rafmacalaba/datause-encoder-data huggingface dataset:
would have happened in that period in the absence of a program but with the same exogenous influences.

The results are shown in Table 3. After explicitly adjusting for the external shocks, initial conditions, levels of external iinancing, and policies followed in the pre-program period, the change in the annual average rate of GDP growth in the _ELAL_ countries was higher but not statistically different from that of all the other countries when measuring changes between 1970-80 and 1985-88. Between 1981-84 and 1985-88, however, adjustment programs are estimated to have boosted the rate of GDP growth by close to 2 percentage points. The International Monetary Fund (IMF) was also supporting Table 3: The effectiveness of adjustment lending

|_PerAil_<br>_dependent_<br>_variabk_|_Ciange in rate'_<br>_of growth of CDP_<br>_(%)_|_Ohnge_n<br>_i,vestment/lDP_<br>_(%)_|_Change_<br>_tn_<br>_domestic_<br>_saving/_<br>_CDP(%)_|_Change_<br>_in_<br>_eport/GDP_<br>_(')_|
|---|---|---|---|---|
|Cument prices<br>1985|||||
|with<br>_1970-80_|1|-4.1"|4.0*|6.4"|
|1985-88<br>with<br>1981-84|2.0*"|OS|4.20|5.0O|
|Constant prices<br>1985-88|||||
|with<br>1970-80|1.0|-5.6"|2.0|1.2|
|1985-88|||||
|with<br>1981-84|1.9"00|-0.1|_5.g8_|23|

1. The rate of grvwth of GDP is measured at constant prces In both cases but the estimatin procedure requires the use of lagged values of all the perfotmance Ind1cstam and that is the reason for slghtly different estimation of the effect of programs on rte of _grwwth_ of CDP in the top and bottom of (ie table. * Statistically sirdficant **at** the 2.5% level Statcally signlficant at the 5% leveL Statistically sidgnicant at the 7.5% leveL StatsdcaUly aignflcant at the 10% leveL
500 firms.

The FactSet database offers unique advantages as it provides information on firms’ fundamentals matched with detailed information on firms’ suppliers and customers, as well as ownership ties. This information is crucial for assessing a company’s dependencies and market position. Given its global coverage, the dataset allows for a comprehensive understanding of the complexity and geographic dispersion between companies. Finally, since the dataset tracks variables over time in a precise manner, it enables the analysis of trends and changes within these relationships over the years.<sup>1</sup> To maximize the utility of the data while minimizing the potential for error, we undertake three primary analyses. Initially, we perform a descriptive exercise using network analysis metrics to examine the role of US Fortune 500 firms, specifically the 48 influential American lead firms in GVCs identified from this list, within the agri-food value chains. This focus is chosen based on the ability of these firms to exemplify significant economic influence within the national agri-food sector.<sup>2</sup> Our findings reveal that the Fortune 500 lead firms are notably more interconnected and increasingly central to the agro-food GVCs. However, since 2018, they seem to be concentrating their activities within their corporate boundaries, in contrast to non-US-based firms.

This observation leads us to conduct a broader and systematic firm-to-firm connectivity analysis. We expand our inquiry to include all firms listed in the FactSet database that operate in agri-food-related sectors. Our findings indicate that the most connected firms are also those that have significantly increased their number of customers and suppliers over time. Furthermore, a relatively small group of highly interconnected firms consistently occupies central positions within the network. Despite their central roles, these firms typically do not diversify their core relationships within narrowly defined indus-

> 1FactSet’s coverage is particularly strong in the United States, where the firms in our sample account for around 80% of aggregate industry output.

> 2The substantial coverage of both Fortune 500 and non-Fortune 500 US agribusinesses in our sample justifies the initial US focus to effectively detect and compare the relevance of these companies with nonFortune 500 firms.

2
TABLE 9 Regression results of labor force participation rates, robustness check: linear-quadratic trend

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 y**|**ears old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Fertility|0.697***||0.059*||0.000||||||
||(0.000)||(0.011)||(0.922)||||||
|Secondary enrollment|0.202***|0.125***|||||||||
||(0.000)|(0.000)|||||||||
|Tertiary enrollment|||-0.113***|-0.180***|||||||
||||(0.000)|(0.000)|||||||
|Completion of secondary<br>dti|||0.040|-0.013|0.236***|0.1340***|0.403***|0.064|||
|eucaon|||(0.233)|(0.642)|(0.000)|(0.000)|(0.000)|(0.218)|||
|Completion of tertiary|||0.158**|-0.100*|0.321***|0.311***|0.490**|0.431**|||
|education|||(0.002)|(0.041)|(0.000)|(0.000)|(0.003)|(0.001)|||
|Life expectancy|||||||0.571***|0.972***|0.101***|0.229***|
||||||||(0.000)|(0.000)|(0.000)|(0.000)|
|Cycle|15.11***|24.22***|0.281|12.72***|3.24**|0.156|-1.56|-2.12*|1.45|17.01|
||(0.000)|(0.000)|(0.684)|(0.000)|(0.003)|(0.470)|(0.101)|(0.014)|(0.512)|(0.491)|
|Cycle * life expectancy|||||||||-0.027|-0.118|
||||||||||(0.275)|(0.348)|
|Fertility * EMDE|-0.630***||-0.067**||||||||
||(0.000)||(0.005)||||||||
|Secondary enrollment *<br>EMDE|-0.342***<br>(0.000)||||||||||
|Completion of secondary<br>education * EMDE|||-0.029|-0.048|||||||
||||(0.482)|(0.133)|||||||
|Completion of tertiary<br>dti * EMDE|||-0.126|0.155*|||||||
|eucaon|||(0.058)|(0.014)|||||||
|Life expectancy * EMDE|||||||||-0.145***|-0.620***|
||||||55<br>||||(0.000)|(0.000)|
- 12 -

- r **is** the ex-Dost after **tax average** real interest rate on banking instruments, **as a** percentage;

- p is a relative price index as **a** percentage, defined **as** the ratio between the gross investment implicit price deflator and the **GDP** deflator; **and**

- PUI is the public sector gross fixed investment in billions of MHeS at 1980 prices.

A simple OLS estimate of Equation 9 for private investment on an annual basis during the period 1962-1987, yields the following resultas

- (10) PRIVt - -339 - 2.87 rt1l - 2.3 Pt + .6 PUlt + 12.4 (Y/K)t **+** .036 kt-1 (-1.3) (-2.1) (-1.8) (5.7) (3.5) (5.6)

- R2 ; 96.3Z R2 . 95.1? DV - 2.07 RHO - .04 (.86) F -81.4

- w,here the values in parenthesis correspond to the t-statistics.

The results indicate that: (i) the capital replacement effect dominated the stock adjustment effect, the latter being too small (possibly, no higher than 22 p.a.);l (ii) public sector investment strongly complemented private investment; (iii) private investment was negatively related to the relative price of investment; (iv) private investment responded negatively to the one-year lagged real interest rate, possibly

- 1I From the Equation 9, the coefficient of the lagged capital stock is equal to d-vo. The estimated value of this coefficient is 3.6Z. Hence, an overall rate of depreciation of 5.62 (higher than that reported in the National Accounts) will be needed to obtain a coefficient of adjustment of 2Z p.a.
# **_Definition Matters: Metropolitan Areas and Agglomeration Economies in a Large Developing Country_**<sup>**_1_**</sup>

Maarten Bosker<sup>2</sup> , Jane Park<sup>3</sup> , and Mark Roberts<sup>4</sup>

**Key words:** metro areas, urban definitions, agglomeration economies, Indonesia **JEL Codes:** O18, O47, C21

> 1 The authors thank Katie McWilliams, Benjamin Stewart and Andrii Berdnyk for their outstanding GIS support, as well as Brian Blankespoor and Shaun Zhang for supplemental GIS support. They also thank Gilles Duranton for very helpful comments and feedback provided during the preparation of the paper. Financial support from both SECO and DFID is furthermore very gratefully acknowledged.

> 2 Department of Economics, Erasmus University Rotterdam, The Netherlands and CEPR <bosker@ese.eur.nl>

> 3 Social, Urban, Rural and Resilience Global Practice, The World Bank, Washington, DC, USA <jpark16@worldbank.org>

> 4 Social, Urban, Rural and Resilience Global Practice, The World Bank, Washington, DC, USA <mroberts1@worldbank.org> (corresponding author).
financial markets, through which people can also access financial instruments such as life, health and auto insurance, for example.

# **4. Methodology**

In order to assess the correlation between insurance penetration and our measures of institutional quality and financial market development taking into account GDP per capita, we estimate the following regression using a panel of 180 countries from year 1996 to 2016: where _i_ indexes the country, _r_ indexes the country region and _t_ indexes the year. As our dependent variable, we use insurance penetration as a percentage of GDP. In the main analysis, we only report the results using life and non-life insurance penetration, but we also report the results using total (life and non-life) insurance penetration in the Online Appendix.

Since we want to estimate partial correlations controlling for GDP per capita, we always include GDP per capita on the right-hand side of our equation. Inspired by the model developed by Greenwood and Jovanovic (1990) and previous work by Enz (2000), among others, we include both a linear (GDPPC) and a quadratic term (GDPPC<sup>2</sup> ) to partially capture the non-linear correlation between insurance penetration and GDP per capita. All variables on the right-hand side are lagged by one year to mitigate concerns of endogeneity. We also include region fixed effects (γr) and year fixed effects (λt) in all specifications and country-specific time trends (tθi) in some specifications.

GDP per capita, institutional quality and financial market development are arguably strongly correlated, so we take a parsimonious approach and run separate regressions for each one of our measures of interest apart from GDP per capita. These variables are included in vector Xit�1. The coefficient of interest is β3 and should be interpreted as the partial correlation between variable X and insurance penetration once GDP per capita is taken into account.

When analyzing institutional quality, not only do we estimate Equation 1 using the index that summarizes the quality of the institutions of each country, but we also run the regressions using each of the index components separately.

To assess the magnitude of the partial correlations that we estimate, we also propose exercises where we calculate the predicted changes to insurance penetration if a country were to move from one part of the distribution of an independent variable to a higher end of the same distribution. For example, we can estimate the predicted change in insurance penetration if a country were to move from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of the governance index. Although not causal, this exercise gives us a sense of what would be the expected growth in insurance penetration in a country that significantly increases its institutional quality, for example. We also perform similar exercises for changes within groups of income. In that case, we predict changes in insurance penetration if a country were to move from one percentile of the income distribution to another within the distribution of low and lowermiddle income countries.

9
update. Inspecting the findings of systematic reviews in the gap map will also reveal where reviews have been unable to reach overall conclusions about intervention effectiveness due to insufficient high quality evidence, indicating a need for additional primary studies.

Finally, an additional way to make use of the gap map methodology which holds much promise for research priority setting in institutions involved in project financing, such as international development organizations, is to overlay a map of the existing evidence on a map of ongoing and planned projects in the sector or subsector in question. An inventory of such projects will help determine which priority research questions can actually be investigated in the short to medium term (Tanner et al., 2013).

# **6. Conclusions**

Evidence gap maps can be used as a tool for informing policy by making evidence more readily available to users in ready packaged collections. They can also inform future research, and allow for a systematic and strategic approach to closing the evidence gap. They enable users to explore and compare the quality, characteristics, distribution and findings of available evidence on the effectiveness of interventions in a particular sector.

Moreover, they allow bigger picture comparisons of the evidence on different interventions within a particular field, exploration of intervention effectiveness along the causal chain, and provide access to user-friendly summaries of systematic reviews and impact evaluations on the 3ie database. While gap maps do not provide context specific evidence and do not draw conclusions or recommendations for policy and practice, their interactive nature enables users to explore the evidence in more detail and to derive their own conclusions and implications for policy making and research agendas.

The methodology used for evidence gap maps can be easily adapted according to the purpose of the gap map, size of the evidence base and available resources. It is work in progress and 3ie is exploring options for improving their presentation, including ways of making it interactive, adding summary of findings tables and addressing cost-effectiveness. A series of gap maps is envisaged over time, integrating the evidence from 3ie’s impact evaluation and systematic reviews databases covering all major sectors. This will allow policy makers, practitioners and researchers easy access to existing research on intervention effectiveness.

20
# Table D5. Distribution of Manufacturing Establishments Vehicles for Shipment by Employment Size, Thailand

VEHICLES **(OWM** VENICLES **FOR** SHIPNENT) FIIMSIZE (EMPLOYEES per FIRM) <!-- Start of picture text --> Fre uic<br>Pecnt<br>Now Pct<br>Col Pet 11-19 120-49 150-99 1100-199 1200-499 1500-999 11000 4 I Totol<br>I I I I I I lover I<br>YES ''' ''''' ~- ''''' 24 4 @ '''' 64 4 ''''-'----4----+ 61 1 41 1 4. 36 .__ _ _ 131 _ _ 4. _ - 5 24<br>I.00 21.33 I 20.33 1 13.67 1 12.00 4.33 1 1.67 81.33<br>- 4 26.23 | 25. 16 80 14.75 533 2:05<br>72n.n3 86.49 89.71 75.n93 76.oO0 81.25 62.50<br>N o 9 1 101 7 131 11 31 3 1 56<br>3.001 3.331 2.33 I 4.331 3.67 I 1.00 1.001 18.67<br>I 16.07 I 17.86 1 12.50 I 23.21 I 19.64 I 5.36 I 5.361<br>- 27.27 I 13.51 1 10.29 1 24.07 1 23.40 18.75 37.50<br>* -4.- 4.- ,. 4.- __ 4.- . . 4.- . 4. -,-, +<br>Total 33 74 68 54 47 16 a 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ (including software platform leaders such as Microsoft, SAP, and Oracle), production equipment manufacturers, distributors, and producers of more generic components and subsystems. The following analysis of how these three firm-level actors have evolved in the industry’s GVCs provides a useful if simplified portrait of the global ICT industry.

**Table 2. Top 15 Importers and Exporters of Intermediate and Final ICT Goods, 2014, billions of current USD, and share of top 30 exporters and importers**

|**Interm**|**ediate ICT Goods**|**Exporters**|||**Importers**||
|---|---|---|---|---|---|---|
|**Rank**|**Top 15 * 2014**|**US$ Billion**|**Share Top 30**|**Top 15 * 2014**|**US$ Billion**|**Share Top 30**|
|1|China|360.4|33.7%|China|284.6|27.6%|
|2|Korea,Rep.|140.5|13.1%|HongKongSAR,China|194.5|18.7%|
|3|Japan|92.4|8.6%|United States|84.0|8.1%|
|4|Malaysia|92.2|8.6%|Singapore|52.5|5.0%|
|5|United States|75.6|7.1%|Korea,Rep.|49.8|4.8%|
|6|Singapore|61.1|5.7%|Japan|46.1|4.4%|
|7|Germany|41.7|3.9%|Germany|41.1|4.0%|
|8|Philippines|31.6|3.0%|Malaysia|35.8|3.4%|
|9|Thailand|23.7|2.2%|Mexico|33.7|3.2%|
|10|Costa Rica|21.8|2.0%|Netherlands|26.2|2.5%|
|11|Netherlands|18.9|1.8%|Vietnam|22.2|2.1%|
|12|Vietnam|14.2|1.3%|Thailand|20.3|1.9%|
|13|France|11.7|1.1%|Philippines|16.9|1.6%|
|14|United Kingdom|11.2|1.0%|United Kingdom|16.7|1.6%|
|15|Mexico|10.5|1.0%|France|16.6|1.6%|
||Top15|1,007.35|94.1%|Top15|940.93|90.4%|
||Total Top 30|1,070.61|100.0%|Total Top 30|1,040.79|100.0%|
|**Final I**|**CT Goods**||||||
|||**Exporters**|||**Importers**<br>||
|**Rank**|**Top 15 * 2014**|**US$ Billion)**|**Share Top 30**|<br>**Top 15 * 2014**|**US$**<br>**Billion)**|**Share Top 30**|
|1|China|580.5|42.9%|United States|269.9|23.0%|
|2|United States|107.3|7.9%|HongKongSAR,China|115.6|9.9%|
|3|Germany|85.3|6.3%|Germany|89.1|7.6%|
|4|Japan|60.2|4.5%|China|78.4|6.7%|
|5|Mexico|59.0|4.4%|Netherlands|70.3|6.0%|
|6|Netherlands|47.5|3.5%|Japan|55.4|4.7%|
|7|Korea,Rep.|45.5|3.4%|United Kingdom|51.0|4.4%|
|8|Vietnam|43.6|3.2%|France|38.4|3.3%|
|9|Switzerland|43.3|3.2%|Korea,Rep.|34.3|2.9%|
|10|Malaysia|35.9|2.7%|Canada|33.1|2.8%|
|11|Thailand|34.0|2.5%|Mexico|30.8|2.6%|
|12|United Kingdom|22.1|1.6%|United Arab Emirates|30.0|2.6%|
|13|Singapore|21.5|1.6%|Italy|26.4|2.3%|
|14|France|21.3|1.6%|Singapore|24.4|2.1%|
|15|Ireland|19.1|1.4%|India|24.3|2.1%|
||Top15|1,207.03|89.3%|Top15|971.26|82.9%|
||Total Top30|1,352.01|100.0%|Total Top30|1,171.79|100.0%|

* While Singapore does import intermediate ICT goods, a significant portion is re-exported. Hong Kong SAR, China, and the Netherlands also have extremely high re-exporters.

Source: World Bank MC-GVC Database using a consistent 175 country panel that accounts for 95-98% of world trade, see: http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator 7
# Fiscal Federalism and Macroeconomic Governance: **For** Better or For Worse?

**By Anwar Shah**<sup>**1**</sup> **World Bank**

Earlier versions of this paper were presented at Decentralization, and Macroeconomic Governance Conference, held in Brasilia, 16-17 June, 1997, sponsored by the Government of Brazil and the OECD; second Swiss Regional Development Seminar held in La Neuveville, Switzerland, December 4-6, 1997 and the FAO/World Bank Technical Consultation on Decentralization held in Rome, Italy, December 15-17, 1997. The author is grateful to Kiichiro Fukasaku for suggesting this topiic and to Murilo Portugal, Vito Tanzi, Guillermo Perry, Joao do Carmo Oliveira, Thomas Courchene, David Sewell, Bemd Spahn, Bert Hoffman and Suman Bery for helpful discussions. The views expressed in this paper are those of the author alone and should not be attributed to the World Bank Group. Please address all comments to Anwar Shah, World Bank, Room G6-079, 1818 H Street, N.W., Washington, DC 20433, USA. Telephone: 202473-7687; Fax: 202-522-3124; E-mail: ASHAH@WORLDBANK.ORG
(Group C), firms did not receive any e-filing training. However, to hold the delivery format of the treatments constant, these firms received general training on taxation not specific to e-filing.<sup>10</sup> Rather, the training included a review of different tax laws and procedures. Due to a requirement by the Tax Committee, this general tax training included one statement about the availability of e-filing on a slide listing the three modes of filing taxes: “by paper, by mail and electronically.” In addition, firms in this group would have been aware of the existence of e-filing from a reference to it in the invitation materials and from some questions in the baseline survey. This observation is important for interpreting our results since it means Group C is not a pure control group but rather a group with a light information treatment.

This design allows us to answer two broad sets of questions. The first set examines firms’ decisions to adopt e-filing: What is the impact of providing information and training about e-filing? What is the additional impact of helping firms to register? What other firm characteristics predict e-filing adoption? The second set of questions focuses on the impact of using electronic filing on firm outcomes, in particular, compliance costs, tax payments and unofficial payments.

# **3.1 Sampling and Randomization**

The study draws from the universe of firms in Dushanbe that are registered in the tax authority database. All legal entities and individual entrepreneurs that are (i) simplified tax regime payers, (ii) have been active in the system for at least two years (i.e. not new enterprises or liquidated ones), and (iii) not currently e-filing, were eligible for the study. There were 5,218 firms in the tax database meeting these three criteria.

We randomly selected a list of 2,004 firms from this overall population with stratification 10 Groups A and B also received this general tax training during their sessions.

10
29 Table A Administrative Costs of Selected NGO Feeding Programs

|Country|Dperating Agency Non-food<br>Num<br>Cost\Beneficiary|ber of Beneficiaries|
|---|---|---|
|Benin|CRS<br>28.33|53,000|
|Ghana|ADRA<br>13.10|41,000|
|Sudan|ADfA<br>76.11|20,000|
|Average, 3 African<br>Programs|---<br>31.24|---|
|Bolivia|ADRA<br>3.24|1,035,300|
|n|CRS<br>1.67|401,100|
|of|FHI<br>17.57|53,100|
|Haiti|ADRA<br>10.39|108,500|
|Average, 4 Latin<br>American Programs|---<br>3.24||

Source: Robert R. Nathan Assoc. Inc. (1987).

Programs were those which applied for a USAID Outreach grant in 1987. Costs are in US $ of 1987
# <u>Administrative regions</u>

Many sub-national descriptive statistics are cited in other publications according to administrative regions corresponding to sub-national government levels. These provide a level of disaggregation that policy makers and anyone more familiar with the geography of a particular country can easily comprehend.

Table 13: Differences in input use between households across administrative regions

||No. of<br>households<br>(weighted)|% using<br>organic<br>fertilizer|% using<br>inorganic<br>fertilizer|Avg total<br>inorganic<br>fertilizer<br>(kg/ha)|% using<br>improved<br>seed|% using<br>agro-<br>chemicals|% using<br>irrigation|% owning<br>any ag<br>equipment|
|---|---|---|---|---|---|---|---|---|
|Ethiopia|||||||||
|Tigray|226|70|70|67|10|19|14|86|
|<br>Afar|28|0|0|0|0|0|47|75|
|Amhara|821|72|52|42|25|21|13|86|
|Oromia|1,029|57|59|42|26|50|8|86|
|Somali|<br>41|15|1|<1|4|2|8|58|
|Benshagul G.|69|43|34|6|21|19|15|62|
|SNNP|851|76|56|51|19|22|3|52|
|Gambelia|14|0|0|0|8|9|0|16|
|Harar|5|70|52|53|13|26|42|84|
|Diredawa|7|41|5|1|19|6|37|80|
|Malawi|||||||||
|North|1,246|11|82|179|-|3|<1|4|
|Central|4,241|25|82|134|-|2|<1|1|
|South|4,598|13|72|148|-|4|<1|<1|
|Niger|||||||||
|Agadez|10|18|16|16|3|30|60|50|
|<br>Diffa|79|20|20|61|2|11|22|53|
|Dosso|331|73|35|5|2|7|7|90|
|Maradi|496|69|15|2|3|8|1|86|
|Tahoua|465|43|4|<1|2|8|8|39|
|Tillaberi|379|46|19|2|2|8|8|85|
|Zinder|472|55|16|8|3|7|6|97|
|Niamey|15|38|31|39|19|6|56|66|
|Nigeria|||||||||
|North central|483|<1|42|107|-|47|4|4|
|North east|417|4|48|102|-|55|3|26|
|North west|809|10|71|238|-|39|8|14|
|South east|558|2|30|132|-|7|3|5|
|South south|351|0|13|33|-|9|1|2|
|South west|321|1|6|15|-|39|2|9|
|Uganda|||||||||
|Kampala|5|33|0|0|-|33|0|0|
|<br>Central|364|20|7|1|-|25|11|<1|
|Eastern|572|5|3|2|-|11|3|0|
|Northern|558|1|3|1|-|6|<1|<1|
|Western|630|26|1|1|-|6|3|<1|

Note: The administrative regions chosen for use vary in size across countries but represent the highest level of zonal disaggregation possible. Tanzania is not included in this table because there are too many regions, the highest level of disaggregation, for the purposes of this table. Average inorganic fertilizer use statistics are unconditional (i.e., they include all farming households, including those that apply no fertilizer) and represent total values, not nutrients.

20