, depending on which regional blocs of countries, e.g. MENA, SA and LAC, a country belongs to. In these countries, the increase in FLFP will almost always have an inegalitarian impact (as they are on the left-hand side of the curve); in continental Europe the impact is mixed, but strongly egalitarian in Euro-Mediterranean and Scandinavian countries, although with different FLFP rates.

As it is important to examine this issue from a broader perspective, we attempt to put together different data sources to come up with a more integrated approach to the problem. Unfortunately, in the micro-analysis, a trade-off needed to be made between the homogeneity of data sources and completeness of information. On the one hand, the choice of the Luxembourg Income Study (LIS) database enabled us to use a very reliable and widely used set of variables; on the other hand, the LIS’s usefulness was limited because it lacked information on developing countries. Whereas LAC countries are broadly represented, MENA countries are only partially included, and there are severe data limitations. Therefore, in order to provide a micro-snapshot of MENA countries, the World Bank’s PovcalNet data set was used, with the Arab Republic of Egypt, Morocco and Jordan subsequently included. However, in addition to the fact that these countries use consumption rather than income data as a welfare variable, the other main limitation is that the main instrument we use for correcting endogeneity – the average women’s wage of the past 10 to 30 years depending on the country (Blau and Kahn, 2007) – is not available for them. Nonetheless, we are comfortable with the results because for these countries we are mostly interested in the sign and significance of the impact, rather than the magnitude. Plus, from these results, we can affirm that the data behave exactly like the data of countries for which we can correct the endogeneity (India and LAC).

3
23 some of the prices, then one must expect the FRG figures to be biased towards the US ones.

# 6. Conclusions

The application of index numbers to disaggregated Korean footwear exports during 1974-86 suggests that the period of binding VERs led to significant price increases, but not to the quality upgrading predicted by early theoretical analyses. The distinctive feature of our results is that, unlike virtually all previous empirical studies of VERs, they are derived from exporters', rather than from importers' data. Hence the results in this paper provide a more direct test of the reactions of exporting countries subject to VERs and to the theoretical analyses of quality upgrading which refer to the exporting countries behavior towards restricted markets. 9 Our comparisons drawn from multilateral indices allowed us to show that, after adjusting for product and country mix changes, the price of Korean footwear exports to the US (and to a somewhat lesser extent to the UK) rose significantly in the years when the VERs were binding (i.e. when quotas were filled). These results confirm and extend earlier work by Aw and Roberts (1988) on US footwear imports, but are more pertinent to testing the price and quality effects of VERs insofar as disaggregated exporters data to different sources provide a better control for product differentiation (etc.) than equally disaggregated importers data from several sources. These results were further confirmed by more disaggregated bilateral calculations.

The multilateral results for product mix change suggest that the VERs were associated, if anything, with less quality upgrading during the
**Table 9. Differences in Fuel Exporter Effects, 1996–2004 and 2005–13**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Constant|-7.52***|-7.08|-0.107|
||(-8.31)|(-4.84)|(-0.07)|
|Dummy 2005–13|0.786***<br>(3.39)|3.11***<br>(6.76)|2.26***<br>(4.55)|
|Dummy fuel > 25%<br>exports|4.25***<br>(8.56)|10.19***<br>(11.86)|6.60***<br>(8.19)|
|Dummy fuel > 25%|0.730|-1.31|-2.59**|
|exports *<br>Dummy 2005–13|(0.94)|(-1.13)|(-2.51)|
|Dummy ores and|0.320|0.385|0.400|
|metals > 10%<br>exports|(1.11)|(0.79)|(0.79)|
|Hard peg dummy|1.38***<br>(2.89)|-2.63***<br>(-4.50)|-4.05***<br>(-7.93)|
|Political stability|0.0605***|0.467***|0.414***|
|(0 to 100)|(4.60)|(20.77)|(17.66)|
|n|1,378|1,392|1,378|
|R-squared|0.143|0.299|0.224|
|RMSE|4.72|8.04|8.02|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996-2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

18
America and the Caribbean currently pursue various export-promotion policies, including trade liberalization, export-processing zones, and export promotion agencies. One of the justifications for such policies is the apparent existence of wage premiums for workers employed by firms that sell a large share of their production abroad. If sectoral wage premiums are in fact related to foreign markets, then export-promotion policies could be welfare enhancing. More generally, industry-specific policies, including other forms of industrial policies, could help reduce the gap between the private and social returns to schooling. The evidence reported in this paper can help guide these policy options.

The rest of this paper is organized as follows. Section 2 reports several estimates of average skill premiums for the countries under investigation: Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay. To test their robustness, we discuss results from various model specifications that differ in terms of definitions of skilled workers, sub-samples of the data, and econometric estimators. In addition, the analyses in Section 2 assess whether international differences in skill premiums are associated with relative endowments of skilled workers, heterogeneity in the composition of skilled workers, or heterogeneity in gender-specific skill premiums. Section 3 presents estimates of industry-specific skill premiums for 60 tradable and non-tradable sectors covered by the employment survey data, including 23 manufacturing sectors. After a brief analytical discussion of inter-industry wage differentials and the role of exports, Section 4 discusses the empirical analysis of exports as determinants of the skilled premium in manufacturing sectors. Section 5 concludes by summarizing the main findings.

# **2 Estimation of National Skill Premiums**

We start by estimating national wage premiums paid to skilled workers using household-level data from sixteen Latin American economies: Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru and Uruguay. The data include information on wages, 3