rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A1:**

|**Variable**|**Defnition**|
|---|---|
|_Household characteristics_||
|Household size|Number of family members living in the household as reported by respondent.|
|Household head is female (1=Yes)|Dummy equal to 1 if the gender of the household head is female.|
|Education of head of household (years)|Number of years of complete education by the household head.|
|Satisfed with ... (1=Yes)|Measures based on the response to the following question: “How satisfed are<br>you with... (i) the quality of your foor; (ii) the quality of your walls; (iii) the<br>quality of your roof; and (iv) the quality of your family life. Would you say you<br>are “Very Unsatisfed”, “Unsatisfed”, “Neither satisfed nor unsatisfed”, “Satis-<br>fed”, or “Very satisfed?" Each measure is a dummy variable that equals 1 if the<br>respondent reports that being “Satisfed” or “Very satisfed” and 0 otherwise.|
|Quality of Life Index (ICV)|Index computed by SIUBEN to assess the poverty status of a given household<br>based on the following characteristics: availability of electricity and water, waste<br>management, fuel used, hosueholds goods, type of house, household head edu-<br>cation, schooling of children, type of foor, walls and roof, type of toilet used and<br>household size divided by house size. The ICV ranges from 1 (lowest quality of<br>life) to 4 (highest).|
|Hours at home per day|<br>Number of hours that the head of the household reported staying at home on a<br>typical day.|
|Trust in others (from 1 to 10)|Based on the response to the following question: "Do you believe most people<br>would take advantage of you, if they could, or would they try to be fair to you?".<br>The answer is a scale from 1 to 10 where 1 reports a strong belief that others<br>would take advantage of the respondent, and 10 a strong disbelief.|
|_House characteristics_||
|House with dirt foor (1=Yes)|House has at least one room with dirt foor.|
|Number of rooms in the house|Number of rooms in the house excluding bathroom and kitchen.|
|Share of bedrooms with dirt foor|Number of bedrooms with dirt foor over total number of bedrooms in the house.|
|Walls made of wood or zinc (1=Yes)|Main material of external walls is either wood or zinc.|
|House is owned (1=Yes)|Dummy that takes value 1 if house is owned and fully paid, 0 otherwise.|
|Monthly rent if leased out (USD)|Based on the response to the following question: "If you were leasing your house,<br>how much would you ask for it monthly?". Respondents could answer using<br>ranges of monthly rents. The value in this variable uses the mid-point in each<br>|
||range.|
|Most important repair was not foor upgrade (1=Yes)|Based on the response to the following question: "What is the most important<br>upgrade or repair that you would do to the house?" The measure is a dummy<br>variable that equals 1 if the respondent did not answer foor upgrade.|
|Distance to closest targeted household (in km)|Km to closest targeted household computed using the GPS location obtained dur-<br>ing the baseline survey.|

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theoretical point of view, as long as the elasticities are within the range [-1,1]. We suggest testing for unit elasticity between sectors, which may be interpreted as "full sectoral spillover." For Ghana, hardly any of the elasticities are close to one; hence, unity restrictions give rise to very large changes in the other parameters. Therefore, we focus on the issue of exogeneity and take the estimated co-integrating restrictions as given. We apply the restrictions directly as error correction terms in the short-run analysis.<sup>9</sup> Table 8 presents the findings. In each case, the first specification applies only a normalizing restriction (except for Ghana, which requires normalizing and identifying restrictions). The second specification is the overidentified, or testable, co-integrating vector. The restricted co-integrating relations are presented in tables 9 and 10.

**Table 8. Tests of Co-Integrating Restrictions**

|Restriction(s)|Log-likelihood|X<sup>2</sup>-stat.|Degrees of<br>freedom|
|---|---|---|---|
|_C6te d'Ivoire_||||
|ri.d=-l|258.919|||
|3inddi-=1ACXserO=|258.124|1.591|2|
|_Zimbabwe_||||
||<sup>3</sup>ind= <sup>1</sup>|221.797|||
|PMindlAagrP-<br>se lAacserO|220.114|3.367|3|
|_Ghana_||||
|rman-1|435.318|||
|Oman=lAaagr=aser-aconagas=0|428.053|14.53|8|

_Note:_ In the subscripts for the variables in the restrictions, ind denotes industry; agr, agriculture; ser, service; man, manufacturing; min, mining; con, construction; and gas, gas and water. Note that for the case of Ghana, the tests implies restrictions on both of the 3 and a parameters, respectively, of the variable in question.

_Source:_ Authors' calculations.

9 The analysis has to apply identifying restrictions equaling the number of co-integrating restrictions minus one, that is, one in the present setting (this explains why all additional restrictions are testable in the case of one co-integrating relation, as this is exactly identified). We choose to restrict the parameter of construction in the first co-integrating relation to its estimated value, that is, take it as given. This merely means that further testing is conditional on this value being "true", that is, it cannot be tested.

14
72 percent of the Brazilian GNP.<sup>9</sup> Our findings are thus representative of the large and modem parts of the Brazilian labor market, but do not necessarily carry over to the rural economy. Because we focus on manufacturing industries, this might not be very problematic.

The data used in this paper consists of people affiliated with any of the 20 manufacturing industries. We include workers or self employed working full-time (defined as working more than 25 hours per week) between ages 15 and 65. We use the data to create several variables that capture worker demographic characteristics such as wage, age, education, geographical location, informal sector of employment, self-employment, and industry affiliation. Our wage measure is hourly wage based on monthly wage divided by 4 times the reported number of hours worked per week. We deflate the hourly wage with the monthly national price index, IPCA. All wages are thus expressed in 1997 September _reals._ The main indicator for education is completed years of schooling, which is computed using an algorithm based on three survey questions on education. 10 Based on completed years of schooling, we classify workers into those with no complete education, complete elementary education, complete lower secondary education, complete secondary education, and complete tertiary education. 1 " We also distinguish whether a worker has formal or informal employment on the basis of _"carteira assinada",_ a signed workcard. A signed workcard entitles a worker to several rights and benefits regulated by the labor market legislations, which enables us to classify whether or not a person works for a formal establishment that complies with labor market regulation. The variable informal is an indicator that is one if the worker is employed in the informal sector of the economy.

> 9 Source: IBGE accounts of gross regional products in current market prices. Brazilian GNP was R$ 864,112 mil and the six states (Sao Paulo, Rio de Janeiro, Rio Grande do Sul, Minais Gerais, Pernambuco, and Bahia) together accounted for R$ 618,728 mil.

**10** The algorithm follows the standard conversion used elsewhere (see Lam and Schonie (1993) and Barros and Ramos (1996)).

"Elementary education in Brazil consists of four years of schooling. Secondary education, ensino medio, is comprised of two parts, 4-8 years of schooling and 9-11 years of schooling. Tertiary education runs from 12 to 15/17 years of schooling.

10
33 _Table_ **_10:_** _Characteristics of MFN tariff schedules in SEE-5 countries (2000)_

|MFNTariff<br>Schedules|Albania|Bosniaand<br>Herzegovin<br>a|Croatia|FYR<br>Macedonia|FR<br>Yugoslavia'"|Memorandum<br>FR<br>Yugoslavia<br>2001'|
|---|---|---|---|---|---|---|
|Simple averagetariff<br>Percentoftarifflines<br>above15%|<br>8.1<br>0|6.2<br>0|10.6|15.0|14.4|(9.2)|
|Agriculturalproducts<br>(HS1-24)|<br>10.9|4.9|15.3|21.8|23.0|n.a.|
|Industrialproducts<br>(HS25-97)|7.3|6.2|9.6|13.2|13.0|n.a.|
|Weightedaverage<br>tariff*|11.6|n.a.|9.0|14.2|9.12**|n.a.|
|Range/Maximum<br>tariffrates|0,2,10,<br>15|0,_5,_10,15|0-25%|0-60%|0-40%|0-30%|
|Standarddeviation|5.7|4.7|7.4|12.4|10.8|n.a|
|Coefficientof<br>variation<br>|0.7|0.8|0.7<br>|0.8|0.8<br>|n.a.|
|Specificrates|No|No|592<br>agricultural<br>and food<br>products<br>(8,4%oftariff<br>lines)subject<br>tocompound<br>rates|No|420agricultural<br>tarifflines (37%)|n.a.|

_Source: Own calculationsfrom national tariff schedules_ _* /Albania: IMF; Croatia: IMF; FYR Macedonia: Daskalov et al. 2001_ _**/Calculatedfrom 2000 tariff schedule and total imports without considering importsfrom FTA countries._ / In May 2001, the government introduced a new tariff schedule reducing levels of protection.

These losses are somewhat mitigated, as SEE-5 economies have relatively low MIFN tariff rates relative to other countries at a similar level of economic development. FR Yugoslavia and FYR Macedonia have the highest simple average rates of 14 percent and 15 percent respectively. But even these do not diverge significantly from the simple average rate of 14 percent of all IMF members. Despite different structures of production, the former Yugoslav republics display significant similarities in their respective tariff schedules. The last decade has not significantly impacted the structure of protection inherited from formerSFRY, although their level declined in all former republics. They all seem to be protecting the same sectors and products. It is interesting to note that Albania's structure of tariff protection does not diverge largely from those in former Yugoslav republics, while that of the EU is dramatically different.

However, the differences become more visible once the cascading effects of tariff structures are taken into account. Note that since tariffs are not uniform, protection (or subsidy) offered to domestic producers is not neutral among products at various stages of processing. SEE-5 tariff structure tend to "discriminate" against low processed products leading to tariff escalation, i.e., tariffs for fully processed products tend to be higher than for semi-processed products and raw materials. But overall the cascading effect is relatively low, as the simple average tariff rates on products in the third stage of processing are not significantly larger than on
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 4. EMPIRICAL ANALYSIS

This section summarizes the results of the analytical methodology described above. The specifications and the estimation techniques have been systematically adapted according to the different assumptions characterizing each model. The baseline model estimates the average empirical associations for all the countries included in the dataset, and is then adjusted to assess heterogeneity between country income groups. Finally, the model was tested on a sub-sample of resource-rich countries<sup>15</sup> , with a view to test the robustness of the empirical association.

## 4.1 The Baseline Model: Pooling All Countries

Table 6 presents the results from the recursive baseline model. The two equations have been estimated separately using a fixed-effect estimator and simultaneously using SURE plus country-level dummy variables. We find that, on average, nonrenewable resource dependence is negatively associated with both income inequality and GDP per capita. In the simultaneous estimation both coefficients increased because of the correlation between the error terms of the two equations.

Table 6: Estimated Results from the Recursive Model Specification (baseline model)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc **|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.059**|-0.087**|-0.149***|-0.132**|
|High-low education ratio|-0.639***|-|-0.203|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.001|-|0.002|-|
|Services value added share|0.078*|-|0.071|-|
|Manufacturing value added share|-0.047|-|0.069|-|
|Agriculture value added share|-0.091*|-|-0.112**|-|
|Log labor force|-|-7.916***|-|-17.991***|
|Log gross fixed capital formation|-|3.802***|-|7.472***|
|Log exports of goods and services|-|2.422**|-|3.422***|
|Log REER|-|5.082***|-|1.353|
|World oil price index|-|0.067***|-|0.067***|
|WorldMetalpriceindex|-|0.013|-|0.006|
|Observations|1015|1034|873|873|
|R2|0.058|0.383|0.910|0.858|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Table 7 shows the results of the non-recursive baseline model specification estimation— that is, the results obtained once the endogeneity related to the feedback effect of including the Gini in the GDP equation, and vice versa, has been accounted for. The estimation results confirm the previous findings from the recursive model, though the

> 15 Based on an IMF definition of ‘resource‐rich’ countries.

10
Table S.26: Impacts on children’s educational achievement - Bangladesh <u>general</u> studies: complementarity (OLS)

|Dependent variable:||Banglad|esh general stu|dies: GPA z|-scores||
|---|---|---|---|---|---|---|
|_X_:|No. of||Schoo|l-level ratio|of||
||students<br>in each|no. of<br>teachers|no. of<br>blackboards|no. of<br>benches|no. of<br>fans|no. of<br>toilets|
||school-|to|to|to|to|to|
||grade|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|0.082|-0.421*|-0.085|0.094|0.035|0.079|
||(0.182)|(0.232)|(0.248)|(0.277)|(0.070)|(0.095)|
|One product|0.645***|-0.054|0.218|0.977***|0.154|-0.041|
||(0.191)|(0.346)|(0.316)|(0.289)|(0.092)|(0.124)|
|All products _× X_|-0.000|20.199**|7.891|-0.064|7.221*|-4.123|
||(0.003)|(8.743)|(11.756)|(0.596)|(3.713)|(21.890)|
|One product _× X_|-0.010***|7.378|-4.991|-1.944***|-6.654|43.299|
||(0.003)|(13.510)|(14.799)|(0.614)|(9.324)|(27.714)|
|No. of students|-0.001|-|-|-|-|-|
|in each school-grade|(0.005)||||||
|R-squared|0.279|0.273|0.270|0.279|0.273|0.273|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.469**|-0.026|-0.187|-0.645**|-0.100|-0.128|
||(0.193)|(0.351)|(0.292)|(0.293)|(0.099)|(0.155)|
|One product|-0.227|0.236|-0.049|-0.179|-0.002|-0.249|
||(0.264)|(0.340)|(0.308)|(0.335)|(0.099)|(0.164)|
|All products _× X_|0.007**|-4.234|3.205|1.184*|-6.201|-0.391|
||(0.003)|(13.329)|(14.375)|(0.629)|(5.100)|(29.385)|
|One product _× X_|0.004|-10.735|1.406|0.359|-4.828|60.456*|
||(0.004)|(12.820)|(15.980)|(0.773)|(8.137)|(30.786)|
|No. of students|-0.007|-|-|-|-|-|
|in each school-grade|(0.005)||||||
|R-squared|0.131|0.128|0.127|0.131|0.128|0.133|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

71
3). In Latin America and the Caribbean the accumulated proceeds amounted to about 11 percent of 2000 GDP, or close to 4 percent of productive assets. Keeping in mind that privately operated assets are more productive than publicly operated assets, the contribution of the privatization process to GDP growth, in static terms, has been substantial. For instance, on the assumption that the productivity of assets is twice as high when they are privately operated than when they are publicly managed, privatization in Latin America and the Caribbean contributed about a quarter of a percentage point annually to growth, which is not negligible. In East Asia, South Asia, and Sub-Saharan Africa the intensity of privatization has been much less and may have contributed, at most, 0.1 percent of growth during this period.

The dominant sectors for privatization during the period 1988-2005 were infrastructure, finance, and energy. This is important because later we argue that these sectors are probably less competitive than the manufacturing sectors, with important implications for the impact of privatization.

Although we do not have reliable information on the size of the public sector at each point of time, we do have some data gathered by the World Bank in the mid-1990s on the share of SOE activity in GDP and the share of SOEs’ investment in gross domestic investment (GDI). These data, combined with the figures on accumulated proceeds as a share of GDP, provide a more complete picture of the extent of privatization in a sample of countries over the last 15 years. In Argentina, for instance, SOE activity was about 3 percent of GDP in 1990, as shown in Figure 5, and the accumulated proceeds from privatization for the period 1988–2005 were about 14 percent of 2000 GDP. Using a capital-output ratio of roughly 3, one can conjecture that in a country such as Argentina, privatization of the economy has basically been completed. In India, by contrast, the assets that have been privatized are small in comparison with the share of SOEs in GDP in 1993, and so the scope for privatization there is still rather large. The same can be said of China and many African countries, such as Côte d’Ivoire.<sup>3</sup> So, privatization is not an issue that belongs to the past and it is all the more important to understand how best to privatize in countries where there is still scope for it.

> 3 Although we lack information on the share of SOEs in GDP in China, it does not seem unreasonable to assume that it should be about half their share in GDI. Then SOEs would still be responsible for 10 percent of GDP in 1997; privatization might have reduced that share by roughly 2 percent since then.

4
current) on the current reliance on research is -0.175 (s.e.=0.027). This suggests that the disparities in usage of research can be expected to decline over time, interpretable as ―convergence.‖ It is of interest to look more closely at where this expected increase in demand for WB research is coming from. Tables 4 and 5 give the summary statistics across VPUs and sectors respectively. The VPU for which demand for research is expected to rise most on average is MNA while the lowest expected increase is for PRM. MNA is also the regional VPU with highest mean score for current reliance on research. The sector where demand is expected to increase most is EM while the lowest is SP. In both cases there is a tendency for the units that currently rely less on WB research to expect larger growth in usage in the future; the correlation coefficients between the expected increase and current levels are -0.32 and -0.22 across VPU and sectors respectively.

However, it remains that expected future reliance on WB research is significantly lower for the sectors with low current familiarity and use. The mean score for future reliance on research for staff in the seven sectors with lowest mean familiarity scores in Table 3—namely ARD, EM, ENV, PSG, SDV, TRN and URB—is 5.55 (s.e.=0.21) as compared to 6.29 (s.e.=0.14) for all other staff and the difference is significant at the 0.3% level (t=2.96).<sup>22</sup> Confining attention to the traditional ―hard sectors‖ (ARD, EM, TRN and URB) their mean expected future reliance on research is 5.34 (0.28) as compared to 6.72 (0.16) for EP, EDU, HNP, POV and SP (and the difference is significant; t=4.31). Even with the signs of convergence we have seen, the hard sectors can be expected to persist in their relatively low reliance on research for their work.

# **4. Accessing research within the Bank**

How do the Bank’s practitioners obtain the research they use? In addition to papers and reports, there are learning programs, seminars, formal and informal contacts with researchers, and formal and informal networks. The survey data on self-assessed familiarity with Bank

> 22 All but one of these sectors is mapped to SDN; the mean future reliance scores are 5.49 (s.e.=0.23) and 6.25 (0.14) for SDN and non-SDN sectors, and the difference is significant (t=2.85). Note that the staff in sectors mapped to SDN need not be in the SDN VPU; many are in regional units. However, there is very little difference in the score between staff in these sectors who are in the SDN VPU versus other VPUs; the mean scores are 5.52 and 5.47 respectively (and the difference is not significant; t=0.09). It is a ―sector effect‖ rather than VPU effect.

12
_where MDEB_<sup>_TistheMDEinablockeddesignwithsaturationμandMDE_</sup> _C_<sup>_TistheMDEin_</sup> _a clustered design with share of treatment clusters μ._

# **2.2 Using the RS Design to Estimate the Pure Control Outcome**

If a study has no pure control group, the counterfactual is at the mercy of within-cluster spillovers. In this context, the RS design has the distinct advantage of allowing a researcher to test for the presence of spillover effects and estimate the unperturbed counterfactual. If the spillover effect is continuous at zero, the researcher can use the variation in treatment saturation to project what would happen to untreated individuals as the saturation approaches ~~ˆ~~ zero.<sup>27</sup> With this unperturbed counterfactual in hand, we can then estimate _SNT_ , and use ~~ˆ~~ this value to correct the estimate of the _ITT_ .

Assumption 6 provides a simple way to estimate the pure control by assuming that the outcome variable is linear with respect to treatment saturation.

**Assumption 6.** _E_ ( _Y |T, π_ ) _is an affine (linear) function of π._

While it is possible to use a more flexible functional form and the specification can be tested, the linear case provides simple intuition for the technique.<sup>28</sup> Given Assumption 6, it is natural to estimate: Given RS design _ω_ with no pure control, estimating equation 4 with saturation weights and equation 5, the hypothesis test _δ_ 2 = 0 determines whether there is variation in the control outcome across saturations. If spillovers are present on untreated individuals, then the counterfactual needs to be corrected. The coefficient _δ_<sup>ˆ</sup> 0 is an estimate of the desired ‘pure’ control outcome, _E_ ( _Yic | Tic_ = 0 _, πc_ = 0), while _β_<sup>ˆ</sup> 0 is an estimate of the within-cluster control outcome actually used as the counterfactual, _E_ ( _Yic | Tic_ = 0 _, πc >_ 0). The difference between _β_<sup>ˆ</sup> 0 and _δ_<sup>ˆ</sup> 0 is the _SNT_ ~~ˆ~~ , which can be used to derive an unbiased estimate of the ~~ˆ~~ _ITT_ .

> 27Although continuity is a reasonable assumption, it is not universally applicable. Consider signalling in a ground-hog colony. Individuals are ‘treated’ by being alerted to the presence of a nearby predator, and the possible individual-level outcomes are ‘aware’ and ‘not aware’. The animal immediately signals danger to the rest of the colony, and control outcomes will be universally ‘aware’ for any positive treatment saturation, but ‘unaware’ when the saturation is exactly zero.

> 28In a panel difference in difference regression, the quantity giving the desired counterfactual would be the un-interacted ‘post-treatment’ dummy. This is the change the control group would have experienced at saturation zero

22