rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. Indicators of infrastructure performance**

|**Dimension**|**Telecom**|**Energy**|**Transport**|**Water & Sanitation**|
|---|---|---|---|---|
|**Quantity**|-Telecommunications density<br>-Internet density<br>-Fixed broadband|-Electricity-generating<br>capacity|-Total road length<br>-Total railroad length||
|**Quality**|-International Internet<br>bandwidth<br>-Number of secure servers|-Energy quality (%)<br>-WEF quality of power<br>supply|-Paved roads (%)<br>-WEF quality of roads<br>-WEFqualityof railroads||
|**Access**||-Access to electricity (%<br>people)||-Access to safe water (%<br>people)<br>-Access to sanitation<br>facilities(%people)|

Sources: see Appendix Note: WEF, World Economic Forum

# **Infrastructure Quantity**

_Telecommunications._ Sub-Saharan Africa has seen a dramatic jump in telecommunication density over the past quarter century. The median number of fixed and mobile phone lines per 1,000 people has risen sharply, from about three in 1990 to 736 in 2014 (Table 2). A similar pattern is observed in other benchmark regions in the world, reflecting the global nature of the boom in mobile phone technology. A comparison of telecommunications density across regions shows that, despite a surge, Sub-Saharan Africa is still far behind other regions.

Modern technological innovation has provided other forms of telecommunications connectivity: the number of Internet users and fixed broadband subscriptions (both normalized by population) are proxies for telecommunications density other than phone lines. Internet density—as measured by the number of users per 100 people—in Sub-Saharan Africa in 2015 was only 16.7, less than that of any other benchmark region. Another indicator of Internet penetration is the number of fixed broadband subscriptions per 100 people.<sup>6</sup> Again, Sub-Saharan Africa, with a penetration rate of only 0.19, lags significantly other regions.

Within Sub-Saharan Africa, progress is observed across all income groups. Telecommunications density expanded at the fastest pace among the region’s LICs, although it started from low levels. Specifically, the number of fixed and mobile phones per 1,000 people among LICs grew from three in 1990 to 687 in 2014. The gap in telecommunications density relative to UMCs has narrowed significantly for LICs and LMCs over the past two decades. For instance, telecommunications density was twice as high in UMCs compared with LMCs in 2014 (while it was 11-fold in 1990). The fast growth of telecommunications density over the past two decades among the region’s UMCs, increasing from 55 lines per 1,000 people in 1990, to 1,605 in 2014, has placed this group above the medians of other regions.

> 6 This indicator refers to fixed subscriptions to high-speed access to public Internet, at downstream speeds equal to or greater than 256 kilobits per second.

_Infrastructure in Sub-Saharan Africa - 4_
15 Over 1990-2000, the 13 destinations accounted for 38 percent of total tourist arrivals in the Caribbean, and 32 percent of the US-Caribbean tourism (US tourists account for more than a half of total arrivals in the Caribbean). These destinations accounted for about 70 percent of the total reported foreign lobbying spent by all the Caribbean destinations (about $306 mn.).

# **5. Empirical Results**

# **5.1 Base Model**

Our empirical strategy is to first estimate a static baseline model with and without fixed-effects in order to observe whether, all else held constant, foreign lobbying is positively associated with tourism. Next, we address the possible endogeneity of lobbying spending and include a lagged dependent variable as in model (1). Most of our inferences come from models estimated using Arellano and Bond’s (1991) GMM method, which solves both the short-panel and endogeneity biases.

Columns 1 and 2 of Table 1 present, respectively, OLS estimates without and with fixedeffects (FE) of the baseline model in equation (1). The simple OLS estimate of 0.12 on ln(Lobbying) implies that a 10 percent increase in foreign lobbying expenditure increase tourism arrivals by 1.2 percent. Including fixed-effects shows that the simple OLS estimates are biased upwards: now a 10 percent increase in foreign lobbying expenditure increases tourism arrivals by 0.49 percent. Although smaller, the elasticity is estimated with precision. The elasticity of tourism with respect to the island’s income is both statistically and economically significant. A 10 percent increase in per capita income increases tourism arrivals by 25 percent. The coefficient on ln(Real Exchange Rate) reveals that this group of islands is quite inelastic (0.82). These coefficients are, however, imprecisely measured across the models.

If the amount of foreign lobbying expenditure is based on expectations about future tourism shocks, then ln(Lobbying) is likely endogenous. In order to correct for possible endogeneity of ln(Lobbying) we use a two-stage procedure in which ln(Lobbying) is predicted using exogenous instruments in addition to the regressors in (1) in the first stage, and the predicted values are used in place of ln(Lobbying) in the second stage.

We use three instruments. The first is the log of the external asset position of reporting banks on the islands, ln(External Asset Positions). Data on the external asset position are from
: v v

> : ~~~~~~ . -:Q ~ ~~~~~~~~~' _C'_

Table 1. The Rdatjve Inportance of OECI) Markets for the TSU and Other Esttrn Em ope Comties

||&E<br>Eastern<br>Europe &|sernEropes<br>OECD|Enr<br>to<br>Other|-U,|.<br>E|FS<br>|Exrs<br>|.o|
|---|---|---|---|---|---|---|---|---|
|Ye.||Markutrs|<br>W<br>||astern<br>le<br>eCh|OECD|Odher|World|
|1970|11.57|4.78|(value o<br>1.74|f total es<br>in ter<br>1810|ms of US $ biHion)<br>676|278|||
|1975|30.48|1210|519|.<br>4777|.<br>|.<br>|3.26<br>|12.80<br>|
|1980|27.90|.<br>2354|,<br>963|.<br>6106|17.99<br>|9.74<br>|7.47<br>|35. 20<br>|
|1985|<br>28.35|.<br>2490|.<br>1027|.<br>6353|17.81<br>|26.70<br>|13.43-<br>|.57.94<br>|
|1990|23.79|.<br>3385|.<br>822|.<br>6586|18.09<br>|23.81<br>|15.42<br>|57.32<br>|
|||.|.|.|11.10|31.05|16.91|59.06|
|1970|64|26|(share<br>10|of total exports in te<br>100|rms of percetge)<br>53|22|||
|1975|64|_25_|<br>11|<br>100|<br>|<br>|25<br>|100<br>|
|1980|<br>46|<br>39|<br>15|<br>0OO|_51_<br>|28<br>|21<br>|tO0|
|1985|<br>45|<br>39|<br>16|<br>100|31<br>|46<br>|23<br>|100<br>|
|1990|<br>36|<br>_51_|<br>13|<br>|32<br>|42<br>|26<br>|100|
|||||tO0|19|53|29|100|

Source: Data compiled from Economic Commission for Europe, Economic Surven of Europe in 1221-2 1992), particularly Table C4 on page 315.

(New York: United Nadons,
classroom) _decreases_ with election (by 5 percentage points ITT, 9 percentage points IV) and training+election interventions (by 7 percent point ITT, 12 IV). So while elections may prompt teachers to increase working hours, there is a significant decrease in the fraction of teachers in the classroom. Particularly for elections, and to a lesser extent for training +elections, these results seem to contradict each other. For this reason we also analyze the impact on each of sub-components of the hours worked variable (not shown in a table). The results indicate that for election it is the lesson preparation time that went up and for training+elections the time grading. These are both non-teaching activities, making it less surprising that no positive effect was found for the fraction of classes observed with a teacher present. For the linkage+election intervention, where there is no contradiction, teaching and grading hours go up, confirming that the intervention has an effect on the time teachers teach children in class.

# **VI. Comparison with qualitative study**

In addition to variables described above and analyzed as part of our quantitative work, we also collected indepth information through focus groups and community interviews about how school committees and communities were responding to the interventions, in a sub-sample of six, purposively-selected schools. Field teams spent about a week with school committees and other education stakeholders in July 2008, just several months before the endline survey (see Table 2) **.** Results in detail are presented in Bjork (2009); in this section we summarize the main conclusions, and compare findings with those from the quantitative research.

The primary focus of the qualitative work was to look at a combination of interventions and their synergies, so the research team selected schools exposed to the linkage+training, linkage+election, training+election treatments. For each combination, the project implementer was asked to select a school where the school committee appeared to be functioning well, and another where it was operating with difficulty. The qualitative and quantitative research were driven by similar evaluation questions, but the qualitative research concentrated on the influence that school committees exerted on school-based management variables, such as parental participation, decision-making, and the quality of services provided in the schools, rather than learning.

The qualitative research underscores school committees‟ appreciation for receiving funding that was directly under its control. Respondents indicated that the grant was the impetus for more face-to-face dialogue among members, which is in line with midline quantitative findings, that the grant is mostly used for meetings. While the grant fostered dialogue, it also caused conflict between the school committee and principal in some cases. The fact that the principal and school claimed authority over the grant indicates that the grant was successful in serving as a counterbalance to principal influence. One other change we see from the 22
<!-- Start of picture text --> 4<br>0<br>> 1<br>Cc<br>+==wra}nax-2<br>-3<br>-4<br>0 1 2 3 4 5 6 7 8<br>Quarters<br>Confidence Interval at 95% — Estimate<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>> 2<br>Cc<br>xo$ona4<br>wx<br>0<br>-1<br>0 1 2 3 4 5 6 7 8<br>Quarters<br>Confidence Interval at 95% —— Estimate<br><!-- End of picture text -->
countries, the number of firms, and the period the ES and COV-ES cover; Table A2 contains a formal definition of all the variables; and Table A3 contains the summary statistics of the variables.

# _3.2 Main variables_

The outcome or dependent variable is the decline in sales in the last month (after the outbreak of COVID-19) compared to sales in the same month one year ago (pre-pandemic), expressed as a percentage of the latter (Sales decline). The mean decline in sales equals 32.2 percent, the median decline is 30, and the standard deviation is 32.7. The decline ranges between 100 and -300 percent. A positive value of Sales decline implies that sales were lower after the outbreak of the pandemic than before, and a negative value implies an increase in sales following the pandemic. About 74.6 percent of the firms experienced a decline in sales, 17.3 percent no change, and 8.1 percent an increase. By country, the mean decline was smallest in Belarus (16 percent) and highest in Niger (53.8 percent).

# _3.2.1 Main explanatory variables_

The main explanatory variable is a dummy variable equal to 1 if the firm is an SME and 0 otherwise (SME). Following the definition used by the ES for survey stratification, SMEs are all firms that had 5-99 full-time workers employed in the pre-pandemic period. The rest (>99 full-time workers) are large firms. There are 4,097 SMEs in our baseline sample, which is about 78.8 percent of all firms. We consider several determinants of the firm’s performance during the pandemic. Many of these determinants are for the pre-pandemic period (taken from ES), and some are for the period after the outbreak of the pandemic (taken from COV-ES). We focus more on the pre-pandemic variables because information is available on a much larger set of variables before the outbreak of 11
- 26 -

at low-to-medium range, also positively affect the probability of project failure (while it has no significant impact once the deficit exceeds about 4 percent of GDP). This "kinked' effect of fiscal balance is detected by the inclusion of two spline variables (BALANKI and BALANK2). BALANK1 is statistically significant in 3 out of 4 equations in Table 6 and 8, suggesting the possibility of a nonlinear or "kinked' relationship between fiscal deficit and project performance. Model 2 controls for the (three-year moving average of) annual GDP growth rate, which has negative and significant coefficients-suggesting that the higher the growth rate, the lower the probability of project failure. Our intention is to show that the effects of economy-wide policies do not go away even when the overall economic dynamism is controlled for, but not to suggest any 'causal" relationship. When multi-policy models are estimated (Table 7), the significant effects of currency overevaluation do not disappear, while the fiscal balance variables become insignificant.

Analyzing the subsample of 228 educational projects (Table 8-9), the results are consistent, except that the effects of black market premium and fiscal balance become more significant. The slope coefficients are quite similar with those estimated for all social projects, except for those which are not significant. In Table 9 when multi-policy models are used on education projects, the effects of both black market premium and fiscal balance are more significant than those for all social projects. Although the trade restrictiveness variable (Thomas-Halevi index) was strongly associated with the probability of project failure in the bivariate cross-tabulation analysis, their effects are not statistically significant here. In part the explanation lies in the fact that in crosstabulations we are analyzing the total effects, whereas in the multivariate analysis we are looking at partial effect, holding everything else constant (some effects are captured by the constant term, or the GDP growth). Furthermore, the number of observations for ftis policy variable is small and there is relatively little variation in it.

Since Probit analysis requires the assumption of normal distribution of the error term, we also performed a Logit regression analysis to test the sensitivity of the results to different assumptions regarding the distribution of the error term Oogistic in this case). Results show that the signs of the estimated coefficients are consistent and the magnitude of the coefficients vary little for those variables which are
Notice from Figures 7(a) and (b), which show savings and growth rates for China and India, that the need for high savings rates for sustained fast growth is hardly extravagant.

**Figure 7. National Savings and Growth: China and India**

<!-- Start of picture text --> (a). China: Growth rate and Savings Rate (b). India: Growth rate and Savings Rate<br>18 60 12 40<br>16<br>50 10 35<br>14<br>30<br>12 8<br>40<br>10 25<br>6<br>8 30 20<br>4<br>6<br>15<br>20<br>4 2<br>10<br>2<br>10<br>0 0 5<br>-2 0 -2 0<br>Growth rate-L/Axis Gross Savings/GDP - R/Axis Growth rate-L/Axis Gross Savings/GDP - R/Axis<br>Source: World Bank WDI data Source: World Bank WDI data<br>% % % %<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br>1980 1983 1986 1989 1992 1995 1998 2001 2004 2007<br><!-- End of picture text --> This exercise brings out two points: first, achieving 8 percent growth over the medium term in Sri Lanka will call for a huge fiscal effort to increase public savings substantially. Second, even assuming the required level of savings is achieved, it will not suffice to maintain an 8 percent growth rate over the long run. Since achieving the desired level of public savings may not be feasible, Figures 8 (a) and (b) shows the growth dynamics consistent with the targeted level of public savings of 1.5 percent of GDP to be attained by 2012. This will constrain the overall level of national savings to a little over 25 percent of GDP in the medium run, leveling off at just below 25 percent over the long run. Sri Lanka does not reach the 8 percent growth target even over the medium run and the growth rate drops to less than 4 percent over the longer run.

Figures 9 (a) and (b) depict Scenario 2 reconciling the government‘s growth targets with its savings target of 1.5 percent of GDP. The key difference from the previous simulation is the assumption that TFP will grow at 2.5 percent per year over the long term. Based on crosscountry experience, this is not impossible but is hard to achieve: this is a highly optimistic scenario. National savings need to rise to approximately 27 percent over the long-term; recall from Figure 7 that this is substantially less than the savings rates achieved by China and India.

22
The second stage estimates are reported in Table 6 (Table A2 presents the first stage estimates). Marginal effects and robust standard errors from IV probit models are presented for private pension planning (the variable takes the value 0 for individuals with public pension funds only and the non-planners).<sup>8</sup> Specifically, all three measures of financial literacy are shown to exert a positive impact on private retirement planning in the baseline estimate of the first three columns. The magnitude of the estimated effect is almost two times higher than that of the baseline probit model in Column 1. However, the estimate of the effect of the number of correct responses on private pension planning in Column 2 is very similar in magnitude to the effect estimated in the probit model of Table 5. Hence, the IV estimates largely confirm the validity of the estimates presented in Table 4.

In Columns3 - 4, and 5 - 6 we perform two additional sets of robustness checks concerning the validity of our instruments. These specifications include control variables for the log values of the regional unemployment rate and the average monthly income per capita in every administrative region<sup>9</sup> . Then, in Columns —5 - 6 we add dummy variables for 1-digit federal regions to the specification. These robustness checks refute the possibility that the impact of our instrumental variables is due to regional differences in living standards and the degree of urbanisation. The results in the last four columns confirm the robustness of our instruments, and the magnitude of the effects remains high and statistically significant at the 1% level.

> 8 The Hansen‘s J statistic of overidentifying restriction, at the bottom of the table, accepts the null hypothesis that the instruments are valid, and the Kleibergen-Paap LM and Wald statistics reject the null hypothesis that the equations are underidentified or weakly identified. The weak-instrument-robust inference tests accept the null hypothesis that the coefficients of the excluded instruments are jointly equal to zero.

> 9 The data is available from the Russian Federation Federal State Statistics service, at: http://www.gks.ru/bgd/regl/b10_06/IssWWW.exe/Stg/1/17-01.htm

1
governing the stochastic process of firm-wide productivity. Taking logs of firm-wide employment in the region where the product range is less than one, we get: where Ψ = <u>�(</u> _fρp_<sup>(</sup><sup>_η_</sup> _−_<sup>_−_</sup> 1<sup>1</sup> <u>)</u><sup>)</sup> <u>�</u> _η−η_ 1<sup>collectsparametersrelatedtothefixedcostofprovisionof</sup> products, the elasticity of substitution across varieties within a product, and the tail of the Pareto distribution of product attributes.

The moment in the data that we target to pin down the step size _h_ is the standard deviation of the distribution of employment growth rates for large firms, which in the US economy is equal to 0.25. It is easy to show that, in the stationary equilibrium : of our model, such variance is approximately equal to<sup>11</sup> Thus, for a given value of _η_ , the standard deviation of employment growth rates in the cross-section of large firms in the United States implies that _h_ = 0.25/ _η_ . We take into account that there exists a small fraction of large firms supplying all the products, for which _η_ no longer plays a role. For these, the variance of employment growth rates is driven entirely by the step size in the process for productivity, so we set _h_ = 0.25 for such group.

In terms of the probability of technological upgrading, _α_ , we set it to match properties of the right tail of the U.S. employment based size distribution. Luttmer (2010) highlights the linearity of the right tail of the US establishment and firm size distribution across employment, as well as the stationarity of the distribution over time, . using various sources of US micro-data<sup>12</sup> For a given value of the productivity step size _h_ and a given value of the exogenous exit probability _δ_ , the slope of the right tail of the firm size distribution in the model is determined by _α_ . To ensure that we are capturing the linear portion of the size distribution, we focus on the slope implied

> 11 ˆ 2 2 2 The exact value for the variance is _Var_ � _L_ � = ( _ηh_ ) _−_ [ _ηh_ ] (2 _p −_ 1) . The approximation is exact in the case of a stochastic process with zero drift, namely _p_ = 0.5. We show below that our calibrated value of _p_ is 0.467, which allows us to qualify the approximated value of the variance as a close approximation. The sole advantage of it is that we can independently identify the values of the parameters of the stochastic process that match their counterparts in the data.

> 12County Business Patterns Database, statistics from the Small Business Administration and the Business Dynamics Statistics from the census.

19
Despite the many studies realized and the extensive empirical evidence produced, however, it is still difficult to identify some sort of “consensus” for the most likely impacts of climate change on agricultural productivity, especially for all world regions. This is because the issue is intrinsically complex and the eventual effect depends on several factors, which are difficult to evaluate ex-ante, for example: (i) the role of adaptation behavior by farmers, firms and organizations, including variety selection, crop rotation, sowing times, etc.; (ii) the amount of fertilization due to higher CO 2 concentration; (iii) the actual level of water available for irrigation, and irrigation techniques.

Some studies in this area are based on controlled experiments. Others are based on crop models applied to different crops in different regions and on the basis of different climate scenarios. This heterogenous information is summarized in the latest IPCC Assessment Report (2014), while efforts are under way to standardize the process of agronomic experiments and modeling (AgMIP, 2014).

Because of the heterogeneity of the underlying available information, we follow here two distinct approaches. The first approach, similar to the one adopted by Roson and Sartori (2010), relies on a meta-analysis provided in the Fifth IPCC Assessment Report (2014), providing central estimates for variations in the yields of maize, wheat and rice. We elaborate on these results to get estimates of productivity changes for these three crops, in all 140 regions and for the five levels of temperature increase, from +1°C to +5°C.

The second approach is similar to that of Cline (2007), and brings about an estimate of productivity changes for the whole agricultural sector in the various regions. The decision about which estimates to use in a general equilibrium simulation depends on the level of industrial disaggregation of the model. We suggest to use the first set of parameters if maize, wheat and rice are considered as separate industries, and the second set for the rest, or for the whole agricultural sector if this is regarded as a single aggregate industry.

# _3.1 Methodology_

The IPCC AR5, similarly to the previous one, provides a graphical summary (Figure 7-4 in IPCC (2014)) for estimates of changes in productivity of maize, wheat and rice obtained by several studies. It distinguishes between tropical and temperate regions and identify a kind of non-linear interpolation function for the two cases, with and without simple agronomic adaptation. The figure is reproduced here below (Figure 2).

6