rafmacalaba/datause-encoder-data huggingface dataset:
the _PTAmx_ coefficient should be interpreted as an elasticity, and its value implies that preferential trade tends to increase by 0 _._ 16 percent for every 10 percent increase in the combined (multiplied) incomes of the PTA partners.<sup>44</sup> This finding offers a potential explanation for the values of the PTA dummy coefficients estimated in this paper, which we noted are much lower than estimates reported in previous studies. Since the trade effects are increasing in income and previous estimates have been mostly confined to PTAs between large partners, it may not be surprising that the estimated magnitudes in the earlier studies were larger. In contrast, our sample includes many PTAs between smaller trading partners, and the estimated coefficients are consequently much lower.

It is important to acknowledge that while the above discussion sheds new evidence on many aspects of preferential trade, many other issues remain outside the scope of our analysis. For example, we do not formally estimate the trade creation and trade diversion effects, and therefore do not know whether the positive association between PTAs and higher bilateral trade is driven by the former or the latter. One implication of this is that we cannot conclude that South-South agreements are more trade-creating or have greater overall effects on trade than North-South agreements. Although the point estimate of the former is much larger than the latter, it could be the case that increased intra-bloc South-South trade is generated entirely through trade diversion, while North-South PTAs create trade in products that would have otherwise been produced domestically. Similarly, our aggregate results offer no insights into the sectoral distribution of the new trade flows, or the identities of the gainers and losers from trade. Thus, our results focus on only a subset of the important PTA issues, and should be interpreted accordingly.

# **5 Conclusion**

Preferential trade agreements have been growing at a pace that has already resulted in PTAs outnumbering WTO members and shows no signs of slowing down. We compile an extensive database of PTAs in force, which shows that by the middle of 2004, 229 PTAs were in effect between 174 countries with an average country having signed at least five agreements. It would thus appear that preferential trade should account for a large share of global trade, but instead,

> 44Recall that _Y m_ and _Y x_ are natural logarithms of importer and exporter incomes, respectively. Therefore, the term ( _Y m_ + _Y x_ ) is multiplicative in levels.

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# **Table 4: Partial credit guarantee schemes across countries**

|**Country**|**Scheme**|**Purpose(s)**|**Launch**<br>**Date**|**Total**<br>**Size**|**Max. Loan**|**Lending**<br>**Interest Rate**|**Max.**<br>**Loan**<br>**Term**<br>**Years**|**Amount**<br>**Financed**|**# of Loans**<br>**Financed**|
|---|---|---|---|---|---|---|---|---|---|
|**Nigeria**<br>(as of end-<br>2011)|**SMECGS**|•<br>80% loan<br>guarantee<br>scheme for SME<br>lendingbybanks|2010|N 200<br>BN|•<br>N 100 MN|Prime|7|N 0.89 BN<br>(0.31 pending)|18<br>(6 pending)|
||**ACGS**|•<br>75% guarantee<br>for agricultural<br>loans<br>•<br>87.5% guarantee<br>for self-help<br>grouploans|1977|N 3 BN|•<br>10 MN<br>cooperatives<br>•<br>1 MN<br>individuals<br>•<br>0.02 MN<br>unsecured|Market<br>(40% interest<br>drawback for<br>performing<br>borrowers)|[?]|N 52 BN|755,000|
||**NIRSAL**(not<br>operational)|•<br>Guarantees for<br>agricultural value<br>chain financing|2012|N 75<br>BN|•<br>Pending|Pending|Pending|N/A|N/A|
|**South**<br>**Africa**<br>(2010)|**Khula**<br>**Indemnity**<br>**Scheme**|•<br>90% loan<br>guarantee<br>scheme for SME<br>lendingbybanks|N/A|N/A|•<br>R 3 MN|N/A|N/A|R 20 MN in<br>2010|50 in 2010|
|**Rwanda**<br>(as of June<br>2012)|**SME**<br>**guarantee**<br>**fund**|•<br>50% loan<br>guarantee<br>scheme for SME<br>lendingbybanks|2010|RWF<br>3.7 BN|•<br>RWF 500 MN|Not<br>prescribed,<br>1% guarantee<br>commission|10|RWF 3 BN in<br>June 2012|14|
||**Agriculture**<br>**Guarantee**<br>**Fund**|•<br>50% guarantee<br>for agricultural<br>loans|2011|RWF<br>911<br>MN|•<br>RWF 500 MN|Not<br>prescribed,<br>1% fee|10|More than<br>RWF 8 BN|169|
|**Tanzania**|**SME Credit**<br>**Guarantee**<br>**Scheme**<br>**(SME CGS)**<br>(suspended)|•<br>50% loan<br>guarantee<br>scheme for SME<br>lending by banks|2005,<br>suspended<br>since 2008|TSh<br>6.5 BN|•<br>TSh 500 MN|Not<br>prescribed,<br>1% guarantee<br>fee|5|TSh 3.1 BN|48|

**S** ource: SME finance surveys. Amounts are in local currency.

16
19 If pooling is not accepted then the equality of slopes across countries does not hold. Put another way, the comnmnon slope as the mean estimate of individual slopes is no longer a good statistic as it is affected by outliers.

In our view, either one tests formally for pooling or if possible, one starts with a random coefficient model. The latter can be presented as follows: This model exploits cross-country properties and time series properties.

Bias resulting from improper pooling can blur our understanding of how key agricultural factors affect agricultural supply.

# 2.5.2. Comparability of variables across countries

The second set of issues is how to capture variables for international comparisons. Aggregate output and prices are the most troublesome variables for international comparison. Below, we summarize how aggregate output and prices can be made internationally comparable first and then discuss prices.

# 2.5.2.1. Output comparability

In a single country context, the aggregate output is obtained as follows'<sup>2</sup> where i stands for commodity, j is the country under study, _Py_ is the price of commodity i received by producers expressed in the local currency of countryj, qq is the quantity of commodity i produced in country j and _Vj_ is the aggregate value of commodities in country j. The use of prices in definition (13) is justified as the quantities of different commodities are not strictly additive.

"<sup>1</sup> Note that the presence of country dummies in the wvithin regressions (OLS wvith country specific variables) is not necessarily an avenue for explaining varying slope estimates. The real issue is whether some panel members (countries, regions, etc) behave like outliers. 12This part is closely based on Rao et al. (1991).
Figure 8. Costs of crime, power outage, security, and bribe by regions The costs of power outage, security, crime, and bribes do not affect homogeneously firms across Nigeria and are negatively associated with investments (Figure 9). These costs are higher for firms in the East and South South regions where the weighted average investment is lower. A regression for determinants of investment estimated for a panel of firms verifies the negative impact of investment climate variables, as well as the positive association of investment and firm size and availability of credit. More importantly, the regional fixed effects reveal the significant differences, with the northeast and the southeast having the largest negative effect.

Figure 9. Investment levels and the costs of an adverse business climate by region* <!-- Start of picture text --> NW<br>NC<br>SW<br>SE<br>SS<br>NE<br>30 40 50 60 70<br>Costs<br>Costs included costs of power outage, insecurity, crime and bribe as a percentage of sales<br>Source: Authors' calculation based on Enterprise Surveys, panel firms 2014<br>12<br>11<br>10<br>9<br>Investment (Log)<br>8<br>7<br><!-- End of picture text --> Source: Authors’ calculations based on panel data from the 2014 World Bank Enterprise Surveys. Note: “Costs” include losses due to power outages, insecurity, crime, and bribery as a percentage of firm sales. NC=North Central; NE=North East; NW=North West; SE=South East; SS=South South; SW=South West.

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**Figure 1 Marginal impacts of tariff reductions on expenditure and tariff revenues.**

<!-- Start of picture text -->
-Δ Exp,<br>-Δ Tariff Rev.<br>x 1<br>x<br>x<br>a<br>x 0<br>0  t 0  Tariff<br>x+t.e<br>pp<br>b<br><!-- End of picture text -->

The tariff revenue effect reduces the decline in tariff revenues for any positive tariff, leaving a net gain from allowing for substitution of area abx1 in the case of full liberalization. Note that this net gain from incorporating the impact of quantity responses on economic welfare is front-end-loaded, generating larger measured gains for initial liberalization than for the same tariff reductions in the later stages of reform, where tariffs are relatively low. The increase in the weight in the expenditure index does not affect the measured welfare benefits within the group because it raises both required expenditure and tariff revenues to the same degree. However, it raises the measured level of the tariff aggregator for this sector at the composite good level, and hence the measured welfare impact of reform. By contrast with the tariff-revenue effect, the weighting effect is likely to be much more important in the later stages of the reform, when the volume of imports has increased.

# **3. Implementation**

We use a two-tier strategy to implement this approach in the World Bank‘s LINKAGE global computable general equilibrium model (van der Mensbrugghe 2005) that has been widely used for analysis of major policy reforms. Our first step was to modify the structure of the model to 10
The climate variables are mostly significant ( χ 2 **>4.0)** . The coefficients on the quadratic terms tend to be positive when significant which implies the response function between the probability of each species being chosen and climate is U-shaped. This is especially true for precipitation. Soils can affect which species is chosen as some soils (such as Acrisols) lead to more productive grazing lands whereas others (Luvisols and Arenesols) are less productive. Households with computers are more likely to pick chickens rather than grazing animals. Female farmers are more likely to pick dairy and sheep. Finally, dairy, sheep, and pigs are more likely in the Andean region.

Figure 1 graphs the relationship between the probability of choosing a species and annual temperature. Note that the mean temperature in Latin America is 18 ° C. The probability of choosing beef cattle and chickens decline as temperatures rise above 18 ° C. By contrast, the probability of choosing dairy and sheep increases. With pigs, the estimated probability first rises and then declines. The graph clearly reveals that the choice of animals in Latin America is temperature sensitive.

Figure 2 displays the estimated relationship between the probability of choosing an animal and annual precipitation. The mean annual precipitation in Latin America is 118 mm/month. The probability of choosing beef cattle declines precipitously as precipitation increases above the mean. By contrast, more rain leads to more dairy cattle. The other species exhibit a U-shaped pattern where they first decline and then increase.

# **5. Climate scenarios**

In this section, we simulate the consequences of climate change using the parameter estimates in the previous section. We examine a set of climate change scenarios predicted by AOGCMs. The climate scenarios reflect the A1 SRES scenarios from the following three 7
left panel of Figure 5. Considering the number of publications, we see no significant effect on the likelihood that the research team has published at least 10 articles in total over this time.<sup>11</sup> We then ask survey respondents to list their five most major publications produced in the last five years from any project, and search for these in Google Scholar. We construct two proxies for overall research quality: the number of these five top publications which were in English, and the number of citations their work has according to Google Scholar. 57 percent of the non-funded projects have produced no publications in English, and 63 percent have no citations. This compares to 44 percent of the funded projects having no publications in English, and 41 percent having no citations. We see positive impacts of funding on the likelihood of producing research that meets either measure of quality at the discontinuity when we use the full range or local mean, but the impacts are not statistically significant. Since these publications are relatively recent, the number of citations is currently low on average (with a mean of 13 and median of 9, conditional on having any citations), but with high variability (standard deviation of 15). The center panel of Figure 5 shows the variation in citations at each score level, which makes detecting any impact difficult.

The next rows of Table 6 examine different types of innovative activities. Survey participants were asked if they had done any of six different types of innovation in the past five years, whether related to the In-Tech project or not. The most common type of innovation for funded projects was to develop a new product prototype, which 84 percent had done. The next most common forms of innovation were developing a new product (78%), and developing a new process (78%), while developing a new industrial design was the least common form (41%). In-Tech funding has small, and not statistically significant, impacts on all of these measures, suggesting that it did not lead to an overall increase in these types of innovative activities.

The overall summary index of research and innovation shows a 0.13 to 0.31 standard deviation increase in research and innovation. This impact is only statistically significant when we use the local mean specification within the optimal bandwidth, and not using a local linear specification. The bottom right panel of Figure 5 shows why this variation in treatment effect arises – the research

> 11 We use 10 or more publications, since the distribution of reported number of publications in the last 5 years is heavily skewed, with a 25<sup>th</sup> percentile of 3, median of 12, mean of 41, and 90<sup>th</sup> percentile of 100. The treatment effect is also insignificant for the number of publications, with an extremely wide confidence interval of (-32.8, 32.0) as a result.

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most being neutral or demand lagging. Supply leading causality was found in more developed Eastern provinces.

In their paper Chang, Lee and Chang (2013) include two tables, the first showing the studies that point to a demand following view and those that point to a supply leading view. Most of the demand following studies used variants of OLS and Granger causality and were carried out before 2004. All the supply leading studies occurred after 2000 and from Arena (2008) onwards most were able to take advantage of large (but still somewhat flat) panels and modern applied statistics methodologies. There is likely to be a growing convergence between the theory showing the importance of the insurance sector and empirical evidence. However it is also clear that country specific conditions can influence the ability of the insurance sector to contribute to economic growth.

The mechanisms underlying the banking/ insurance sector linkage, such as credit risk transfer to insurers, group dynamics and cross holdings of assets, have been noted by policymakers concerned about financial sector contagion (Trichet, 2005). However there is virtually no literature on the impact of shocks arising from insurer failure on economic growth. Anecdotal evidence from the few cases that have been documented (e.g. Jamaica in the later 1990s, the Southern and Eastern Caribbean following the 2007/ 8 liquidity crisis, AIG following the same crisis) show that that these events can have a long term dampening effect on growth. In each of these cases poor governance and risk management, combined with links to banking systems contributed to the systemic aspect of the failure: more work needs to be done to understand and cauterize these potential leakages into the broader economy. In the meantime regulatory frameworks have evolved, particularly since the 2007/9 financial crisis, under G20 and Financial Stability Board oversight (FSB, 2013): the 2011 IAIS<sup>15</sup> Insurance Sector Assessment Methodology, which is heavily influenced by the recent financial crisis, includes ICP 23, Group Supervision, which requires cooperation between the various financial sector supervisors in a country in identifying risk concentrations and a much strengthened Governance ICP. Nine international insurance groups are now classified as being systemically important and are subject to enhanced supervision.

# **Insurance and the poor – reducing poverty**

## **Thesis and theory**

Clarke and Dercon (2009) point out that the poor are subject to both liquidity and risk constraints in raising productivity and income/ consumption (Table 2). However the traditional view of poverty has assumed a world without risk – an asset focus - and that in analogy to standard growth theory ‘there may not be any convergence in the asset accumulation process, but divergence. Left to their own resources and efforts there may be a limit to the amount [the poor] can accumulate, leaving them in a perpetual cycle of poverty’.

> 15 International Association of Insurance Supervisors

12
While the estimates in these three regressions are interesting, the links between policy and performance outcomes are weaker than they are for the model of time in customs. We confine the rest of our analysis to variables that track time outcomes, and apply the results from the discrete time transition model. Our governing assumption will be that the economic value of the TFA is reasonably well summarized by the value of estimated reductions in the expected time to trade.

# **6 Implications**

The goals of this study are threefold (in diminishing value of importance, and confidence): First, identify the trade facilitation policy bundles that can be linked, statistically, to better performance in customs. This exercise also requires an evaluation of the quantitative implications of the coefficients (i.e. what is the implied reduction in reported days in customs associated with a move to best practice). Second, apply external estimates of the value of time-in-trade to calculate tariff-reduction equivalents of the policy changes. Third, calculate an estimate of the monetary value of time savings our empirical model attributes to such reforms.

Having already accomplished the first objective, we proceed to make a tentative effort to accomplish the second and third. In doing so, we must take a stronger stand on the merits of the TAB data than was heretofore necessary. So far, the data need not be understood to measure anything but private-sector _perceptions_ of the time that goods spend in import and export customs. Perceptions may or may not reflect actual time in customs for a representative shipment.

To go a bit further we rely on an assumption that the reported days in customs are indicative of the actual number of days in customs. We also take an estimate from the literature of the tariff equivalent value of a day in customs, assuming the common iceberg form of trade costs. Using trading firms’ freight mode choices in US imports, Hummels and Schaur (2013) estimate that an additional day in transit is approximately equal to an _ad valorem_ tariff equivalent trade cost of between 0.6 percent and 2.1 percent. We take these as the best available evidence on the value of time in trade. Since most of the reforms we consider are in developing countries, where firms and consumers may not be willing to pay as much for time reductions, we take the lower figure as better for our purposes. We use this figure, and our estimate of the reduction in expected days in customs that accompanies a move to best practice in all policies to calculate country-specific estimates of the tariff equivalent reduction implied by the TFA. We calculate that the average tariff-equivalent 31
# **Appendix S6. Results for Various Macroeconomic Indicators**

<!-- Start of picture text --> Figure S6.1: Baseline Results for Trade Balance and Inflation<br>Panel A: The Effect of Exchange Rate on<br>Panel B: The Effect of Tariffs on Inflation (%)<br>Trade Balance-to-GDP (ppt)<br>0.20 10<br>0.00 8<br>-0.20<br>6<br>-0.40<br>4<br>-0.60<br>-0.80 2<br>-1.00 0<br>-1.20<br>-2<br>-1.40<br>-1 0 1 2 3 4 5<br>-1 0 1 2 3 4 5<br><!-- End of picture text --> _Source:_ Authors’ estimations. _Note:_ The solid line in Panel A indicates the response of trade balance-to-GDP to a one standard deviation increase in (one-year lagged) change in log real exchange rate (0.12). The solid line in Panel B indicates the response of inflation to a one standard deviation increase in tariff. The dotted lines in both panels correspond to 90% confidence bands. The x- axis denotes time. t=0 is the year of the change. The estimates are based on equation (1).

<!-- Start of picture text --> Figure S6.2: Selected Asymmetric Results for Consumption and Investment<br>The Asymmetric Effect on Consumption<br>Panel A: Tariff Increases Panel B: Tariff Decreases<br>0.5 1<br>0<br>0.5<br>-0.5<br>-1 0<br>-1.5<br>-0.5<br>-2<br>-2.5 -1<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>The Asymmetric Effect on Investment<br>Panel D: Tariff Decreases<br>Panel C: Tariff Increases<br>2 3<br>1<br>2<br>0<br>-1 1<br>-2<br>-3 0<br>-4<br>-1<br>-5<br>-6 -2<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br><!-- End of picture text --> _Source:_ Authors’ estimations. _Note:_ The solid black line indicates the response of consumption (investment) to a one standard deviation increase in tariff; the dotted lines correspond to 90% confidence bands; estimates are based on equation (2). Dashed red lines indicate the response of consumption (investment) to a one standard deviation increase in tariff in the baseline; estimates based on equation (1). The x-axis denotes time. t=0 is the year of the tariff change.

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# **TABLE A6. Employment by Worker Type.  ICT intensity.**

||Ma|nagers|Skilled|Workers|Unskille|d Workers|
|---|---|---|---|---|---|---|
||FE|FE‐2SLS|FE|FE‐2SLS|FE|FE‐2SLS|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ICT|0.41|14.3|7.86**|25.2|6.11**|‐12.2|
||(2.07)|(16.9)|(3.27)|(25.5)|(2.69)|(26.0)|
|ICT|‐1.57|‐45.5**|4.85|‐11.5|2.37|‐48.0|
||(1.83)|(21.4)|(3.42)|(23.8)|(1.65)|(29.3)|
|ICT * Growth|4.40|60.6***|6.49|52.4**|8.09|90.6***|
||(4.17)|(17.6)|(6.05)|(20.9)|(5.30)|(32.1)|
|Observations|2,431|2,431|2,419|2,419|3,462|3,462|

Analogous to Table 16. Regressor: average firm investment in ICT over firm sales during sample period.

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