rafmacalaba/datause-encoder-data huggingface dataset:
research in universities in a way that builds intellectual capability and encourages long-term and high-risk research projects. The comparative advantage of universities vis-à-vis industry is clearly their solid academic competence. Hence, the importance of high quality research in universities should not be underestimated even if the link between academic research and industrial application is often ambiguous.

# **3.1 Academic research in Latin America**

The quality of research in Latin American universities is not uniformly good, particularly considering the funds allocated them (Holm-Nielsen and Agapitova 2002; Lundvall 2000). Figure 3 shows scientific publications relative to the level of income for a sample of 123 countries. The estimated trend line reflects the expected log of ISI publications per million people when a linear regression is performed on the log of per capita GDP. The data indicate that all countries in Latin America publish below what is expected based on their level of income. Researchers in countries such as Argentina, Brazil and Mexico produce on average an international peer-reviewed article only every five years. Moreover, the quality of research, as indicated by the citation impact factor is low in the region. Articles published by researchers in Argentina, Brazil and Mexico are cited roughly 1.3 times, which should be compared to 2.7 and 2.0 times for articles published by their colleagues in Finland and Spain, respectively (Thorn 2005b).

**Figure 3. Publications per million people relative to GDP per capita, 1999**

<!-- Start of picture text --> 3.50<br>3.00<br>Israel Finland<br>UK<br>USA<br>Singapore<br>2.50 Spain<br>Hungary<br>Portugal<br>Russia Croatia<br>2.00 Bulgaria<br>Lithuania Chile Argentina<br>1.50 Ukraine Romania TurkeyBrazilUruguay<br>Georgia Egypt VenezuelaMexico<br>Gambia Morocco Panama<br>1.00 Iran<br>Kenya Senegal Thailand<br>Guinea-Bissau Colombia<br>Bolivia<br>0.50 Malawi Benin<br>0.00 Ethiopia NigerSudanTogoBangladeshHondurasCote d'IvoireGuatemalaEcuadorPeru<br>2.00 2.50 3.00 3.50 4.00 4.50 5.00<br>Log of per Capita GDP, 1999<br>Note: Some country names have been omitted to make the graph more legible<br>Source: Thorn 2005b<br>Log of SCI publications per million people, 1999<br><!-- End of picture text --> Research production and quality is dependent on various factors. As noted earlier, the insufficient qualifications of academic research staff is an issue facing most countries in Latin America. University professors often see their main role in instruction rather than research, which is the heritage from the old university model in Latin America where universities were primarily teaching institutions. A low emphasis on research is particularly evident in private universities, which generally have a high proportion of part-time staff and tends to be specialized in less research-intensive fields. Moreover, major universities enjoy great independence of public oversight and maintain incentive structures that do not reward excellence. Despite these challenges, there are pockets of quality research in Latin America, even if the average level does 11
|||**Correspon**<br>|**ding coeffic**<br>|**ients for ca**<br>|**pital and la**<br>|**bor in the a**<br>|**bove regre**<br>|**ssions for in**<br>|**dustry * fir**<br>|**m category**<br>|**:**<br>|<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Material,<br>Large|Material,<br>Medium|Material,<br>Small|Light,<br>Large|Light,<br>Medium|Light,<br>Small|Chemical,<br>Large|Chemical,<br>Medium|Chemical<br>Small|, Machine,<br>Large|<br>Machine,<br>Medium|<br>Machine,<br>Small|
|OLS|ln(capital-labor<br>ratio)|.750**|*.065|.274***|.500***|.264***|.411***|<br>.212***|.050|.017|.261***|.513***|.547***|
|pooled||(.093)|(.049)|(.046)|(.058)|(.051)|(.061)|(.096)|(.074)|(.091)|(.076)|(.051)|(.087)|
|OLS|ln(number of|.057*|.054*|.087**|-.0003|-.048*|-.030|-.004|-.012|-.077*|-.027|-.016|.003|
|pooled|employees)|(.032)|(.032)|(.037)|(.024)|(.027)|(.032)|(.038)|(.042)|(.045)|(.029)|(.033)|(.037)|
|FE|ln(capital-laborratio)|-.020|-.288***|-.148*|-.021|.020|-.036|-.083|-.110|.103|.289***|.368**|_.195***_|
|pooled||(.100)|(.075)|(.078)|(.090)|(.056)|(.071)|(.104)|(.073)|(.075)|(.064)|(.053)|(.063)|
|FE|ln(number of|.326|.002|-.649***|-.355|.067|-.025|.043|.491 * *|.295*|.674***|.113|.998***|
|pooled|employees)|_(.305)_|(.197)|(.152)|(.309)|(.132)|(.144)|(.242)|(.193)|(.159)|(.0192)|(.111)|(.171)|

o Empty cells means that the coefficient is constrained to be zero, so assumed because in no specification it approached statistical significance.

**Table 3. The Benchmark Results of PC Effects** ( **Dep.=ln(value added per employee) ): Controlling for Other Reforms**

|Sample:|Pooled S|ample: both<br>PC-P|PC Particip<br>articipants|ants and Non-||PC-part|icipants O|nly|
|---|---|---|---|---|---|---|---|---|
||FE (2)|FE (3)|FE (4)|FE (5)|FE (7)|FE (8)|FE(9)|FE (10)|
|PC-Participants dummy (i.e. di=l) * number<br>of years in the samnple|.004<br>(.009)|.010<br>(.008)|.010<br>(.008)|.010<br>(.008)|No need|once contr|ol for year|dummies|
|Dununy: the year before PC-adoption (i.e., Dt<br>1)<br>PCi,t_I|-.013<br>(.030)<br>.076**<br>(.038)|-.025<br>(.030)|-.025<br>(.029)|-.025<br>(.029)|-.004<br>(.029)<br>.017<br>(.039)|-.008<br>(.029)|-.007<br>(.029)|-.006<br>(.029)|
|PCi,t-2||.005<br>(.042)||||-.034<br>(.041)|||
|PCi,t-3|||.008<br>(.073)||||-.013<br>(.070)||
|PCit-4||||.038<br>(.103)||||.06<br>(.029)|
|Number of observations|6176|6176|6176|6176|5471|5471|5471|5471|
|R square|.455|.455|.455|.455|.489|.489|.489|.489|

Note. (a) Standard errors in parentheses. ", **, and *** represents significance levels of 10, 5, and I percent. (b) R<sup>2</sup> 's reported under FE models are R<sup>2</sup> within. (c) In all the contests between OLS, RE, and FE specifications, the FE models win decisively. (e) Other control variables for all specifications include capital and labor terms by industry-size category, dummies of year 1984-1989, as well as other reforms: marginal profit retention rate and its missing indicator, firm wage elasticity and its missing indicator, the dummies of managerial wage discretion, of production autonomy, of the presence of new management.

36
Table 8: Regression Discontinuity with Before and After Approach

||1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|Treatment|0.618***|0.550**|0.520**|0.542**|0.571***|0.541**|0.578***|0.586***|0.528**|0.446*|
||(0.222)|(0.219)|(0.218)|(0.217)|(0.217)|(0.217)|(0.222)|(0.221)|(0.238)|(0.228)|
|Urban_×_Dept|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|HH Indigenous status||✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|||✓|✓|✓|✓|✓|✓|✓|✓|
|Gender||||✓|✓|✓|✓|✓|✓|✓|
|# Children in HH|||||✓|✓|✓|✓|✓|✓|
|# Children under 5||||||✓|✓|✓|✓|✓|
|# Adults|||||||✓|✓|✓|✓|
|Parent present||||||||✓|✓|✓|
|Father/Mother_>_65|||||||||✓|✓|
|Father/Mother educ||||||||||✓|
|Observations|3645|3645|3645|3645|3645|3645|3645|3645|3645|3645|
|_R_<sup>2</sup>|0.242|0.254|0.283|0.286|0.291|0.293|0.296|0.301|0.302|0.356|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
_33_ _WATER WHEN IT COUNTS_ TABLE 3—PLOT-CROPS MOVED FROM INSUFFICIENT TO SUFFICIENT WATER AVAILABILITY

||(1)|(2)|
|---|---|---|
|Post|-0.140<sup>_∗∗∗_</sup>|-0.198<sup>_∗∗∗_</sup>|
||(0.023)|(0.028)|
|Individual feedback|-0.0216|-0.0369|
||(0.022)|(0.028)|
|Individual feedback * post|0.00818|0.0177|
||(0.034)|(0.043)|
|High water gap||-0.155<sup>_∗∗∗_</sup><br>(0.038)|
|Post * High water gap||0.136<sup>_∗∗∗_</sup><br>(0.043)|
|Individual feedback * High water gap||0.0455<br>(0.049)|
|Individual feedback * Post * High water gap||-0.0344<br>(0.066)|
|Observations|3724|3709|
|Adjusted_R_<sup>2</sup>|0.201|0.208|
|Randomization pairs fxed-effects|Yes|Yes|
|Month ofplantingfxed-effects|Yes|Yes|

Notes: Observations at are at household-round-plot-crop-growth stage level. Sample is restricted to plots that were cultivated. Standard errors are clustered by household to account for autocorrelation.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01

# _B. Robustness_

In the previous section, we find that the share of people with a negative water gap falls after the initiation of information provision, but that there is no additional gain from individualized feedback. However, because everyone receives general information, we cannot rule out that a factor other than information caused the improvement in people meeting requirements. The most obvious candidate for an external factor would be increased rainfall. Figure 10 shows the share of plots under cultivation with a negative
In order to ensure that we have a relatively good match between the crop revenue (and other socio-economic) data and climate information, we restrict our sample to only those households in counties with meteorological stations. In total, this means that our final sample has 8405 households in 915 villages, in 124 counties in 28 provinces.<sup>1</sup>

# **Model Specifications**

In order to capture the expected nonlinear relationship between net revenue and climate, we specify the following model to examine the impacts of climate change on agriculture in China: where the dependent variable, _V_ , is net crop revenue per hectare (as defined above). The variables _T_ and _P_ represent vectors of temperature and precipitation (four seasons). In addition, we include _Z_ , a vector of county-, village- and household-level socio-economic and other control variables. Included in _Z_ are our measures of soil type (Z1), elevation of the county (Z2), terrain (Z3=1 if the village is located on a plain and 0 if the village is in a mountain), the share a village’s cultivated area that is irrigated (Z4), the conditions of a village’s road (Z5=1 if there is a road that connects the village to the outside world and 0 if there is not) and a variable measuring the distance between the village and township government (Z6). There are also a series of household-level variables in Z, including the average education level of the laborers in the family (Z7), a household’s land area (Z8) and whether or not a household belongs to a production cooperation (Z9=1 if yes and 0 if no). The symbols bk and dj are vectors of the coefficients to be estimated, and _e_ is an error term.

In order to assess the robustness of the model, we try a number of alternative specifications of equation 3. For example, we also try using the log of net revenue as the dependent variable. We test whether precipitation and temperature are independent by adding

> 1 We have tried various approaches (such as linear and non-linear regression; GIS methods) to extrapolate the climate data from the location of the meteorological stations in the counties with weather data across the landscape of counties and villages in adjacent counties (or those counties without weather data).  However, our results suggest that such extrapolation methods introduce substantial amounts of data measurement error into the analysis.  In order to avoid such measurement error in the climate variables, we have chosen to drop all farm households that are in counties that do not have climate data (i.e., that do not belong to a county with a meteorological stations).  In addition, we dropped those households which did not cultivate any crops (characterized with total cropping sown areas of zero).

8
_3. Methodological framework_

**Table 1 Summary of series organized by SDG**

Source: Authors’ calculations

# **Construction of metrics derived from network theory**

The construction of the network of SDG indicators is based on two ideas:

- (i) The probability of a country to perform above expectations in a given SDG indicator is conditioned by the probability of performing above expectations in other SDG indicators; and

- (ii) SDG indicators that have similar SDG‐delivery mechanisms are more likely to be jointly observed across countries.

Hence, achievements in the SDGs mirrors the underlying similarities in SDG‐delivery mechanisms across countries.

## Concept 1: Performance in SDG indicators

The performance measure is based on the World Bank’s Country Development Diagnostics Framework (CDDF) (Gable et al., 2015), though with a few changes. This paper incorporates a similar classification that measures progress on SDGs considering a country’s current level of capacities, proxied by GNI per capita. For any combination of country and SDG‐series, a country can be classified as an over‐performer if the SDG‐series is statistically _better_ than what is expected given the country’s capacities.

Formally, a country _c_ has a revealed comparative advantage in the SDG‐series _i_ if the value of the SDG‐series is above the 95% confidence interval of the mean: ��,� �����.�� �����,�� �����, ����0,<sup>1,</sup> ��������� The CDDF uses log‐on‐log linear regressions on low‐ and middle‐income countries to estimate the expected value of the formula above. Our approach differs in the following ways: _i_ ) we include high‐income countries in the sample, and _ii_ ) kernel‐weighted local polynomial estimators are used instead of the constant elasticity assumption, due to the varying relationship especially at higher levels of GNI per capita. Figure 1 illustrates the approach with a cross‐country scatter plot of an SDG‐series, CO2 emissions (kg per 2005 5
**Table 9 Effects of fiscal policy volatility on GDP per capita growth**

|**Dependent variable: G**|**DP per capit**<br>|**a growth**<br>|||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Fiscal policy volatility|-0.022|-0.116***|-0.110***|0.079*|
||(0.060)|(0.022)|(0.036)|(0.042)|
|Government size||0.013|0.020|0.015|
|||(0.012)|(0.013)|(0.012)|
|Initial GDP per capita||-0.659***|-0.767***|-0.774***|
|||(0.138)|(0.147)|(0.138)|
|Capital account openness||1.210***|1.230***|0.930***|
|||(0.302)|(0.314)|(0.342)|
|Investment price||-1.481***|-1.548***|-1.546***|
|||(0.392)|(0.432)|(0.318)|
|Commodity exporter||||-0.734**|
|||||(0.318)|
|Constant|1.986***|8.416***|9.202***|9.708***|
||(0.533)|(1.157)|(1.332)|(1.284)|
|Observations|177|161|128|128|
|_R_-squared|0.006|0.356|0.403|0.433|

_Source_ : World Bank.

_Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Fiscal policy volatility in all specifications is measured using primary expenditures. The estimates in columns (1) and (2) are based on ordinary least squares (OLS) regressions. Columns (3) and (4) is based on instrumental variables (IV) estimation, with the "political constraints" and "control of corruption" used as instruments.

53
Table 8 presents the summary statistics for the standard deviation of labor productivity and for the covariance term. The summary statistics show that the mean and median standard deviation in log labor productivity increase with age suggesting that the heterogeneity in labor productivity increases with firm age. When we look at the size-productivity covariance term, we find that the mean and median across countries and ages is negative suggestive of resource misallocation and selection issues.<sup>28</sup> For instance, the median covariance term of -0.858 for young firms suggests that the labor productivity in the median country in the average young firm is 86% lower than it would be if employment shares were randomly allocated. However, the mean and median values of the OP covariance term also seem to become less negative with age indicating that the older firms in our sample are more efficient.<sup>29</sup> For instance, the median covariance term of -0.263 for mature firms suggests that the labor productivity in the median country in the average mature firm is 26% lower than it would be if employment shares were randomly allocated. The increase in the size-productivity covariance over the lifecycle provides suggestive evidence that the size-age profiles established in sections A and B are partly driven by the selection of more efficient firms.

# **VI. Conclusion**

The role of size and age in influencing firm dynamics is crucial to understanding productivity differences across firms and countries. In this paper, we use unique survey data across 118 developing countries to examine the relationship between firm size and age in the

> 28 For comparison sake, the OP covariance term for labor productivity reported by Bartelsman et al. (2013) for the United States is 0.51 which implies that the industry index of labor productivity in the average US manufacturing industry is 50% higher than it would be if employment shares were randomly allocated within industries. 29 For comparison sake, the OP covariance term for labor productivity reported by Bartelsman et al. (2013) for the United States is 0.51 which implies that the industry index of labor productivity in the average US manufacturing industry is 50% higher than it would be if employment shares were randomly allocated within industries.

31
# **III. REAL EXCHANGE RATE BEHAVIOR IN THE TWO ZONES**

## **A. UEMOA ZONE**

The evolution of the real effective exchange rate since the 1999 introduction of the euro can be divided into three distinct phases. Firstly, from January, 1999 to April, 2002 there was close to 5 percent improvement in the external competitiveness of the UEMOA zone due to a combination of a protracted slide in the value of the euro against the dollar and other major currencies due to higher projected growth in the US as well as interest differentials between the euro zone and others. By the beginning of 2000, the euro fell below parity with the dollar. Furthermore, lower inflation rates in the UEMOA zone relative to many of the competitor nations helped keep the REER low. In the second phase, from May, 2002 to September, 2003 there was quite a significant appreciation of close to 12 percent due to the gain in the euro against dollar. This was partially prompted by the twin fiscal and current account deficits in the United States, which have required possible corrections through a sustained dollar depreciation, as well as greater market confidence in the euro. Finally, after October, 2003, the REER has revolved around a stable mean. The chart below documents the trends, with an increase in the REER indicating an appreciation and a decrease indicating depreciation. Over the entire period, the REER appreciated by slightly over 8 percent.

An analysis of the movements of the bilateral exchange rates provides evidence of composition of changes in the REER. The volatility in the euro-dollar exchange rate has been the primary influence on the REER since close to 30% of the zone’s trade occurs with the United States, other countries pegged to the dollar like China and the Republic of Korea, or to African countries like Nigeria and Ghana, whose exchange rate fluctuations closely parallel the dollar. Since close to one-third of the zone’s trade was with the euro zone in 2000-2001 and the inflation differentials between the zone and Europe were insignificant due to prudent monetary policy in both places, there was not much volatility in the bilateral real exchange rates with the European countries.

<!-- Start of picture text --> CHART 8: REAL EFFECTIVE EXCHANGE RATE FOR UEMOA<br>USING CPI: 1999-2004<br>110<br>108<br>106<br>104<br>102<br>100<br>98<br>96<br>94<br>92<br>90<br>88<br>1999 M11999M051999M092000M012000M052000M092001M012001M052001M092002M012002M052002M092003M012003M052003M092004M12004M52004M9<br><!-- End of picture text --> 12
<!-- Start of picture text --> N Bangladesh: Spousal age difference<br>om83<br>ott<br>c<br>£3<br>2 a Urban<br>ec °o<br>aoa)<br>§2<br>£3 -<br>g 2 Higher education<br>og<br>is<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> India: Spousal age difference<br>N<br>3S<br>st<br>OFT<br>&3 Urban<br>g8<br>nom)<br>o55<br>go<br>£235 Higher education<br>Lor<br>8 =<br>is<br>%<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Spousal age difference<br>N<br>on<br>oz.77<br>gc<br>ego _<br>s—TME<br>5 g Higher education<br>goryi]og<br>is<br>4<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Nepal: Spousal age difference<br>N<br>os<br>5xSe<br>lc<br>eBo<br>52 |<br>§ g Higher education<br>2o4iyoe<br>hip<br>4<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing age difference of spouse on wealth, urban, higher education and<br>interactions withyear-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
3. Section 4 defines the equilibrium and analyzes its properties. Section 5 discusses the conditions under which multiple equilibria may emerge, when unit monitoring costs are endogenous. Section 6 draws together the policy implications of the analysis, and the final section offers some concluding remarks.

# 2 Finance and Innovation: Recent Evidence

Recent contributions on the link between access to finance and innovation include Savignac (2008), Ang (2010, 2011), Ayyagari et al. (2011), Ilyina and Samaniego (2011), Brown et al. (2012), Efthyvoulou and Vahter (2012), Hottenrott and Peters (2012), Maskus et al. (2012), Popov and Roosenboom (2012), Silva and Carreira (2012), Gorodnichenko and Schnitzer (2013), and Hsu et al. (2014).

Using a direct measure of financial constraints faced by French firms, Savignac (2008) estimated simultaneously the probability to have innovative activities and the probability to face financial constraints. She also accounted for the endogeneity of the financial constraint variable, by relating it to firms’ ex ante financing structure and economic performance. She found that financial constraints significantly reduced the likelihood of firms engaging in innovative activities. Similarly, Efthyvoulou and Vahter (2012) found that lack of appropriate sources of finance is an important hampering factor to innovation performance across European countries, whereas Hottenrott and Peters (2012) found that external financial constraints are more binding for R&D and innovation activities of small Ayyagari et al. (2011) exploited the responses of some 17,000 firms in 47 countries– many of them low- and middle-income economies–to questions on enterprise innovation. Taking an average of each firm’s responses to the innovation-related questions, they assembled a range of country- and firm-level variables likely to be associated with firm innovation, including information about the structure of each firm’s financing. Accounting for various control variables and controlling for reverse causality by using instrumental variable techniques, they found that firms’ use of external finance was one of the most important constraints that formal private firms–small and medium-size alike–face in developing countries. See for instance World Bank (2008, 2012), Dinh and Clarke (2012), and International Finance Corporation (2013).

7
# **Figure 2 Global factors**

<!-- Start of picture text --> CIF<br>COD<br>1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br><!-- End of picture text --> 46