rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Impacts of Engagement on Payment

||Made at <br>payment|least one<br>within. . .|Total a<br>paid wi|mount<br>thin. . .|Propor<br>bala<br>paid wi|tion of<br>nce<br>thin. . .|
|---|---|---|---|---|---|---|
||1 month|9 months|1 month|9 months|1 month|9 months|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Coefcient|-0.005|-0.002|0.049|-0.575|0.013|0.008|
||(0.018)|(0.019)|(0.642)|(2.011)|(0.017)|(0.015)|
||r0.776s|r0.928s|r0.939s|r0.775s|r0.436s|r0.616s|
|Observations|2584|2584|2584|2584|2584|2584|
|Control Mean|0.301|0.558|6.958|38.343|0.497|0.484|

*** ** * _Notes_ . Standard errors in parentheses; P-value in brackets. p<0.01, p<0.05, p<0.1. Sample includes all compounds that were included in the randomization procedure to assign the tenant-level engagement intervention. Time periods are based on the end date of the intervention (November 2018) and use data downloaded from the Nairobi Water billing data for December 2018, and September 2019 to estimate impacts 1 and 9 months after the intervention respectively. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated.

Table 5: Impacts of Contract Enforcement on Payment

||Made at <br>payment|least one<br>within. . .|Total a<br>paid wit|mount<br>hin. . .|Propor<br>bala<br>paid wi|tion of<br>nce<br>thin. . .|
|---|---|---|---|---|---|---|
||1 month|9 months|1 month|9 months|1 month|9 months|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Coefcient|0.300***|0.195***|8.783***|9.078**|0.113***|0.078***|
||(0.039)|(0.043)|(1.714)|(4.154)|(0.025)|(0.027)|
||r0.000s|r0.000s|r0.000s|r0.031s|r0.000s|r0.005s|
|Control Mean|0.110|0.334|5.026|24.722|0.268|0.300|
|Observations|1001|1001|1001|1001|1001|1001|
|Number of Clusters|142|142|142|142|142|142|

_Notes_ . Clustered standard errors based on the level of randomization (street) in parentheses and associated p-value in brackets. *** p<0.01, ** p<0.05, * p<0.1. Sample includes all compounds that were included in the randomization procedure to assign the disconnection notices (enforcement) intervention. Time periods are based on the end date of the intervention (November 2018) and use data downloaded from the Nairobi Water billing data for December 2018, and September 2019 to estimate impacts 1 and 9 months after the intervention respectively. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated.

33
incentives and inclination to create ‘good will’ with the less developed riparian. Consequently we expect more cooperative behavior in economically asymmetric dyads.

To reflect the economic and welfare asymmetry discussed above, we use annual countrylevel real GDP and per capita GDP data from Gleditsch (2004). Taking annual GDP and GDP per capita as units of analysis, average values were calculated for each of the dyad countries for the period of time data was available for both parties, such that an identical time period was used for both to ensure comparability. Ratios of these average values were then calculated for each dyad, with the larger value country serving as the numerator and the lesser value the denominator, such that larger values always indicate greater asymmetry. The ratio of total GDPs is a measure of overall power asymmetry, or economic power, while the ratio of the per capita GDP is a measure of wealth asymmetry, or welfare power.

# <u>4.2. Regression Models</u>

In order to evaluate the above hypotheses we ran a series of ordinary least squares regressions on cross-sectional data, using riparian treaty member dyads as the units of observation. The basic regression model used was of the form:

# **_Eq. (1)_ AvgBAR =** **_B0_ +** **_B1_ Allocation +** **_B2_ Institutions +** **_B3_ Controls + e**

Where:

**AvgBAR** is a measure of the mean of the antilogged values of conflictive and cooperative relations between the dyad in question since treaty signature based on the event rankings in the Basins at Risk database.

**_Bi_** = coefficient parameters to be estimated

**Allocation** = types of allocation mechanism

Different specifications of the variable were used in separate regression runs.

- **_Allocation_** - a binary dummy variable indicating whether or not an allocation method was specified in the treaty.

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# **2.2. Current market status**

The installation of solar energy technologies has grown exponentially at the global level over the last decade. For example, as illustrated in Figure 2(a), global installed capacity PV (both grid and off-grid) increased from 1.4 GW in 2000 to approximately 40 GW in 2010 with an average annual growth rate of around 49% (REN21, 2011). Similarly, the installed capacity of CSP more than doubled over the last decade to reach 1,095MW by the end of 2010. Non-electric solar thermal technology increased almost 5 times from 40 GWth in 2000 to 185 GWth in 2010 (see Figure 3). The impetus behind the recent growth of solar technologies is attributed to sustained policy support in countries such as Germany, Italy United States, Japan and China.

# **2.2.1 Solar PV**

By December 2010, global installed capacity for PV had reached around 40 GW<sup>4</sup> of which 85% grid connected and remaining 15% off-grid (REN21, 2010). This market is currently dominated by crystalline silicon-based PV cells, which accounted for more than 80% of the market in 2010. The remainder of the market almost entirely consists of thin film technologies that use cells made by directly depositing a photovoltaic layer on a supporting substrate.

**Figure 2: Total Installed Capacity of PV at the Global Level**

<!-- Start of picture text --> Rest of the<br>World; 8 %<br>40<br>China; 2 %<br>30 Japan; 9 %<br>United States;<br>20<br>6 % Germany;<br>44 %<br>10 Other EU;<br>7 %<br>0 Czech Republic;<br>5 %<br>Italy; 9 %<br>Year Spain ; 10 %<br>2004 2010<br>Total Installed Capacity |GW|<br>2000 2002 2006 2008<br><!-- End of picture text -->

(a) **Trend of global installed capacity**

**(b) Country share in the global installation in 2010** Source: REN21, 2011

> 4 This, however, represents only about 0.8% of the total global installed power generation capacity of about 4,600 GW in 2008.

7
72 protection for value added in a production process while nominal rates show the protection for the final good. Stated differently, ERPs indicate the reduction in returns to productive factors (labor and capital) a foreign supplier must absorb in order to compete with domestic producers in the protected markets.<sup>3 3</sup> For example, if the effective rate of transport protection for a specific good is 10 percent African producers would have to reduce payments to domestic labor and capital that much below what these factors earn in the United States in order to export competitively. While some of the margin probably could be absorbed in the wage bill, the scope for doing so may be more limited than often thought due to relatively low African labor productivity, and to the fact that some African wages may be quite close to subsistence levels. Furthermore, the cost of capital in Africa is almost certainly to be considerably higher than in North America due to uncertainty and political instability. Given the profit maximizing objectives of many international firms even relatively low effective rates of transport protection, or small differences in transport ERPs within and outside the region, may reflect a major disincentive for the location of export oriented production activity in Africa.<sup>3</sup> Table 16 provides estimates of U.S. nominal and effective rates of transport cost protection facing African 33For early formulations and applications of the concept of effective protection see Balassa (1965) or Basevi (1966). Grubel (1971) provides a useful nontechnical discussion of the concept. The effective rate of transport protection in the US against African exports of product j, (Etj) can be calculated from a modification of the normal formula, where rj is the nominal freight rate for African exports of product j to the United States, r1 is the average freight rate on all U.S. imports of product i used in the production of j, and aj' is the observed Leontief coefficient. Following Waters (1970) we utilized the observed production coefficients (and not the theoretically preferable zero transport cost coefficient). As a result, our ERP estimates will be downward biased. The information on production coefficients was drawn from the U.S. Census of Manufactures.

34The influence of even small variations in international transportation costs on the location of global production may not receive adequate recognition. For example, in a Nobel Symposium on the location of international economic activity Assar Lindbeck argued that "given other costs, firms chose between alternative international locations in order to minimize transport costs. These costs, therefore, may become low precisely because they have been highly important for location -- high transport cost locations are avoided if other costs are equal". Similarly, Jagdish Bhagwati observed that 'even if transport costs for any alternative location were a small proportion of total product price, they could still affect location if they varied geographically more than other costs of production". See (Ohlin et. al. 1977, p. 276).
# **Figure 3 Work-Intensity of Older Workers and Elderly in China**

Panel A Average Hours of Work Per Week (Unconditional on Work Status) <!-- Start of picture text --> Urban Male Urban Female<br>80 80<br>60 60<br>40 40<br>20<br>20<br>0<br>0<br>45 55 65 75<br>age 45 55 65 75<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Rural Male Rural Female<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>45 55 65 75 45 55 65 75<br>age age<br>CHNS (1991) CHARLS (2008) CHNS (2009) CHNS (1991) CHARLS (2008) CHNS (2009)<br>Lowess Plot bw=0.3<br>Working Hours<br>Working Hours<br><!-- End of picture text --> Panel B Average Hours of Work Per Week (Conditional on Working) <!-- Start of picture text --> Urban Male Urban Female<br>80 80<br>60 60<br>40 40<br>20<br>20<br>0<br>0<br>45 55 65 75<br>age 45 55 65 75<br>CHNS (1991) CHNS (2009) CHNS (1991) CHNS (2009)<br>Rural Male Rural Female<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>45 55 65 75 45 55 65 75<br>age age<br>CHNS (1991) CHNS (2009) CHNS (1991) CHNS (2009)<br>Lowess Plot bw=0.3<br>Working Hours<br>Working Hours<br><!-- End of picture text --> Notes: Average hours worked per week by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. Panel A presents results unconditional on working which use the 1991 and 2009 waves of the CHNS, and the 2008 CHARLS pilot. Because of the small sample of respondents who were working in urban areas in the CHARLS pilot, results conditional on working (Panel B) are only estimated using the CHNS.

31
other Spoke countries as well as the RoW, rather than the EU. Concerning the coefficient of the change in tariff, ∆ _τj_ , this is always negative and significant. This suggests that the change in preferential tariff reduced Spoke’s imports from the rest of the world compared to the increase in preferential access provided by the EU15.

Table 4: PECS and imports from RoW and EU15

|Dep Var:||Change in log Impor|ts: RoW and EU15||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|weighted IO-RoO_j_|0.044***<br>(0.007)|0.032**<br>(0.013)|||
|IO-RoO_j_|||0.012|0.037**|
||||(0.010)|(0.018)|
|∆_τj_||-1.773***<br>(0.314)||-1.749***<br>(0.314)|
|Observations|130,894|32,117|130,894|32,117|
|R-squared|0.106|0.163|0.105|0.162|
|Importer FE|Yes|Yes|Yes|Yes|
|Exporter FE|Yes|Yes|Yes|Yes|

_Notes_ : OLS estimation. The dependent variable is the difference between changes in log imports of intermediate _j_ from nonparticipating countries between 1995 and 2002, and the corresponding change of log imports from the EU15, ∆ _impj,srow −_ ∆ _impj,sEU_ 15. ∆ _τj_ is the change in preferential tariff (where we use the applied MFN in case of missing preferential tariff information). IO-RoO _j_ and weighted IO-RoO _j_ represent our simple and weighted average measures of the restrictiveness in RoO respectively. Importing countries include: Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland, and Romania.

In sum, diagonal cumulation appears to have also allowed producers to rethink sourcing decisions more in line with comparative advantage reasons. Before the introduction of PECS, producers could cumulate inputs only from their original FTA and the EU15 to obtain preferential access for their final good in the EU15 market. Following PECS, producers could cumulate inputs from a larger set of countries to fulfill rules of origin and obtain preferential access to the EU15. This may have led producers to change the composition in terms of origin of their input basket and still obtain preferential tariffs.

This mechanism seems also confirmed in Table 5, where we consider the impact of diagonal cumulation on changes in imports of each Spoke country from the other Spoke countries compared to changes in imports from the EU15. The coefficient of the weighted average IORoO index is positive and significant, while the coefficient of the simple average Io-RoO Index is positive but not significant when controlling for tariffs, and is negative and not significant when changes in tariffs are not controlled for. Concerning the coefficient of the change in tariff, ∆ _τj_ , this is always positive but not significant.

17
**Table 2 (continued)**

|Retail|0.138|-0.177|0.172|-0.112|0.265|-0.000|
|---|---|---|---|---|---|---|
||(0.012)**|(0.020)**|(0.011)**|(0.018)**|(0.011)**|(0.017)|
|Services I|0.025|-0.596|0.093|-0.406|0.207|-0.140|
||(0.013)|(0.017)**|(0.012)**|(0.017)**|(0.011)**|(0.016)**|
|Services II|0.268|-0.110|0.304|-0.074|0.427|0.146|
||(0.018)**|(0.027)**|(0.017)**|(0.026)**|(0.015)**|(0.022)**|
|Transport/Comms|0.332|-0.035|0.357|0.053|0.440|0.234|
||(0.015)**|(0.042)|(0.014)**|(0.039)|(0.013)**|(0.033)**|
|Social Services|0.171|-0.132|0.176|-0.089|0.376|0.138|
||(0.018)**|(0.018)**|(0.017)**|(0.017)**|(0.015)**|(0.016)**|
|Public|0.256|0.017|0.232|0.018|0.497|0.322|
||(0.014)**|(0.024)|(0.014)**|(0.022)|(0.013)**|(0.020)**|
|No Labor Card|-0.371|-0.356|-0.420|-0.376|-0.276|-0.220|
||(0.008)**|(0.009)**|(0.007)**|(0.009)**|(0.007)**|(0.008)**|
|Intercept|-1.310|-1.274|-1.028|-0.879|-0.943|-0.735|
||(0.019)**|(0.028)**|(0.018)**|(0.027)**|(0.017)**|(0.025)**|
|N by gender|65002|35042|68102|38620|74378|45798|
|N by year|100|044|106|722|120|176|
|R-squared|0.49|0.55|0.46|0.49|0.49|0.49|

Notes: Standard errors in parentheses. * denotes significant at 5%; ** at 1%. Services I corresponds to the services sector; Services II corresponds to the PNAD classification of auxiliary services of economic activity.

Source: computed from PNAD 1988, 1992 and 1998 23
Policy Research Working Paper 5681

# **Abstract**

Conditional cash transfers are programs under which poor families get a stipend provided they keep their children in school and take them for health checks. Although there is significant evidence showing that they have positive impacts on school participation, little is known about the long-term impacts of the programs on human capital. This paper investigates whether cohorts of children from poor households that benefited up to nine years from _Familias en Acción_ , a conditional cash transfer program in Colombia, attained more school and performed better on academic tests at the end of high school. Identification of program impacts is derived from two different strategies using matching techniques with household surveys, and regression discontinuity design using a census of the poor and administrative records of the program. The authors show that, on average, participant children are 4 to 8 percentage points more likely than nonparticipant children to finish high school, particularly girls and beneficiaries in rural areas. Regarding long-term impact on tests scores, the analysis shows that program recipients who graduate from high school seem to perform at the same level as equally poor non-recipient graduates, even after correcting for possible selection bias when low-performing students enter school in the treatment group. Although the positive impacts on high school graduation may improve the employment and earning prospects of participants, the lack of positive effects on test scores raises the need to further explore policy actions to couple the program’s objective of increasing human capital with enhanced learning.

This paper is a product of the Public Sector Unit, Independent Evaluation Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jbaez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
set through bilateral negotiations between employers and employees on a highly decentralized basis. The survey results indicate that there is little difference in the basis on which wages are set between formal and informal enterprises. Wages are based first and foremost on the workers’ skills, followed by experience and finally affordability of the enterprises (Figures 18 and 19). Other factors like age of worker and comparative wages appear to play a very small role, and workers’ demands play almost no role at all.

48. (a) <u>Wage Levels: The survey asked about wages and non-wage payments for both</u> formal and informal sector workers. Unfortunately, the quality of responses from informal sector establishments was so poor that the present report does not feel confident to report results. This is regrettable, given the dominance of the informal sector in employment. Nonetheless, some observations and inferences can be drawn from the formal sector wage information, which was considerably more reliable.

49. The average wage in urban areas in Timor Leste was USD 150 in 2001.<sup>7</sup> Results from the 2004 urban survey are only obliquely comparable to the 2001 TLSS, but in 2004, the average wage was USD 158 for urban formal enterprises. This suggests that average nominal wages have not risen at all in the aggregate, given that the 2001 figure includes both formal and informal sector urban workers. In addition, annual inflation rates in the intervening period were not insignificant, at 10 percent in 2001-02 and around 4 percent in the 2002-2004 period. It therefore seems safe to conclude that real wages in urban areas have fallen in the 2001-2004 period. The extent of the fall could be significant, but robust quantification will require better data on urban informal sector compensation.

50. A disaggregated comparison of wages between 2001 and 2004 is only partly possible. It is not possible to compare the average wages by occupational/skill category between the 2004 survey and the urban workers in TLSS. However, the average wage of professional workers, which includes technical, administrative, managerial workers, was USD 209 in the TLSS. If these workers are taken together in the 2004 urban survey, their average wage was USD 206 per month. If one takes the groups as reasonably comparable, this suggests that real wages for professional workers in urban areas have fallen by around 15 percent between 2001 and 2004.

**Figure 18: Main factors in Wage-Setting Reported by Formal Enterprises**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Skills of Experience Age of Affordability Comparative Demand by Comparativ<br>worker of Workers Worker wages in Worker e wages<br>private in public<br>sector sector<br>Very important Important Somewhat important<br>Not very important Not important at all DK<br><!-- End of picture text --> Source: UES, 2004

> 7 See Das, 2004.

23
<!-- Start of picture text --> lyndall’Centreyndall Centre global temperature TimioonMitchell<br>ATF AZ<br>6 6 — csiroe<br>°. Lf.ve | °- FE cGcma2HadCM3<br>4 4 Z. — PCM<br>a) im) 7<br>2 Z e wl<br>= 2 eae S 2 a<br>on | wk<br>— iE<br>0 a<br>2000 2020 2040 2060 2080 2100 2000 220 2040 2060 2080 2100<br>B1 B2<br>66<br>55<br>4_4<br>22<br>= 3 B 3 —|<br>E | Cc5 |<br>| me<br>0 a<br>2000 «2020 240 2060 2080 2100 2000 2020 2040 2060 2080 2100<br><!-- End of picture text -->
scenarios that are relevant to understanding climate policy–induced shifts in employment. Here, we utilize simulation results from the global carbon tax scenario. Figure 1 displays the time-path for the global carbon tax, rising from $8 to $151 over time.

**Figure 1: Global Carbon Tax Time-Path**

<!-- Start of picture text --> $160<br>$140<br>$120<br>$100<br>$80<br>$60<br>$40<br>$20<br>$0<br>2016 2020 2025 2030 2035 2040 2045 2050<br>$/tCO2<br><!-- End of picture text --> Table 1 reports the distribution of unskilled and skilled employment across 26 sectors in 2020 in three types of emitter countries under business as usual.<sup>5</sup> The global economy is divided into large emitters (LEs), swing emitters (SEs), and carbon dependent countries (CDCs). Employment in traditional dirty industries (fossil fuel producers, coal, or gas-based electricity) and traditional clean industries (biodiesel, wind, solar power, and so forth) is small in all regions. For example, in carbon dependent countries, dirty industries (coal, oil, natural gas, refined oil, coal-based power, and gas-based power) employ only about 4 percent of unskilled workers; in SEs and LEs, the same industries employ only 1.1 and 0.7 percent of unskilled workers, respectively. Therefore, even if climate policy produces large changes in employment in these industries, we expect climate policy–induced job shifts to be a relatively small share of total employment.

> 5 The 40 sectors are aggregated into 26 sectors for the sake of presentation.  Here, we use the standard technology reference case.

24