rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6017

# **Abstract**

The research and development (R&D) sector is considered one of the main driving forces of sustainable growth in the long run. The sector, however, also shows excessive volatility which raises interesting questions regarding the sources of this volatility as well as the nature of the relation between the sector and aggregate fluctuations. Using data from the United States Bureau of Economic Analysis and National Science Foundation, we show that technology innovations are the main source of fluctuations in R&D investment while R&D technology shocks are important in driving aggregate output fluctuations. After taking nominal innovations into consideration, such as shocks in monetary policy and inflation, capital investment-specific shocks explain 70 percent of fluctuations of R&D investment, while R&D technology shocks explain 30 percent of the variation in the output of the non-R&D sector. Technology innovations jointly explain most of the variation of output in the R&D sector and 78 percent of the variation of output in the rest of the economy.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eartuc@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
<!-- Start of picture text --> 160<br>140<br>120<br>=<br>E 100<br>3<br>w80<br>QN<br>=z ©<br>40<br>| (ul<br>0<br>2020 2021 2022 2023 2024<br>Date<br><!-- End of picture text -->

<!-- Start of picture text --> 20<br>”<br>S<br>= 15<br>fe)<br>e<br>2<br>% 10<br>i)<br>.<br>E<br>3 5<br>nn<br>2020 2021 2022 2023 2024<br>Date<br><!-- End of picture text -->
Table 9 Correlation Analysis for the Economies in Europe/Mideast/Africa 1978-1990

> Pearson Correlation Coefficients / Prob > I R I under HO: Rh0 =0 / Number of Observations

||Greece<br>MCY|Jordan<br>MCY|Nigeria<br>MCY|Portugal<br>MCY|Turkey<br>MCY|
|---|---|---|---|---|---|
|M1Y|0.700|0.388|0.109|-0.504|-0.627|
||0.0053|0.2126|0.7220|0.0789|0.0291|
||14|12|13|13|12|
|QMY|-0.284|0.123|0.425|-0.245|0.485|
||0.3247|0.7042|0.1474|0.4199|0.1102|
||14|12|13|13|12|
|DCPY|-0.264|0.238|0Q222|-0.744|0.002|
||0.3615|0.4564|0.4655|0.0035|0.9960|
||14|12|13|13|12|
|GNPCAP|0.126|-0.962|0.066|0.896|0.629|
||0.6664|0.0021|0.8312|0.0001|0.0283|
||14|6|13|13|12|

23
across a number of World Bank ICRs.<sup>**11**</sup> The argument is that without clearly articulating how the FMIS would address these problems, investments could result in a system that does not match actual requirements, which would fundamentally undermine its relevance and effectiveness. It is also important for minimizing financial and institutional resources, as clearly articulated in the Bolivia ICR.

3.3 **A detailed diagnostic assessment led to an effective engagement** in countries where it preceded investments and then guided the solutions’ design. In Honduras, Kazakhstan, the Russian Federation, and Ukraine, for example, a detailed diagnostic of the underlying economic problems and their causes preceded work on setting up the treasury systems. The diagnostic emphasized a fiscal situation under stress, a deficit exceeding its targets and needing cash rationing, and a rapid arrears accumulation. Among the causes identified were weaknesses in the legal framework, institutional structures, and accompanying systems required for managing government finances. Following this, the reform directions had three distinct characteristics:

- Implementing a centralized TSA, including closing all spending unit bank accounts.

- Establishing a treasury-centric payment mechanism that routed all government payment and receipts through the treasury (or out-posted treasury staff).

- Setting up a basic budget execution system with line item control.

These three elements of the reform program led to the following:

- Better fiscal control by ensuring that expenditures complied with budget appropriations, commitments and cash allocations, close monitoring of outstanding bills, cash in government bank accounts, arrears and fiscal deficits.

- Better cash management by bringing all government accounts under the treasury’s control.

- Timely and accurate reporting for economic management, and preparing statutory financial statements, and improved baseline data quality for budget preparation.

3.4 **Lack of a thorough diagnostic led to ineffective engagement and disappointing results** in several cases.<sup>12</sup> Ghana and Zambia, for example, faced similar problems to

> 11 See project documentation for FMIS operations in Bolivia, Cameroon, Colombia, the Democratic Republic of Congo, Ecuador, Honduras, Kazakhstan, Kenya, Madagascar, Russia, Sierra Leone, the Slovak Republic, and Ukraine.

> 12 See project documentation for FMIS operations in Zambia, Ghana, Cambodia, Russia, Kazakhstan, and Ukraine.

**21**

**21**
**Table 4. Differences in non-Response Rates**

||Sequence|Coaches|Coaches|
|---|---|---|---|
||vs.|vs.|vs|
||Control|Control|Sequence|
||(1)|(2)|(3)|
|Missing (or omitting)student test|0.008|0.025|0.017|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ Each entry indicates the mean difference between the two experimental groups in the column for the students who missed or did not complete the student test. * indicates that the difference of means test is significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

# **Table 5. Mean and Standard deviation of learning outcomes**

|Variable|C<br>|ontrol G<br>|roup<br>|Se<br>|quence G<br>|roup<br>|C<br>|oaches G<br>|roup<br>|
|---|---|---|---|---|---|---|---|---|---|
||N|Mean|Sd|N|Mean|Sd|N|Mean|Sd|
|Science score|790|0|1|771|0.506|1.119|801|0.681|1.156|
|Science score (Basic skills)|790|0|1|771|0.185|0.998|801|0.292|0.966|
|Science score (Medium skills)|790|0|1|771|0.554|1.088|801|0.583|1.102|
|Science score (Higher-order<br>skills)|790|0|1|771|0.386|1.134|801|0.654|1.195|
|Percent of correct answers|790|0.326|0.202|771|0.409|0.24|801|0.436|0.243|
|Percent of incorrect answers|790|0.217|0.16|771|0.166|0.158|801|0.161|0.153|
|Percent of omitted answers|790|0.203|0.203|771|0.145|0.171|801|0.113|0.166|

Note: N means number of observation and Sd means standard deviation.

Source: Authors analysis based on experimental data generated for this study.

**Table 6. Results on Science learning**

2
# **2 Methodology**

In this study, we consider some of the welfare and poverty impacts of changes in the world prices of key staple food commodities. Basic food commodities that are traded globally and are important for small farmers and consumers in the developing world include wheat, rice, dairy products, maize, sugar, beef and poultry. We analyze the impacts of changes in the prices of these commodities using household-level data for nine low income countries.

To undertake the analysis, we needed information on households’ production, purchases, own-consumption and sales of these agricultural products, a need that limited us to countries with high-quality household surveys containing this information. This need also precluded using many of the models and databases prepared for the Hertel and Winters (2006) volume, since many of these did not include detailed information on enough of the commodities central to our analysis.

To assess the household impacts of changes in commodity prices, we use a very simple methodology based on Singh, Squire and Strauss (1986) and Deaton (1989; 1997, p185) (see Appendix A). We represent the impact of price changes on an individual household, using an expenditure function to characterize household consumption and factor supply behavior and a profit function to represent household production activities through unincorporated enterprises such as family farms. This yields a simple expression for the welfare impacts of small price changes that involves multiplying the price changes resulting from trade reform by the shares of income and expenditure affected by these price changes. Where a household consumes its own output of, for instance, staple foods, the share of the good’s production in total output value is offset by its share in consumption so the only effect on income is through the household’s net sales of the good.

Our framework is partial equilibrium in that we consider only the direct impacts of changes in the prices of staple foods on households, except that, like Ravallion (1990), we 3
12

**Table 2: Overview of Risk Management Instruments**

|**Product**|**Benefits**|**Costs / Risks / Constraints**|
|---|---|---|
|Forwards|• The risk management solution is embedded in the physical supply<br>contract and there is no need for a separate contract /<br>documentation.<br>• Pricing of forward contracts can be customized to the needs of<br>the hedger<br>• Depending on the pricing formula used, forwards will have same<br>benefits as the financial products described below.|• May be complex for government to implement if importers are<br>privately held.<br>• Depending on the pricing formulas used, forwards will have<br>same costs/risks/constraints as the financial products described<br>below.|
|Futures|• Provides ability to lock in forward prices through a financial<br>contract.<br>• No upfront costs.|• Prices are “locked in” and hedger has limited ability to take<br>advantage of positive price movements that may occur in the<br>future.<br>• Creates unknown and unpredictable future liability since hedger<br>will owe the market counterparty if the market moves in an<br>adverse direction.<br>• Requires financing of a credit line or a credit guarantee and<br>managing cash flow /liquidity requirements to support<br>(potential)dailymargin calls.|
|Options|• Provides ability to lock in maximum (minimum) prices while still<br>providing hedger with ability to take advantage of positive price<br>movements that mayoccur in the future.|• Has an upfront cost, which is market-driven and volatile but<br>can range from 5-12% of the underlying price for a 6-18 month<br>coverage.|
|Risk Reversals|• Limits price exposure to within a price band or “collar” that has<br>both a ceiling and a floor.<br>• Upfront costs can be lower since hedger is simultaneously buying<br>a call option and selling a put option.|• Creates unknown and unpredictable future liability since hedger<br>will owe the counterparty if the market moves below the price<br>floor.<br>• Requires financing of a credit line or a credit guarantee.<br>• Requires managing cash flow /liquidity requirements to support<br>(potential)dailymargin calls.|
|Swaps|• Provides ability to manage two commodity exposures, or<br>financial flows, at the same time.<br>• No upfront costs.|• Creates unknown and unpredictable future liability.<br>• Requires financing of a credit line or credit guarantee.<br>• Requires managing cash flow requirements to support<br>(potential)dailymargin calls.|
|Commodity-Linked<br>Bonds or Loans|• Could be used on more macro level to connect borrowing or<br>financing programs to the performance of a specific commodity<br>index.|• Can be more complex to structure.<br>• May not be effective as a hedge for specific commercial<br>exposures.|

Source: Yépez-García and Dana 2012. Benefits and Costs/Risks/Constraints are discussed from the point of view of an importer using these instruments to manage against the risk of increasing prices.
factors are important in both rural and urban areas, accounting for more than 60 percent of sibling correlations for men and 65 percent for women. The contribution of common neighborhood factors to variance in education is larger for rural women whereas parental education is relatively more important for both men and women in urban areas. Overall, common family backgrounds are perhaps most important factor for rural women for whom less than 30 percent of variations in education can be explained by individual effort, choices and other unobserved idiosyncratic factors.

To summarize, though the influences of parental education and common family background are smaller in magnitude in rural areas, there has been little or no progress in mobility in education. The largest improvement in educational mobility has been experienced by urban women while men experienced effectively no improvement regardless of their location. We find common neighborhood environment and parental education as the most important source of sibling correlations in both urban and rural areas. The influences of common neighborhood factors are particularly important for rural women.

# **(4.2.2) Less Developed vs. Developed Regions**

Living standards in India vary widely across states. The incidence of poverty among poorer states in India is amongst the highest among developing countries. On the other side of the spectrum, many states such as Punjab have low poverty rates that are comparable to richer countries (e.g. Turkey) (World Bank (2011)). The NFHS 1992/93 identifies the ―backward‖ districts.<sup>35</sup> The NFHS 2006 does not identify any district because of confidentiality considerations with respect to AID/HIV testing results. The states where the most backward districts are located can be matched between the two surveys. The backward districts in 1992/93 are located in five states: Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh and West Bengal. These states are among the poorest in terms of income in 1993/94 (four of them belong to the so-called BIMARU), and also suffer from poor educational attainment and infrastructure indicators (Kingdon (2007), Deaton and Dreze (2002)).<sup>36</sup> We take the

> 35 The Ministry of Health and Family Welfare, Government of India, has defined backward districts as those having a crude birth rate of 39 per 1,000 population or higher, estimated on the basis of data from the 1981 Population Census. 36 Note that there may not be a one to one correspondence between backward districts in our sample and backward states according to income and education indicators, because there are backward districts even in developed states. The estimates of Head Count Ratio by Deaton and Dreze (2002) show that Rajasthan has a lower incidence of poverty, but the evidence reported in Kingdon (2007) shows that it lags in terms of schooling indicators, even though it has made substantial strides in primary schooling.  West Bengal, on the other hand, suffers from higher poverty, but has better schooling indicators.  As noted by Deaton and Dreze (2002), West Bengal belongs to the same ―slow growth‖ group with the BIMARU states

21
<!-- Start of picture text --> ©<br>@®e%e aoe”OS) e% ®.<br>e°. ee &° See, @<br>e @ “6&6<br>8 © °6/ @ ®<br>QeON s “2e : e®©<br>2 © ON _o%= 80°08Hl 2) @|<br>ee Se e6el®<br>£220, C6 ~8®<br>C.@@® e e o©&<br>S) a) / ®\e © a®<br>6Se me “.- 0°e®<br>ee © ea @<br>[ \8<br>@<br><!-- End of picture text -->

<!-- Start of picture text --> ©<br>©2466~ ae<br>@ oo 66@2cSse®<br>e 9° | eSege<br>eee? ee e6<br>©ea?506.00? @ ° ©es ©<br>Se eS- 68 & 6i @<br>Seao° 6 ©<br>A Of @<br>Ce 22 .@ 68<br>ee . @8 © @ &<br>@ ee oe O86<br>ao) @ @ @®<br>a<br>*<br><!-- End of picture text -->
Table 7: Access to markets and connectivity

||(1)|(2)|(3)|
|---|---|---|---|
|Dep. Var.:Growth inper capita income(1|996-2011)|||
|Homeland (=1)|1.552***|0.701|1.552***|
||[0.495]|[0.468]|[0.497]|
|Share Employed mining1996|-0.472*|-0.452*|-0.465*|
||[0.249]|[0.254]|[0.250]|
|Homeland*Share Employed mining1996|0.907***|0.862***|0.908***|
||[0.276]|[0.279]|[0.277]|
|Distance to major cities|-0.002**||-0.002*|
||[0.001]||[0.001]|
|Homeland*Distance to major cities|-0.001***||-0.001***|
||[0.000]||[0.000]|
|Distance to main road||-0.001|-0.001|
|||[0.001]|[0.001]|
|Homeland*Distance to main road||-0.001|0.0002|
|||[0.001]|[0.001]|
|Observations|9,583|9,583|9,583|
|R-squared|0.636|0.635|0.636|
|1996 Controls|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log per capita income for 2011-1996. Distance to major cities defined as the population-weighted average of the linear distance from given community to the 10 largest cities in South Africa (as explained in section 7.2). Distance to main road is the linear distance from given community to the nearest main road as defined by Statistics South Africa in 2001. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
and adoption to limiting or banning certain crypto-assets activities – El Salvador has adopted bitcoin as legal tender.

Understanding the main drivers behind crypto-assets usage is important for policy makers, investors, and industry alike. Although Bitcoin, the original crypto-asset, was conceived as a peerto-peer electronic cash system without the need for a trusted third party such as a central bank or financial intermediary, crypto-assets are currently not widely used as a medium of exchange, although recent research finds evidence that bitcoin is used as a vehicle for domestic transactions and international payments (e.g., Graf von Luckner et al (2021)). Moreover, crypto-assets users may not be motivated by security concerns related to payments in cash or commercial banking services, at least in advanced economies such as the United States (e.g., Auer and Tercero-Lucas (2021)). And while growing quickly, DeFi is still nascent. We therefore explore two other often discussed drivers behind crypto-assets activity in this paper: i) crypto-assets serve as a speculative or risky investment vehicle (e.g., Baur, Lee and Hong (2018) and Athey, et al (2016)) and ii) crypto-assets are perceived as an emerging “digital, scarce, speculative store of value” (Gensler (2021)) which may act as a macro hedge providing protection against monetary and macrofinancial weaknesses such as excessive and persistent inflation (e.g., Blau et al (2021) and Conlon, et al (2021)) and currency depreciation or volatility.

Our paper contributes to a small, but burgeoning literature which empirically investigates the potential drivers and motivations of crypto-assets activity and usage in various ways. First, using a rich monthly data set of on-chain crypto-assets transaction volume estimates at the country level, we document the evolution of usage around the world and analyze both trends at the global level and in EMDEs. Second, using panel regressions we empirically assess the association between crypto-assets volumes and a country’s macro-financial fundamentals and relevant global financial conditions. Third, we use cross-sectional regressions to take initial steps to identify other potentially relevant country characteristics such as financial sector development and ICT adoption.

6
The selected implied volatilities come from option contracts with 3, 12, and 24-month maturities to represent the uncertainty for the current year, next year, and two-year ahead horizons. The procedure for recovering the variance and skewness of the equity and commodity prices from implied volatility series—assuming the Black-Scholes-Merton option pricing model—is discussed in Appendix A6.

Panel A. Ex-Ante Variance of Crude Oil Prices Panel B. Ex-Ante Skewness of Crude Oil Prices <!-- Start of picture text --> 3-month horizon Median: 3-month horizon 3-month horizon Median: 3-month horizon<br>12-month horizon Median: 12-month horizon 12-month horizon Median: 12-month horizon<br>24-month horizon Median: 24-month horizon 24-month horizon Median: 24-month horizon<br>5 1<br>4 .8<br>3 .6<br>2 .4<br>1 .2<br>0 0<br>Panel C. Ex-Ante Variance of the S&P 500 Index Price Panel D. Ex-Ante Skewness of the S&P 500 Index Price<br>3-month horizon Median: 3-month horizon 3-month horizon Median: 3-month horizon<br>12-month horizon Median: 12-month horizon 12-month horizon Median: 12-month horizon<br>24-month horizon Median: 24-month horizon 24-month horizon Median: 24-month horizon<br>4 1<br>3 .8<br>.6<br>2<br>.4<br>1<br>.2<br>0<br>FIGURE 3. EX-ANTE VARIANCE AND SKEWNESS OF CRUDE OIL AND S&P 500 INDEX PRICES<br>Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015 Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015<br>Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015 Oct 2005 Jan 2007 Apr 2008 Jul 2009 Oct 2010 Jan 2012 Apr 2013 Jul 2014 Oct 2015<br><!-- End of picture text --> Source: Bloomberg and author’s calculations.

Note: Ex-ante variance and skewness of crude oil and the S&P 500 index prices are estimated via implied volatilities of option contract prices. Bloomberg data uses a variation of the option pricing model of BlackScholes-Merton. The option contracts series are at the money. The variance series is scaled down using the sample standard deviation of the original series. The skewness series is scaled down using the maximum value of the corresponding original series.

21
<!-- Start of picture text --> 4<br>35 ———~ Natural change<br>3 ——— Net migration<br>25<br>2<br>1.5<br><!-- End of picture text --> <!-- Start of picture text --> a<br>0.5<br>0<br>-0.5 fF<br>-1<br>1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> <!-- Start of picture text -->
1.2<br>——— Natural change<br>1<br>08 ——— Net migration<br>0.6<br>04<br><!-- End of picture text -->

<!-- Start of picture text --> 0.2<br>0 —-~ : =<br>-0.2<br>-0.4<br>1960 1970 1980 1990 2000 2010<br><!-- End of picture text -->