rafmacalaba/datause-encoder-data huggingface dataset:
metal gates used to block access to a route and similarly to the other gates usually have varying opening times. For each barrier observed in a specific year, we have information on its geographical coordinates and type. Figure 2 shows a map of the West Bank including the checkpoints (both permanent and partial) along with the localities’ centers in 2007. It is clear from the figure that these barriers were spread all over the West Bank territory thus affecting virtually all movements within the region.

ARIJ also collected data on the length of the wall built for each West Bank village crossed by the path of the wall for the years 2002, 2004, 2006, 2008 and 2010. In cases in which a village contains more than one locality, we assign a quota of the village’s length of the wall to each locality on the basis of the share of the village’s areas covered by that locality. We use linear interpolation to obtain the data on the wall length for the missing years.

Data on the number of Palestinians fatalities in each locality since 2000 are taken from B'Tselem, The Israeli Information Center for Human Rights in the Occupied Territories.<sup>17</sup> Widely thought to be accurate and reliable, the data published by B'Tselem record in detail every fatality on both sides during the Second Intifada. Finally the data on the size of localities’ labor markets before our period of analysis come from the 1997 Palestinian population census administered by the PCBS. Summary statistics for key variables are provided in Table 1.

# **5. Estimation strategy**

The identification of the effects of the barriers on the West Bank labor market relies on time varying measures of the ‘closeness’ of the physical barriers from the individual’s locality of residence, which proxies for the intensity of the restrictions imposed by the barriers. The main measure we use is constructed as the count of the barriers within 30 minutes of travel time (by existing roads) from the locality’s centroid, weighted by the inverse of their travel time.<sup>18</sup> This weight captures the idea that the more distant a checkpoint is (taking into account the road system) the less it will affect mobility to and from a certain locality. More formally:

> 17 Available at: http://www.btselem.org.

> 18 We use the network analysis algorithm in ArcGIS in order to compute the distance and the travel time between the locality’s centroid and each barrier. The computation is based on georeferenced data on the existing road network in the West Bank in 2001 (which has not changed over the period of analysis). The model takes into account also the ruggedness of the terrain to compute the speed over the road network, which is necessary to calculate the travel time.

15
From a theoretical standpoint, the issue is complex; some types of clauses would necessarily be associated with lower trade volumes, such as those that reduce illicit trade. These clauses might limit imports from factories that underpay their workers, take advantage of discriminatory wage practices, or do not comply with domestic and international standards. Other clauses, such as those that lower trade costs by harmonizing standards or by promoting good institutions, would be associated with more trade. These “good institutions” include Freedom of Association, Collective Bargaining, and efforts to end forced and child labor.

This paper seeks to improve on previous work, which has been limited by the lack of relevant coded data. It uses the new Handbook of Deep Trade Agreements and recent advances in gravity model estimation techniques to generate estimates of the relationship between very detailed labor clauses and bilateral trade flows. The results of this paper are robust not only when limited to manufacturing trade and trade excluding the U.S., but also change little when tariff margins and other clauses are factored in. Different results do emerge, however, when trade is limited to lower-income exporters. In this case, enforcement and clauses that are related to nonderogation of domestic labor law are associated with less trade. This relationship is a promising sign of a reduction in illicit trade, as factories that do not comply with international labor standards are excluded. This finding also supports the existing body of work that finds that labor clauses in trade agreements are associated with improvements in working conditions.

pg. 17
**<u>Table 8: Impact of proximity to large farms on yields of main crops</u>**

||**Maize**|**Cassava**|**Sorghum**|**Peanuts**|**Rice**|**Sugarcane**|
|---|---|---|---|---|---|---|
||||<br>**Pane**|**l A**|||
|Any new same-crop large farm <= 25 km|0.034|0.082|0.306|-0.050|0.197|-4.083|
||(0.072)|(0.150)|(0.445)|(0.126)|(0.402)|(4.791)|
|Any new same-crop large farm 25-50 km|0.023|-0.029|-0.192|-0.142|0.532*||
||(0.068)|(0.135)|(0.510)|(0.110)|(0.297)||
|Any new same-crop large farm 50-100 km|0.056|0.165|0.167|-0.161|-0.046|2.715|
||(0.065)|(0.131)|(0.354)|(0.111)|(0.210)|(2.469)|
|Observations|9,483|6,479|2,767|4,879|1,573|169|
|R-squared|0.276|0.262|0.280|0.345|0.473|0.934|
||||**Pane**<br>|**l B**<br>|||
|New same crop large farm area <= 25 km|-0.002|0.084|-0.000|-0.030|-0.081|-0.583|
||(0.026)|(0.084)|(0.082)|(0.093)|(0.122)|(0.771)|
|New same crop large farm area 25-50 km|0.007|0.068|0.002|-0.069|0.142||
||(0.023)|(0.067)|(0.123)|(0.074)|(0.091)||
|New same crop large farm area 50-100 km|0.021|0.007|0.092|-0.050|0.024|0.336|
||(0.016)|(0.052)|(0.096)|(0.049)|(0.041)|(0.306)|
|Observations|9,483|6,479|2,767|4,879|1,573|169|
|R-squared|0.276|0.262|0.280|0.344|0.473|0.934|

Note: Dependent variable is ln of output (kg/ha) for maize, cassava, sorghum, peanut and rice and ln of output value (MT/ha) for sugarcane. New large farms growing the same crop are included if they cultivate more than 1 ha of the crop in question. Control variables include ln farm size, quadratic ln farm size, indicator variables for crop-specific use of fertilizer, pesticide and irrigation, hiring of temporary or permanent workers, use of animal traction, crop rotation, inter-cropping, and line sowing at household level, a rainfall shock, household composition (no. of children, adults and old people), headship, head’s age, and highest education in the household and a time dummy. EA fixed effects are included throughout and standard errors are clustered by EA. *** p<0.01, ** p<0.05, * p<0.1.

23
**<u>Table 1: Balance between control and treatment groups</u>**

||Con|trol group|Treatm|ent group|Mean|
|---|---|---|---|---|---|
||No. obs.|Mean|No. obs.|Mean|difference|
|Household head male (0/1)|558|0.738|977|0.713|0.025|
|Age of household head|558|44.26|977|44.663|-0.403|
|Household head literate (0/1)|558|0.76|977|0.738|0.022|
|Household head married (0/1)|564|0.739|982|0.732|0.007|
|Household head Christian (0/1)|558|0.81|977|0.801|0.009|
|Household head main occupation (farming) (0/1)|564|0.761|982|0.746|0.014|
|Household engaged in nonfarming (0/1)|564|0.124|982|0.119|0.005|
|Area: self-reported, post planting (acre)|564|0.642|982|0.610|0.032|
|Area: GPS (acre)|564|0.558|982|0.507|0.051<sup>*</sup>|
|Area: self-reported, post planting (log, acre)|564|-0.755|982|-0.796|0.041|
|Area: GPS (log, acre)|564|-1.042|982|-1.119|0.077|
|Farm size (acre)|564|1.474|982|1.398|0.076|
|Plot acquired through local admin or inherited|564|0.243|982|0.22|0.023|
|Plot acquired through rental or purchase|564|0.073|982|0.116|-0.043<sup>***</sup>|
|Plot under customary tenure system|564|0.832|982|0.833|-0.001|
|Pure stand cropping (0/1)|564|0.426|982|0.412|0.013|
|Soil type sandy or clay (0/1)|564|0.555|982|0.601|-0.046<sup>*</sup>|
|Soil color red or brown (0/1)|564|0.381|982|0.376|0.005|
|Slope of plot is flat (0/1)|564|0.644|982|0.678|-0.035|
|Soil quality good (0/1)|564|0.495|982|0.529|-0.034|
|Soil texture fine or very fine (0/1)|564|0.413|982|0.395|0.018|
|Soil texture coarse or very coarse (0/1)|564|0.138|982|0.153|-0.014|

**Notes:** This table compares characteristics of control and treatment plots using information collected before the treatment. The last column provides mean differences across treatment and control group characteristics.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

Table 2 reports the distribution of plot sizes and measurement error in the post-planting and post-harvest interviews. We present these discrepancies across quartiles of "true" plot size (based on GPS measurement) and report differences in terms of biases relative to the true size. That is, for each quartile and survey round we compute, (i) R R R R =<sup><u>(XX�S</u></sup> for the<sup><u>P−XX�T)∗100</u></sup> post-planting, pre-treatment round and (ii) R R R R =<sup><u>(XX�S</u></sup> bbR bb: P for the post-harvest, XX<sup>�</sup> TT<sup><u>P−XX�T)∗100</u></sup> post-treatment round.<sup>10</sup> Although the overall relative bias (at the sample mean) in self-reported bbR bb: P XX<sup>�</sup> TT plot size in the post-planting survey is just 18 percent, this disguises a strong, systematic pattern across the plot size distribution. Farmers overestimate the size of the smallest quintile of plots by

> 10 S and S stand for average self-reported plot size in the post-planting and post-harvest interviews, respectively. stands for average true plot size (measured using GPS device). XX� XX� TT XX<sup>�</sup> 8
with strong e-commerce legislation are much more likely to promote sharing of both public and private intent data. Of some concern is the finding that governments with a well-established regulatory framework to facilitate e-commerce/transactions, appear not to be adopting adequate safeguards to protect personal data. Take Indonesia as an example, where Law No. 11 of 2008 on Electronic Information and Transactions (and amendments to the law) establishes an enabling environment for e-commerce and e-transactions by granting the equivalence of paper-based and electronic communications, recognizing e-signatures, and adopting the technological neutrality principle. But there is no general law protecting data, despite scattered, sector-specific regulations.

**Table 4. Regulatory good practices, by country income across dimensions (in percentages)**

|||_Safeguards_<br>|<br>||_Enab_<br>|_lers_<br>||
|---|---|---|---|---|---|---|---|
|**Country income**<br>**level**|**Personal**<br>**data**|**Non-personal**<br>**data**|**Cybersecurity**<br>**and**<br>**Cybercrime**|**Cross**<br>**border data**<br>**flows**|**E-commerce**<br>**/transactions**|**Public**<br>**intent data**|**Private**<br>**intent data**|
|High income|59|43|73|42|86|69|30|
|Upper middle income|46|29|57|30|74|62|20|
|Lower middle<br>|43|38|55|24|72|44|15|
|income||||||||
|Low income|31|47|39|13|59|28|3|
|**Total**|**44**|**38**|**56**|**27**|**73**|**50**|**17**|

_Source:_ Author’s calculation based on results from the diagnostic.

The high correlation between personal data and cross-border data flows merits further scrutiny. Countries with sufficient regulatory safeguards on personal data may not only engender trust for domestic market but also enhance its competitiveness in international digital trade, thus further incentivizing governments in adopting regulatory practices to facilitate cross-border data transactions. The correlation between nonpersonal data protection and other dimensions is at a rather low level, even falling into negative territory, suggesting lack of attention to this issue.

**<u>Table 5. Enablers and safeguards: Correlations among regulatory scores across dimensions</u>**

||E-commerce/<br>transactions|Public<br>intent data<br>enablers|Private<br>intent data<br>enablers|Personal<br>data<br>protection|Non-<br>personal<br>data<br>protection|Cybersecurity<br>and<br>cybercrime|Cross-<br>border data<br>flows/<br>transactions|
|---|---|---|---|---|---|---|---|
|E-commerce/transactions|1|||||||
|Public intent data|0.2979*|1||||||
|Private intent data|0.3496*|0.3998*|1|||||
|Personal data|0.0875|0.1736|0.4589*|1||||
|Nonpersonal data|-0.1046|-0.2214*|-0.0853|0.1249|1|||
|Cybersecurity and<br>cybercrime|0.2924*|0.4031*|0.6232*|0.5423*|-0.1744|1||
|Cross-border data<br>transactions/flows|0.1329|0.2458*|0.5573*|0.8216*|0.0348|0.6087*|1|

_Source:_ Author’s calculation based on results from the diagnostic. _Note:_ * indicates that correlation coefficients are significant at the 5 percent level or better.

13
**_Energy demand models for policy_** **_<u>formulation</u>_**

Bhattacharyya (1997) further noted that “The application of economic theories that presuppose the existence of monetized markets and are concerned only with agents involved in such markets faces serious conceptual problems in dealing with economies that do not conform to such stereotypes. Serious conceptual difficulties and incompatibilities arise in the valuation of goods for which no tangible payment is made. For energy goods, the problem is further complicated by the fact that these are not goods for direct consumption but intended to derive certain end-use, which could be satisfied by a number of substitutes. Evaluating the contribution of these energies in monetary terms when some are acquired through non-money activities still rests problematic. Because non-money activities often occupy a far greater share than the monetized part in the rural energy of a developing economy, it is thus imperative that any and every economic indicator for these sectors and the whole economy should take into account both the monetized and the non-monetized sectors, as well as their mutual interaction”.

While the theory is capable of capturing non-price variables in principle, the implementation in actual models would show how far this is captured. Similarly, the reliance on consumption data implies that only the satisfied demand is captured in the energy statistics. Using consumption and demand interchangeably implies that the nonmanifested demand is not taken into consideration in practice. This again can introduce a bias in the analysis by providing an inaccurate picture in developing countries. Hence, the prescriptions based on standard economic theories can be misleading.

# **_3.2 Energy demand forecasting techniques_**

A review of the demand forecasting approaches suggests existence of a large variety of techniques used by different sets of users. Werbos (1990) presents the distinction between modeling approaches very succinctly through an example. Let us assume that we want to forecast population in the following year based on present year information. We write the following relationship: 24
Table 4: Conflict

||ACD|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|ACD Confict|0.307<sup>_∗∗∗_</sup><br>(0.06)|||
|ACD Post-Confict|0.057<br>(0.057)|||
|WGI Instability||0.106<sup>_∗∗_</sup><br>(0.045)||
|WGI Instability>Median|||0.15<sup>_∗∗_</sup><br>(0.059)|
|Log Ruggedness|0.065<sup>_∗∗_</sup>|0.098<sup>_∗∗∗_</sup>|0.099<sup>_∗∗∗_</sup>|
||(0.027)|(0.033)|(0.032)|
|Log of Rainfall|-.107<sup>_∗_</sup>|-.105|-.107|
||(0.063)|(0.068)|(0.069)|
|Log dist to coast|-.016|-.040|-.044|
||(0.039)|(0.043)|(0.042)|
|Population Density|0.11<sup>_∗∗∗_</sup><br>(0.016)|0.088<sup>_∗∗∗_</sup><br>(0.016)|0.082<sup>_∗∗∗_</sup><br>(0.015)|
|Log of Surface Area|0.047<sup>_∗∗_</sup>|0.062<sup>_∗∗∗_</sup>|0.072<sup>_∗∗∗_</sup>|
||(0.019)|(0.02)|(0.018)|
|Length>than 50km|-.128<sup>_∗∗∗_</sup>|-.107<sup>_∗∗_</sup>|-.113<sup>_∗∗∗_</sup>|
||(0.041)|(0.044)|(0.043)|
|Estimate|-.011|-.016|-.017|
||(0.055)|(0.058)|(0.057)|
|Contract|-.062|-.050|-.064|
||(0.074)|(0.073)|(0.072)|
|Log of GDP pc (1985)|-.024|-.027|-.044|
||(0.043)|(0.044)|(0.042)|
|Foreign frm or JV|0.242<sup>_∗∗_</sup>|0.277<sup>_∗∗_</sup>|0.264<sup>_∗∗_</sup>|
||(0.12)|(0.122)|(0.123)|
|World Bank|0.007|0.028|0.007|
||(0.097)|(0.096)|(0.093)|
|Bilateral Donor|0.191|0.196|0.18|
||(0.138)|(0.141)|(0.138)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.899|0.898|0.898|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

33
**Table 4: Selected Sample Summary Statistics**

|||New<br>mtC|CERS<br>O2e|Stock o<br>mtC|f CERs<br>O2e|Withdrawn<br>CERs|
|---|---|---|---|---|---|---|
|Proet start|Number of|2008-|2008-|2008-|2008-|Share of 2012|
|jc|projects|2012|2020|2012|2020|stock|
|2003 (1 month)|4|9.08|8.92|9.08|8.92|-|
|2004|48|7.91|7.14|16.98|16.05|0.028|
|2005|441|120.70|120.86|137.68|136.92|0.025|
|2006|664|131.93|159.62|269.61|296.55|0.067|
|2007|1165|124.50|171.34|394.11|467.88|0.132|
|2008|1464|111.55|174.55|505.66|642.43|0.139|
|2009(9 months)|845|51.70|99.22|557.36|741.64|0.122|

Source: UNEP Risoe (2009) and authors’ calculations.

17
within 0.5 percent of GDP in Burkina Faso, Cape Verde, and Niger. If benchmark performance is assumed in addition to the 2015 annual average fuel prices, the total number of countries with below zero QFD increases from 13 to 21.

Table 14 presents the impact on countries using oil products for generation of different oil prices, ranging from US$35 to US$70 per barrel. Diesel and HFO prices corresponding to different prices of Brent were estimated by regressing historical fuel prices on Brent crude oil prices.

**Table 14: Fuel cost savings at different prices of Brent (US$/barrel), savings as percent of GDP**

||**$30**|**$35**|**$40**|**$45**|**$52**|**$55**|**$60**|**$65**|**$70**|
|---|---|---|---|---|---|---|---|---|---|
|São Tomé and Príncipe|3.2%|3.0%|2.7%|2.4%|2.0%|1.9%|1.6%|1.4%|1.1%|
|Seychelles|2.9%|2.7%|2.5%|2.3%|2.0%|1.9%|1.7%|1.5%|1.3%|
|Gambia, The|3.0%|2.8%|2.5%|2.3%|1.9%|1.8%|1.5%|1.3%|1.1%|
|Cape Verde|1.9%|1.8%|1.7%|1.6%|1.4%|1.3%|1.2%|1.0%|0.9%|
|Comoros|2.4%|2.2%|2.0%|1.9%|1.6%|1.6%|1.4%|1.2%|1.1%|
|Senegal|2.2%|2.0%|1.9%|1.7%|1.5%|1.4%|1.3%|1.1%|1.0%|
|Mauritius|1.0%|1.0%|0.9%|0.8%|0.7%|0.7%|0.6%|0.5%|0.5%|
|Mauritania|1.0%|0.9%|0.9%|0.8%|0.7%|0.7%|0.6%|0.5%|0.4%|
|Burkina Faso|0.8%|0.8%|0.7%|0.6%|0.5%|0.5%|0.4%|0.4%|0.3%|
|Madagascar|0.6%|0.5%|0.5%|0.4%|0.3%|0.3%|0.2%|0.2%|0.1%|
|Rwanda|0.5%|0.5%|0.4%|0.4%|0.4%|0.3%|0.3%|0.3%|0.2%|
|Togo|0.5%|0.4%|0.4%|0.4%|0.3%|0.3%|0.3%|0.2%|0.2%|
|Mali|0.5%|0.5%|0.4%|0.4%|0.3%|0.3%|0.3%|0.2%|0.2%|
|Cameroon|0.4%|0.4%|0.3%|0.3%|0.2%|0.2%|0.2%|0.1%|0.1%|
|Liberia|0.4%|0.4%|0.4%|0.3%|0.3%|0.3%|0.2%|0.2%|0.2%|
|Guinea|0.3%|0.3%|0.3%|0.3%|0.2%|0.2%|0.2%|0.2%|0.1%|
|Sudan|0.3%|0.3%|0.2%|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|
|Tanzania|0.3%|0.3%|0.2%|0.2%|0.2%|0.2%|0.2%|0.1%|0.1%|
|Burundi|0.2%|0.2%|0.2%|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|
|Sierra Leone|0.2%|0.2%|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|0.1%|
|Niger|0.2%|0.2%|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|0.1%|
|Gabon|0.2%|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|
|Kenya|0.2%|0.2%|0.2%|0.1%|0.1%|0.1%|0.1%|0.0%|0.0%|
|South Africa|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|0.0%|0.0%|0.0%|
|Botswana|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|0.1%|0.0%|0.0%|
|Uganda|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Central African Republic|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Zambia|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Benin|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Ethiopia|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Lesotho|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Congo, Rep.|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|Mozambique|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|

_Source_ : World Bank staff calculations.

# 8.2 Hydrology risk

The reference year being an outlier is a particular concern in countries with a high dependency on hydropower, which are vulnerable to years of drought. In 2015 and 2016, for example, Zambia and Zimbabwe have faced significant declines in hydropower generation from the Kariba dam, due to an ongoing drought.

59
Another factor that can affect the extent to which providers can influence prices is the level of financial literacy of remittance senders. Since we cannot capture this directly, we include a measure of the level of education of migrants in each corridor. In particular, we include the ratio of migrants with a secondary and/or tertiary education over the total number of migrants from the receiving country, residing in the sending country. This variable comes from the OECD Database on Immigrants and Expatriates. We expect this variable to be correlated with financial literacy and, to the degree that financial literacy enables consumers to make better informed choices, costs should be lower. The ratio of secondary and tertiary educated migrants varies from 21 percent for Chinese migrants in Italy to 91 percent for Nigerians residing in the US. Because this variable is only available for 88 out of the 119 corridors for which we have cost data, we do not include it in the baseline regressions, but only show it as an additional variable.

Third, we control for different government policies relating to the exchange rate, the capital account and the regulation of the remittance market. We include a dummy variable for receiving countries with pegged exchanged rates (including cases of no separate legal tender, currency boards or _de-facto_ pegged regimes). Lower exchange rate volatility should reduce costs, by lowering the exchange rate spreads and we, therefore, expect this dummy to be negatively associated with the cost of sending remittances. At the same time, we expect the cost of sending remittances to be higher in countries that impose controls on remittance transactions, since these operate like a tax that is likely to be passed onto recipients. Both the dummy for pegged exchange rate regimes as well as the capital controls dummy come from the IMF Annual Report on Exchange Arrangement and Restrictions. In 39 corridors (close to 33 percent of the sample) there is no exchange rate variability (since the exchange rate is pegged or the economy is fully dollarized) and in 22 corridors (18 percent of the sample) there are controls on gifts from abroad.

Finally, we control for the breadth of regulation of remittance service providers in sending and in receiving countries by creating an index of regulation which can take values from 0 to 5 depending on whether providers must be: (a) registered, (b) licensed, (c) are subject to specific safety and efficiency requirements, (d) need to comply with AML regulations, and/or (e) need to comply with laws and regulations of general applicability. Data to create the indexes 10
26 Figure 25 <!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVCONP<br>0.02<br>0.01<br>0.00<br>-0.01<br> LKSTPUBPC<br>-0.02<br>-0.03<br>-0.04<br>-0.05<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text --> The other two types of government expenditures, namely the subsidies and social security transfers have negligible effects on GDP in the medium term and opposing effects in the long run. Given the small size of this type of expenditure, we will focus here on the effect of social security transfers (see appendix for results of subsidies). Social security transfers have a negative growth effect (Figure 26), primarily because of the associated reduction in the public sector capital (Figure 27). A 5 percent increase in the social security payments is associated with a fall of 3 percent in the public capital stock.

# Figure 26

<!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVSSTP<br>0.03<br>0.02<br>0.01<br>0.00<br> LGDPPC<br>-0.01<br>-0.02<br>-0.03<br>-0.04<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text -->