rafmacalaba/datause-encoder-data huggingface dataset:
Table E9: Share of Significant Temperature Coefficients in Niger

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.50|0.58|0.58|
|Median Daily Temperature|0.50|0.67|0.67|
|Variance of Daily Temperature|0.50|0.83|0.50|
|Skew of Daily Temperature|0.50|0.50|0.58|
|Growing Degree Days (GDD)|0.50|0.50|0.75|
|Deviation in GDD|0.50|0.50|0.75|
|Z-Score of GDD|0.33|0.67|0.67|
|Maximum Daily Temperature|0.83|0.58|0.83|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Niger.

Table E10: Share of Significant Temperature Coefficients in Nigeria

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.58|0.67|0.58|
|Median Daily Temperature|0.67|0.75|0.67|
|Variance of Daily Temperature|0.75|0.83|0.58|
|Skew of Daily Temperature|0.50|0.42|0.33|
|Growing Degree Days (GDD)|0.75|0.92|0.83|
|Deviation in GDD|0.67|0.67|0.83|
|Z-Score of GDD|0.67|0.83|0.67|
|Maximum Daily Temperature|0.67|0.58|0.58|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Nigeria.

135
century with many of the world’s poorest countries, with average income per capita lower than at the end of the 1960s (World Bank, 2000). While post-1995 growth rates have been higher than in <!-- Start of picture text --> previous years, Sub- CHART 1: REAL GDP GROWTH:<br>CHINA VS SSA (%)<br>Saharan Africa (SSA)<br>18<br>has more people living 16<br>14<br>on less than a dollar a<br>12<br>day than in the 1980s. 10<br>8<br>In fact, while the 6<br>4<br>poverty rate in East<br>2<br>Asia fell from almost 0<br>-2<br>80 percent to under 20 -4<br>percent over this<br>period, it stayed at Africa: Sub-Sahara China<br>19801982 19841986 19881990 19921994 19961998 2000 20022004 20062008<br><!-- End of picture text --> around 50 percent in Sub-Saharan Africa, though with signs of progress since the mid 1990s (Chen and Ravallion, 2008). If current demographic and mortality trends continue, Africa will be the only region where the number of poor people in 2015 will be higher than in 1990. African populations missed out on the growth that was taking place in the developing world, particularly in Asia, in the second half of the 20<sup>th</sup> century (O’Connell and Ndulu, 2006).

Moreover, there has been slow and erratic growth of large countries, especially conflictridden countries such as Sudan and the Democratic Republic of Congo. Ethnic and national violence have led to pronounced output declines in a number of countries, rendering macroeconomic and monetary management even more difficult. Growth, investment, and fiscal and current account positions are significantly correlated with violence in many of the SSA countries. Also, there has been quite a significant fluctuation in growth rates in SSA due to country vulnerability to exogenous terms of trade shocks. Since most countries in Africa are primary commodity exporters, they are significantly vulnerable to trade shocks.

6
- Latvia scores lowest in both absolute and relative terms on the Innovation Index. While all NMS’s rank low on this variable, Latvia ranks below most NMS’s.  And Latvia has made no appreciable progress on this variable since 1995.

**Figure 21 Innovation Index in Global Context**

<!-- Start of picture text --> 7.00<br>6.00<br>5.00<br>Ireland<br>D en m ar R ussia<br>F inlandk<br>4.00 C zech R ep.S loveniaS w eden<br>H ungary S lo v ak ia<br>3.00 E stonia B ulgaria<br>L A T V IA P oland<br>L ithuania<br>R om ani<br>2.00<br>a<br>1.00<br>0.00<br>0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00<br>1995<br>Source : WBIKAM<br>Most Recent<br><!-- End of picture text --> In sum, this benchmarking exercise suggests that Latvia has a highly educated population, fairly good communication links with the outside world, and an acceptable economic incentive regime. Improvements can undoubtedly continue in all of these areas. But Latvia’s fundamental weakness remains the NIS: Latvia does not yet have an effective system for converting knowledge – both domestic and foreign – into wealth.

# <u>C. Porter and Stern:  National Innovation Capacity</u>

A third benchmarking exercise ranks countries according to their “national innovation capacity,” which is defined as a “country’s potential...to produce a stream of commercially relevant innovations....National innovation capacity is also distinct from both the purely scientific or technical achievements of an economy, which do not <u>necessarily</u> involve the economic application of new technology.”<sup>28</sup> According to this analysis, national innovation capacity depends on four broad elements:

- The proportion of scientists and engineers.

> 28  Michael E. Porter and Scott Stern, “National Innovation Capacity,” Chapter 2.2 in Porter, Sachs, Cornelius, McArthur and Schwab, The Global Competitiveness Report 2001-2002, New York, Oxford University Press, 2002.

32
<u>Table 9 Hourly earnings differentials between teachers and other wage workers</u>

||<br>All tea|<br>chers|<br>Primary<br>teachers|<br>Secondary<br>teachers|<br>Public<br>school<br>teachers|<br>Public school teachers<br>relative to other public<br>sector employees|
|---|---|---|---|---|---|---|
||OLS|Median|||OLS||
||(1)|(2)|(3)|(4)|(5)|(6)|
|Burkina Faso|0.631***|0.763***|0.652***|0.517**|0.696***|0.491***|
||(0.107)|(0.093)|(0.114)|(0.245)|(0.117)|(0.134)|
|Côte d'Ivoire|0.733***|0.792***|0.663***|0.810***|1.045***|0.538***|
||(0.143)|(0.140)|(0.188)|(0.196)|(0.169)|(0.178)|
|DR Congo|0.043|0.107***|0.039|0.063|0.017|0.018|
||(0.040)|(0.040)|(0.043)|(0.085)|(0.043)|(0.044)|
|The Gambia||||N.A.|||
|Ghana|0.324***|0.281***|0.294***|0.364***|0.435***|0.234***|
||(0.051)|(0.054)|(0.063)|(0.072)|(0.057)|(0.060)|
|Liberia|-0.059|0.023|-0.034|-0.158|0.041|0.011|
||(0.091)|(0.081)|(0.099)|(0.186)|(0.106)|(0.120)|
|Malawi|0.169**|0.198***|N|A|0.225***|0.124|
||(0.073)|(0.073)|.|.|(0.080)|(0.092)|
|Namibia|0.563***|0.522***|0.597***|0.502***|0.581***|0.383***|
||(0.062)|(0.077)|(0.073)|(0.093)|(0.064)|(0.058)|
|Niger|0.096|0.061|0.158|-0.098|0.199|-0.089|
||(0.145)|(0.100)|(0.163)|(0.271)|(0.159)|(0.205)|
|Nigeria|-0.146*|-0.180**|-0.206**|-0.020|0.061|-0.058|
||(0.086)|(0.079)|(0.099)|(0.131)|(0.095)|(0.088)|
|Senegal|0.564***|0.571***|0.587***|0.530***|0.620***|0.431***|
||(0.059)|(0.063)|(0.075)|(0.087)|(0.061)|(0.058)|
|Sierra Leone|0.047|-0.035|0.094|-0.037|-0.020|-0.016|
||(0.167)|(0.131)|(0.195)|(0.246)|(0.188)|(0.223)|
|Tanzania|0.003|0.022|-0.010|0.035|0.020|-0.138***|
||(0.044)|(0.050)|(0.050)|(0.072)|(0.046)|(0.043)|
|Uganda|-0.027|-0.142|-0.085|0.260|0.017|-0.138|
||(0.095)|(0.101)|(0.100)|(0.180)|(0.111)|(0.144)|
|Zambia|0.697***|0.524***|0.659***|0.797***|0.785***|0.349***|
||(0.050)|(0.063)|(0.056)|(0.084)|(0.053)|(0.045)|
|**Average**|**0.260**|**0.251**|**0.262**|**0.274**|**0.337**|**0.153**|

Notes: Each column reports the coefficient in the regressions of ln (monthly earnings) on (a dummy for) a type of teachers with controls. The comparison group are other wage workers in columns 1-5 and other public sector employees in column 6. Controls include gender, age, age-squared, urbanicity and (a dummy for) post-secondary education. Sample is restricted to workers with secondary education or more. The survey in Malawi did not differentiate the levels of teachers and the survey in The Gambia did not report weekly earnings. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Surveys listed in Table 1.

42
major issues here (Chattopadhyay and Duflo 2001; Hsieh and Urquiola 2002).

Educational levels raise the return to creating a job in a particular location, and are thus an element of 1<sup>st</sup> advantage. The human capital embodied in skills that workers acquire while doing a job can also spillover to other firms, for example via job turnover. A worker may become familiar with a particular technology and can use this knowledge in other firms. The magnitude of these spillovers will increase as the level of economic activity in a given location increases, and constitute a source of 2<sup>nd</sup> advantage. There are both micro-level and more aggregate studies of the importance of these effects. Firm level studies are reviewed in Evenson and Westphal (1995) who find extensive evidence of learning by doing within firms and of spillovers between firms. They also report that diffusion of technology between firms comes largely from the turnover of skilled workers and managers. There is also evidence of human capital externalities whereby increasing the number of skilled workers in a location brings external benefits for other workers (Moretti 2003; Klien 2003).

# _Trade and Market Access_

We have already argued (section 2) that exporting is an important aspect of modern economic growth. Barriers to exports are, in our terminology, a source of 1<sup>st</sup> _dis_ advantage, reducing the return to job creation. While this suggests the case for liberal trade policy, several caveats need to be made. Indeed, the role of trade policy in economic development has been the subject of an intense and passionate debate over the last decade.

Our central thesis is that the mapping from 1<sup>st</sup> advantages to outcomes is not straightforward. The removal of trade barriers is not a sufficient condition for growth, as has been confirmed by the empirical literature. While samples of successful countries indicate strong export performance (Dollar and Kray 2000, Bhagwati and Srinivasan 1999), crosscountry regressions attempting to establish a causal link between open trade policy and 17
<!-- Start of picture text --> Figure 7A. Evolution of world house and equity prices<br>(in percent)<br>World House Price Growth<br>9<br>6<br>3<br>0<br>-3<br>-6<br>-9<br>1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013<br>World Equity Price Growth<br>40<br>20<br>0<br>-20<br>-40<br>1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013<br>Note: Each panel shows the four-quarter average of market-weighted growth rates of the respective variables for<br>advanced and emerging market economies. All variables are in real terms. House price data start in 1970. Growth<br>in world equity prices starts in 1962; the market-weights are three-year rolling averages. Shaded bars indicate<br>global recessions. The last observation is for 2014.<br><!-- End of picture text --> 58
government education spending, most strongly for private consumption. The structural rankings declined for government consumption, total gross fixed capital formation (sum of private and government), government health spending, and tax revenue. Among all structural percentile ranking changes, the strongest is the loss of 30 points for tax revenue. In sum, this global comparison suggests that Pakistan’s progress for different objectives is quite similar to other countries at its level of development. Compared to other countries, the striking structural change deviation for Pakistan is a decline in tax revenues, matched by a less dramatic decline in the role of government final demands compared to private final demands (both consumption and investment; cf. Figure 2.4).

Figure 2.18. Percentile rank for Pakistan among all countries -- selected indicators <!-- Start of picture text --> 15-24 literacy rate<br>Adult literacy rate<br>Primary net intake rate<br>Tertiary enrollment rate<br>Secondary enrollment rate<br>Primary net enrollment rate<br>Primary completion rate<br>Water access rate<br>Sanitation access rate<br>Maternal survival rate<br>Under-five survival rate<br>Equality<br>Net ODA (% of GNI)<br>Tax revenue (% of GDP)<br>Gov't health spending (% of GDP)<br>Gov't education spending (% of GDP)<br>Private GFCF (% of GDP)<br>GFCF (% of GDP)<br>Gov't consumption (% of GDP)<br>Private consumption (% of GDP)<br>GNI per capita (US$2000)<br>0 10 20 30 40 50 60 70 80 90<br>2008 1992<br><!-- End of picture text --> Note: Percentile rank is defined as the % of all countries with data that fall below Pakistan's value. Data is for averages around the central years 1992 and 2008 (averages of central year ± 2 years if multiple observations are available). Indicators for which a high value is undesirable have been redefined so that a high value is desirable (e.g. mortality rate changed to survival rate).

14
<!-- Start of picture text --> 0.4<br>_<br>x)<br>© 0.2<br>&<br>weoO<br>S<br> 00P-- 7-5 rrr rn nnn nnn nnn nee<br>()<br>2<br>bat<br>oO<br>OS o><br>-0.4<br>0) 1<br>Female President<br><!-- End of picture text --> <!-- Start of picture text --> _~ 0.00 ------------------------<br>x)<br>oO<br>&<br>weoO<br>© -0.04<br><<br>()<br>i)<br>bat<br>oO<br>OS -0.0.08<br>-0.12<br>0 1<br>Female President<br><!-- End of picture text -->
One, Two, and Three 2 general equilibrium effect of the workfares is approximately three times larger in the high-expenditure districts. However, owing to the large standard errors, the two coefficients’ equality cannot be rejected ( _p_ - _value_ = 0 _._ 44).

The differences in elasticity estimates are consistent with previous studies like Imbert and Papp (2015) who find a more significant general equilibrium effect for the “star-states” that implemented NREGA well. It is worth mentioning that the above differences in elasticities represent a lower bound on the difference between the program’s impact due to the heterogeneity in program implementation across districts. Any scale effects based on how well the program is implemented are partialled out by fixed effects and differential trends in equation 2 in the main article..

# **S2. Effect on Male and Female Wages**

Here I check whether the public workfares affected male and female wages differently. Using the same set of controls as in equation 2, table S6 reports wage elasticities based on gender as well as over different program regimes. Column 1 of table S6 reports the elasticity of male wages over the aggregate sample, while column 2 shows male wage elasticities separately for SGRY and NREGA regimes. Columns 3 and 4 show the same set of results but for female wages. Based on the average growth in _ed,t_ and the elasticities of male and female wages reported in columns (1) and (3), respectively, public workfares are shown to increase male wages by 0.3 percent ( _t_ = 1 _._ 92) and female wages by 0.4 percent ( _t >_ 2) per annum on average.

The more significant impact of employment programs on female wage rates is expected since, relative to men, women earn 18-20 percent lower on average. On the other hand, employment programs offer equal wages for men and women, inducing a higher growth in female wage rates. Columns (2) and (4) show that the wage elasticities for male and female wages under the SGRY regime are noticeably larger than the respective wage elasticities during NREGA. However, a test on the equality of coefficients over the two program regimes cannot be rejected at a 95 percent confidence level for both male and female wages.

# **References**

> Altonji, J. G., T. E. Elder, and C. R. Taber. 2005. “Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.” _Journal of Political Economy_ 113 (1): 151–184.

- Imbert, C., and J. Papp. 2015. “Labor Market Effects of Social Programs: Evidence from India’s Employment Guarantee.” _American Economic Journal: Applied Economics_ 7 (2): 233–63.

- Oster, E. 2019. “Unobservable Selection and Coefficient Stability: Theory and Evidence.” _Journal of Business & Economic Statistics_ 37 (2): 187–204.
market” pricing, and refers to the idea that holding liquidity is costly because less liquid assets have higher expected returns, but agents may hold liquidity because on occasion they are able to make a profit by buying assets at fire-sale prices (Allen and Gale, 1994, 1998; Allen and Carletti, 2008).

To shed light on the rebalancing effects, we display the behavior of short-term assets during crisis times. We focus on pension funds because we have exclusive access to high frequency (daily) portfolio data.<sup>17</sup> Pension funds experience significantly less outflows than mutual funds (as we show later), thus they should be especially able to use their liquidity to take advantage of market opportunities in turbulent times instead of meeting redemptions.

Several papers have proposed that crisis periods in emerging economies are frequently related to a lack of liquidity, when foreigners sell assets at fire-sale prices (Krugman, 1998; Aguiar and Gopinath, 2005). The natural question here is whether the domestic investors, who know the domestic market and have deep pockets, are the ones on the other side of those selloffs. To analyze changes in the short-term portfolio during crises, we study the evolution of short-term assets held by pension funds during the Asian and Russian crises of 1997-1998. This period was characterized by a significant degree of volatility that is especially useful for our purposes. Also, the 1997-1998 crises had a substantially larger impact in Chile than the 2008-2009 global financial crisis.

> 17 In unreported results, we analyze many regulatory changes related to pension funds. Since these regulatory changes have typically been announced in advance, PFAs could accumulate liquidity prior to the deregulation to take advantage of such changes. In other words, if PFAs hold liquidity to take advantage of investment opportunities we should expect an increase in short-term holdings before the limits change, and a reduction after their implementation. We find that around the regulatory events the portfolio share of short-term assets does not change significantly. Namely, we find no evidence of liquidity hoarding before the regulatory changes.

22
# **Table 10 Determinants of the Stock Return Variability**

The dependant variable is the _Stock return variability_ representing the annualized standard deviation of weekly stock returns. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the squares of fee income and non-deposit funding, respectively. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We obtain bank level data from Bankscope and macroeconomic data from World Development Indicators (WDI) of World Bank. We estimate all regressions using country and time fixed effects and clustering at bank level. Standard errors are in parentheses. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|-0.010***|-0.008***|-0.010***|-0.010***|
||(0.003)|(0.002)|(0.003)|(0.003)|
|Equity|-0.123*|-0.089|-0.139**|-0.142**|
||(0.072)|(0.062)|(0.067)|(0.068)|
|Assets growth|0.023|0.013|0.020|0.018|
||(0.017)|(0.018)|(0.018)|(0.018)|
|Overhead|0.433***|0.476***|0.511***|0.493***|
||(0.128)|(0.146)|(0.172)|(0.175)|
|Fee income|0.157***||0.111***|0.073|
||(0.035)||(0.030)|(0.067)|
|Non-deposit funding||0.124***|0.071**|0.060|
|||(0.024)|(0.029)|(0.064)|
|Fee income sq||||0.044<br>(0.070)|
|Non-deposit funding sq||||0.009<br>(0.074)|
|Inflation|0.234|0.290|0.126|0.119|
||(0.346)|(0.331)|(0.334)|(0.336)|
|GDP growth|0.313|0.357|0.247|0.246|
||(0.313)|(0.284)|(0.289)|(0.289)|
|GDP per capita|0.015**|0.014**|0.014**|0.014**|
||(0.007)|(0.006)|(0.006)|(0.006)|
|N|4343|4312|4256|4256|
|R-sq|0.313|0.315|0.331|0.331|
|Country fixed effects|Y|Y|Y|Y|
|Time fixed effects|Y|Y|Y|Y|
|Clusteringlevel|Bank|Bank|Bank|Bank|

48
power for which firms take up the intervention: there are no significant differences in take-up for those that already had paid workers, for those firms that were formally registered, for firms that had more assets at baseline, or by firm age. Instead the skills of the owner appear to matter more. More highly educated owners, and those employing better business practices at baseline are more likely to use the subsidy.

# **4.2 Who did they hire?**

In October 2009, we surveyed both the workers hired under the subsidy program and the employers who hired them. These surveys provide data on the characteristics of the workers and the methods the owners used to find them. The hired workers are 31.5 years of age and have 9.8 years of schooling on average. Close relatives of the owners and those living in the owner’s household were not eligible to be hired, but 31.3 percent of hired workers are related to the owner in some way; 15.6 percent are female. Most (83.4 percent) were known to the owner before the hiring, and almost half (48.4 percent) say they live within 1 kilometer of the business. Workers report being paid 1,860 LKR per week, with just under one-third of them being paid the subsidy amount or less.<sup>8</sup> The excess hiring of relatives is concentrated among lower-profit firms, with 41 percent of hires by firms with lower than median baseline profits being related, compared with only 20 percent of hires by firms with above median baseline profits (p=0.08). These low profit firms are also the ones paying the lowest wages: 60 percent of their hires are paid the subsidy amount or less, compared with only 4 percent of the hires by higher-profit firms (p<.01).

To provide context, we can compare these characteristics with two other groups of hired workers. The first is workers who were working for control firms in April 2010, and who were hired between 2009 and 2010.<sup>9</sup> We have more limited data on these employees, but we find that they are slightly older (33.6 years of age) and less likely to be female (9.4 percent). They are much less likely to be related to the owner (9.4 percent) and are paid a higher wage (3,217 LKR per week). A second

> 8 There is no significant difference in reported hours worked between those paid 1,000 LKR (51.6 hours) per week or less and those paid more than this amount (52.8).

> 9 This period encompasses the subsidy period, but is longer so that we have a somewhat larger sample. Among employees at control firms, 32 were hired in 2009 and 21 in 2010. Note that since this sample is workers employed in 2010, it does not include workers hired but no longer working for the enterprise. We note also that this survey provides more limited information about the employees, and that wages are reported by employers (rather than employees and in categories of daily wages, 0-199, 200-399, etc. We estimate average wages from these responses by using the midpoint of the categorical response, which may over- or under-state the level relative to the continuous response given in the survey of hired workers.

14