rafmacalaba/datause-encoder-data huggingface dataset:
1980]. In this case, the adding-up restriction is automatically satisfied.

Banks et al. [1997] have extended this system to a quadratic form (QUAIDS) which then writes as follows: To estimate the above equations, we would need detailed information on prices in Brazil which we do not have for all relevant goods. This constitutes an important limitation to our analysis. If prices were uniform in Brazil, this would not be a concern. However, this seems a too strong assumption given the great heterogeneity in living conditions, which vary greatly according to the region of residence (see descriptive statistics). It is important to take this regional heterogeneity into account in our estimations, due to the fact that it might be correlated with important differences on relative prices, particularly for non-tradeables, including health and education services. In addition, this source of heterogeneity could be correlated with unobservables in the quality of the services provided (e.g. public schools and public hospitals), the opportunity cost to access them, household preferences and returns on investment in child human capital.

If the true specification of the empirical model is linear as in equation 17, prices only affect the intercept _α_ . In this case, one possibility to control for regional variation in prices is the inclusion of dummies based on a spatial definition (see also Attanasio et al. [2011]). Indeed, Banks et al. [1997] shows that the linear formulation appears to be a reasonable approximation for the food expenditure share curves if compared with a nonparametric estimation. The same applies for fuel and other goods, apart from alcohol and clothing.

We make use of the sampling strata of the POF survey and include 549 regional dummies, which are defined on the basis of the sampling units, in our specification to control for the regional variation in prices.<sup>68</sup> We work with the maintained hypothesis that prices differ between those strata but are uniform within spatial strata. Given the spatial proximity within any of the 549 strata, we believe that this is a reasonable approximation. We modify the above equation to allow for an error term _ε_ and add the regional dummies whereby _rj_ refers to the dummy variables for the regional strata from 1 to 549.

> 68There are 550 regional survey strata in our data set. They include the division of areas in urban, rural, states, metropolitan region, among others.

30
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-8-

# School and individual contribution to variance in achievement

12. The first step in the HLM estimation process involves fitting an unconditional, or random regression model. We do this in two steps. In the first step, we partition the variance in the mathematics and Portuguese test scores into their between unit (school) and within unit (individual) components. The results are presented in Table 1; they show that school level factors account for nearly two-thirds of the variance in mathematics achievement but only about one third of the variance in Portuguese achievement. This result is consistent with research from other countries showing that, as one might expect, the more abstract the subject matter, the more important formal schooling is for developing student achievement.

Table 1: Results of variance component analysis: Brazil secondary, 1988

|Score|School|Individual|
|---|---|---|
|Mathematics|62.38|37.62|
|Portuguese|36.36|63.64|

13. In the second step, we fit an unconditional model that includes the SES-achievement gap. Table , presents the results. The average mathematics score is 14.29 (out of a maximum possible score of 45) and the average Portuguese score is 17.36 (out of a maximum possible score of 35). The average social class differentiation (the average within-school social classachievement slope) is .14 for mathematics and .31 for Portuguese. All are significant at probability levels less than .05. The school average achie.vement estimates are highly reliable for both mathematics and Portuguese, but the social class differentiation estimates are considerably less so. This
In this paper, we construct cost-of-living indexes for different groups of households in order to quantify differences in the inflation burden across the populations of countries in Europe and Central Asia (ECA). We use microdata on household expenditures to construct consumption baskets for each household and use detailed price data to track the evolution of the price of such baskets. We generate detailed inflation estimates for 19 emerging market and developing economy (EMDE) countries in ECA and provide a limited set of estimates for 25 countries in the European Union (EU) and the European Economic Area (EEA).

Our results show that the high inflation of 2022 had a heterogeneous impact on European populations. The burden of inflation was spread unevenly across households, with poor households appearing to have suffered the most from rising food and energy prices. Households in the bottom decile of the consumption distribution faced inflation, that is, for the average country in EMDE ECA, 2.3 percentage points higher than the inflation faced by households in the top decile, with the difference reaching 5 percentage points in some countries. We also show that poverty and inequality rates and the profiles of the poor derived from household-specific inflation rates differ from those based on the standard CPI approach. In all countries in our sample, the poverty headcount rate, the poverty gap, and the Gini index of inequality are higher when using COL household-specific indexes than when using the CPI.

The rest of the paper is organized as follows. Section 2 reviews the literature. Section 3 presents the methodology for constructing household-specific COL indexes, and section 4 describes the data used for our analysis. Section 5 presents a descriptive analysis of the differences in inflation rates across goods and services and in consumption patterns across households. Section 6 presents the main findings. Section 7 discusses the implications of our approach for the analysis of poverty and inequality. Section 8 draws policy implications.

# **2 Literature review**

Official measurements of the cost of living started in the late 19<sup>th</sup> century. In 1914, the US and UK governments constructed an index to adjust wages during the period of high inflation driven by World War I. After World War II, the COL index was renamed the Consumer Price Index (CPI) in the US and the Index of Retail Prices (IRP) in the UK (USBLS 1966; Courtney 2014). The longstanding debate among researchers about the limitations of these indexes in informing economic 3
**Table 4 : Cities grow faster than regencies**

||**1993-97**|**1999-2001**|**2001-05**|
|---|---|---|---|
|Regencies_(Kabupaten_)|5.4%|2.5%|3.0%|
|Cities_(Kota_)|6.2%|7.3%|3.6%|

Source: Authors’ calculations based on GRDP data from BPS. Real data (constant 1993 prices).

By contrast, being landlocked and having poor roads does not always mean slow growth. Contrary to the often cited disadvantages of landlocked countries, the growth rate of landlocked districts does not appear to be systematically lower than that of coastal districts. More surprising still is that the (perceived) quality of roads is not related to growth rates. Districts where villages report worse perceptions of road quality do not have slower growth rates than those that report higher quality roads, and having a higher share of villages with asphalt roads is only very weakly associated with higher growth.

While there is a very strong relationship between being poor and being far from a large city, there is no obvious connection between remoteness and growth **.** There are no districts with a GDP larger than Rp 2 trillion (around US$222 million) that are more than 1,000 km from one of Indonesia’s five large cities. On average, the growth rates of these smaller and more remote districts are no lower than those of districts far closer to the main economic centers of the country (Figure 7).

<!-- Start of picture text --> Figure 7: Does distance matter?<br>Further away places are poorer... ... but they don’t necessarily grow more slowly<br>0 500 1000 1500 2000 2500<br>Distance to one of the five large cities<br>0 500 1000 1500 2000 2500<br>Distance to one of the five large cities<br>.15<br>.1<br>00,000,000,000<br>0 60,<br>.05<br>00,000,000,00<br>0 40,<br>0<br>Geometric Average Growth 2001-2005<br>Real Income (GRDP) BY=2000 200520,000,000,000,00 -.05<br>0<br><!-- End of picture text --> Source: Authors’ calculations based on GRDP data from BPS. Real data (constant 1993 prices).

Districts’ growth rates do, however, appear to be positively correlated with their neighbors’ growth rates. Figure 8 shows the correlation between districts’ growth rates with those of their neighbors. During the pre-crisis (1993-97) and post-crisis (1999-2001) periods there appears to be a positive association between the per capita growth of a district and that of its neighbors. After decentralization, however, this relationship appears rather weaker, although a positive 14
larger effects, consistent with using a more stringent innovation measure. Workers capture part of the rents of firms.

The last two columns of both Table 9 and Table 10 show the dynamic effect of innovation on wages. The effect on wages of a firm becoming innovative is positive and lasting, around 2 log points and constant for _t_ and _t_ +2. This suggests that workers also appropriate some of the rents or competitive advantages associated to innovation in a way that is persistent over time. Table E1 and Table E2 re-estimates the same models but without allowing transitions from innovators to non-innovators. The results are qualitatively identical.

Table 9: Do firms pay higher wages after becoming innovative?

|||y: log of|wage||
|---|---|---|---|---|
||(1)<br>Mean|(2)<br>Mean|(3)<br>Dynamic|(4)<br>Dynamic|
|Mean efect|0.0169***<br>(0.00327)|0.0168***<br>(0.00328)|||
|Efect at t|||0.0195***<br>(0.00345)|0.0193***<br>(0.00345)|
|Efect at t+1|||0.0181***<br>(0.00355)|0.0180***<br>(0.00355)|
|Efect at t+2|||0.0197***<br>(0.00347)|0.0195***<br>(0.00347)|
|Individual-Year|Yes|Yes|Yes|Yes|
|Region-Sector||Yes||Yes|
|Observations|949896|949896|949896|949896|
|R-squared|0.946|0.946|0.946|0.946|

_Note:_ The dependent variable is log of hourly wage. Innovative firms is measured by a dummy = 1 if there is a PoTec workers for firm _f_ in year _t_ but none in _t −_ 1. Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, ** and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

As a robustness check, Figure 4 plots the wage differences between treated and untreated workers of firms that become innovative after 2014. The figure provides some visual reassurance that changes in the wage premium between the two groups start after firms become innovative and last on time. Specifically, this positive effect persist over time on about 2 log points more wages. The figure shows a higher effect on wages on the year of becoming an innovator, and then a lower increase but constant over time. While the period used for the analysis does not allow to analyze the dynamic effect over a longer horizon, it is likely that the persists even longer than _t_ + 2. Overall, the results provide strong evidence for a causal effect of innovation on a wage premium, supporting other studies in developed countries (Reenen (1996);Martinez-Ros (2001)).

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**Figure 3. The Growth Deceleration Effects of the Risk of Civil War and Riots (1970-99)**

<!-- Start of picture text --> Civil Wars Riots<br>-0.080% OECD<br>-0.36%<br>-0.480% Other Developing<br>-0.37%<br>-0.756% SSA<br>-0.31%<br>-0.585% All<br>-0.36%<br><!-- End of picture text --> Note: Change in growth-civil wars=Beta(civil wars)*St.Dev(civil wars) Change in growth-riots=Beta(riots)*St.Dev(riots) where Beta(civil wars)=-0.24 and Beta(riots)=-0.02. St.Dev.(civil wars)=0.02,0.03,0.02, and 0.003 for all sample, SSA, Other developing, and OECD, respectively. for St.Dev.(riots)=0.17,0.14,0.17, and 0.17 all sample, SSA, Other developing, and OECD respectively.

**Figure 4: East Asia-Sub-Saharan Africa Income Gap (log y(EA)/y(SSA))**

<!-- Start of picture text -->
1.35<br>1.25<br>1.15<br>1.05<br>1970- 1978 1982 1986 1990 1994 1998<br>74<br>Civil War Effect Income Ratios EA:SSA<br>Real Income Ratios EA:SSA<br>Ratio 1970-74<br><!-- End of picture text -->

34
42

# **13. Literature**

- Bergl6f, E. (1995): Corporate governance in transition<sup>economies -The theory and its policy</sup> implications, in: Aoki, M. and H.-K.<sup>Kim (eds.),</sup><sup>_Corporate governance in_</sup> _transitional economies: insider control and the role_<sup>_of banks,_The World Bank, EDI</sup> development studies, Washington DC.

- Blommestein, H. and M. Spencer (1993): The Role<sup>of Financial Institutions in the Transition</sup> to a Market Economy, _International Monetary_<sup>_Fund,_IMF working paper no. 93-75,</sup> Washington DC.

- Borish, M., M. Long, and M. Noel (1995): Restructuring<sup>banks and enterprises,</sup><sup>_The World_</sup> _Bank,_ Discussion paper no. 279, Washington DC.

- Boycko, M., A. Shleifer, and R. Vishny (1994):<sup>Mass privatization in Russia, OECD, OECD</sup> advisory group on privatization, Fifth plenary<sup>session, Mass privatization: a first</sup> assessment of the results, 2-4 March 1994, Paris.

- Brom, K. and M. Orenstein (1993): The 'privatized<sup>sector' in the Czech Republic:</sup> Government and bank control in a transitional<sup>economy,</sup><sup>_Institute of EastWest_</sup> _Studies,_ Prague.

- Cable, J. (1985): Capital market information<sup>and industrial performance: the role of West</sup> German banks, _The Economic Journal,_ vol.<sup>95, 118-32.</sup>

- Claessens, S. (1995): Corporate governance and<sup>equity prices: evidence from the Czech and</sup> Slovak Republics, _The World Bank,_ Policy<sup>research working paper, no. 1427,</sup> Washington DC.

- Coffee, J. (1994): Investment privatization funds:<sup>The Czech experience, Joint Conference of</sup> the World Bank and the Central European University<sup>Privatization Project; 15-16</sup> December 1994, _The World Bank,_ Policy Research<sup>Department, Transition</sup> Economics Division, Washington DC.

_Creditanstalt_ (1994): Central & Eastern Europe weekly<sup>market review, Vienna.</sup>

- Dittus, P. (1994): Corporate governance in Central Europe:<sup>the role of banks,</sup><sup>_Bank for_</sup> _International Settlements,_ Economic paper no. 42, Basle.

- Dittus, P. and S. Prowse (1994): Corporate control<sup>in Central Europe and Russia: Should</sup> banks own shares?, Joint conference of the World Bank<sup>and the Central European</sup> University Privatization Project; 15-16 December 1994, _The World_<sup>_Bank,_Policy</sup> Research Department, Transition Economics Division, Washington DC.

- Drabek, Z. (1993): Capital markets in Central Europe,<sup>_Charles University,_Center for</sup> Economic Research and Graduate Education (CERGE), and Economics<sup>Institute of</sup> the Academy of Sciences of the Czech Republic (El), Working paper<sup>series no. 43,</sup> Prague.
# **IV. Infrastructure and Growth: Regression Analysis**

The purpose of this section is to study the effect of infrastructure on the rate of per capita GDP growth. The goal is to obtain a sensible measure for this effect that can be used in the quantitative projections for Egypt. To perform our estimations, we use pooled cross-country and time-series data covering 78 countries over the period 19612005. The data is organized in non-overlapping five-year periods, with each country having at most 9 observations. The panel is unbalanced, with some countries having more observations than others. We build on the panel-data growth regression literature that uses a GMM procedure to address endogeneity and control for unobserved countryspecific factors. This was introduced by Arellano and Bond (1991 and 1998) and applied, for example, in Levine, Loayza, Beck (2000) and Dollar and Kraay (2004).<sup>24</sup> The econometric methodology is explained in detail in Appendix 3.

## **A. Data and Regression Specification**

Our point of departure is a standard growth regression equation designed for estimation using (cross-country, time-series) panel data: Where the subscripts _i_ and _t_ represent country and time period, respectively; _y_ is the log of output per capita, _CV_ is a set of control variables, and _I_ represents infrastructure;  _t_ and  _i_ denote unobserved time- and country-specific effects, respectively; and  is the error term. The dependent variable ( _yi,t-yi,t-1_ ) is the average rate of real output growth, that is, the log difference of GDP per capita normalized by the length of the period. The regression equation is dynamic in the sense that it includes the level of output per capita ( _yi,t-1_ ) at the start of the corresponding period in the set of

> 24 The estimation results presented in the text follow the standard procedure.  To check robustness, we applied the standard-error correction proposed by Windmeijer (2005) in exercises not presented here.  The qualitative results were, however, the same.  In particular, the coefficients related to the infrastructure indices remained statistically significant.

25
poverty rate in the country, being representative at the national, provincial, and urban/rural levels. Across the four provinces, 24,809 households were interviewed, broken down into 15,936 rural and 8,873 urban households.<sup>4</sup> Table 1 shows that the official (national) poverty rate in FY19 was 21.9,<sup>5</sup> but it varied between the provinces and urban and rural areas. The spatial distribution of the poor varies, as the province with the lowest population share (Balochistan) also has the highest poverty rate, almost twice as high as the national poverty rate.

**Table 1: Official Poverty rate in Pakistan FY19**

||**National**|**Punjab**|**Sindh**|**KPK**|**Balochistan**|**Urban**|**Rural**|
|---|---|---|---|---|---|---|---|
|Population (%)|-|53.9|23.0|17.1|5.9|36.4|63.6|
|Poverty rate|21.9|16.3|24.1|29.5|42.7|10.9|28.1|
|Share of thepoor|-|40.0|25.4|23.0|11.6|18.12|81.9|

Source: Author calculations using HIES 2018/19 Note: Population shares use the survey expanded weights Table 2 provides information on the macroeconomic indicators, including growth rates, inflation, and population. PBS publishes data on population growth rates, inflation, and the GDP series between FY20 to FY22.<sup>6</sup> Growth rates, along with inflation forecasts, between FY23 andFY25 are projected by the World Bank, and published through the semiannual Macro Poverty Outlook.<sup>7</sup> While headline inflation is projected until FY25, Classification of Individual Consumption According to Purpose (COICOP) category-level projections are not available beyond FY23. Additionally, Table 2 showcases one of the main development challenges in Pakistan: The need for substantial growth to outpace population increases. This can be seen in the situation in the FY25, when real GDP growth reached 2.4 percent, but no per capita changes were achieved.

**Table 2: GDP and Sectoral Growth Rates, Inflation, and Population**

||FY20|FY21|FY22|FY23*|FY24*|FY25*|
|---|---|---|---|---|---|---|
|GDPgrowth rates,nominal(%)<sup>a</sup>|8.8|16.8|21.1|24.7|32.0|19.7|
|GDPgrowth rates,real(%)<sup>a</sup>|-0.9|5.8|6.1|-0.6|1.7|2.4|
|GDPper capitagrowth rates, real(%)<sup>a</sup>|-2.6|3.8|4.1|-2.5|-0.6|0.1|
|_Sectoral growth rates, real_<sup>_a_</sup>|||||||
|Agriculture|3.9|3.5|4.3|1.0|2.2|2.4|
|Industrial|-5.7|8.2|6.8|-2.9|1.4|2.3|
|Services|-1.2|5.9|6.6|-0.5|1.5|2.4|
|Mining and Quarrying|-7.2|1.7|-7.0|-4.4|0.5|1.0|
|Manufacturing|-7.8|10.5|10.9|-3.9|1.7|2.6|
|Electricity Generation and Gas Distribution|3.5|9.0|3.1|6.0|1.4|2.5|

> 4 The HIES (2018/19) was conducted in the four provinces of Punjab, Sindh, Khyber Pakhtunkhwa (including merged FATA districts), and Balochistan.

> 5 The national Cost-of-Basic-Needs (CBN) poverty line in Pakistan was estimated in 2013–14.  All poverty estimates provided are based on this official measure of poverty, unless otherwise mentioned. See Appendix A for details.

> 6 https://www.pbs.gov.pk/sites/default/files/tables/national_accounts/2021-22/Table_4.pdf

> 7 The projections are taken from the Macro Poverty Outlook published in October 2023.

4