rafmacalaba/datause-encoder-data huggingface dataset:
**Implications of public investment efficiency.** The efficiency of public investment plays a crucial role in driving its growth effects.<sup>26</sup> The estimates suggest a greater effect on GDP in response to public investment shocks in EMDEs with the highest efficiency, culminating in an increase in output of about 1.6 percent after five years—one-half percentage point higher than the effect of public investment in EMDEs with the lowest efficiency (figure 5). In countries with the lowest efficiency, the effects of public investment are lower and not statistically significant (albeit still positive).

**Figure 5. Effects of public investment on output conditional on public investment efficiency**

# **A. High investment efficiency B. Low investment efficiency**

<!-- Start of picture text --> Percent Baseline response Percent Baseline response<br>3.0 3.0<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Year Year<br><!-- End of picture text --> _Source:_ Authors’ estimates.

_Note:_ Responses of real GDP (cumulative change in year _t_ relative to year _t_ = -1, in percent) to a public investment shock equivalent to 1 percent of GDP, conditional on fiscal space. _t_ = 0 is the year of the shock. Dashed line indicates the baseline unconditional response. Shaded area denotes 90 percent confidence bands, based on standard errors clustered at the country level. High-efficiency and low-efficiency samples are based on the top and bottom quartiles of the IMF (2021b) public infrastructure efficiency index, which ranges from 0 to 100 (the values above 81 and below 47, respectively).

These results are consistent with empirical studies using other samples and methods and provide support for the argument that low public investment efficiency is problematic.<sup>27</sup> Poor design, evaluation, and implementation of investment projects, including issues with corruption and governance, can deplete valuable fiscal resources without necessarily increasing the quantity or quality of public infrastructure that supports growth (Dabla-Norris et al. 2012; IMF 2014; Pritchett

> 26 The analysis uses the IMF (2021b) public infrastructure efficiency index, which is a cross-sectional index available for 120 countries (including 93 EMDEs), produced using the data envelopment analysis. The index ranges from 0 to 100, with higher values indicating better efficiency. The model was also estimated using the Devadas and Pennings (2018) infrastructure efficiency index and the Dabla-Norris et al. (2012) public investment management index. Estimations using alternative measures also suggest statistically insignificant and lower output effects of public investment in low-efficiency economies (see the results in the robustness section).

> 27 See, for instance, Cavallo and Daude (2011); Furceri and Li (2017); IMF (2014); Izquierdo et al. (2019); Leduc and Wilson (2012); and Leeper et al. (2010).

20
# **References**

- Aghion, Philippe, Celine Antonin, Luc Paluskiewicz, David Stromberg, Raphael Wargon, Karolina Westin, and Xueping Sun. “Does Chinese research hinge on US co-authors? Evidence from the China initiative”. 2023.

- Akcigit, Ufuk, Sina T Ates, Josh Lerner, Richard R Townsend, and Yulia Zhestkova. “Fencing off Silicon Valley: Cross-border venture capital and technology spillovers”. _Journal of Monetary Economics_ , vol. 141, 2024, pp. 14–39.

- Alnuaimi, Tufool, Jasjit Singh, and Gerard George. “Not with my own: Long-term effects of cross-country collaboration on subsidiary innovation in emerging economies versus advanced economies”. _Journal of Economic Geography_ , vol. 12, no. 5, 2012, pp. 943–68.

- Archibugi, Daniele, and Jonathan Michie. “The globalisation of technology: a new taxon-

- omy”. _Cambridge journal of Economics_ , vol. 19, no. 1, 1995, pp. 121–40.

- Azoulay, Pierre, Wesley H Greenblatt, and Misty L Heggeness. “Long-term effects from early exposure to research: Evidence from the nih “yellow berets””. _Research Policy_ , vol. 50, no. 9, 2021, p. 104332.

- Azoulay, Pierre, Shumin Qiu, and Claudia Steinwender. “Paper Tiger? Chinese Science and Home Bias in Citations”. _National Bureau of Economic Research Working Paper Series_ , no. 32468, 2024.

- ———. “Who Stands on the Shoulders of Chinese (Scientific) Giants? Evidence from Chemistry”. _National Bureau of Economic Research Working Paper Series_ , no. 30772, 2022.

- Bergeaud, Antonin, and Cyril Verluise. “The rise of China’s technological power: the perspective from frontier technologies”. 2022.

- Bian, Bo, Jean-Marie Meier, and Ting Xu. “Cross-border institutions and the globalization of innovation”. 2023.

- Blomstr ̈om, Magnus, and Ari Kokko. “Multinational corporations and spillovers”. _Journal of Economic surveys_ , vol. 12, no. 3, 1998, pp. 247–77.

28
that have been superseded by a deeper one are not included. For example, the association agreements of the EU with the new member states that preceded the Eastern Enlargement are not included. As we do not have any information about their depth, we assume that they are shallow ones.

As an alternative measure for depth, we use on the DESTA database provided by Dür et al. (2014), which focuses on only seven policy areas (c.f., Hofmann et al. (2019) for a discussion of the two methodologies). We classify an agreement as deep whenever the depth index is larger than three. DESTA provides for almost all agreements information about their depth; if no information is available, we assume that the trade agreement is shallow.

# **4 The NTB Effects of Deep Trade Agreements Revisited**

Next, we show how accounting for misreported tariffs and globalization trends affects the finding of the literature that deep trade agreements reduce NTBs. We also discuss potential treatment heterogeneity and perform numerous robustness checks.

## **4.1 Baseline Results**

Table 1 shows the baseline results. We start by estimating Equation (4) without controlling for (1 + _τijkt_ ), which yields the combined effect of tariff reductions and NTB effects without accounting for globalization trends. The results can be found in column (1). To tease out the pure NTB effect, we control for tariffs, first using information downloaded from WITS (column (2)), then from the cleaned GTD that accounts for misreporting (columns (3) and (4)). Lastly, we add the border dummies to control for globalization trends (columns (5) and (6)). All specifications include importer-sector-time, exporter-sector-time, and pair-sector fixed effects.

Panel (A) includes data for the three broad sectors of agriculture, manufacturing, and services and the years 2000 to 2015. When using all provisions of the World Bank’s DTA data to determine the depth of trade agreements, we find that deep agreements increase overall trade by 25% while shallow ones and agreements notified under the Enabling Clause do not activate trade (column (1)). The size of the effect is very standard in the literature. The size of the sample used to obtain these results is large: _N_ = 474 _,_ 312 sector-pair combinations. If one accounts for ad valorem tariffs from the standard WITS data set in the specification, the sample shrinks to _N_ = 360 _,_ 522 observations as there are many missing values.

Specification (2) suggests that deep TAs continue to remain statistically significant determinants of bilateral trade, with a trade-creating effect of 16%. Shallow TAs and the enabling 13
<!-- Start of picture text --> Decrease in firm productivity from 1% increase in power outages<br>4.0 7,000<br>3.5 6,000<br>3.0<br>5,000<br>2.5<br>4,000<br>2.0<br>3,000<br>1.5<br>2,000<br>1.0<br>0.5 1,000<br>0.0 0<br>GDP/cap<br>Percentage decrease in productivity resulting from one percent increase in outages<br>%<br>GDP pc<br>South Africa Angola Nigeria Ghana Zambia Kenya Cameroon Tanzania Mali Rwanda Burkina Faso Uganda DR Congo Malawi<br><!-- End of picture text --> _Figure 2: Decrease in firm productivity from 1% increase in power outages (left axis) and national‐level GDP per capita in 2019 USD (right axis) (Source: Mensah, 2016)_ A meta‐review of literature analyzing constraints to growth in developing countries finds that over 40% of such studies classify electricity as a binding constraint (McCulloch and Zileviciute, 2017). Based on the World Bank Enterprise Survey, Bbaale (2018) assesses the impact of electricity outages on manufacturing firms in 26 African countries. The study shows that power outages lead to self‐ reported losses in sales of 11‐12% for small, medium, and non‐exporting firms. For large and exporting firms, the study observes that the substitution of grid supplied power with generators tends to increase productivity.

Using Enterprise Survey panel data for 15 Sub‐Saharan African countries, Mensah (2016) identifies reductions in firms’ productivity by 0.6% to 1.1% for a percentage increase in outage intensity. A similar study based on a panel of 23 African countries estimates that a one percent increase in electricity outages would account for a loss in firms’ total factor productivity of 3.5% (Mensah, 2018). Ramachandran et al. (2018) use Enterprise Survey data as well but allow for heterogeneity among firms’ experiences of outages. By clustering firms according to their affectedness by outages and their growth behavior, they find a large group of firms seemingly unaffected by outages, a group that seems to be able to cope with outages through the usage of generators, and a group severely affected by power outages that cannot cope even when using generators.

Increasing the reliability of infrastructure services has been found to significantly improve outcomes both at the firm‐ and macro‐economic level. For instance, Ilmi (2011) estimates the marginal impact of electricity reliability on firm costs, using firm‐level data collected by the Business Environment and Enterprise Productivity Survey (BEEPS) in 26 countries in Europe and Central Asia. Eliminating all electricity outages would allow firms to reduce costs by about 1.4%, on average. In the Western Balkans, firms loose on average 5.8 percent of their annual sales due to electricity issues, according to the same survey (Kresic, Milatovic and Sanfey, 2017). The losses caused by outages are particularly pronounced in Africa. One study 9
Table A.9: Parameter Estimates: Electrical and Optical Equipment

||VC|M|P-|R|
|---|---|---|---|---|
||coef.|s.e.|coef.|s.e.|
|Labor Share Equation: Wage|0.07<sup>_∗∗∗_</sup>|(0.009)|0.06<sup>_∗∗∗_</sup>|(0.009)|
|Labor Share Equation: Capital|0.05<sup>_∗∗∗_</sup>|(0.01)|0.05<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Energy Price|0.06<sup>_∗∗∗_</sup>|(0.01)|0.06<sup>_∗∗∗_</sup>|(0.01)|
|Labor Share Equation: Materials Price|-0.01|(0.02)|0.002|(0.01)|
|Labor Share Equation: Output|-0.09<sup>_∗∗∗_</sup>|(0.009)|-0.09<sup>_∗∗∗_</sup>|(0.009)|
|Labor Share Equation: Time Trend|-0.004<sup>_∗∗∗_</sup>|(0.0007)|-0.006<sup>_∗∗∗_</sup>|(0.0007)|
|Labor Share Equation: Constant|0.5<sup>_∗∗∗_</sup>|(0.009)|0.5<sup>_∗∗∗_</sup>|(0.007)|
|Energy Share Equation: Wage|-0.002<sup>_∗_</sup>|(0.001)|-0.002|(0.001)|
|Energy Share Equation: Capital|0.009<sup>_∗∗∗_</sup>|(0.002)|0.006<sup>_∗∗∗_</sup>|(0.002)|
|Energy Share Equation: Energy Price|0.004<sup>_∗∗_</sup>|(0.002)|0.008<sup>_∗∗∗_</sup>|(0.002)|
|Energy Share Equation: Materials Price|0.010<sup>_∗∗∗_</sup>|(0.003)|-0.002|(0.002)|
|Energy Share Equation: Output|-0.006<sup>_∗∗∗_</sup>|(0.001)|-0.004<sup>_∗∗∗_</sup>|(0.001)|
|Energy Share Equation: Time Trend|-0.0001|(0.0001)|-0.0001|(0.0001)|
|Energy Share Equation: Constant|0.02<sup>_∗∗∗_</sup>|(0.001)|0.02<sup>_∗∗∗_</sup>|(0.001)|
|Euler Equation for Capital: Adjustment Term|0.4<sup>_∗∗_</sup>|(0.2)|0.3<sup>_∗_</sup>|(0.2)|
|Euler Equation for Capital: Wage|0.4<sup>_∗∗∗_</sup>|(0.1)|0.4<sup>_∗∗∗_</sup>|(0.09)|
|Euler Equation for Capital: Capital|0.1|(0.1)|0.2|(0.1)|
|Euler Equation for Capital: Energy Price|0.03|(0.1)|-0.08|(0.1)|
|Euler Equation for Capital: Materials Price|-0.3<sup>_∗_</sup>|(0.2)|0.04|(0.1)|
|Euler Equation for Capital: Output|-0.6<sup>_∗∗∗_</sup>|(0.09)|-0.7<sup>_∗∗∗_</sup>|(0.09)|
|Euler Equation for Capital: Time Trend|0.008|(0.007)|0.004|(0.008)|
|Euler Equation for Capital: Constant|-2.1<sup>_∗∗∗_</sup>|(0.09)|-2.0<sup>_∗∗∗_</sup>|(0.07)|
|Number of observations|26|3|26|3|
|Labor Share Equation: _R_<sup>2</sup>|0.|93|0.|93|
|Energy Share Equation: _R_<sup>2</sup>|0.|79|0.|77|
|Euler Equation for Capital Stock: _R_<sup>2</sup>|0.|87|0.|86|
|LR Test: _φl_ =_φe_ =_φm_ =_ζ_ = 0, _χ_2 (pval)||18.54|(0.00)||

Notes. VCM: Vintage Capital Model. P-R: Pindyck and Rotemberg (1983) Model. coef.: estimated coefficient. s.e.: standard error.<sup>_∗∗∗_</sup> p _<_ 0.01,<sup>_∗∗_</sup> p _<_ 0.05,<sup>_∗_</sup> p _<_ 0.1 44
Table 29 Composition of Expenditure _(percent)_

|||||_1993 Priority_|_survey results_|||
|---|---|---|---|---|---|---|---|
|_Expenditure Categories_|_CPI basket_<br>_Abidjan blue_<br>_collar worker_<br>_households_|_Abidjan_<br>_poor_|_Abidjan_<br>_non- poor_|_Abidjan_|_Savannah_|_C6te_<br>_d 'Ivoire_<br>_poor_|_C6te_<br>_d 'Ivoire_<br>_non- poor_|
|Food|48.0|44.7|34.7|34.8|6.1|56.8|46.0|
|Lodging and utilities|16.3|27.8|20.0|20.1|16.2|21.4|17.9|
|Clothing|10.1|5.4|6.3|6.3|6.1|6.8|6.7|
|Transport|12.2|5.6|12.5|12.4|3.3|2.8|7.7|
|Household maintenance|4.0|0.9|2.0|1.6|1.2|1.1|1.6|
|Hygiene|1.0|2.5|2.0|2.0|2.1|2.4|2.1|
|Health|0.7|5.4|6.7|6.7|1.3|2.3|5.0|
|Education|1.0|4.0|5.0|5.0|0.9|1.9|3.5|
|Miscellaneous|6.7|0.6|1.1|1.1|1.0|0.7|1.1|
|Remittances sent||3.0|9.7|9.6|5.2|3.6|8.3|
|Non-food in kind receipts||0.1|0.1|0.3|0.4|0.3|0.2|

Table 30 Consumer Price Index Blue - Collar Worker

|_(November 1992 - October_|_1993 = 100)_|||
|---|---|---|---|
|_Expenditure Categories_|_December 1993_|_March 1995_|_December 1995_|
|Food|100.3|144.8|142.3|
|Lodging|100.0|114.7|114.7|
|Utilities|104.7|119.4|129.2|
|Furnishing|103.0|166.4|161.6|
|Clothing|100.0|168.3|167.9|
|Transport|100.0|138.4|138.4|
|Vehicles|108.0|141.9|141.9|
|Household Maintenance|100.3|131.7|131.2|
|Hygiene|100.1|149.2|156.3|
|Health|101.4|147.3|154.9|
|Miscellaneous|104.6|172.9|171.9|
|Total|101.4|145.0|144.4|

over time for each region. Other countries, such as Ghana, have a rural price index available. The urban and rural deflators for the Ghana poverty analysis are based on rural and urban price indices. Given the difference in expenditure shares between the survey and CPI baskets in Ghana, the food and nonfood price indices were weighted by the survey shares to derive the deflators. Such a procedure might be envisioned for CMte d'Ivoire, though in the absence of regional price indices over time and given the differences even in the composition of food expenditure between the survey and the CPI basket, it is not clear that the result would necessarily be more accurate.

The use of the Abidjan price deflator as the price deflator for other regions could potentially be a problem, since there is a big difference between Abidjan and the Savannah in the share of expenditure on food. The food share is 62 percent in the Savannah compared with 35 percent in Abidjan, according to the 1993 survey (table 31). The higher share on food in the Savannah was offset by a somewhat lower share of expenditure on lodging and utilities, health and education, transport and remittances. Since the price increase in lodging and utilities was far less than the overall increase in prices, this would have contributed to the Savannah having a slightly higher rate of inflation than Abidjan, if the price index were recalculated on the basis of the Savannah consumption basket.

34
Policy Research Working Paper 7153

# **Abstract**

This paper assesses the link between democracy and inequality. Inequality is measured at the cohort level with pseudo-panel data built from nine Latin American countries’ household surveys (1995–2009, biannual). Democracy is measured as a stock during long periods of time both before and after each cohort’s year of birth. The paper presents evidence that long-run historical patterns in the degree of democracy relate to income inequality. However, this relationship is non-monotonic: inequality first increases with the stock of democracy before falling. The paper also presents evidence that education may be a mechanism explaining this result.

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbalcazarsalazar@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table A10:  Global E-Governance Index (EGV)**

|**Data Provider**<br>|Brown University Center for Public Policy<br>||||||||
|---|---|---|---|---|---|---|---|---|
|**Description**|University located in Providence, United States||||||||
|**Website**|www.insidepolitics.org||||||||
|**Data Source**|Global E-Governance Index||||||||
|**Type**<br>|Expert assessment<br>||||||||
|**Respondents**<br>|Research team led by Professor Darrell M. West<br>||||||||
|**Frequency**|Annual since 2002||||||||
|**Coverage**|Global sample of countries||||||||
|**Public Access**|Yes||||||||
|**Description**|This source reports an assessment of the quality of e<br>government websites. Features assessed include on<br>clips, video clips, non-native languages or foreign l<br>premium fees, user payments, disability access, pri<br>online services, number of different services, digita<br>address, comment form, automatic email updates,<br>assistant (PDA) access, and an English version of t<br>100-point scale with 72 points for availability of pu<br>the number of online services available.|-gover<br>line p<br>anguag<br>vacy p<br>l signa<br>website<br>he web<br>blicati|nment<br>ublicat<br>e tran<br>olicy, s<br>tures,<br>perso<br>site. A<br>ons an|based<br>ions, o<br>slation,<br>ecurity<br>credit c<br>nalizati<br>ssessm<br>d datab|on revi<br>nline d<br>comm<br>featur<br>ard pa<br>on, pe<br>ents a<br>ases a|ews of<br>atabase<br>ercial<br>es, pre<br>yments<br>rsonal d<br>re scor<br>nd 28 p|official<br>, audio<br>advertising,<br>sence of<br>, email<br>igital<br>ed on a<br>oints for||
|**Voice and Acco**|2007<br>**untability**|2006|2005|2004|2003|2002|2000<br>1998|1996|
|NA|..|..|..|..|..|..|..<br>..|..|
|**Political Stabili**|**ty and Absence of Violence**||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Government Ef**|**fectiveness**||||||||
|Global E-govern|ance Index<br>X|X|X|X|X|X|..<br>..|..|
|**Regulatory Qua**|**lity**||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Rule of Law**|||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Control of Corr**|**uption**||||||||
|NA|..|..|..|..|..|..|..<br>..|..|
|**Country Covera**|**ge**<br>**196**|**196**|**195**|**192**|**195**|**194**|**..**<br>**..**|**..**|
|**Year of Publica**|**tion**<br>**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**<br>**..**|**..**|

48
_Table 8: Summary statistics - credit constraint index and its components before and after imputation_

||**N.**<br>**Obs**|**Mean**|**Std. dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**_Before imputation_**||||||
|Is access to finance an obstacle?|15,694|0.47|0.50|0|1|
|Is access to finance the main obstacle faced by the firm?|14,509|0.15|0.36|0|1|
|-100*Proportion of investments financed by banks|7,364|-26.71|34.92|-100|0|
|Is the firm credit constrained|12,820|-3.30|0.76|-4|-1|
|The firm is partially credit constrained|13,873|-0.16|0.37|-1|0|
|**_Post imputation_**||||||
|Is access to finance an obstacle?|16,000|0.47|0.50|-0.93|1.83|
|Is access to finance the main obstacle faced by the firm?|16,000|0.15|0.36|-1.05|1.34|
|-100*Proportion of investments financed by banks|16,000|-25.61|35.15|-163.23|96.55|
|Is the firm credit constrained|16,000|-3.31|0.74|-5.61|-0.93|
|The firm is partially credit constrained|16,000|-0.16|0.37|-1.40|1.07|
|**Latent measure of Credit Constraint**|**16,000**|**0.00**|**0.89**|**-3.10**|**2.90**|

25
Table 5: ITT Treatment Effect Estimates, Business Processes

|Outcome variable|OLS<br>ITT Treatm<br>Estim|ent Effect<br>ates|Observations|Control group<br>mean<br>(std. dev.)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Id f ll litd bl|0.072|0.035|378|-0.036|
|nex o a process measures se eow|(0.104)|(0.098)||(0.975)|
|Index components|||||
|Developed new products during last year dummy|-0.048|-0.046|378|0.531|
||(0.055)|(0.053)||(0.500)|
|Attracted new clients during last year dummy|-0.020|-0.033|376|0.789|
||(0.046)|(0.045)||(0.409)|
|Implemented new process during last year dummy|-0.062|-0.070|378|0.617|
||(0.053)|(0.052)||(0.487)|
|Attracted new investors during last year dummy|0.027|0.024|378|0.074|
||(0.032)|(0.031)||(0.262)|
|Began process to register a patent during last year|0.045||376|0.079|
|dummy|(0.034)|||(0.270)|
|Began certification process for an international|-0.024||378|0.156|
|standard (e.g. ISO)|(0.035)|||(0.364)|
|Made new marketing effort during last year|0.129**||378|0.440|
|dummy|(0.055)|||(0.497)|
|Expanded installations during last year dummy|-0.030||377|0.240|
||(0.045)|||(0.428)|
|Remodeled installations during last year dummy|0.022||377|0.459|
||(0.054)|||(0.499)|
|Human resources management index|-0.062|-0.061|363|0.022|
||(0.152)|(0.146)||(1.450)|
|Keeps formal accounts dummy|0.076**|0.069**|378|0.852|
||(0.030)|(0.029)||(0.356)|
|Controls for baseline value of outcome|No|Yes||-|

Note: The index follows the methodology in Kling, Liebman, and Katz (2007) and is the normalized average of z- scores for all non-missing process measures, using mean and standard deviation in the control group to calculate the z-scores. Each row in Columns 1 and 2 contains the treatment effect point estimates and robust standard errors for separate OLS estimations. All regressions include controls for strata dummies and re-randomization variables, as well as a dummy for having been surveyed in March 2009 (vs. April, May or June) at follow-up. Some variables are not available at baseline, which is why the corresponding cells in Colum 2 are empty. Column 4 contains means and standard deviations for the control group at follow-up. Significance levels: *10 percent, **5 percent, ***1 percent.

27