rafmacalaba/datause-encoder-data huggingface dataset:
estimator and is composed of the OLS estimator (covariance over variance), plus a term that corrects for the effect of measurement error underlying the first-stage equations.<sup>9</sup> The final step in Deaton’s methodology is to derive a consistent set of expenditures (proxy for income) ε _ex_ and price elasticities ε _pr_ from φ<sup>ˆ</sup> and the following relationships: ( _a_ ) between the price and income elasticities of unit value (θ2 and β2 ); ( _b_ ) between price and expenditure shares (θ1 and β1 ); and ( _c_ ) between price and quantities (ε _pr_ andε _ex_ ). In Deaton’s model, the theoretical relationship between quality, price and expenditure relationship is given by: Now, from (1) and (2), the expenditure elasticity is given by β _1 / w_ , but since the budget share is unit values times quantity divided by total expenditure, we have: Similarly, the price elasticity is: Substituting (13) and (14) into (12) and rearranging, we have: Substituting φ = θ _1_<sup>_/_θ</sup> _2_ into (15) and solving for θ _1_ yields:

> 9 As a clarification, the covariance ( _y_<sup>ˆ</sup> 1 _ct_ , _y_<sup>ˆ</sup> 2 _ct_ )  is calculated directly from the _y_<sup>ˆ</sup> 1 _ct_ and _y_<sup>ˆ</sup> 2 _ct_ corrected from the effects of total expenditures as in (5) and (6).

9
# COVID-19 and Children’s School Resilience: Evidence from Nigeria<sup>*</sup>

Sylvain Dessy<sup>†</sup> Horace Gninafon<sup>‡</sup> Luca Tiberti<sup>§</sup> Marco Tiberti<sup>¶</sup> _Keywords:_ COVID-19’s lockdown measures; School attendance; Schools’ disruptions; Gender inequality in education; Nigeria _JEL codes:_ C13, H52, I21

> *Luca Tiberti acknowledges financial support from the Partnership for Economic Policy (PEP), which is funded by the Department for International Development (DFID) of the United Kingdom (or UK Aid), the Government of Canada (through the International Development Research Centre (IDRC) and Global Affairs Canada (GAC)), and the William Flora Hewlett Foundation. The authors would like to thank Christina Jenq, Kevin McGee and Tara Vishwanath (World Bank) for very valuable comments on an earlier draft. Usual disclaimers apply.

> †Department of Economics, CRREP and CREATE, Université Laval, Quebec City, QC, Canada. sdes@ecn.ulaval.ca

> ‡Department of Economics, Université Laval, Quebec City, QC, Canada. horace-mahugnonakim.gninafon.1@ulaval.ca

§Université Laval (Canada), University of Florence (Italy), PEP and CREATE. luca.tiberti@ecn.ulaval.ca ¶Development Data Group, The World Bank, Rome, Italy. mtiberti@worldbank.org. Via Labicana, 110. 00184, Rome, Italy. +39 0677710245. Corresponding author.
**Figure 8: Output growth volatility vs. Financial Depth**

<!-- Start of picture text --> 9<br>TCD<br>8 DZASYR<br>CHN<br>7<br>SLELSO<br>6 COGZARNEMWICMRSDNATGO R BDIG NICSYC<br>5 ZMBCIVVENPNGPERBWAKENDOMURYTTO GUY CHL<br>432 CAFMDGHTIBENBFAPRYGHAMEXSENECUIDNGTMPAKPHLCOLBOLBGDBRACRISLVINDHNDEGYBLZHUNMAR GRCFINBELTHAPANISRITASGPFRAKORMYSAUSAUTMLTSWE HKGNZL PRTLUXZAFESPCHEGBRNLDJPNIRLDNKCANUSA ISL<br>1<br>0<br>0 50 100 150 200 250 300 350<br>Ratio of Domestic Credit to GDP<br>Output Volatility<br><!-- End of picture text --> Sources: author’s calculations using WDI data. Output Volatility is the average standard deviation of GDP per capita growth for the years 1960-2006.

**Figure 9: Private sector domestic credit to GDP**

<!-- Start of picture text --> 154<br>147<br>110<br>103<br>91 91<br>73<br>61 60<br>53<br>18 23 20<br>12<br>4<br>NON-OECD NON-EAP OECD EAP NON-OECD<br>1961-70 1971-80 1981-90 1991-00 2001-05<br>Domestic Credit (% of GDP)<br><!-- End of picture text --> Sources: author’s calculations using WDI data. Domestic Credit to GDP is the unweighted average for the group.

# **Trade Openness**

Traditionally, the literature has identified trade openness as an important factor affecting volatility (Figure 10).<sup>8</sup> Trade openness can lead to higher volatility through several channels: it increases, for instance, concentration and specialization in production through dynamic comparative advantage, so that countries suffer to a greater extent when international prices

> 8 While the positive association between trade openness and volatility has been repeatedly found, there is an ongoing empirical debate regarding the connection between financial openness and output growth volatility (see Box 2).

13
the ...nancial accounting framework is deterministic and ...xed for each model<sup>15</sup> the crucial part in this case is the model simulating the paths of the underlying economic variables. The work at the Bank of Canada, Danmarks Nationalbank focuses on simulation of the term structure of interest rates as the interest rate risk is of a major focus there. Korean model combines the concept of the e¢ cient frontier and CaR penalties to derive the optimal benchmark portfolio for government debt. In the Peruvian model the process used to generate discrete-time observations of ...nancial variables is derived from a multivariate continuous-time model. On the other hand, the Swedish model is a discrete-time model based on univariate autoregressive processes with regime switches characterizing the data generating processes (DGPs) of the economic variables. Finally, the UK model uses a reduced form of an implicit structural model in combination with regime switches to generate the likely paths of economic variables.

(ii) The second group includes work of e.g. Garcia and Rigobon (2004), or Xu and Ghezzi (2004). In general, this approach again uses simulated paths of economic variables and the debt structure to compute the corresponding government debt-toGDP ratios. The numerous possible paths of the variables give rise to a distribution of the debt/GDP realizations at each point in time. The probability mass in the tail of these distributions cut o¤ by the speci...ed debt/GDP default threshold is then used as a measure of the probability that the government ...nances will be considered unsustainable at a given point in time. The two referenced approaches di¤er mainly in the aspect of using discrete- and continuous-time model speci...cations, respectively. Garcia and Rigobon (2004) use an unrestricted vector autoregressive (VAR) model to simulate the economic variables whereas Xu and Ghezzi (2004) use a system of Brownian motions as the basis for their simulation.

(iii) Gapen, Gray, Lim and Xiao (2005), Gray, Merton and Bodie (2005) work with an explicit measure of sovereign credit risk derived from a contingent claim analysis. The latter uses option pricing formulas to capture possible non-linearities in the relationships of interest. They start from determining a distress barrier analogous to

> 15The ...xed structure can however di¤er across models depending on the selected level of aggregation of the borrowing instruments (categories).

16
economic growth. However, the rural sector remains the main contributor to economic growth with 2.3 percentage points, a performance similar to that of the preceding five year period.

The modern sector, which includes the oil industry, grew annually by 9 percent during the period 2007 to 2012 and contributed half of Niger’s overall economic growth, despite the fact that it only accounts for 31 percent of the economy. The growth of the extractive industries and modern sectors bodes well for Niger’s capacity to generate government revenue.

# **_Trade and Balance of Payments_**

During the period 2007 – 2012, the nominal value of both exports and imports grew at an annual average rate of 20 percent. Uranium has been Niger’s main export, accounting for 43 percent of total exports in 2012. Exports of refined oil products started in 2012 and contributed already 13 percent of total exports, despite start-up problems which resulted in production and exports being significantly below capacity. The remaining exports are mainly agricultural products. Livestock contributes 8 percent to total exports. Exports of cowpeas contribute another 8 percent to total exports and onions account for 5 percent.

**Table 3: Exports and Imports, Million CFAF, 2007-2012**

||**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**Average annual**<br>**growh 2007-2012**|**Share in**<br>**total 2012**|
|---|---|---|---|---|---|---|---|---|
|**_Exports_**|||||||||
|Uranium|143|198|196|242|301|339|19%|43%|
|Oil|0|0|0|0|0|102|0%|13%|
|Livestock|38|50|61|58|51|63|11%|8%|
|Cowpeas|19|42|58|53|54|60|26%|8%|
|Onions|42|42|40|40|39|43|1%|5%|
|Other|50|50|87|131|111|134|22%|17%|
|Re-exports|26|26|29|46|44|46|13%|6%|
|**Total**|**318**|**409**|**471**|**570**|**601**|**788**|20%|100%|
|**_Imports_**|||||||||
|Food|88|120|206|182|228|237|22%|22%|
|Oil products|66|91|126|133|140|16|-25%|1%|
|Capital goods|112|227|302|290|344|424|30%|39%|
|Intermediate goods|59|31|132|251|99|170|23%|15%|
|Other goods|113|136|81|116|242|251|17%|23%|
|**Total**|**438**|**604**|**847**|**973**|**1052**|**1097**|20%|100%|

_Source_ : Government of Niger, IMF Niger data base, and World Bank WDI.

Capital goods accounted for 39 percent of total imports in 2012, reflecting large scale investment in the extractive industries and energy sectors. Food imports have grown rapidly during the period 2007 – 2012 – partly as a result of repeated harvest failures that made donor funded food imports necessary in order to meet the basic needs of the population - and account now for 22 percent of total imports. Other goods, which include a wide range of 7
# 4. References

Bangladesh, Bureau of Statistics. 1992. _Report on Labour Force Survey 1989._ Dhaka.

China.1993a. _Chinese Social Statistics 1993_ China.1993b. _Chinese Yearbook of Labor Statistics 1993._ European Bank for Reconstruction and Development. 1994. _Transition Report 1994._ London.

Indonesia, Biro Pusat Statistik. 1993. _Labor force situation in Indonesia:. May 1993._ Indonesia Central Bureau of Statistics. Jakarta.

Nigeria, Federal Office of Statistics. 1991. _Annual Abstract of Statistics 1991._ Lagos.

ILO. 1986. _Estimates and projections 1950-2025._ Geneva.

ILO. 1994. _World labor report 1994._ Geneva.

ILO various years. _Yearbook of Labor Statistics._ Geneva.

- Faruqee, Rashid. 1994. "Nigeria: ownership abandoned", in _Adjustment in Africa: Lessons_

- _from case studies,_ Ishrat Husain and Rashid Hussain eds. The World Bank. Washington.

- India, National Sample Survey Organization. 1992. _Tables with notes on fifth annual survey_

-

- _on consumer expenditure and employment and unemployment NSS 47th round (July-_

_December 1991),_ Government of India. New Delhi.

10
# **II.** **<u>Data, Variables, and Conjectures</u>**

We rely on the CHFS of 2013, which was collected by the Chinese Household Finance Research Center of Southwestern University of Finance and Economics. The data set is representative of Chinese households due to its stratified random sampling design in three stages: first, random sampling of counties; then communities within a county; finally random sampling from within a community.<sup>5</sup> When choosing sample counties, all were first ranked according to GDP and classified into groups. Within each GDP group, a random sample of counties was then selected. In the end, a total of 262 counties, roughly 10% of the total number in China, were selected to be part of the sample. Within each county, four communities were chosen. And within a community, 25-50 households were chosen for urban areas, and 20 households for rural areas. The final CHFS sample consists of 28,100 households and 98,000 individuals in 1,029 communities, from 262 counties, spanning 29 provinces.

The CHFS provides detailed information on households’ demographic characteristics, assets and liabilities, insurance and social protection, and income and expenditures. More importantly for our research purposes, the CHFS contains detailed information on household liabilities, such as debts incurred for agricultural production, for non-agricultural production of businesses owned by family members, for purchases of cars and housing, for schooling tuition, and many other purposes. Furthermore, the survey covers not only borrowing from banks but also debt from informal channels such as relatives, friends, and informal financial organizations. Moreover, we have information on whether the maturities of specific debts exceed one year, which allows us to classify some loans as long-term.<sup>6</sup>

> 5 A community is either a village in rural areas or a neighborhood committee (Ju wei hui in Chinese) in urban areas.

> 6 For some loans, there are direct questions about maturity. For others, we know the timing of the debt, and whether it has an outstanding balance. If the loan has an outstanding balance and was incurred at least a year earlier, we classify it as long‐term.

7
households ranked by income normalized by the relevant DB line — with and without DB receipts for both participants and the full (35-city) sample.

The program is having a sizeable impact on poverty amongst the participants (Table 3). The proportion of the participant population falling below the DB line is 45% with DB transfers, but it would have been 57% without them. However, the impact on poverty in the population as a whole is much less. The proportion falling below the DB lines falls from 7.7% to 7.3% after DB transfers. Proportionate impacts are slightly higher for _PG_ than for the headcount index (and slightly higher again for the squared poverty gap); this indicates that the program has increased the mean income of those below the DB line and reduced inequality amongst them. _Targeting and poverty across cities of China_ Given the scheme’s decentralized financing and implementation and the differences observed across China’s cities, heterogeneity in outcomes across municipalities is to be expected. There will, of course, be differences in local resources and administrative capabilities, but there will also be (less obvious) differences in the local political economy. Here I only aim to describe the differences in DB performance across municipalities, and to use these differences to assess how well prevailing targeting measures perform in predicting impacts on poverty.

There is considerable variation in targeting performance across municipalities. (The Appendix gives results by city.) _S_ varies from 31% to 98%; _NS_ varies from 2.8 to 18.8, and _CI_ varies from 0.64 to 0.93, while _TD_ varies from 0.06 to 0.53. (All cities except one, Kunming, have _NS_ higher than the best performing program surveyed by Coady et al., 2004.) Recall that _TD_ automatically gives equal weights to the Type 1 and Type 2 error proportions. The weights for the three measures based on the concentration curve depend on the analytic properties of these measures and how the design features and setting influence the overall program participation rates (as discussed above). A simple way of summarizing this (potentially complex) relationship, is to regress each measure of targeting performance on _T_ 1 and _T_ 2 (as defined in Table 1). Table 4 gives the regressions. _T_ 2 has only a small and statistically insignificant effect on _S_ , _NS_ or _CI_ ; by contrast, _T_ 1 has a strong and significant effect for _NS_ and _S_ ; the coefficient is also significant at the 3% level for _S_ if one drops _T_ 2.<sup>20</sup> However, the normalizations used in defining _T_ 1 and _T_ 2 have bearing on the weights attached to Type 1 and Type 2 errors. If instead one normalizes by total populations of the

> 20 The regression coefficient on Type 2 errors is then -1.361 (t=-2.28; prob=0.03).

14
17 <!-- Start of picture text --> Figure 3: Weighted Average Unit Values<br>U.S. Imports of HS 61 and 62<br>98 00 02 04 06 08 10 12 14<br>time<br>Sri Lanka Cambodia<br>60<br>50<br>40<br>30<br><!-- End of picture text --> _Source:_ Data from Office of Textiles and Apparel (OTEXA), U.S. Department of Commerce. _Note:_ Data represent the weighted U.S. dollar values per square meter equivalent of U.S. imports of HS 61 and HS 62 of each country. Data are smoothed using a six-month moving average.
- Masyn, Katherine. 2013. Latent class analysis and finite mixture modeling. In T. D. Little (Ed.) _The Oxford handbook of quantitative methods in psychology. Volume 2_ . pp. 551-611. New York, NY: Oxford University Press.

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- <mark>Moran, Peter, and Sumantra Ghoshal. "Markets, firms, and the process of economic development."</mark> _<mark>Academy of Management Review</mark>_ <mark>24, no. 3 (1999): 390-412.</mark>

- Noordewier, T. G., G. John and J. R. Nevin. 1990. “Performance Outcomes of Purchasing Arrangement in Industrial Buy-Vendor Relationships.” _Journal of Marketing_ 54: 80-93.

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- <mark>Poppo, Laura and Zheng Cheng. 2017. "Trust and Contracts: Complements versus substitutes in business-to-business exchanges." in Rosalind Searle, Ann-Marie Nienaber, and Sim Sitkin eds.</mark> _<mark>The Routledge Companion to Trust</mark>_ <mark>. New York : Routledge.</mark>

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58
# 1. Introduction

The promotion of investments in research and development (R&D) and innovation is a standard component of `‘` stimulus packages `’` adopted by advanced economies to counterbalance the effects of the recent global crisis (OECD 2012). For example, according to Eurostat, government budget appropriations or outlays for research and development (GBAORD) increased 46 percent in the Slovak Republic, 33 percent in Korea and 20 percent in Germany in the period 2007-11 (Eurostat, 2011).

Governments have been particularly concerned with the possible decline in R&D investments by the private sector and its impact on innovation and productivity. Approximately threequarters of OECD economies adopted new measures to foster business investments in R&D to counter this - including higher tax-credits, additional direct support or both, as in the cases of France, Japan, Norway, and the U.S. (OECD, 2011). Direct support to business R&D corresponds to about 1.27 percent of GDP on average, and overall spending in R&D (including the government and higher education sectors) reached 2.06 percent.<sup>2</sup> This consensus among policy-makers, however, needs to be supported by empirical evidence to substantiate the causal links that are assumed when they allocate public funds to private R&D projects, more specifically the stage multiplier effects of R&D subsidies on R&D expenditures, especially for input, output, and outcome additionality. This literature also needs to be supplemented with a systematic review that aggregates findings to offer policy directions.

One first attempt to collect and review empirical literature on the impact of direct public support to private investment in R&D was performed by David et al. (2000). The majority of studies surveyed in this paper point out the following conclusions: Government R&D and tax

> 2 Business investments in R&D were considered to be pro-cyclical due to the reduction in firm’s cash flows or simply worsening of financial market conditions. Internal funds are the preferential source of financing for R&D and innovation investments. With the global downturn, firm’s revenues declined substantively. Also, financial market conditions (cost and availability of capital) worsened significantly, reducing the availability of internal and external funds for research and innovation. For example, using a French firm-level panel data set over the period 1993-2004, Aghion et al. (2008) show that the share of R&D investment over total investment is countercyclical without credit constraints, but it becomes procyclical as firms face tighter credit constraints. Other studies have argued that business R&D is pro-cyclical even when firms are not financially constrained (Barlevy, 2007).

2
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# Table 2. Descriptive Statistics of Variables

Panel A. Bond tranche data

|Variable|Obs.|Mean|Std.<br>Dev.|Min|Max|
|---|---|---|---|---|---|
|Bond Variables||||||
|Fixed Yield-to-Maturity|5962|5.06|3.25|0.20|12.31|
|Maturity of Bond issued|6804|6.00|7.31|0.10|100.08|
|Log of Size of Bond Issued|6925|18.63|1.49|11.51|22.63|
|Global Push Factors||||||
|VIX|6925|2.86|0.28|2.41|3.95|
|RISK|6925|0.69|0.29|0.15|1.97|
|FED|6925|14.44|0.67|13.08|15.23|
|LIBOR|6573|2.84|0.49|1.92|5.09|
|MOVE|6925|4.38|0.24|4.03|5.22|
|USREER|6925|-0.002|0.026|-0.074|0.098|
|XRATE|6883|-0.015|0.057|-0.319|1.449|
|UST10Y|6925|3.08|1.14|1.66|6.36|
|Domestic Pull Factors||||||
|GDPPC|6894|8.62|0.59|6.10|9.63|
|GROWTH|6897|6.02|3.06|-14.80|34.50|
|EXT|6918|27.63|29.28|1.30|203.70|
|CA|6922|-0.15|4.48|-39.50|35.50|
|PCRED|6905|84.56|48.16|2.23|135.76|

29