rafmacalaba/datause-encoder-data huggingface dataset:
services (33.89%), construction (30%), social services (28.38%), trade and transport (23.95%) as well as in food processing (25.93%) and agriculture (13.65%) (Figure 6).

**<mark>Figure 6. Trade barriers Indonesia faces on the RCEP markets, %</mark>**

|T|ariffs||NTMs|
|---|---|---|---|
|Social services||||
|Communication and...||||
|Finance and other business...||||
|Trade and transport||||
|Construction||2015||
|Utilities||||
|Machinery and equipment...<br>Other manufacturing||2030||
|Electronic equipment||||
|Transport equipment||||
|Metals||||
|Chemical, rubber, plastic...||||
|Wearing apparel and leather||||
|Textiles||||
|Food, beverages, tobacco||||
|Natural resources / mining||||
|Other agriculture||||
||0|5<br>10|0<br>20<br>40<br>60<br>80|

As a result of the low initial level of protection and the assumed less ambitious level of commitments for RCEP vis-à-vis the other agreements, the expected reduction of tariffs and NTMs is relatively small. The highest reduction in the trade-weighted import tariff is expected in India (2.9 percentage points) and China (1.6 percentage points) while the average NTM that Indonesia faces on the RCEP markets falls only by 1.64 percentage points. As illustrated in Figure 6, the highest reduction of trade barriers should occur in services sectors (communication, finance and other business services, construction, trade and transport) as well as in food processing, agriculture, wearing apparel and textiles.

Although the reduction of trade barriers is quite low, RCEP agreement enables a better access to a large number of growing markets and therefore some increase in participation in global value chains. Economic integration with 16 countries of the region induces a boost of Indonesian income by almost 10 billion USD, a GDP increase of 0.18% by 2030 compared to the baseline. Reduction of trade barriers leads to higher exports and imports, which expand by over 1.5% relative to the 15
|_Secondary_|30(16%)|30(17%)|0(0%)|
|---|---|---|---|
|_Bothprimary and secondary_|24(13%)|16(9%)|8(57%)|
|_Other_|14(7%)|14(8%)|0(0%)|
|Location||||
|_Urban_|22(11%)|22(12%)|0(0%)|
|_Rural_|74(39%)|64(36%)|10(71%)|
|_Both_|96(50%)|92(52%)|4(29%)|
|Components||||
|_Single_|80(42%)|74(42%)|6(43%)|
|_Multiple_|112(58%)|104(58%)|8(57%)|

# **Table 2: Tests for statistically significant differences in distribution of intervention effect sizes**

|**Access outcomes**||<sup>h</sup> <sup>h</sup>|<sup>h</sup>|
|---|---|---|---|
||Mean|Mean within 50<sup>t</sup>– 75<sup>t</sup>percentile|Mean from 90<sup>t</sup>percentile and above|
||**Girl‐tar**<br>|**geted vs. General interventions**<br>||
|Girl‐targeted|0.048<br>(0.039)|0.037***<br>(0.012)|0.228<br>(0.200)|
|Constant|0.111***|0.106***|0.519***|
|N|212|52|22|
|||**Girls vs. Boys**<br>||
|Girl|0.033*<br>(0.019)|0.031***<br>(0.005)|0.142<br>(0.100)|
|Constant|0.086***|0.079***|0.423***|
|N|400|100|40|
|**Learning outcomes**||||
||Mean|50<sup>th</sup>– 75<sup>th</sup> percentile|90<sup>th</sup> percentile and above|
||**Girl‐tar**|**geted vs. General interventions**||
|Girl‐targeted|‐0.027<br>(0.064)|0.020<br>(0.018)|‐0.404<br>(0.242)|
|Constant|0.184***|0.190***|0.853*|
|N|452|117|47|

27
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA

# **Male landlords discriminate more against gay couples than female landlords**

Male landlords rejected gay couples significantly more often than lesbian couples. As much as 36 percent of male landlords refused gay couples, while only 7 percent refused lesbian couples.<sup>xxx</sup> On the other hand, female landlords rejected gay and lesbian couples to the same extent.<sup>xxxi</sup> Male landlords rejected lesbian couples at similar levels of female landlords (figure 7).

Figure 7: Acceptance and refusal of lesbian and gay couples by female and male landlords <!-- Start of picture text --> 100% 93% 92%<br>90%<br>79%<br>80%<br>70% 64%<br>60%<br>50%<br>36%<br>40%<br>30% 21%<br>20%<br>7% 8%<br>10%<br>0%<br>Gay couples Lesbian couples<br>Accepted (male landlord) Refused (male landlord) Accepted (female landlord) Refused (female landlord)<br><!-- End of picture text --> Database: 86 male landlords—42 observations with gay couples and 44 with lesbian couples; 74 Female landlords—38 observations with gay couples and 36 with lesbian couples.

# **Homophobia was Openly Expressed by Many Landlords**

Analysis of the interview transcripts shows open homophobia by landlords. Once informed that the potential tenants were a gay or a lesbian couple, the landlords reacted in one of two ways.

One group of landlords immediately stated that they were not willing to rent the apartment to a gay or lesbian couple: _Two men, what do you mean a couple, what kind of couple? You mean two men in emotional relationship? Oh no, no! Goodbye._ _Oooooh, well, I have never rented a flat to a male couple, or something like that, they were always a young couple or married couple. I am sorry, but not me, not like that._ _Well I don’t know, I wouldn’t really want to. I have nothing against it, but I don’t want to._ _What can I say, it is a problem. (Could we see the apartment?) No. Bye._ 13
Thorbecke, W., 1997, On stock market returns and monetary policy, _Journal of Finance_ 52, 635654.

Todorov, K., 2020, Quantify the quantitative easing: Impact on bonds and corporate debt issuance, _Journal of Financial Economics_ 135, 340-358.

Veronesi, P., and L. Zingales, 2010, Paulson’s gift, _Journal of Financial Economics_ 97, 339-363.

31
|||||(1.723)|
|---|---|---|---|---|
|**Administration and procedures**||||-0.9973|
|||||(1.365)|
|**Chuquisaca**|0.3503***|0.3273***|0.3411***|0.3331***|
||(0.115)|(0.117)|(0.117)|(0.121)|
|**Cochabamba**|0.1067|0.1136|0.1066|0.1141|
||(0.093)|(0.092)|(0.094)|(0.094)|
|**Oruro**|0.1036|0.1256|0.1224|0.1206|
||(0.102)|(0.103)|(0.103)|(0.103)|
|**Potosí**|0.3239***|0.3096***|0.3300***|0.3192***|
||(0.095)|(0.096)|(0.095)|(0.095)|
|**Tarija**|-0.2877***|-0.3020***|-0.2832***|-0.2883***|
||(0.086)|(0.074)|(0.090)|(0.088)|
|**Santa Cruz**|-0.2919***|-0.2965***|-0.2893***|-0.2816***|
||(0.062)|(0.061)|(0.063)|(0.064)|
|**Beni**|-0.3139***|-0.3130***|-0.3063***|-0.3046***|
||(0.068)|(0.063)|(0.072)|(0.072)|
|**Pando**|-0.3353***|-0.3765***|-0.3251***|-0.3303***|
||(0.064)|(0.034)|(0.074)|(0.071)|
|<br>**Observations**|327|327|327|327|
|**Pseudo R2**|0.223|0.218|0.227|0.235|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

# _<u>Differential estimation</u>_

We further explore the inter-temporal effects of agricultural spending on VAM by controlling for an initial level of spending, A0 (2003 level), and estimating the effect that the inter-temporal change in spending, ΔA (the increase between 2006 and 2007), has on the observed risk of 2007 food insecurity. We call that change ̳incremental spending‖. Hence, estimates in this section inform about the association between such spending increases and vulnerability changes over time. Equation (2‘‘‘) introduces the new probit specification to be modeled: The dependent variable takes the value of 1 when the municipality moved into or remained at a high or very high vulnerability status (that is, VAM = 4 or 5) between 2006 and 2007.

Results show that 2003 spending still has a positive association with future levels of high or very high vulnerability (Table 6). It also shows that increases in agricultural spending between 2006 and 2007 are associated with reductions in vulnerability (Column 1). However, even though that association is statistically significant, it is negligible in size. This conclusion is robust to a non19
Table 2: Estimates of the Eects of DTAs on FDI

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PTA|DTA|INV|DEPTH|
|_PTAij,t_|-0.029|-0.100|-0.162|-0.134|
||(0.072)|(0.102)|(0.101)|(0.100)|
|_DTAij,t_||0.072|-0.089|-0.120|
|||(0.075)|(0.084)|(0.083)|
|_INVij,t_|||0.295|0.582|
||||(0.108)<sup>_∗∗_</sup>|(0.251)<sup>_∗_</sup>|
|_INV_DEPTHij,t_||||-0.010<br>(0.009)|
|_BITij,t_|0.017|0.017|0.023|0.028|
||(0.104)|(0.104)|(0.107)|(0.108)|
|_SANCTij,t_|-0.019|-0.017|-0.012|-0.012|
||(0.049)|(0.048)|(0.048)|(0.048)|
|_WTOij,t_|0.465|0.476|0.469|0.463|
||(0.356)|(0.353)|(0.359)|(0.363)|
|_N_|18,158|18,158|18,158|18,158|

Notes: This table reports estimates of the eects of trade agreements on FDI over the period 1990-2011. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, and directional pair xed eects. The estimates of all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Column (3) isolates the DTAs with investment provisions. Finally, in addition to PTAs, DTAs, and DTAs with investment provisions, column (4) introduces a continuous variable for investment depth. See text for further details.

25
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33
|_________________<br>**Table III.6: Expor**|___________<br>**t starters an**|____________<br>**d non-starte**|_______________<br>**rs three years be**|_____________<br>**fore the start**|_____________|
|---|---|---|---|---|---|
|Country|Year of<br>start|Labor produ<br>of export-sta<br>|ctivity premia<br>rters<br>|No. of<br>observ.|No. of<br>starters|
|_________________|___________|(percent)<br>____________|[p-value]<br>_______________|_____________|_____________|
|Belgium|1999|31.206|[0.051]|457|28|
||2000<br>|-1.438<br>|[0.909]<br>|476<br>|31<br>|
||2001|10.256|[0.289]|568|54|
||2002|16299|[0068]|545|50|
||<br>2003|.<br>15.923|.<br>[0.202]|<br>526|<br>45|
||2004|22.569|[0.078]|511|39|
||2005|12.044|<br>[0.210]|451|23|
|Chile|1993|25.031|[0.001]|2,824|89|
||1994|14.534|[0.020]|2,851|92|
||1995|23.043|[0.014]|2820|65|
||<br>1996|<br>7.226|<br>[0.220]|,<br>2,796|<br>88|
||1997|25.160|[0.012]|2,614|57|
||1998|5.678|[0.528]|2,456|50|
||1999|14.299|<br>[0.234]|<br>2,452|38|
|China|2001|22.624|[0.000]|44,272|1,017|
||2002|29.353|<br>[0.000]|<br>47,268|<br>1,186|
||2003|24.892|[0.000]|47,017|1,183|
||2004|17.308|<br>[0.000]|52,745|3,339|
||2005|13.123|<br>[0.000]|<br>58,019|<br>3,476|
|Colombia|1984|-1.836|[0.779]|3,751|55|
||1985|3859|<br>0553|<br>3843|76|
||<br>1986|.<br>10.021|[.]<br>[0.091]|,<br>3965|<br>86|
||<br>1987|<br>17.606|<br>[0.014]|,<br>4,013|<br>61|
||1988|13.780|[0.090]|4,216|69|
||1989|18.649|<br>[0.011]|<br>4,482|101|
||1990|17.071|[0.002]|4,734|151|
||1991|24.614|[0.000]|4,758|236|
|Denmark|1998|36.895|[0.185]|42|3|
||1999|-6.543|[0.675]|45|4|
||2000|-17.126|[0.189]|47|5|
||<br>2001|<br>10.875|<br>[0.490]|<br>42|<br>1|
||2002|29.875|[0.672]|37|5|
|Republic of Ireland|1994|-11.311|[0.144]|643|42|
||1995|11.612|[0.160]|645|44|
||1996|14.770|[0.053]|677|56|
||1997|22.119|[0.004]|664|53|
||<br>1998|<br>-2.487|<br>[0.758]|<br>665|<br>45|
||1999|6.891|[0.355]|673|59|
||<br>2000|<br>-1.919|<br>[0.754]|<br>666|<br>70|
||2001|6211|[0511]|626|55|
||<br>2002|.<br>-1.181|.<br>[0.901]|<br>633|<br>38|
||2003|10935|<br>0152|667|41|
||<br>2004|.<br>-2.753|[.]<br>[0.711]|<br>641|<br>25|
|Slovenia|1997|24.292|[0.345]|130|15|
||1998|-6.778|<br>[0.703]|160|17|
||1999|-15.022|[0.143]|186|35|
||<br>2000|<br>-0.572|<br>[0.958]|<br>193|<br>31|
||2001|25.961|[0.156]|186|23|
||2002|23.140|[0.052]|202|31|

78
# _Granger Causality from Panel Data—Health in relation to income_

The purpose of the Granger (1969) causality test is to determine whether one selected variable is helpful in forecasting another variable in the context of time series data. According to Sims, causality here refers to a “recursive ordering among things determining something [and it] has also been used to refer to one-sided distributed-lag relationships in which the right-hand-side variables are exogenous” (Hansen 2004, p.278). A time series X is said to Granger-cause Y if lagged values of X provide statistically significant information on future values of Y. For example as Hamilton (1994) shows for a linear function: X fails to Granger-cause Y if Usually we conduct an F test of the lagged values of X. But with panel data, the estimation becomes much more complex. Holtz-Eakin, Newey, and Rosen (1988) proposed an estimation and testing of vector autoregression with panel data by applying instrumental variables to the quasi-differenced autoregressive equations.<sup>43</sup> From this they have devised a crude test in the spirit of Granger-causality using fixed and random effects models where fi is an unobserved individual effects, m is the number of lagged periods.

Using this equation, we test for the direction of causality (from per capita income to longevity and from longevity to per capita income) with different values of m (1-3). The F test results for the joint significance of the lagged independent variables are shown in Table 2. Only the fixed effects model results are presented in Table 2 because the sample represents close to the universe of the available countries and also because it is hard to think of countries as random draws from a large population (Wooldridge 2002).

> 43 Erdil and Yetkiner (2004) classify two types of approaches that deals with this: the first one is pioneered by Holtz-Eakin, Newey, and Rosen (1988) along others which considers estimation and testing vector autoregression (VAR) coefficients in panel data leaving the autoregregressive coefficients and regression coefficients slopes as variable; the second approach proposed by Hurlin and Venet (2001), Hurlin (2004a), Hurlin (2004b), and Hansen and Rand (2006) treats the autoregregressive coefficients and regression coefficients slopes as constant.

21
components are zero. The LM is distributed as chi-squared with one degree of freedom. If the null is rejected, the random effect model is more appropriate.

# **3. Analytical approach**

## **3.1 Data and definition of indicators**

## **_Our data set is based on a panel of 22 countries for a period beginning in 1989 and extending through 2009._**

The outcomes used for our econometric as well as our case study analysis are the four indicators of power sector performance listed in Table 1 that are used to measure the determinants of power sector performance, namely residential access to electricity, energy sold per employee of electricity suppliers as an indicator of labor productivity, average electricity tariff level as an indicator of financial efficiency, and the index of carbon dioxide emissions as an indicator of environmental sustainability.

**Table 1 Selected Power Sector Performance Indicators**

|**Variables**|**Definition**|
|---|---|
|**Ridtil**|**ACCESS**|
|**esena access**<br>(%population)|= number of residential connections divided by the total population<br>**OPERATIONAL EFFICIENCY**<br>|
|**Energy sold per employee**<br>(MWh per employee)<br>|= Amount of generated consumed from the interconnected system divided<br>by the number of employees of the power supply entities for the whole<br>power market (MWh per employee)<br>**FINANCIAL EFFICIENCY**|
|**Tariff level**<br>(US$c per KWh)|= average tariff (US$c per KWh)|
|<br>**Carbon Emission Index**<br>**(CO2 ton/Kwh)**|**ENVIRONMENTAL SUSTAINABILITY**<br>= energy generated from each energy source weighted with the following<br>coefficients of CO2 emission intensity by weight per unit of electricity<br>generated divided by total amount of energy generated (coal = 1.00;<br>petroleum fuels = 0.94; natural gas = 0.63; renewable energy and nuclear<br>power = zero)|

Tables 2 and 3 report the explanatory variables that our model will use to determine the power sector performance. Table 2 includes a comprehensive list of policy variables, such as the degree of vertical unbundling together with the degree of concentration and private sector participation of each segment of the market, as well as the introduction of an autonomous regulator.

8
We find that the incidence of village forest institutions can be explained by their obvious proximity to forests, the presence of an educated person in the village, and a clear dependence on fuelwood. Our results reinforce traditional wisdom that local leadership is critical for collective action institutions to begin and be sustained. They also highlight the importance of fuelwood as an impetus for community forestry.

An interesting policy finding is that a household in MP and UP is more likely to participate in community forestry relative to a household in Orissa. Thus, forest policies in these two states seem to better facilitate CF participation. Another key result is that scarcity is strongly correlated with participation. JFM has, in its second phase, expanded to nondegraded forests as well as degraded forests. While this is generally considered a good idea, our findings suggest that some caution needs to be exercised in rapidly advancing JFM to areas where scarcity may not be such a large issue.

Our most important findings are that fuelwood consumption and participation are linked and participation has a significant positive impact on consumption. Village participation has a positive but insignificant effect. This is a very interesting finding. It suggests that JFM and other forms of community forestry are benefiting participants in the short to medium term. However, these benefits cannot really be called program benefits because the presence of the program at the village level does not seem to matter. We think that participants benefit because they are able to take part in forest related activities and/or because they are better informed about rules and regulations. Our results also do not support any negative impacts of community forestry programs on fuelwood consumption.

Finally, we acknowledge that JFM, the state-initiated community forestry program, has changed in important ways in the last five years. Formerly nationalized NTFP revenues and medium-term timber benefits are now accruing to village communities. The number of JFM communities has also expanded. Thus, our results, which focus on fuelwood consumption in 1998, are indicative of impacts but do not present the full picture. Further, the effect of people’s use of natural resources on forests can change over time, depending on markets and institutions as well as the resilience of forests themselves. In order to probe these dynamic effects, we would need time-series data. Future studies would benefit tremendously from carefully obtained base-line surveys and time-series information.

20
|South Africa|2,071|2,689|0|38,442|1,940|1,885|**47,027**|
|---|---|---|---|---|---|---|---|
|South Sudan|0|0|22|0|0|0|**22**|
|Sudan|1,599|0|1,688|400|0|0|**3,686**|
|Swaziland|60|25|10|0|0|0|**95**|
|Tanzania|565|574|450|0|0|20|**1,609**|
|Togo|68|0|263|0|0|0|**331**|
|Uganda|692|65|136|0|0|0|**892**|
|Zambia|2,228|0|140|0|0|0|**2,368**|
|Zimbabwe|750|0|0|1,220|0|0|**1,970**|
|**Total**|**23,628**|**16,134**|**8,376**|**41,161**|**1,940**|**2,490**|**93,729**|
|% of total capacity|25%|17%|9%|44%|2%|3%|**100%**|
|**Total excluding South**||||||||
|**Africa**|**21,557**|**13,445**|**8,376**|**2,719**|**0**|**605**|**46,702**|
|% of total capacity|46%|29%|18%|6%|0%|1%|**100%**|

_Sources_ : Utility annual reports and other documents _Note:_ Data include grid connected commissioned generation plants including isolated grids. Data do not include decommissioned plants or plants under construction. Other renewable energy includes geothermal, solar, wind, and biomass. Data include regional projects physically located within national boundaries. Dual fuel and cogeneration are recorded under natural gas.

# **Table A3.2: Installed capacity and other parameters – in the reference year or latest year with available data**

||**Total**<br>**capacity**|**Available**<br>**capacity**|**Availability**<br>**factor**|**Peak**<br>**demand**|**Reserve**<br>**margin**|
|---|---|---|---|---|---|
||**MW**|**MW**|**%**|**MW**|**%**|
|Angola_2011|1,586|1,083|68%|1,034.0|5%|
|Benin_2013|169|40|24%|—|—|
|Botswana_2013|893|—|—|572.1|—|
|Burkina Faso_2014|285|—|—|218.0|—|
|Burundi_2014|45|—|—|57.47|—|
|Cameroon_2014|1,249|—|—|713.0|—|
|Cape Verde_2012|138|—|—|68.3|—|
|Central African Republic_2014|26|—|—|—|—|
|Chad_2012|125|—|—|—|—|
|Comoros_2012|27|—|—|22.6|—|
|Congo, Dem. Rep._2013|2,444|1,502|61%|—|—|
|Congo, Rep._2012|600|—|—|265.0|—|
|Côte d'Ivoire_2014|1,632|—|—|1,148.0|—|
|Equatorial Guinea_2011|46|—|—|—|—|
|Eritrea_2011|141|—|—|—|—|
|Ethiopia_2012|2,167|1,881|87%|1,237.0|34%|
|Gabon_2014|507|353|70%|353.2|0%|
|Gambia, The_2014|101|57|57%|—|—|
|Ghana_2013|2,812|2,267|81%|1,943.0|14%|

86
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