rafmacalaba/datause-encoder-data huggingface dataset:
**Table S2.S continued**

||(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|reput_4|reput_5|pcf_rep|lvademp|dlemp|growth1|growth3|pcf_perf|
|supbuy|0.287|0.392|0.281|0|-0.541*|-0.551|1.573|0|
||(0.646)|(0.645)|(0.798)|(0)|(0.277)|(0.785)|(2.537)|(0)|
|samesup|-0.0995|-0.0905|-0.0443|-1.434**|-0.284|-1.043***|-0.802**|-0.629**|
||(0.162)|(0.178)|(0.156)|(0.559)|(0.185)|(0.318)|(0.394)|(0.284)|
|samecus|-0.105|0.118**|0.0414|-0.946|0.0868|-0.624***|0.284|-0.0639|
||(0.112)|(0.0493)|(0.0863)|(0.684)|(0.139)|(0.209)|(0.451)|(0.219)|
|same_sec|-0.0118|0.0208|-0.00343|-0.320|-0.0704|-0.0915|-0.0903|-0.239|
||(0.0340)|(0.0316)|(0.0342)|(0.291)|(0.0618)|(0.114)|(0.212)|(0.167)|
|ldist|-0.00328|-0.00496|0.00566|-0.0662*|-0.0177|-0.0526**|-0.0522|-0.0146|
||(0.00301)|(0.00487)|(0.00701)|(0.0399)|(0.0113)|(0.0251)|(0.0351)|(0.0200)|
|ad_fmage|-0.000696|0.00154|-0.00117|-0.00498|0.00111|0.00861|0.00458|0.00600|
||(0.000812)|(0.00156)|(0.000985)|(0.00781)|(0.00216)|(0.00650)|(0.00745)|(0.00460)|
|ad_meduc|-0.00143|0.00724|0.00139|0.0468|-0.00464|-0.0332|-0.0201|0.0120|
||(0.00704)|(0.00944)|(0.00863)|(0.0477)|(0.0116)|(0.0229)|(0.0267)|(0.0251)|
|ad_mexper|-0.000772|-0.00118|0.000485|-0.00728|0.000767|0.00304|-0.00385|-0.00181|
||(0.000947)|(0.00144)|(0.00169)|(0.00579)|(0.00132)|(0.00695)|(0.00580)|(0.00314)|
|ad_mfemale|0.0157|0.0262|0.0370|0.376|-0.00715|-0.161|0.0561|-0.138|
||(0.0387)|(0.0461)|(0.0481)|(0.582)|(0.0613)|(0.134)|(0.274)|(0.0959)|
|ad_lemp|-0.0267***|-0.0311**|-0.0111|0.00746|0.0654**|0.0906|0.102|0.0611|
||(0.00876)|(0.0133)|(0.0136)|(0.110)|(0.0258)|(0.0709)|(0.114)|(0.0599)|
|secdist|0.00247|0.00173|0.00412|0.0382|0.00321|0.00876|0.00547|0.0307|
||(0.00628)|(0.00628)|(0.00674)|(0.0511)|(0.00845)|(0.0259)|(0.0315)|(0.0217)|
|seclink|-0.00581|-0.0456|0.133|-0.244|0.0255|0.315|0.183|0.373|
||(0.171)|(0.142)|(0.118)|(0.646)|(0.238)|(0.247)|(0.451)|(0.275)|
|Constant|1.004***|0.894***|1.126***|3.775***|0.716***|1.523***|2.060***|0.994***|
||(0.0242)|(0.0489)|(0.0506)|(0.379)|(0.0995)|(0.290)|(0.337)|(0.222)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 31
2. Two factors for Google search terms related to labor: money, salary, LinkedIn, unemployment, employment 3. Two factors for all Google mobility data 4. One factor for food and assistance search terms: assistance, money, bread, milk, wheat 5. One factor for education and sport search terms: school, student, exercise 6. One factor for religion search terms: prayer, bible A key question in the regression results is what share of the total r-squared is attributable to different combinations of variables. To shed some light on this issue, following Shorrocks (1982), we investigate the contribution of each group of real-time indicators to the overall goodness of fit of each regression, using Shorrocks–Shapley decomposition of r-squared.<sup>8</sup> The resulting decomposed values of r-squared allow checking the fraction of the goodness-of-fit contributed separately by each group of real-time indicators and fixed effects, with the latter especially pertinent in the quarterly results where we include both quarter and country fixed effects.

# 3. Findings

# 3.1 Satellite indicators

- a. Nitrogen dioxide

Figure 2 presents trends in median NO2 emissions across seven different geographic regions: East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, South Asia, Sub-Saharan Africa, and North America.

At first glance, there is a noticeable decrease in NO2 emissions in all regions – Latin America being a possible exception – with declines in Europe/Central Asia and Sub-Saharan Africa starting in February. Interestingly, the largest decrease is in Europe and Central Asia, and it began in February, even though the large changes in human behavior began in March. The decreases in the Middle East/North Africa, South Asia, and East Asia and the Pacific took place in March and generally did not recover until much later in the year. Trends in North America and East Asia and the Pacific are very comparable in the first quarter. However, North America faced a steeper increase in NO2 since April 2020, followed by a steady decline during the next three months and another rebound in the last quarter of the year.

Figure 3 presents results disaggregated by country income levels instead of geographic regions. We see a noticeable decrease across all income categories except low-income countries. The largest decrease is in high-income countries, but this decrease again starts in February. The upward trend is more consistent in this figure than in the previous one; after April, there is a noticeable uptick across all four income categories, with numbers returning to 2019 levels in the last quarter of the year.

b. Nightlights

> 8 We implement this using _shapley_ in Stata (Kolenikov, 2000).

7
Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2014). “The New Empirical Economics of Management”, _Journal of the European Economic Association,_ 12(4):835–876 Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2016). “International Data on Measuring Management Practices”, _American Economic Review: Papers & Proceedings,_ 106(5): 152–156 Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten. and J. Van Reenen. (2019a) “What Drives Differences in Management Practices?”, _American Economic Review_ , 109(5): 1648-1683 Bloom, N., L. Iacovone, M. Pereira-López and J. Van Reenen. (2019b) “Spillovers, Market Size and Misallocation: Management in Mexico”, mimeo Chacko, T. I. and J. G. Wacker. (2001) "An Examination of Strategic Goals and Management Practices of Russian Enterprises," _International Business Review_ , 10(4): 475-490, Cusolito, A.P. and H. Winkler. (2015). “Productivity Growth and Convergence in Russia: A Dynamic FirmLevel Diagnostic”, Mimeo.

Danilin, I. (2015) “State-Owned Enterprises as innovation agents in Russia: new developments or innovation dead end?” Institute of World Economy and International Relations Working Paper, Russian Academy of Sciences, Moscow, Russian Federation.

Estrin, S.; S. Poukliakova and D. Shapiro (2009) “The Performance Effects of Business Groups in Russia”, _Journal of Management Studies_ , 46(3): 393-420 Friebel, G. and H. Schweiger. (2013) “Management Quality, Ownership, Firm Performance and Market Pressure in Russia”, _Open Economies Review_ , 24(4): 763-788 Giorcelli, M. (2019) “The Long-Term Effects of Management and Technology Transfers _”, American Economic Review_ , 109(1): 121–152

- Grover, A., D. Medvedev. and E. Olafsen. (2019) High <u>Growth Firms: Facts, Fiction, and Policy Options for Emerging Economies, World Bank, Washington, DC</u>

Grover, A., L. Iacovone and P. Chakraborty. (2019) “Management in Croatia: Drivers and consequence for firm performance”, mimeo Haltiwanger, J., R.S. Jarmin and J. Miranda. (2013) “Who Creates Jobs? Small versus Large versus Young”, _Review of Economics and Statistics_ , 95(2): 347-361 Hopenhayn, H. (1992,) “Entry, Exit, and Firm Dynamics in Long-Run Equilibrium,” _Econometrica,_ 60(5): 1127–1150.

Hsieh, C.-T. and P. J. Klenow. (2014) “The Life Cycle of Plants in India and Mexico”, _Quarterly Journal of Economics_ , 129(3): 1035-1084 Jovanovic, B. (1982) “Selection and Evolution of Industry.” _Econometrica,_ 50(3): 649-670 Kilinc, U. (2019). “A technical note on productivity estimations for Russia using Ruslana”, mimeo Lemos, R., A. Choudhary, J. Van Reenen, and N. Bloom. (2016) “Management in Pakistan: First Evidence from Punjab”, International Growth Centre Working Paper, London, UK.

25
# **Is there any improvement in PCAs in 2007-08 and 2008-09 to reflect the EDIs?**

The improved attention on districts, that too on the basis of criteria such as SFDs were expected to improve allocations to these districts to reflect their needs vis-à-vis other better off districts. What is the impact of such attention on SFDs in terms of planning and allocations? In order to analyze this, the PCA under SSA for 2007-08 and 2008-09 are compared to PCA for the year 2005-06. We used the same 2003-04 EDIs since we were not comparing the improvements in PCA vis-à-vis improvements every year in EDIs, but with respect to the PCA of 2004-05 in the context of EDI 2003-04.

The analysis shows that overall funds for SSA have increased from Rs. 11030 crores<sup>13</sup> in 2004-05 to Rs. 21383 crores in 2007-08, and 20153 in 2008-09. More importantly, the Per Child Allocations (PCA) have increased, and on an average the PCA (for the 500 districts from the major states studied here) has increased by 194% from 2004-05 to 2007-08. This definitely means that all districts have more funds now to implement SSA. However, are these funds distributed equitably so that the worse off districts receive more funds than the better off districts?

In all the states, average PCA has increased, and so also is the case of minimum PCA in a district and the maximum in a district. The increase is the highest for those districts which are in the bottom 100 in terms of EDI ranking (an increase of 365% over the PCA of 2004-05) and the increase is least in districts that are in the top 100s in terms of EDI ranking. See graph 4.

# **Graph 4.**

<!-- Start of picture text --> Per Child Allocations under SSA by EDI quartiles<br>2000<br>1500<br>1000<br>500<br>0<br>EDI Q1 EDI Q2 EDI Q3 EDI Q4<br>2005-06 2006-07 2007-08 2008-09<br>Rupees per Child<br><!-- End of picture text --> In around 10 districts, PCA has declined between 2004-05 and 2007-08. Six of these districts are from Kerala, all of which belong to the top 100 districts in terms of EDI rankings. See table 7.

Is decline in PCA a problem in these districts? Not necessarily. Because the fund requirement of these districts, especially those in Kerala is much less given that there are very little physical infrastructure to be constructed. Moreover, there is no need to plan for anticipated increase in educational services either due to large out of school 20
14 expansion was concentrated in long-term credits (with repayment periods over five years) which went up 80 percent to US$11 billion. In 1989, however, the growth in OSECs was due to medium-term credits, and in fact the long-term commitments declined.

In 1988 and 1989, OSEC commitments to low income developing countries increased parallel with the general development. SSA experienced a slight growth in OSECs from US$3.3 to 3.6 billion in 1988, followed by a 23 percent rise in 1989 (see Table 3).

# The Profile of OSECs

Statistics on new commitments allow a multi-dimensional breakdown of the OSECs. Since the mid-1980s, one quarter of all medium- and long-term commitments were made to industrial countries and high income developing countries (from Table 2). Middle-income developing countries accounted for half of the total, and low income countries received the remaining one quarter. One third of the new cornmitments to low income developing countries was due to SSA (see Table 3).

The share of long-term credits in total OSEC commitments (with an initial term of over one year) fluctuated around 20 percent. This ratio used to be somewhat lower in the case of high income countries, and higher for low income countries. Recently this trend reversed and the industrial and high
Table 3: GGDC10: ICT Abundance and Industrial Composition

|||A. 2000|||B. 2010||C.|1994 & 20|11|Ind.<br>Pred.<br>1980|ln( <sup>Obs.</sup><br>Pred.<sup>)</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(A.1)|(A.2)|(A.3)|(B.1)|(B.2)|(B.3)|(C.1)|(C.2)|(C.3)|(D)|(E)|
|ln(GDP/L)|0.32***||0.034|0.029||-0.088|0.19**||-0.0081|-0.048|-0.037|
||(0.075)||(0.14)|(0.11)||(0.12)|(0.074)||(0.099)|(0.11)|(0.056)|
|Ind. Pred.||1.16***|1.06**||0.48**|0.64**||0.86***|0.88***|0.79***||
|||(0.20)|(0.42)||(0.18)|(0.27)||(0.17)|(0.30)|(0.27)||
|t=2000|||||||-2.23***|-0.80*|-0.79|-1.33***|-0.58**|
||||||||(0.35)|(0.48)|(0.53)|(0.41)|(0.29)|
|t=2010|||||||-2.41***|-0.81|-0.78|-1.41***|-0.55**|
||||||||(0.31)|(0.49)|(0.58)|(0.44)|(0.25)|
|Constant|-1.66***|0.025|-0.093|-3.13***|-1.88***|-1.80***||||||
||(0.35)|(0.57)|(0.66)|(0.45)|(0.54)|(0.56)||||||
|Obs.|32|32|32|32|32|32|64|64|64|62|64|
|F-Stat.|17.9|33.3|17.2|0.1|7.1|3.9|984.6|1116.7|827.3|764.7|17.2|
|R<sup>2</sup>|0.4|0.4|0.4|-0.0|0.1|0.1|1.0|1.0|1.0|1.0|0.4|

_Notes:_ The table reports coefficient estimates from linear regressions of ln <u>�</u> _pph,nh,ikkhh,n,i_ <u>�</u> on log real GDP per capita and predicted values based on country specific industrial composition, both separately and jointly. Predictions based on industrial composition are based on equation (12) and computed as ln � _<u>rr</u>_ <u>++</u> _<u>δδni</u>_<sup>_·_</sup> <u>��</u> _<u>njnj</u>_ =1=1<sup>_ααj,nj,iYYhh,j,j_</sup> �, with _r_ = 0 _._ 03, _δn_ = 0 _._ 06, and _δi_ = 0 _._ 2. Robust standard errors are reported in parentheses below each coefficient. Significance levels are indicated by * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, and *** _p <_ 0 _._ 01.

Since our estimates for ICT start in 1993 and the GGDC10 database reports values every 10 years, we can only use the years 2000 and 2010 to conduct our cross country analysis for this data source. Panel A.1 suggests a remarkably close fit of our predicted values for both years. Likewise, Panel A.2 reveals a similar fit based on the much coarser WDI sectors, yet for a much larger number of countries and reported at an annual frequency for the period 1993-2011. Given this fairly tight fit it is perhaps not surprising that panel B of Figure 7 suggests a strong positive correlation between our predicted values and log income per-capita for both data sources.

Inspired by this graphical illustration we use our predicted values to evaluate the degree to which cross country variation in ICT abundance is accounted for by industrial composition. Table 3 reports a regression of log nominal ICT/NICT on log real GDP per capita and the log of our predicted values from equation (12) based on the GGDC10 database. While this database has fairly detailed information on value added by sector, we only have data for 32 countries in 2000 and 2010 in overlap with our ICT measures.

Panel A displays the results for 2000, panel B for 2010, and panel C pools both years but controls for year effects. Columns (A.1) and (A.2) in panel A indicate that, in 2000, both income and 24
<!-- Start of picture text --> 80<br>75 SN —erural —TM—urban —#—country<br>a oN<br>t./& 6S<br>= 60<br>S|<br>a 55 =<br>Sg) ee.<br>i<br>& 45<br>40<br>35<br>30<br>2003 2007 2009<br><!-- End of picture text -->

<!-- Start of picture text --> 90 100<br>80 m 2003 90 GBAO<br>80<br>@ © Khatlon<br>B 60 so<br>= 40 sod<br>vu 50 =<br>-—mEkin<br>240 20 § Dushanbe<br>"2 &EeE ie<br>20 BEE E | 2008 2009<br>Dushanbe Sogd Khatlon RRP GBAO<br><!-- End of picture text -->
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Public**<br>**Admin**<br>**istrati**<br>**on**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Educa**<br>**tion**|0.00|0.02|0.04|0.06|0.06|0.06|0.06|0.06|0.06|0.06|0.06|
|**Health**|0.00|0.02|0.05|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.07|
|**Other**<br>**Servic**<br>**es**|0.00|5.16|1.96|-1.09|-1.10|-1.10|-1.12|-1.13|-1.14|-1.15|-1.16|

_Table B. 7. Annual spending associated with investments and households’ consumption under the Smart Meters Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forest**<br>**ry**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Minin**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**g**||||||||||||
|**Food**<br>**Manuf**<br>**acturi**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ng**||||||||||||
|**Textile**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Wood**<br>**and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Paper**||||||||||||
|**Chemi**<br>**cal and**<br>**Plastic**<br>**Produ**|0.00|1.50|2.00|2.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**cts**||||||||||||
|**Metal**<br>**Produ**|0.00|0.75|1.00|1.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**cts**||||||||||||
|**Machi**<br>**nery**<br>**and**<br>**Equip**<br>**ment**|0.00|0.75|1.00|1.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Energy**|0.00|-0.03|-0.06|-0.09|-0.20|-0.31|-0.42|-0.54|-0.55|-0.55|-0.56|
|**Constr**<br>**uction**|0.00|7.50|10.00|10.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Trade**|0.00|0.46|0.62|0.63|0.07|0.11|0.15|0.20|0.20|0.20|0.20|
|**Accom**<br>**modat**<br>**ion**<br>**and**<br>**Food**|0.00|0.00|0.01|0.01|0.02|0.04|0.05|0.06|0.06|0.06|0.06|

41
going to multiply and amplify exogenous shocks, thus highlighting the importance of a more competitive regulatory environment for continued convergence.

**Figure 24: Simulation of the effect of ETCR on GDP**

<!-- Start of picture text --> Increase in level of Real GDP per capita in ppp by<br>Improvement of ETCR index<br>to Median level to 75% level to the Best level<br>2.77<br>Sys GMM 1.65<br>0.82<br>1.35<br>RE GLS 0.81<br>0.40<br>0 3 %<br><!-- End of picture text --> 26
# **_Sowing and Reaping:_**

# **_Institutional Quality and Project Outcomes in Developing Countries_**

## **_By David Dollar and Victoria Levin_**

Much of the academic debate on the effectiveness of foreign aid is centered on the relationship between aid and growth. Different aid-growth studies find conflicting results: aid promotes growth everywhere; aid has a zero or negative impact on growth everywhere; or the effect of aid on growth depends on recipient-specific characteristics, such as the quality of institutions and policies. Although these studies fuel an interesting debate, cross-sectional macroeconomic studies cannot be the last word on the topic of aid effectiveness. In this paper, Dollar and Levin introduce microeconomic evidence on factors conducive to the success of aid-funded projects in developing countries. The authors use the success rate of World Bank-financed projects in the 1990s, as determined by the Operations Evaluation Department, as their dependent variable. Using instrumental variables estimation, the authors find that existence of high-quality institutions in a recipient country raises the probability that aid will be used effectively. There is also some evidence that geography matters, but location in Sub-Saharan Africa is a more robust indicator of lower project success rate than tropical climate. The authors proceed to disaggregate the success rate of World Bank projects by lending instrument type and by investment sector, finding that different institutions are more important for different types of projects. The finding of a strong relationship between institutional quality and project success serves to provide further support to the hypothesis that aid effectiveness is conditional on institutions and policies of the recipient country.

World Bank Policy Research Working Paper 3524, February 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
# **Figure 3. Total volume of fish landings in Kenya by source, 1990-2001:**

<!-- Start of picture text --> 250000<br>200000<br>150000<br>100000<br>50000<br>0<br>Lake Victoria Other freshwater Marine<br>Tonnes<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> _Source: CBS._ The development of the Nile perch fishery also served to enhance the dominance of Lake Victoria as Kenya’s main fishing resource (Bokea and Ikaiara, 2000). Thus, whilst landings of fish at Lake Victoria accounted for around 50 percent of total fish supply in 1970, by the mid1990s this had increased to 94 percent. Today, when discussing domestic production and trade in fish and fishery products within a Kenyan context, one is inevitably referring to Lake Victoria.

During the early to mid-1980s, despite significant expansion, the Kenyan Lake Victoria fishery continued to be almost exclusively exploited by small-scale local fishers. There were no major changes in technology and practices and many women were involved in the processing and marketing of fish locally. However, towards the end of the 1980s, the Nile perch fishery changed dramatically. Thus, the number of fishers increased steadily from 11,000 in 1971, to 22,000 in 1989 and 30,000 in 1995. There was also significant growth in the number of registered vessels from around 4,000 in the early 1970s, to 5,000 in the late 1980s and over 11,500 in 2000. Simultaneously, there was investment in more advanced fishing equipment, in particular gill nets with larger mesh sizes (Abila and Jansen, 1997).

The growth of the Nile perch fishery has been accompanied by a significant expansion of the population of districts bordering the lake, from 2.55 million in 1989 to 3.73 million in 2001 (On’yan’ga, 2002). Around 60 percent are below 15 years of age. Thus, the fishery has become the major source of livelihood for a significant proportion of the Kenyan population. However, at the same time this expansion in population has meant that any growth in aggregate income in the area has been ever more widely dissipated.

Despite the expansion of the Lake Victoria fisheries, with the exception of a small number of trawlers that operate illegally, fishing is still undertaken from wooden boats with a crew of between two and four fisher-folk. Relatively few of the boats are motorized and the main technological advance has been in the type and size of net. Until the end of the 1990s, facilities 15