rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Long Term Female Primary Education Trends<br>by District Conflict Intensity<br>Western Calendar Year of Birth<br>1973-1974 1978-1979 1983-1954 1988-1989<br>oo<br>cr | | et<br>2wecuWO = _ tee—-—== “ |<br>Sa [ee | |<br>De | 77 Tage | |<br>Boy sams 26 | |<br>oe an --Cantral-- <--Excluded--> | <--Treated--> |<br>a Ms | | likely still in prirnary | started school |<br>a schooling in 1996 around 1996<br>2030 2035Nepalese Calendar 2040Year of Birth. 2045<br>— Low Intensity Tercile<br>sees Intermediate Intensity Tercile<br>—-—-—-—- High Intensity Tercile<br>Long Term Male Primary Education Trends<br>by District Conflict Intensity<br>Western Calendar Year of Birth<br>1973-1974 1978-1979 1983-1954 1988-1989<br>Bo | | | |<br>Bin eee |<br>Wun : re | | | |<br>=~ --Contob <--Excluded--> | <--Treated-> |<br>= m | | likely still in primary | started school |<br>os! schooling in 1996 around 1996<br>2030 2035Nepalese Calendar 2040Year of Birth. 2045<br>— Low Intensity Tercile<br>sees Intermediate Intensity Tercile<br>—-—-—-—- High Intensity Tercile<br><!-- End of picture text -->
**Table 7**

|C6te d'lvoire: Mean Household Size with and without Household-Size Weights. by Region 1985-88|
|---|

|||Abidjan||Oth|er Urb|an|Ea|st Fore|st|We|st Fore|st|S|avann|ah|C6te d'l|voire|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**A.**|B.|%|**A.**|B.|%|**A.**|**B.**|**%**|**A.**|B.|%|**A.**|B.|%|**A.**<br>B.|%|
||||Diff.|||Diff.|||Diff.|||Diff.|||Diff.||Diff.|
|1985|6.87|6.18 -|10.0|8.52|6.41|-24.8|8.44|6.60|-21.8|8.00|6.03|-24.6|8.04|7.24|-10.0|7.98 6.51|-18.4|
|1986|6.57|5.90 -|10.2|8.53|6.54|-23.3|8.22|6.56|-20.2|7.63|5.76|-24.5|7.39|6.32|-14.5|7.70 6.25|-18.8|
|1987<br>|6.06<br>|5.79<br>|-4.5|7.20|6.21|-13.8|7.49|6.74|-10.0|6.59|5.84|-11.2|6.70|6.21|-7.3|6.84 6.20|-9.4|
|1988|5.88|5.88|-|6.33|6.33|-|6.46|6.46|-|5.62|5.62|-|6.35|6.35|-|6.16 6.16|-|

A. Mean household size computed without household-size weights. B. Mean household size computed with household size weights.

14
Policy Research Working Paper 5682

# **Abstract**

This paper offers an empirical evaluation of the output contribution of infrastructure. Drawing from a large data set on infrastructure stocks covering 88 countries and spanning the years 1960–2000, and using a panel time-series approach, the paper estimates a long-run aggregate production function relating GDP to human capital, physical capital, and a synthetic measure of infrastructure given by the first principal component of infrastructure endowments in transport, power, and telecommunications. Tests of the cointegration rank allowing it to vary across countries reveal a common rank with a single cointegrating vector, which is taken to represent the long-run production function. Estimation of its parameters is performed using the pooled mean group estimator, which allows for unrestricted short-run parameter heterogeneity across countries while imposing the (testable) restriction of long-run parameter homogeneity. The long-run elasticity of output with respect to the synthetic infrastructure index ranges between 0.07 and 0.10. The estimates are highly significant, both statistically and economically, and robust to alternative dynamic specifications and infrastructure measures. There is little evidence of longrun parameter heterogeneity across countries, whether heterogeneity is unconditional, or conditional on their level of development, population size, or infrastructure endowments.

This paper is a product of the Macroeconomics and Growth Team, Development Research 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 ccaldero@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 4 Continued**

|**Panel C: Returns to Years of Schooling -- Piecewise Linear Spline**|||
|---|---|---|
|Model<br>1|2|3|
|Variable<br>ln(wage)|ln(wage)|ln(wage)|
|Years of Schooling for Years>12<br>0.119|0.097|0.087|
|(0.007)|(0.039)|(0.039)|
|Years of Schooling for Years<=12<br>0.053|0.071|0.071|
|(0.008)|(0.026)|(0.026)|
|Significance of Instruments|||
|Years of Schooling >12: F-Test<br>-|53.46|24.85|
|F-Probability<br>-|0.000|0.000|
|Years of Schooling<=12: F-Test<br>-|159.13|67.20|
|F-Probability<br>-|0.000|0.000|
|Over-Identification Test|||
|Hansen J-Statistic<br>-|-|3.21|
|Chi-Square P-Value<br>-|-|0.361|
|Observations<br>3610|3598|3598|
|R-Squared<br>0.341|-|-|
|Notes To Table 3:|||

(1) All models include jointly significant three year birth-cohort X city dummies, school quality and location variables, parent occupation dummies and educational attainment variables, number of siblings, age and age-squared (with age measured from month of birth to November 2001), gender (female=1), height, and gender x height, and jointly significant three-year birth cohort x city dummy variables. We report robust standard errors that are cluster corrected at the three-year birth cohort x city level.

(2) Instruments for high school and college in model (2) are predicted probabilities of college completion calculated from model (4) and of high school completion from model (7) in Table 1. Note, this model is exactly identified in Panels A and C, and so we do not report an over-identification test. (3) Instruments in model (3) are predicted probabilities of high school attainment from models (5) and (6) of Table 1, and predicted probabilities of college attainment using models (1), (2) and (3) from Table 1.

38
addition, the reallocation of workers to above-average productivity growth sectors made positive contributions to growth. However, only a small portion of growth in 1998-2014 can be traced to within-sector productivity growth. The remaining portion is explained by the lower employment rate and an increase in the share of working-age population.

**Table 4. Sectoral contributions to Inter-sectoral shifts (2006-2015)**

||Direction of<br>employment<br>share shift|Contribution to<br>inter-sectoral<br>shifts (%)|
|---|---|---|
|Agriculture|-|59.16|
|Mining|+|0.27|
|Utilities|+|0.45|
|Manufacturing|+|4.29|
|Construction|+|6.53|
|Commerce|+|-27.04|
|Transport|+|10.42|
|Finance|+|2.39|
|Other services|+|43.62|
|_Total_||_100_|

Source: Author’s estimation based on data described in Section 2.

The sectoral decomposition results suggest that ‘other services’, agriculture, and transport together explained nearly 70 percent of the structural change-induced growth. ‘Other services’ contributed by about 0.8 percent, nearly 30 percent of per capita growth. Agriculture’s small (negative) contribution is the combined outcome of a static loss and dynamic gain.<sup>33</sup> The remaining sectors are generally characterized by static gains but no dynamic gains/losses. In contrast, commerce made only meager contributions, despite employing over 50 percent of the workforce that left agriculture. Manufacturing made barely any contribution to growth since the late 1990s. Table 5 shows sectoral contributions to structural change. ‘Other services’ was the largest contributor, followed by agriculture and commerce.

**Table 5. Burkina Faso: Sectoral decomposition of GDP per capita growth: 1998-2014 (Annual percentage changes)**

|||% C<br>|ontributio|n of<br>|||
|---|---|---|---|---|---|---|
||Within-sector|Employment||Inter-sectoral||Total|
||productivity|rate|Static|Dynamic|total|(%)|
|Agriculture|0.56|-1.20|-0.38|0.83|0.45|-0.19|
|Mining|0.21|0.03|0.18|0.00|0.18|0.42|
|Utilities|-0.50|0.05|0.27|-0.01|0.26|-0.19|
|Manufacturing|0.15|0.00|-0.05|0.00|-0.04|0.11|
|Construction|-0.19|0.04|0.26|0.00|0.26|0.12|
|Commerce|-0.03|0.33|0.18|0.00|0.18|0.48|
|Transport|0.06|0.04|0.42|0.00|0.42|0.52|
|Finance|0.25|-0.01|-0.03|0.00|-0.03|0.21|
|Other services|-0.03|0.11|0.77|0.00|0.77|0.85|
|Subtotals|**0.47**|**-0.60**|**1.63**|**0.82**|**2.45**|**2.32**|
|Demographic co|mponent|||||**0.63**|
|Annual % chang<br>Source: Author’s est|e in GVA per cap<br>imation based on da|ita<br>ta described in Sect|ion 2.|||**2.94**|

Structural change played an important positive role during 2005-2014, but it has been dominated by static gains. Static structural change contributed by 1.74 ppts, roughly 70 percent of the 2.5 percent growth per capita, while dynamic gains made a insignificant contribution. By contrast, a decline in

> 33Dynamic productivity gain in agriculture reflects the fact that this sector shrank in terms of employment while productivity growth is below-average. Conversely, some of the labor that left agriculture moved to sectors that experienced higher productivity growth.

19
<!-- Start of picture text --> 14.0<br>12.0 |-@—@<br>®<br>=zt °<br>8 100 |+-@ @<br>N fie<br>f°<br>¥30 @ ee e°<br>£oD @ ee $ ? .<br>= _&®<br>60 z °<br>5 "08 @@ %<br>a F io6 ea...®| %,Ae ee<br>= a0 owes 5 SU... o* e<br>zs" e ee oe... e<br>2.0 e ° ® oe oo e %e<br>e ry & ® *teeray<br>e e e e ® * e e<br>e e e e sina] e<br>0.0 ib,<br>0.0 20.0 40.0 60.0 80.0 1000 1200 140.0 160.0 180.0 200.0<br>Domestic credit to private sector (% of GDP, 2005-14)<br>14.0<br>12.0 »<br>=a8o<br>410.0 %<br>g, PF |? °<br>N 2.<br>= 30 ss° °<br>coc @@ e<br>=Oo %° eo* °<br>E 60 e..4 rey ef Cy)<br>2 PoeYY | ° ® °<br>2 #e ’ ad Treessee.,<br>E40 ® ®ée e® Cas ee e<br>2.0 e ° e ce e<br>ra . Ps ‘ ° an ;<br>0.0<br>- 100 200 300 400 500 600 700 800 900 1,000<br>Borrowers at commercial banks per 1,000 adults adults (2005-14)<br><!-- End of picture text -->

<!-- Start of picture text --> 2 PoeYY | ° ® °<br>2 #e ’ ad Treessee.,<br>E40 ® ®ée e® Cas ee e<br>2.0 e ° e ce e<br>ra . Ps ‘ ° an ;<br>0.0<br>- 100 200 300 400 500 600 700 800 900 1,000<br>Borrowers at commercial banks per 1,000 adults adults (2005-14)<br><!-- End of picture text -->
# **3.3 Model validation and AI impact channels**

Most of the six model propositions are intuitive and have been observed for an extended period. Rigorous empirical analysis, presented in Appendix A, strongly supports these propositions. The analysis draws upon multiple datasets covering the past seven decades, providing robust evidence for the model’s validity.

The model demonstrates how technology influences relative prices, nominal and real outputs, sectoral employment shares, as well as real income levels and growth trajectories. It also highlights the distinct effects of technology on large and small open economies through trade. Given that generative AI primarily enhances productivity in high-skill services, the model predicts the following:

- Impact on real income: If AI’s productivity gains are confined to the high-skill service sector, the aggregate growth effect will be limited, as this sector represents a relatively small share of total output and employment. However, AI’s growth impact will be significantly greater if it drives productivity increases across all sectors.

- Impact on sectoral composition: AI’s effect on sectoral employment shares is ambiguous, contingent on its impact on real income and sectoral productivity. In general, AI tends to accelerate the shift toward low-skill services. If its productivity impact is most pronounced in high-skill services, as evidence suggests, it will result in an earlier and lower peak in high-skill services employment at a lower real income level. Similar to the phenomenon of premature deindustrialization, generative AI could lead to ”premature de-professionalization” in many developing countries.

- Impact on comparative advantage and trade: In large open economies, AI is unlikely to alter comparative advantage, as productivity changes are offset by corresponding shifts in relative prices. Over time, AI is expected to reduce employment in high-skill services and drive down global relative prices in this sector. For small open economies, AI presents an opportunity to gain comparative advantage in high-skill services, increasing production, exports, and employment in this sector.

The analysis above assumes that AI does not alter consumer preferences. In the following section, I use model parameters drawn from the literature and empirical evidence to quantify AI’s impact under various scenarios. Beyond exploring different productivity boost scenarios, I introduce a more ambitious scenario in which AI creates entirely new products and industries, leading to a permanent shift in consumer preferences.

26
19

## **Box 3:  SOEs in Mexico**

The role of SOEs in the Mexican economy has dropped dramatically over the last two decades. In 1982, there were 1,155 SOEs that accounted for 4.4 percent of the labor force and 14 percent of GDP. By 1990, the number of SOEs had dropped to 418 accounting for 3.7 percent of employment and 10.1 percent of GDP. These figures decreased even further during the 1990s when the government continued to withdraw from many economic activities, including in strategic and priority areas. The privatization process included ports, several airports, and railroads. Progress was also made in allowing private participation in secondary petrochemicals and electric power generation.

<!-- Start of picture text --> SOEs share in GDP and employment Number of SOEs<br>15% 1250<br>1000<br>10%<br>750<br>500<br>5%<br>250<br>0% 0<br>1982 1990 2001 1982 1990 2001<br>GDP Employment Running Divestment in process<br><!-- End of picture text --> In 2001 there were 205 SOEs accounting for 3 percent of GDP and 1.5 percent of employment. In this year 20.3 percent of the federal budget was assigned to subsidies and transfers of which 31.4 percent was accounted for by SOEs. Three major SOEs in the energy sector accounted for 2.9 percent of GDP, 0.62 percent of employment, and contributed about 55.9 percent of the federal budgetary income. At the end of 2003, about 80 of the 210 remaining SOEs performed commercial activities in the following areas: petroleum, electricity, transport, communications, postal services and the commercialization of basic or mass consumption products. The rest were financial and social institutions with programs in health, housing, education, culture, food and other products and services. SOEs still maintain control over important industries such as petroleum (Pemex) and electricity (CFE and LFC) that remain reserved areas for the State.

_Source_ : _Regulating Market Activities by Public Sector, 01-February-2005, Organization for Economic Co-operation and Development_

# **B. Sectoral highlights**

35. Government ownership in existing infrastructure and financial sector entities continues to exist despite increasing emphasis on private sector participation over the past decade.

# _Infrastructure_

36. Power utilities in nearly 85 developing countries--or 55 percent of all developing countries in the PPI database--are still owned and operated by the state. In a survey of 52 developing countries with generating capacity of between 29 megawatts (the Gambia) and 318 gigawatts (China), almost 70 percent had not started or completed the process of brining private sector participation into the sector, and a further 18 percent had just begun the process; by contrast, independent power providers had been established in 67 percent of the countries with
need a variable that predicts exports from Egypt based solely on its trading partners internal demand growth, rather than supply-side determinants. Hence, we construct our instrument using time-series regressions of Egypt exports to its trading partners on the trading partner’s GDP by industry at the four-digit level as follows: here, <u>j, ,</u> denotes country _j_ ’s share of industry i’ exports; is the change in real GDP in QQ ss tt destination country QQss,tt _j_ . ΔΔYYjj,tt+h Predicted values or exports from these regressions would serve as a proxy for Egypt’s exports to its trading partners explained exclusively by the latter's domestic aggregate demand.

These predicted exports will be combined with I-O coefficients to generate total exports accounting for supply chain linkages. Subsequently, these total exports will be used to generate the export exposure at the area level in Egypt.

Then, estimation will take the form of two-stage least squares, with the first stage being: and the second stage: 0 1 2 aa aa aa tt,tt+1 tt,tt+1 tt aa where are the predicted values of the first stage regression. yy = ββ + ββ ΔΔXX<sup>�</sup> + ββ KK<sup>′</sup> + εε aa tt,tt+1 ΔΔXX<sup>�</sup>

# **5. Results**

To assess the impact of total export shocks on area-level wages, informality, and female labor force participation rates in Egypt, we divide the analysis into two distinct periods: 2007-2012 and 2012-2018. The IV estimates of total export exposure on local labor market outcomes, presented in Table 2b in annex 3, reveal that areas (markaz and kizm) that are more exposed to total exposure per worker do not exhibit any significant effects on key labor force variables such as real wages, female labor force participation, and informality.

These findings align with previous studies conducted by Robertson, Vergara et al. (2021) and Berg & Vergara (2023). The former study discovers that export exposure, when not accounting for input-output linkages, remains statistically insignificant for most worker categories, thus reinforcing the broken link hypothesis posited by an earlier paper. The latter study finds that, in the short run, subdistricts experiencing higher export exposure observe a decline in average real wages, but this effect diminishes over time. Regarding informality and female labor force participation rates, no significant impact is observed in either the short or long term.

# **6. Conclusion**
# Table 6

# U.S. Imorts under CoERA by Duty Treatent and Country (Value in millions of dollars)

||Costa<br>1989|RIca<br>1990|**Guatm**<br>1989|**ala**<br>1990|El Sal<br>1989|vador<br>|Hon<br>|duras<br>|
|---|---|---|---|---|---|---|---|---|
|||||||1990|1989|1990|
|TotalImports|968|1,007|608|791|244|238|468|486|
|Dutiable Value|200|220|136|194|43|60|65|64|
|HTS.9802|90|148|38|59|14|19|18|22|
|Other Dutiable|110|72|97|135|29|41|37|42|
|Duty freo Value|768|785|476|596|201|177|402|422|
|MFN<br>|297|261|294|345|131|107|279|269|
|CBERA|193|218|116|154|28|28|53|68|
|GSP<br>|90|93|24|38|13|8|20|19|
|HTS.9802|188|218|42|59|30|33|50|66|

Source: USITC, U.S. Department of Commerce.

Note: Totals may not **add** up due to different data bases.
<!-- Start of picture text --> Global Factor<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>1980 1985 1990 1995 2000 2005 2010 2015<br>Advanced-country factor Emerging-country inflow factor<br>3<br>2<br>2.5<br>1.5<br>1 2<br>0.5 1.5<br>0 1<br>-0.5 0.5<br>-1 0<br>-1.5 -0.5<br>-2 -1<br>-2.5 -1.5<br>-3 -2<br>1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Figure A.6: Estimated common factors. In each graph, the thick solid line corresponds to the factor estimated using the complete sample. Dashed lines correspond to the factor estimated over the corresponding 20-year window, re-scaled to have the same mean and variance as the full-sample factor over the window.

49
However, national efforts at stimulating economic growth were partly annulled by expenditure contraction, tax increases, or both at the subnational level in some federal countries. The negative impact of the crisis on state and local government finances in the United States was large and potentially long lasting (Box 1). In general, subnational fiscal outcomes in large federal countries varied, depending on inherited levels of debt, historically evolved fiscal behavior, and the rules governing borrowing by subnational governments.

**Box 1 Working at Cross-Purposes: Intergovernmental Disharmony in the United States** Subnational fiscal policy in the immediate aftermath of the 2008 crisis was pro-cyclical in many advanced economies. The problem was especially pronounced in the United States. While the federal government cut taxes, increased spending, and expanded its deficit to stimulate aggregate demand, states cut a broad range of spending items. Balanced budget rules limited their options when revenues fell, as a result of weaker economic growth, rising unemployment rates, and a sharp decline in housing prices. Total nominal spending by state governments fell by almost 4 percent in fiscal year 2008/09 and by more than 6 percent in 2009/10—the most rapid declines since World War II. California cut spending by about $31 billion in 2009/10, despite federal stimulus funds providing an additional $8 billion. There have been some cases of financial distress and subnational government bankruptcy (examples include Alabama; Harrisburg, Pennsylvania; and Vallejo, California), though average default rates for states and municipalities have remained low.

Growing political pressure on the federal government to reduce its fiscal deficit means that states and municipalities will receive less federal financial aid in the future. Moreover, despite some recovery in revenues following moderate economic recovery, state and local tax collections are projected to be weak to moderate due to the lagged response to falling house prices. These trends imply additional discretionary expenditure cuts, particularly as entitlement programs (e.g. Medicare/Medicaid) continue to rise rapidly. Hence the procyclical behavior of sub-national finances is likely to continue.

_Source_ : Jonas 2012 and authors.

Recent research (Canuto and Liu 2010a,b) discusses the pressures on subnational finance arising from the global crisis and from the debt created by subnational units through special4