rafmacalaba/datause-encoder-data huggingface dataset:
# **1- Introduction**

Few would deny that water, as a necessity for life, is central to all forms of economic activities. And yet attempts to quantify its contribution to economic growth remain elusive and uncertain. The earliest efforts to establish such links relied upon simple correlations between rainfall and economic growth. Charts showing comovements of rainfall and GDP growth are still presented as prima facia evidence of a link between the availability of water and economic growth by the World Bank and others (World Bank 2006). While perhaps a useful tool for policy discourse, such correlations provide little information about causal patterns.

More recently a number of papers have attempted to investigate the effects of either rainfall or water availability on economic growth. Two canonical papers in the growing climate economy literature empirically examine the joint effects of rainfall and temperature changes on aggregate economic outcomes (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015 – hereafter DJO and BHM). After many robustness checks, both papers conclude that there is little discernible, robust evidence that rainfall has a statistically significant and consistent impact on GDP growth. However, temperature is shown to have a consistent and non-linear effect on GDP growth, with productivity being maximized at 13�C in BHM. Following this pioneering work, recent research in the economics literature has focused upon the consequences of rising temperatures (Deryugina and Hsiang, 2017; Diffenbaugh and Burke, 2019; Henseler and Schumacher, 2019; Burke and Tanatuma, 2019). A typical finding is that temperature has negative economic impacts, but precipitation has no consistent and statistically significant impact.

To our knowledge there are only four recent papers that seek to specifically explore whether rainfall has an impact on aggregate economic activity at a global scale (Barrios et al. 2010, Brown et al. 2013, El Khanji & Hudson 2016, Sadoff et al 2016 – See Table Sup 1).<sup>2</sup> All these studies use national data. Only Brown et al. (2013) find an impact of precipitation shocks on GDP growth, noting that both dry and wet episodes of rainfall lead to a decline in per capita GDP growth rates. Surprisingly they find no impact on agricultural GDP growth, though it is the sector that is most affected by rainfall. None of the other papers finds a robust relationship between the chosen measure of precipitation or water availability (which varies across papers) and indicators of economic growth. Further details are provided in Sup 1.

> 2 In this discussion we exclude a handful of country-specific studies and studies that use simple correlations between GDP and rainfall or runoff, or cross sectional regressions as the latter group can make no claim about causal impacts and the findings of the former are similar to those of the global studies – no consistent pattern is found between GDP or GDP growth and rainfall.

2
the robustness of our results by considering a depth of 50m. This increases the fraction of croplands satisfying our conditions to 62.05%; the high fraction of croplands satisfying our conditions provides further evidence for the feasibility and potential for sustainable groundwater irrigation in Sub-Saharan Africa. Our results remain consistent with this change in the depth constraint (refer to Table A3. Refer to the map of production gains in Figure A1 and Figure A2 for the SD and SRHS approaches, respectively. Figure A4 and Figure A6 plot the country-wise gains for maize and rice, respectively, using the SD approach. For the SRHS approach results, refer to Figure A3 and Figure A5.

Our findings align with those in existing literature. For the Indian context, Duflo and Pande (2007) found surface water irrigation boosts cereal crop production by 34%-53%. Similarly, in Rwanda, Jones et al. (2022) observed a 43%-62% increase in annual cash profits. Dyer and Shapiro (2023) also reported a 41% increase in net revenues from fruits and vegetables through irrigation pumps in Kenya. Our results for most of the crops fall within the range of gains reported in these studies, but vary by crop type.

## **6.1 Income Gains**

Groundwater irrigation is one of several input factors that are determinants of agricultural productivity. Small to modest simulated income growth should be expected from filling these crop water deficits. Our modelling approach does not account for the possibility of water transportation arrangements from areas with low water deficits to areas with large water deficits. Consequently, we simulate expected income growth (over the annualized 1.90 USD International Poverty Line) ranging from 0% to 3.1% across African countries with simulated productivity gains.

The gains appear to be highest in the Sahel countries with Togo (3.1%), Benin (2.4%) Guinea (2.4%), and Nigeria (1.0%). These results are driven by large deposits of available groundwater in areas where crop water deficits exist and are thereby correlated with pre-existing low levels of crop production. It should also be noted that because we are unable to estimate the number of farmers in these specific areas, we use World Bank national estimates of number of people in agriculture. Hence, our results are likely quite conservative in terms of per-farmer earnings gains corresponding to filling in existing crop water deficits.

# **7 Robustness Checks**

## **7.1 Considering a Different Combination of Conditions**

We test the robustness of our results by relaxing each of the three conditions. Table 5 and Table 6 present the simulation results we obtained using SD and SRHS, respectively. Each row in the tables corresponds to a unique combination of conditions that were imposed while running the simulations. The first row for each crop corresponds to the results in Table 4 where all three conditions hold.

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Policy ReseaRch WoRking PaPeR 4460

# **Abstract**

This paper presents a survey of recent research on the economics of infrastructure in developing countries. Energy, transport, telecommunications, water and sanitation are considered. The survey covers two main set of issues: the linkages between infrastructure and economic growth (at the economy-wide, regional and sectoral level) and the composition, sequencing and efficiency of alternative infrastructure investments, including the arbitrage between new investments and maintenance expenditures; OPEX and CAPEX, and public versus private investment. Following the introduction, section 2 discusses the theoretical foundations (growth theory and new economic geography). Section 3 assesses the analysis of 140 specifications from 64 recent empirical papers— examining type of data used, level of aggregation, econometric techniques and nature of the sample—and discusses both the macro-econometric and microeconometric contributions of these papers. Finally section 4 discusses directions for future research and suggests priorities in data development.

This paper—a product of the Development Research Department, Research Support Unit (DECRS)—is part of a larger effort by the World Bank’s Research Committee, in consultation with Regions and Networks, to commission surveys of recent policy research and diagnostic analyses of the current state-of-the-art in priority areas for developing countries.. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@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 2: REAL CONSOUDATED PUBLIC SECTOR DEFICIT<sup>(Cant)</sup> (Pereag of GNP)

|TURKIEY|1910-11|1962-44|196548|YUGOSLAVIA|1961|196244|1967|
|---|---|---|---|---|---|---|---|
|Total Real PS Defcoi<br>(Excluding K lwses ol Fof. Debt)|-0.9|e.7|5.4|Real Quasi-Fiscal Deficit|3.7|6.2|8.7|
|||||A. Money Finance|2.2<br>|2 7<br>|4.3<br>|
|A. Adjusted Money Finance|0.5|4.5|2.7|Inflation Tax|3.6<br>|3.6<br>|4.9<br>|
|||||Seignorage|-1.4|-0.9|-0.7|
|Inflation Tax|1.4|3.0|2.9|||||
|Seignorage|-0.9|1.5|-0.1|S.Debt Finance|-1.5|-3.4|-2.4|
|B. Not Domnetic Debt Fince|-2.3|-0.5|1.4|||||
|Bonds and BiNl|-0.2|0.3|0.7|||||
|Net Bank Crdt|-1.6|-45|0.4|||||
|**ESFe**|00|00|0.0|||||
|C.NetFofeign Debt Finn<br>(Excluding K**lome**of For. Debt)|0.9|2.7|1.3|||||
|D. Net Fofeign Debt Finance<br>(Including K**km"**of For. Debt)|53|5.1|1.8|||||
|MEMO: Fofeign Exchange Losses(D-C)|4.4|2.4|0.6|||||

SOURCE: See Annex.
**Figure 3  Job Creation an Employment by Firm Size**

<!-- Start of picture text --> Employment by Firm Size<br> 2000<br>3%<br>13%<br>Micro<br>Small<br>50%<br>Medium<br>34% Large<br><!-- End of picture text --> <!-- Start of picture text --> Job Creation by Firm Size<br>2000-2001<br>21% 19%<br>Micro<br>Small<br>Medium<br>26% Large<br>34%<br><!-- End of picture text --> Source: Annual Survey of Enterprises, 2001; author’s calculations.

Although the small firm sector as such is expanding, this is associated with substantial job reallocation among small firms. To illustrate, one out of eight jobs is moved from one small firm to another within a year. This high job turnover epitomizes a dynamic labor market and implies that those who are looking for a job have a better chance to find one. At the same time, however such high job turnover contributes to frictional unemployment, as it takes time for workers to move from old to new jobs.

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further heightened **by** the importance of total external trade in **GDP.** It is clear that the combination of large deviations and large trade shares in **GDP** provides prima facie evidence that adjustment of official exchange rate as a general purpose conversion factor should be accorded serious consideration.

46. IEC has expanded its collection of various exchange rates to include parallel or black market rates. For Operational Guideline purposes, black market rates have zero weights. They are used only to shed light on conversion issues and set outer limits on any possible adjustment of conversion factors.

47. Should "unofficial" exchange rates, in addition to officially recognized multiple exchange rates, be used to obtain reasonably meaningful estimates for per capita **GNP?** In principle, the conversion factor used **by** the Bank to transform major macroeconomic aggregates in local currencies into a common numeraire should reflect the effective transaction rates used in international transactions. In addition, for the resulting overall conversion factor to be analytically relevant and meaningful, the major macroeconomic variables, such as **GNP,** should reflect consistently the relative prices implicit in the effective transaction rates believed to apply. Therefore, the effective transaction rates should refer to all the various rates at which foreign transactions take place, including "unofficial" exchange rates. Failure to attach any importance to the unofficial rates which are known to prevail (because they are "unofficial" or "illegal") gives rise to both price and conversion distortions which will tend to show up in erratic trends in derived per capita **GNP** estimates.
correlations (column 6) – doubling firm size is associated with approximately 0.047 point increase in management score. Going across columns, this relationship appears to be stronger for manufacturing and particularly low-tech. Services, and especially knowledge intensive services, have a weaker relationship of management capabilities with firm size. This result is analogous to the finding in Mexico where the relationship between firm size and management is weaker in services relative to that in manufacturing (Bloom et al., 2019b).

_Second,_ as noted in the descriptive in Section 3, regressions confirm that firm age in Croatia is negatively correlated with management score, that is, adoption of structured management practices is lower among older firms (column 2). Following similar analysis in Bloom et al. (2019b) on Mexico and the work of Hsieh and Klenow (2014) regarding the life cycle of firms, this finding appears to be indicative of poor allocative efficiency among firms in Croatia where clearly, market selection is not operating perhaps due to the lack of pro-competitive conditions, especially in services. Nevertheless, the finding in Croatia is somewhat similar to that observed in a survey of manufacturing firms in Russia such that firms also do not improve with age (Grover and Torre, 2019), however, in Croatia this negative association is primarily driven by the services sector.

_Third,_ demonstrate that management score is positively and significantly correlated with the likelihood being an exporter, and having higher a share of tertiary educated workers (columns 3-4). Exporters are more likely to be better managed, and significantly so in manufacturing. More precisely, the management score of a manufacturing exporter in Croatia is higher by 7 percent than a non-exporter. This result is mainly driven by high-tech manufacturing firms, where the same difference is likely to around 14 percent, thereby suggesting that management capabilities can be improved among other firms in all sectors. Column (5) uses an indicator for ownership of firms. It takes value 1 if a firm is family-owned and run by a family member of the founder and 0 otherwise. We do not find any relation between ownership and management practices of a firm.

Column (6) puts all these together and the results remains the same, qualitatively, that is, – firm size, export status and education of employees are significantly correlated with management score. The overall explanatory power of firm characteristics in explaining management score is still quite limited – in the most complete specification (column 6) explains only 20 percent of the overall variation in the management score, implying that nearly 80 percent of the variation in management practices is explained by idiosyncratic, firm-specific factors.
Monitoring Financial Stability in Developing and Emerging Economies Practical Guidance for Conducting Macroprudential Analysis Miquel Dijkman* World Bank

# **JEL Codes: G01; G18; G2**

# **Keywords: Financial Crises; Government Policy and Regulation; Financial Institutions and Services**

## _____________

*  The author is a Senior Financial Sector Specialist in the Financial and Markets Global Practice of the World Bank. The views expressed in the paper are the author’s alone. The author gratefully acknowledges comments and inputs received from Attila Csajbok, Erik Feyen, and Eva Gutiérrez (all of the World Bank), as well as the Banco Central de Costa Rica, the Banco Central de Reserva de El Salvador, and the Bank Indonesia. The author can be contacted at mdijkman@worldbank.org.
Table 7 Investment Ratios (average of annual figures, percent of GDP)

||1979-82|1983-86|
|---|---|---|
|Reschedulers|||
|Argentina<br>|20.6|12.5|
|Brazil<br>|21.9|17.9|
|Chile<br>|18.2<br>|13.0|
|Costa Rica|26.4|_23.5_|
|Ecuador|25.0|18.3|
|Mexico|26.1|21.0|
|Morocco|23.2|21.1|
|Peru<br>|19.2|16.7|
|Philippines|30.3|18.1|
|Uruguay|16.1|8.8|
|Yugoslavia|42.6|29.8|
|Non-Reschedulers|||
|Colombia|19.6|18.2|
|Egypt|32.2|23.4|
|India|24.5|24.1|
|Indonesia|24.9|28.2|
|Israel|23.2|20.6|
|Korea|30.8|29.7|
|Malaysia|32.9|30.7|
|Venezuela|26.3|15.4|

# Note: Mexico is 1983-85 rather than 1983-86.

# Sources: International Financial Statistics (various years) and World Development Report (various years).

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And adding one point for each of the following that the business has done in the last 3 months

- Attempted to negotiate with a supplier for a lower price on raw material

- Compared the prices or quality offered by alternate suppliers or sources of raw materials to the business’ current suppliers or sources of raw material

The _records score_ ranges from 0 to 8, and it is calculated by adding one point for each of the following that the business does

- Keeps written business records

- Records every purchase and sale made by the business

- Able to use records to see how much cash the business has on hand at any point in time

- Uses records regularly to know whether sales of a particular product are increasing or decreasing from one month to another

- Works out the cost to the business of each main product it sells

- Knows which goods you make the most profit per item selling

- Has a written budget, which states how much is owed each month for rent, electricity, equipment maintenance, transport, advertising, and other indirect costs to business

- Has records documenting that there exists enough money each month after paying business expenses to repay a loan in the hypothetical situation that this business wants a bank loan

The _financial planning score_ ranges from 0-12, and it is calculated by adding up to three points for each of the following two questions

- How frequently do you review the financial performance of your business and analyze where there are areas for improvement

- How frequently do you compare performance to your target

- Zero points for “Never”

- One point for “Once a year or less”

- Two points for “Two or three times a year”

- Three points for “Monthly or more often”

And adding one point for each of the following that the business has

-

- A target set for sales over the next year

- A budget of the likely costs your business will have to face over the next year

- An annual profit and loss statement

- An annual statement of cash flow

- An annual balance sheet

- An annual income/expenditure sheet

# _Wage worker and SME surveys_

We also make use of data from surveys of wage workers and larger firms. The wage worker survey was conducted in all urban areas in Sri Lanka at annual intervals from 2008-2011. The initial 51
Africa and Asia the bottom. The literature offers various possible explanations. Griffith et al. (2004) postulate the wedge between private and social returns; Aghion et al. (2010), Bond et al. (2010), Hall and Lerner (2009), Mulkay et al. (2000) have explored the role of credit constraints; Bloom (2007), the depressing impact of uncertainty, all of which would arguably be more binding in LDCs. However, this still raises the question of why, given these high rates of return, developing countries do not take measures to offset the implicit market failures in credit, appropriability, or insurance and facilitate private sector R&D investment, or even undertake it directly. In fact, to re-frame Lucas’ famous observation about growth, confronted with the rates of return found in the literature, it would be hard for governments to think of anything else. Indeed, looking again at figure 2, the dramatic trajectories of two important exceptions, China and India (following Israel, Finland, Korea previously), suggest that they, in fact, think about it a lot.

However, this paper argues that developing country governments may well be rational: there may be countervailing forces that prevent Griffith et al. (2004)’s tendency from continuing monotonically with distance from the technological frontier. In particular, another literature stresses the necessary complementarities to R&D expenditure which are likely to diminish with distance from the frontier and hence reduce the efficacy of a given unit of R&D.<sup>3</sup> Howitt (2000); Aghion et al. (2005); Howitt and Mayer-Foulkes (2005) for example, postulate a simple parameter on efficacy of R&D that could capture any number of institutional, and educational factors which can offset the Schumpeter catch-up effect.<sup>4</sup> In particular, analogous to (Lucas, 1988)’s classic argument for the complementarity of human and physical capital, a large literature sees education broadly construed as a necessary complement, especially for technological diffusion (Benhabib and Spiegel, 1994; Caselli, 2001; Klenow and Rodriguez-Clare, 2005; Benhabib and Spiegel, 2005; Howitt and Mayer-Foulkes,

> 3More generally, recent literatures have stressed the very large impact that missing complementarities can have in stymieing development (see Kremer (1993); Jones (2011))

> 4Aw et al. (2008) argue from Taiwanese data that a larger export market increases the expected returns to R&D. To the degree that poorer countries have smaller markets and fewer export linkages, the returns may be lower.

2
# **3. Data characteristics**

The panel data used in this paper, collected by the Bangladesh Institute of Development Studies (BIDS), are based on household and community surveys prior to, and following, implementation of two World Bank funded projects that allowed identification of control and treatment villages. The data are used here to calculate economic returns to roads and its impact on poverty, and overcome some of the pitfalls of past road evaluations that have relied mostly on cross-sectional household survey data. The datasets have a true before/after and with/without structure and are reasonably large, allowing a study of household-level impacts, especially with reference to households above and below the poverty line. They cover not just the standard road project outputs such as trip frequency and duration, but also key outcomes (consumption, employment) and a broad range of market interactions.

The data collection was financed under the World Bank-funded projects and conducted by BIDS as part of the government’s efforts to analyze and quantify both the short-term and long-term impacts of rural road improvements. The first survey covered the Rural Roads and Markets Improvement and Maintenance Project – I (RRMIMP-I), which was a component of the Rural Development Project-7 (RDP-7). For the purposes of this paper, we will define this project as RDP. The RDP initiative entailed improvement of 47 FRBs to bitumen surfaced standard, upgrading of 65 secondary markets and construction of 3700 meters of culverts and small bridges. Its physical works were completed during 1995-96. The first phase of the survey collected benchmark information in the study area prior to the project work, and the second phase collected the same information during the first half of 2000. The next phase included 24 project and 18 control villages, and 1,260 households.

The second survey studied the Rural Roads and Markets Improvement and Maintenance Project – II (RRMIMP-II), which included improvement of 574 kilometers of FRBs to bitumen-surfaced standard, construction of 1,900 meters of culverts, 1,750 meters of bridges and 2,200 meters of small drainage structures on rural roads, and improvement of 136 Growth Center Markets and 41 Ghats (river jetties for boats and vessels). The first phase of the RRMIMP survey collected benchmark information on 872 households from 18 villages during May-September 1997, and the second phase covered the same households between August, 2000-February, 2001. Both projects were funded by the World Bank as part of its effort to promote rural infrastructural development, and, consequently, rural growth and poverty reduction.

In both project and control road areas, one roadside village was selected for each road project. The selected roadside villages indicate the immediate influence areas of road intervention. In addition, one remote village was selected for each of the sample roads to assess the decay effects of road development. The remote villages were similar to the roadside villages and were chosen such that they 7