rafmacalaba/datause-encoder-data huggingface dataset:
Despite these concerns, nightlights have several advantages as a data source. They are globally available, providing coverage even in regions where economic data might be scarce or unreliable. Nightlights also have a standard spatial resolution and time intervals, which allows for consistent comparisons over time and across different geographic areas. When processed and interpreted correctly, taking into account the potential limitations and biases, nightlight data can indeed serve as a useful proxy for the intensity of economic activity (Gibson et al., 2021).

2. Relationship between disaster and conflict-affected population traction over the past decade (Siddiqi, 2018), specifically focusing on co-location and causation debates associated with climate-related hazards, violent and armed conflict, and insecurity (Gemenne et al., 2014; Gleditsch, 2012). Often, previous studies have focused on the impacts of disasters on conflicts – whether they exacerbate existing conflicts, ignite new ones, or in some cases halt ongoing conflicts (Nel & Regharts, 2008; Schleussner et al., 2016; Slettebak, 2012; Ghimire et al., 2015; Nardulli et al., 2015). Due to such uncertain impacts of disasters and disaster recovery effort on conflicts, other studies explore how disaster risk reduction and recovery measures should be done differently in conflict contexts (Brzoska, 2018; Peters et al., 2019; World Bank, 2016).

attention has been given to understanding and quantifying the influences of conflicts on disaster impacts – the additional economic impacts of disasters should they take place in conflict areas and its effect on conflict-affected population – as well as the causal pathways and mechanisms behind such additional impacts. The absence of comprehensive economic data and ground truth data to validate disaster impacts, coupled with the complexity of defining conflict-affected populations are among the scientific challenges prohibiting analyzing the influence of conflicts on disaster impacts. This paper seeks to address this gap and support further quantitative analyses on the additional impact on households’ welfare and nations’ economic growth in countries experiencing these compounded crises.

# Empirical strategy

# Data

density, and administrative boundaries, to econometrically analyze the specific effects of flood events in Mozambique as well as Nigeria's conflict and non-conflict affected regions.

## _Nightlights data_

Furthermore, we utilize composite images of nighttime radiance data captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NASA-NOAA Suomi satellite. These monthly composites are available since 2012 at a resolution of 15 arcseconds by 15 arcseconds (approximately 463 meters at the equator). VIIRS Day Night Bands (DNB) data exclude grid cells affected by lightning, stray light, lunar illumination, and cloud cover (Elvidge et al., 2017). We favor VIIRS data over traditionally used data from the Defense Meteorological Satellite Program (DMSP) 3
Table 9

# THE EFFECTS OF A FIVE YEAR SUSTAINED ANNUAL INCREASE OF S20 BILLION IN THE FINANCIAL<sup>FLOWS</sup> TO DEVELOPING COUNTRIES

|P<br>E<br>R<br>0<br>S|GNP|Uni<br>PGNP|ted State<br> <br>ERAN|s<br>RS|CURBL|GNP|<br>PGNP|Germany<br>ER|<br>RS|CURBL|GNP|PGNP|Japan<br>ER|RS|CLRBL|Large<br>GNP|Industr<br>PGNP|Other<br>ialized<br>ER|Coun<br>RS|tries<br> <br>CURB|Develop<br>L GIP|ing Co0<br>PGNP|Ltrfes<br>_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1988|0.27|0.08|-0.22|0.05|-3.05|0.43|0.20|0.24|0.09|0.23|0.43|0.21|0.19|0.09|2.19|0.43|0.28|0.23|0.09|0.11|0.02|0.24|0.27|
|1989|0.33|0.26|-0.15|0.08|-3.69|0.49|0.58|0.18|0.14|0.25|0.49|0.57|0.12|0.14|2.44|0.50|0.72|0.16|0.16|-0.46|0.05|0.57|1.26|
|1990|0.31|0.52|-0.05|0.11|-4.42|0.41|1.00|0.09|0.18|0.68|0.42|0.95|0.02|0.18|2.86|0.38|1.13|0.06|0.20|-1.25|0.05|0.91|1.93|
|1991|0.23|0.82|0.06|0.14|-5.58|0.28|1.34|-0.02|0.21|1.11|0.30|1.24|-C.09|0.20|3.13|0.22|1.39|-0.05|0.21|-1.96|0.04|1.16|2.47|
|1992|0.11|1.09|0.17|0.16|-6.99|0.14|1.48|-0.13|0.20|1.42|0.20|1.33|-0.19|0.20|3.31|0.13|1.41|-0.15|0.20|-2.23|0.03|1.24|2.67|
|193|-0.18|1.29|0.26|0.13|-5.4|-0.27|1.42|-. 23|0.13|1.53|-0.18|1.23|-0.27|0.13|1.55|*0.25|1.22|-0.23|0.12|2.61|0|<br>1.12|1.76|

GDP Gross Domestic Product GNP = Gross National Product PGNP = GNP Price Deflator

- KERN = MERN Weighted Effective Exchange<sup>Rate</sup>

- RS = Short-Term Interest Rate

CURBL = Current Account Balance ND = Net Debt

- . The numtbers in GNP, PGNP, MERN<sup>and ER are percentage deviation from the baseline.</sup> R AS is the change in the tevel.

-

- CURBL and ND are in biLlions of dollars.

w
# **Table S22. Estimated total number of households using a coping strategy in response to COVID-19related income loss and average household-level incidence of using a coping strategy**

||Any<br>Coping<br>Strategy|Relied on<br>Saving|Sale of<br>Assets|Reduced<br>Food<br>Consumption|Reduced<br>Non-Food<br>Consumption|Received<br>Assistance<br>from<br>Friends and<br>Family|Received<br>Any<br>Assistance|
|---|---|---|---|---|---|---|---|
|Total|33,171,234|12,219,426|3,462,072|21,370,172|6,491,568|8,084,976|4,270,560|
||(578,286)|(473,945)|(284,763)|(518,857)|(354,462)|(422,270)|(313,064)|
|Mean|0.564|0.294|0.083|0.514|0.156|0.194|0.073|
||(0.010)|(0.011)|(0.007)|(0.012)|(0.009)|(0.010)|(0.005)|
|Observations|8,721|5,595|5,595|5,595|5,595|5,595|8,719|

_Note_ : Each column reports the estimated total number of households using a coping strategy in response to COVID-19-relaated income loss and average household-level incidence of using a coping strategy. Adoption of a strategy is condition on experiencing a COVID-19 related loss of income per Table S8. Data come from the most round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Standard errors on the estimates are in parenthesis.

49
where OUTCOME is one of the key outcome metrics of looms, looms/employee, etc. We report statistical significance using both conventional inferential procedures based on normal approximations as well as using permutation tests that have exact finite sample size to allay sample size concerns.<sup>11</sup> We start in column (1) of Table 5 in the top panel looking at the number of looms (in logs), which is a basic measure of production capacity. In panel A, we regress this on a dummy for the year being greater or equal to 2011 - a post-intervention dummy - finding a statistically insignificant coefficient of -0.032. In panel B, we break down this impact by year, with the point estimates suggesting a 16.1 percent increase in capacity by 2017, but this is also not statistically significant.

In column (2) we examine employment. The point estimates suggest a relatively large drop in employment, of 23 to 24 percent on average over the full period, and in 2017. However, this drop is also not statistically significant. There are two reasons why employment may have fallen. The first is that, at baseline, firms employed many workers fixing quality defects and would need less of this sort of labor as quality improved. Second, production processes improvements and fewer breakdowns can enable the same worker to be in charge of more looms.

Column (3) then combines these measures to focus on our main measure of long-term firm productivity, which is log looms per employee. This is a classic productivity measure in the literature (see, for example, Clark 1987 or Braguinsky _et al_ . 2015). One reason is that employees spend much of their time dealing with malfunctioning looms, so that a higher number of looms per employee indicates fewer breakdowns and higher rates of production uptime (the time the loom is producing output rather than being repaired). As such, column 3, panel A, shows that the average treatment effect over the full post-intervention period was to increase looms per employee by a statistically significant 26.7%. Panel B suggests this efficiency improvement was rising over time in that the coefficients are generally larger for 2017 that 2011, with the long-run impact a statistically significant 51.0 percent increase in this productivity measure. However, despite the trend of rising coefficients, we cannot reject that this productivity impact is constant over time.

We also want to investigate the impact on labor productivity. While we did not collect information on labor productivity in 2017, we can use the survey data from the initial wave to

> 11 We also estimate the regression at the plant level and the results are qualitatively similar.

16
# **_Table A4.4e Relationship between country-level characteristics and IEG outcome rating, results for IBRD/IDA_**

|||**De**<br>|**pendent vari**<br>**By**<br>|**able: IEG 1-6**<br>**lending sour**<br>|**outcome rati**<br>**ce**<br>|**ng**<br>||
|---|---|---|---|---|---|---|---|
||**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|
|CPIA rating at|0.159||0.423***||||0.390***|
|approval year|(0.107)||(0.112)||||(0.140)|
|CPIA change over||0.338***|0.611***||||0.514***|
|project lifetime||(0.122)|(0.154)||||(0.174)|
|Log(GDPpc PPP$||||-0.104||-0.005|-0.142|
|at approval year)||||(0.152)||(0.140)|(0.135)|
|Real GDPpc growth|||||4.488***|4.502***|1.800|
|over project lifetime|||||(1.413)|(1.389)|(1.427)|
|Constant|3.924***|4.525***|3.083***|5.407***|4.499***|4.600***|4.504***|
||(0.504)|(0.416)|(0.452)|(1.308)|(0.400)|(1.190)|(1.086)|
|Observations|1,247|1,236|1,236|1,198|1,195|1,143|2,837|
|Adjusted R-squared|0.073|0.084|0.107|0.077|0.070|0.077|0.109|

**Dependent variable: IEG 1-6 outcome rating**

||||**By**|**lending sour**|**ce**|||
|---|---|---|---|---|---|---|---|
||**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|
|CPIA rating at|0.169*||0.414***||||0.439***|
|approval year|(0.090)||(0.108)||||(0.134)|
|CPIA change over||0.323**|0.572***||||0.572***|
|project lifetime||(0.126)|(0.157)||||(0.169)|
|Log(GDPpc PPP$||||-0.080||-0.091|-0.112|
|at approval year)||||(0.067)||(0.071)|(0.068)|
|Real GDPpc growth|||||0.944*|1.087*|0.658|
|over project lifetime|||||(0.529)|(0.547)|(1.330)|
|Constant|2.504***|3.179***|1.901***|3.300***|3.085***|3.476***|2.537***|
||(0.460)|(0.304)|(0.442)|(0.556)|(0.350)|(0.584)|(0.635)|
|Observations|1,543|1,531|1,531|1,537|1,470|1,437|1,386|
|Adjusted R-squared|0.067|0.073|0.089|0.059|0.065|0.061|0.080|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05,

*p<0.10). All regressions are estimated using OLS, and controlling for region, sector, original commitment amount of project, original length of project and IEG evaluator fixed effects.

50
f f ~~_~~ f f ~~_~~ <!-- Start of picture text --> b<br>¥<br>P 0.733<br>1539<br>1539 1.73<br>1538<br>1538 0.732<br>1538<br>2.252.52.75 3 3.25335 3.74 ab 2.95 79.527.75 2 2.25 225.3.75 a:<br>1537<br>1537 0.731<br><!-- End of picture text --> <!-- Start of picture text --> P<br>1539<br>1539<br>1538<br>1538<br>1538<br>2.252.52.75 3 3.25335 3.74 ab<br>1537<br>1537<br><!-- End of picture text --> <!-- Start of picture text --> ¥<br>3457<br>3456<br>345345345<br>345<br>2.25 2.5 2.75 3 3.253.5 3.75 4 i<br><!-- End of picture text --> <!-- Start of picture text --> U<br>ag.7 3457<br>a 3456<br>89.7a9.7 a 345345345<br>345<br>2.5 2.75 3 3.25 3.5 3.75 at 2.25 2.5 2.75 3 3.253.5 3.75 4 i<br><!-- End of picture text -->
29 and link managerial compensation to profits, at the same time clarifying the position and rewards for management following privatization so that the maximization of the firm's value becomes paramount.

# III.3 Managerial Attitudes and Incentives

In the pre-ETP days, the prime managerial attribute was ability to negotiate with the central authorities on subsidies and allocations of inputs and investments funds under the Central Investments program. A typical manager was an engineer, whose entire professional career was connected with the same enterprise, a specialist in production, knowing little of marketing and financial management. On the other hand, good managers knew how to deal with social conflicts within firms, a quality still in high demand in Poland.

Following the shock therapy of the ETP, the emphasis shifted tc profits and marketing, away from the old "production target". At first, managers seemed overwhelmed by the changes they had to deal with: big, sudden changes in relativ'e prices; a demand constraint; and import competition. In addition, the SOEs with their social assets, old technology, excess employment, and quality problems hardly seemed the ideal springboard to a market economy. However, even early in the ETP, it was clear that there were good and far-sighted managers. Phase 1 contained two important findings as SOEs struggled to cope with the new system: o Managers were typically the moving force and inspiration behind change. Workers' Councils played at best a facilitating role o Judging from PPWW payments and wage behavior, SOEs were myopic, with considerable focus on current wages ("high discount rate") and little emphasis on long-term restructuring.

Firm governance was marked by tension between managers and workers (Workers' Council and Trade Unions), with limited labor shedding relative to output fall and high PPWW payments. This collection of findings led to the conclusion that macro stringency while necessary, would not suffice to introduce firm-level change without addressing firm governance, clarifying accountability and responsibility and introducing managerial incentives that would emphasize long-run restructuring.

In this third year of the ETP, published surveys indicate that optimism is up, in spite of huge problems in embattled firms. Managers are looking to the longer run, there is more self-confidence and in many cases successful measures been taken to improve enterprises. What has accounted for this remarkable change?

# III.3.A Optimism

Managers of sample firms agreed with the published survey results of the Warsaw Main School of Commerce (based on polls of SOE managers) that optimism is up."<sup>6</sup> ' On a 0-5 scale, they rated the growth in their optimism in 1992 at 2.5 on average. Managers in the commercialized and privatized firms in the sample were visibly more optimistic than those that had remained SOEs. Managers in AAA enterprises expressed much stronger optimism (2.8) than AA (1.9) and the loss16<sup>1</sup> "Business Survey Poland', Research Institute of Economic Development, Warsaw School of Economics, various monthly issues in 1992.
De, Supriyo. 2009. “Intangible Determinants of Market Value in the New Economy: A Dynamic Panel Data Analysis of the Indian Software Industry.” _The Singapore Economic Review_ 54 (3): 379–98.

De, Supriyo, and Dilip Dutta. 2007. “Impact of Intangible Capital on Productivity and Growth: Lessons from the Indian Information Technology Software Industry.” _Economic Record_ 83 (August): S73–86.

DeStefano, Timothy, Richard Kneller and Jonathan Timmis. 2023. “Cloud Computing and Firm Growth.” _Review of Economics and Statistics_ (published online): 1–47.

Demir, Ayse, Vanesa Pesqué-Cela, Yener Altunbas, and Victor Murinde. 2022. “Fintech, Financial Inclusion and Income Inequality: A Quantile Regression Approach.” _The European Journal of Finance_ 28 (1): 86–107.

de Haan, Jakob, and Jan-Egbert Sturm. 2017. “Finance and Income Inequality: A Review and New Evidence.” _European Journal of Political Economy_ 50: 171–95.

de Vries, Gaaitzen J., Elisabetta Gentile, Sébastien Miroudot, and Konstantin M. Wacker. 2020. “The Rise of Robots and the Fall of Routine Jobs.” _Labour Economics_ 66 (October): 101885.

Dhyne, Emmanuel, Joep Konings, Jeroen Van den Bosch, and Stijn Vanormelingen. 2018. “IT and Productivity: A Firm Level Analysis.” Working Paper No. 346, National Bank of Belgium, Brussels.

Di Ubaldo, Mattia, and Iulia Siedschlag. 2021. “Investment in Knowledge-Based Capital and Productivity: Firm-Level Evidence from a Small Open Economy.” _Review of Income and Wealth_ 67 (2): 363–93.

Diao, Xinshen, Mia Ellis, Margaret S. McMillan, and Dani Rodrik. 2021. “Africa’s Manufacturing Puzzle: Evidence from Tanzanian and Ethiopian Firms.” NBER Working Paper No. 28344, National Bureau of Economic Research, Cambridge, MA.

<mark>Dinerstein, Michael, Liran Einav, Jonathan Levin, and Neel Sundaresan. 2018. “Consumer Price Search and Platform Design in Internet Commerce.”</mark> _<mark>American Economic Review</mark>_ <mark>108 (7): 1820– 59.</mark> Draca, Mirko, Raffaella Sadun, and John Van Reenen. 2007. “Productivity and ICTs: A Review of the Evidence.” In _The Oxford Handbook of Information and Communication Technologies_ , edited by Robin Mansell, Chrisanthi Avgerou, Danny Quah, and Roger Silverstone, 100–47. Oxford University Press, Oxford.

Dustmann, Christian, Johannes Ludsteck, and Uta Schönberg. 2009. “Revisiting the German Wage Structure.” _Quarterly Journal of Economics_ 124 (2): 843–81.

30
OZIER - REPLICATION REDUX – p. 29 of 41 _abstract_ , Aiken, et al. (2015) write that “For school attendance, re‐analysis showed benefits similar to those originally found in intervention schools for both children who did and those who did not receive deworming drugs. However, ... there was little evidence of an indirect effect on school attendance among schools close to intervention schools.” So, though Aiken, et al., (2015) are thorough in their text, their abstract shortens the message to a more negative one in terms of spillovers. The associated Cochrane Review text (Taylor‐Robinson, et al., 2015, p. 10) reads: “the indirect effects of the intervention on adjacent schools disappeared.” A press release from Aiken and Davey’s home institution then appeared simultaneously with the Cochrane Review in July of 2015 (LSHTM 2015), proclaiming that “deworming children may not improve school attendance.” In summary: two very detailed and nuanced replication reports appeared, with commentary from the original study authors. The abstracts of two very detailed replication efforts took a relatively negative view of the replication’s outcome, despite the very different interpretation that the original study authors and some consumers of the replication came to. The Cochrane review took the pessimism to heart, a press release led with the negative angle, the _Guardian_ headline followed, and with it, the “worm wars.” Perhaps the over‐simplified message of the press release is a cautionary tale about producing inaccurate summaries of research; perhaps the incredible ambition of this replication effort was simply too much; or perhaps simply with modern trial registries and widespread data availability requirements, this series of misunderstandings would not be likely to happen again.

# 4. Lessons

We have seen how the replication, reanalysis, and review that considered the work of Miguel and Kremer (2004) made substantial progress in pointing out the ways analysis may or may not be robust,
through both participation and better governance--can lead to greater efficacy in government action, including development assistance (Picciotto 1994; OECD 1995).

On the other hand, the empirical evidence does not provide evidence of a relationship between "democracy' and the particular performance indicator that we examine (although we hasten to emphasize the statistical fact that lack of evidencefor is not necessarily evidence _against)._ This merely suggests with microeconomic data that which is known from aggregate data: while some authoritarian govermnents have not provided economic benefits, other countries under authoritarian regimes (particularly in East Asia) have experienced efficacious governments, widespread economic growth, and enormous reductions on poverty.

- 39 -
45

**Figure 4.3: First order dominance Test 1985-92.**

<!-- Start of picture text --> 100<br>80<br>60<br>C<br>20j 0_<br>0 20 40 60 80 90<br>Percent ofpoverty line<br>0 1985/86 .~.. .- 1992/93<br><!-- End of picture text -->

**Table 4.30: Projected Impact of (5%) Distributionally Neutral Growth in Real per Capita Expenditure on Poverty (1994-2005)**

|**Year**|**Mean Per Capita**<br>**Expenditures**|**Head Count (P0 %)**|**Depth Index (PI %)**|**Severity Index**(P2%)|
|---|---|---|---|---|
|1994|825.34|32.54|13.78|7.85|
|1995|842.18|31.95|13.41|7.61|
|1996|859.37|31.30|13.03|7.38|
|1997|876.91|30.54|12.66|7.16|
|1998|894.80|29.95|12.31|6.94|
|1999|913.07|29.22|11.95|6.72|
|2000|931.71|28.41|11.61|6.51|
|2001|950.72|27.80|11.27|6.31|
|2002|970.12|27.05|10.94|6.11|
|2003|989.92|26.03|10.62|5.92|
|2004|1010.12|25.39|10.32|5.73|
|2005|1030.73|24.75|10.01|_5.55_|

Source: NCS, 1985/86 and 1992/93.

As mentioned earlier, poverty is widespread and cannot be eliminated without sustained economic growth. Nigeria needs to choose a growth strategy which ensures that the poor benefit. A poverty reducing growth strategy for Nigeria, will need to start with removing any existing restrictions on labor mobility and moving agricultural policy and practices away from a bias towards specific crops to encouraging an environment where producers will shift towards the most profitable crops. A long term vision for growth will be based on broad based export production to provide the cash stimulus to get the economy moving. Under such a strategy, the government would promote exports by creating appropriate conditions by removing those fiscal, regulatory and monetary restrictions that tend to hamper export development. Labor intensive exports would create employment and incomes which would increase the demand for upstream activities and for domestically produced food.
24

- Media Eghbal (2008), 'BRICS Economies Withstand Global Financial Crisis', <u>Euromonitor International from National Statistics.</u>

- Pine, J. B. (1993), Mass Customization: The New Frontier in Business Competition, Cambridge, Mass: Harvard Business School Press.

- Piore M. J. and C. Sabel (1984), <u>The Second Industrial Divide: Possibilities for Prosperity, N. York: Basic Books.</u>

- Prahalad, C. K. (2005), <u>The Fortune at the Bottom of the Pyramid: Eradicating Poverty through Profits, Upper Saddle River, NJ: Pearson Education/Wharton</u> School Publishing.

- Solow, R. (1957) 'Technical Change and the Aggregate Production Function' in <u>Review of Economics and Statistics, Vol 39. August, pp.312-20.</u>

- Von Braun J. (2007) "The World Food Situation: New Driving Forces and Required Actions," Food Policy Reports 18, International Food Policy Research Institute (IFPRI).

- Wade R. H. (1990), <u>Governing the Market: Economic Theory and the Role of Government in East Asian Industrialization, Princeton: Princeton University</u> Press.

- Wilkinson, R. and K. Pickett (2009), <u>The Spirit Level: Why More Equal Societies Almost Always Do Better, London: Penguin.</u>

- Womack, J. P. and D. T. Jones (1996), <u>Lean Thinking: Banish Waste and Create Wealth in Your Corporation, N. York: Simon & Schuster</u>

- World Bank (2009), <u>Global Economic Prospects 2009: Commodities at the Crossroads, Washington D.C.</u>

- World Bank (March 2009), China Quarterly Update. Beijing, World Bank.

- Wright, G. and J. Czelusta (2004), “The Myth of the Resource Curse”, <u>Challenge,</u> Vol. 47, No. 2, pp. 6-38.

- Zhang, Y. and C. Zheng (2008), “The Implications of China's Rapid Growth on Demand for Energy and Mining Products Imported from Australia.”, <u>Economic Papers 27 (1), 95-106.</u>