rafmacalaba/datause-encoder-data huggingface dataset:
Table 14. Interactive Effect by Business Environment

||Pa|nel A: Full sam|ple|Pane|l B: African Su|bsample|
|---|---|---|---|---|---|---|
||TFP|logLP|LP Growth|TFP|logLP|LP Growth|
|FemHead|0.042|-0.111|-0.010|0.071|0.475|0.390**|
||(0.191)|(0.096)|(0.024)|(0.722)|(0.322)|(0.176)|
|Trade Creditc|0.040|0.502***|0.065***|0.923|0.284|0.075|
||(0.091)|(0.167)|(0.012)|(0.692)|(0.486)|(0.061)|
|FemHead*Trade Creditc|-0.005|-0.362**|-0.056*|-1.663|-0.141|-0.154|
||(0.159)|(0.143)|(0.032)|(1.658)|(0.831)|(0.171)|
|Overdraftc|0.127|0.276*|0.029*|-0.426|-0.358|-0.094|
||(0.105)|(0.160)|(0.017)|(0.522)|(0.340)|(0.065)|
|FemHead*Overdraftc|-0.055|-0.123|-0.035*|0.396|-0.586*|-0.234***|
||(0.133)|(0.108)|(0.019)|(0.823)|(0.295)|(0.078)|
|Webc|0.397***|0.429***|0.035**|0.031|0.879**|0.123*|
||(0.084)|(0.099)|(0.016)|(0.510)|(0.353)|(0.067)|
|FemHead*Webc|0.144|0.304**|0.071***|-0.355|-0.471|-0.059|
||(0.212)|(0.134)|(0.027)|(1.455)|(0.401)|(0.119)|
|Inf. Competititonc|0.050|-0.013|-0.001|0.074|-0.157|-0.034|
||(0.122)|(0.091)|(0.011)|(0.323)|(0.323)|(0.056)|
|FemHead*Inf. Competititonc|-0.287**|-0.098|-0.027|-1.540*|-0.631|-0.175|
||(0.132)|(0.135)|(0.023)|(0.826)|(0.403)|(0.145)|
|Corruptionc|0.092|0.112|0.028**|-0.407|0.093|0.066*|
||(0.077)|(0.076)|(0.011)|(0.239)|(0.228)|(0.038)|
|FemHead*Corruptionc|0.108|-0.068|-0.010|0.971|0.411|0.022|
||(0.131)|(0.122)|(0.017)|(1.084)|(0.253)|(0.084)|
|Outagec|-0.073|-0.064|-0.014|1.417*|0.454|0.018|
||(0.054)|(0.088)|(0.010)|(0.742)|(0.364)|(0.058)|
|FemHead*Outagec|-0.199|0.084|0.027|0.811|0.247|-0.161*|
||(0.126)|(0.100)|(0.018)|(0.907)|(0.317)|(0.086)|
|capAggc|0.418*|0.107|-0.008|-1.635|0.873|0.045|
||(0.219)|(0.216)|(0.027)|(0.966)|(0.902)|(0.143)|
|FemHead*capAggc|0.119|0.209|0.021|0.458|-0.741|0.016|
||(0.312)|(0.294)|(0.040)|(1.681)|(0.644)|(0.188)|
|Firm Controls|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|16,638|41,248|37,365|863|3,323|2,895|
|Adjusted R<sup>2</sup>|0.265|0.360|0.285|0.123|0.279|0.502|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

44
bureaucratic procedures involved in undertaking these activities divert their attention away from the needs of water-users and towards the objectives of higher levels of state. These objectives include building new water projects rather than maintaining existing infrastructure. The state’s focus on building new infrastructure significantly challenges water access in rural areas of the country, where 46% of the 74,000 water points are not functioning at any given point in time (Coville and Rogger 2015). Moreover, a full 25% of all water points break down within two years of being constructed. Consequently, nearly 7.5 million people – 30% of Tanzania’s rural population – live without reliable access to clean water. While the country has made significant strides by expanding the number of water projects in rural areas, the lack of attention to maintenance by local governments severely limits the impact of these investments in the water sector.

In order to address the problem of maintenance, the Department for International Development (DfID) proposes directing funds towards a Payment by Results Program (PbR). The PbR program is a form of ‘results-based-aid’ (RBA) that provides financial incentives to Local Government Authorities (LGAs) to expand rural access to water by disbursing funds in response to a set of pre-determined, measurable results. These results include increasing the number of functioning water points by continuing to build new infrastructure and by maintaining existing projects. To understand the impact of the PbR program, DfiD, along with the Ministry of Water and Irrigation (MoWI), is working to collect data on the number of functioning water points in the country. To collect this data, the MoWI are asking district water departments to count the number of functioning and non-functioning water points in their districts. These numbers are then submitted to the MoWI on a monthly basis. This focus on collecting and maintaining accurate data on the number of water points is a result of how the PbR program is 5
auto-correlation.<sup>24,25</sup> What do our marginal estimates imply about total yield losses from exposure to wildfire smoke plumes in the American Midwest? In panels **B** and **C** of Figure 2, we show the total impact of exposure to smoke on yields in an average year for every county in our sample. In an average year, crop-growing counties in our sample are exposed to approximately 24 days under a low-density smoke plume and 7 days under a high-density plume. The larger number of days under low-density smoke plumes, combined with the positive impact that these days have on yields, results in a positive impact on yields for both soy and corn across nearly all of the counties in our sample.

Because of the larger positive effect of diffuse light on soy yields, the overall effect is more positive for soy. On average, soy yields increase 1.7% in a typical year as a result of smoke exposure, although in counties with the smallest impacts the effect is only slightly greater than zero. Corn yields increase by an average of 0.5%, but because of the smaller positive impact of diffuse light and subsequently smaller positive impact of days with low-density smoke plumes, there are some counties that experience yield losses in corn in an average year. In the most negatively impacted counties, corn yields appear to decline by approximately 0.5% in a typical year. The absolute impact of these yield changes on production, measured in bushels, is highly concentrated in counties in Illinois, Iowa, and Nebraska as these are the states with the largest levels of absolute production (Figure SI2).

# **2.1.1 Mechanisms**

To test the hypothesis that wildfire smoke plumes affect crop yields by changing the amount and type of sunlight available for photosynthesis rather than changing meteorological determinants of yield, we examine the extent to which smoke plumes impact weather inputs directly and how smoke plumes change aerosol optical depth (AOD).

We use satellite-derived measures of the AOD to verify that plumes of both types increase AOD relative to no plumes and that the effect of a high-density plume on daily AOD is roughly 3 _×_ greater than that of a low-density plume (Table SI2). Turning to impacts on meteorological variables, we do not find evidence that smoke plumes of either density meaningfully impact the number of growing degree days or extreme degree days during a growing season (Figure SI1).

We do find some evidence that there is less precipitation on days with plumes of either density. We also see that cloud cover is lower on days with smoke plumes, which suggests that the impact on daily precipitation may be an artifact of the construction of the smoke plume data—it is more difficult for analysts to draw smoke plumes on days with dense cloud cover, which are the same days when precipitation is most likely. At the same time, there is evidence from the Amazon that smoke plumes can directly impact cloud formation, with the specific mechanics depending 5
identify shippers’ demand and practices related to perceived and actual cargo dwell time and how they are linked to private sector market structure.

The surveys were conducted in five Sub-Saharan African countries—Kenya, Nigeria, South Africa, Uganda, and Zambia—from May to July 2011. Each of the surveys includes about 100 observations (per country), yielding 506 validated records. Participants in the survey are shippers importing containerized cargo through the ports of Durban (for South African and Zambian importers), Mombasa (for Kenyan and Ugandan importers), and Lagos (for Nigerian importers). Both manufacturers and retailers in the most important sectors are represented in the survey.

# **Data Collection Problems Encountered during Fieldwork**

Several problems were encountered while collecting data in the field. In many instances, the respondents were not able to answer all of the questions, mostly because they did not have the information (they had to check with other employees or the forwarding agents). For example, many respondents did not know clearance times in harbor or customs, as clearance procedures are generally handled by their C&F agents, who do not necessarily share the information with them. Many shippers were only concerned with the final on-site delivery dates. Some respondents did not understand the questions, even though pilot surveys had been conducted to eliminate this problem<sup>5</sup> . These issues reveal the information asymmetry problem between importers and their C&F agents, owing mainly to the lack professionalism and transparency of C&F agents, who do not provide feedback about their work or exchange information about the clearance process with their shippers.

Another issue, particularly in Kenya, Nigeria, and Uganda, is that some of the potential respondents were suspicious about the survey and not willing to participate in studies of this nature. They considered the questionnaire to be seeking sensitive or private information.

However, several respondents, mostly in South Africa, felt that the interview was interesting and expressed appreciation that it survey was being conducted because they felt that something needed to be done to ―improve the red-tape of getting goods out of the harbour in time‖.

Data collection issues demonstrate two major problems that inhibit change: lack of information and low expectations.

# **Dwell Time Statistics and Expectations**

The perception about what is ―normal‖ cargo dwell time varies between countries and regions. Refas and Cantens (2011) present a detailed discussion regarding what is considered ―normal‖ dwell time in SSA. According to them, it is around 11 days (close to the free time period) in Douala and in most ports in SSA. This is a particularity of the region, because the dwell time perceived as ―normal‖ in most international ports in East Asia or Europe is around four days.

# **Dwell Time as Reported by Shippers**

> 5 In Kenya, pilot surveys were conducted in order to check whether the questions were understood by the interviewees and to verify the length of the questionnaire.

21
in Central America and the Caribbean, which are more influenced by the United States’ economic cycle (and which fared less positively in the 2000s). In the group of South American countries experiencing high growth during the boom (and to a lesser extent, in those experiencing low growth), employment grew, attracting previously inactive workers into the market, both low- and high-skilled, accompanied by wage increases between 2005 and 2011 (while in Mexico, employment and wages declined). Overall, during the expansion, the composition of employment shifted away from self-employment into salaried work, and from small firms to employment in medium and larger firms – i.e., on average, there was a shift towards more formal, higher productivity, better paid work. With the economic slowdown, however, this trend appears to be undergoing a reversal for some countries. The analysis suggests that the contraction in domestic demand, as a result of the adverse terms of trade, is leading, among others, to adjustments in quantity in the labor market that have particularly affected young and less educated workers. Moreover, the rate of progress in inequality reduction is also slowing down. After a solid decade of performance, recent research suggests signs of stagnation in the decline in income inequality in the region between 2010 and 2013 (Cord et al., 2014). As in the case of growth, there is much heterogeneity within: many countries still display substantial declines, while the pace of reduction has become negligible in some, and even increased in others.

The rise of labor market income and poverty reduction has not progressed in the same way across countries. Overall, labor income explained 58 percent of poverty reduction in Latin America and the Caribbean during 2003-08, and 49 percent over 2008-13 (World Bank, 2014). Yet, this trend was not uniform: income from labor 3
# **Figure 1: GATS score commitments for environmental services and other services**

1a. ES: narrow definition 1b. ES: wide definition <!-- Start of picture text --> Environamental services Other services Environamental services Other services<br>HIC (15) HIC (15)<br>UMIC (12) UMIC (12)<br>LMIC (10) LMIC (10)<br>0 20 40 60 80 0 20 40 60 80<br>Score (min=20; max=100) Score (min=20; max=100)<br><!-- End of picture text --> Source: Melo and Vijil (2014; figure 3). There are no available data for LIC in the service commitments database. The narrow definition only considers ES as defined by the W/120 list; the wide definition adds to these ES the following W/120 sectors: professional services, research and development services, other business services, and construction and related engineering services. The qualitative ESL index covers all 4 services modes. It aggregates two indexes, one for commitments to national treatment, another for commitment to market access (see Melo and Vijil 2014; annexes 1 and 2 for the derivation and choice of weights) In Melo and Vijil (2014) we construct a similar Environmental Services Liberalization (ESL) index to check if there is any difference in commitments for ESs vs. other services. Figure 1 confirms greater commitments for high-income countries and slightly higher commitments for Environmental Services than for the rest. However, the informational value of the index is limited because actions to improve the environmental use of services go beyond those categorized as ES in the CPC, which are the only services considered as belonging in the ES category.<sup>13</sup> It is likely that negotiations on ESs will also be off the Davos agenda, as negotiators will hold off taking them on board, pending the outcome of the Trade in Services Agreement (TiSA) negotiations. Should this agreement result in participants exchanging the best commitments they have so far undertaken in their PTAs (Marchetti and Roy, 2013), ESs could be substantially liberalized, as most of the opening has occurred on a North-South preferential basis. Because complementarities between trade in EGs and trade in ESs are especially

> 13 In Melo and Vijil (2014), we used ESL indexes to suggest that liberalization in ESs might go further through North-South RTAs than multilaterally. This might be expected as most of the world market, particularly for infrastructure ES, is in the hands of firms from HIC that have strong interests in prying open developing countries’ domestic markets (Kirkpatrick, 2006). These patterns suggest that developing countries are only likely to continue liberalizing trade in ESs as part of RTAs. Similarities in commitments across modes might be sufficiently high to allow these liberalization steps to be multilateralized, especially if the Davos-led negotiations gather momentum. Moreover, as rules of origins are difficult to impose on services trade, RTA commitments could be extended on a multilateral basis.

18
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

3% price reductions, while the government gained about £5 billion in privatisation proceeds and taxes.

# **4 Summary of tested hypothesis and variables**

# **4.1 Econometric studies**

In relation to the generic model of electricity sector reform analysis outlined in Section 2, the existing empirical econometric studies have at best partially addressed the performance and determinants aspects of reform. Broadly, the findings of this literature may however be summarised as follows:

- Institutional determinants of reform: Higher country policy and institutional quality is positively correlated with extent of reform (Bacon and Besant-Jones, 2002). The effect of judicial independence on competition level and ownership type is ambiguous and/or sensitive to model specification. Market-oriented economic ideology is positively correlated with higher competition and private ownership. Distributional conflict is positively correlated with higher level of monopoly, and ambiguous in relation to ownership type. Judicial independence, economic ideology, and distributional conflict are positively correlated with the overall reform score (Ruffin, 2003).

- Privatisation and efficiency: In developed countries, privatisation has improved operating efficiency and capital utilisation (Steiner, 2001). In developing countries, privatisation has improved efficiency when accompanied by independent regulation (Zhang et al., 2002).

- Privatisation and prices: In developed countries, the cost-reflective-pricing effect of privatisation (i.e. lower industrial prices and industrial/residential price ratios) has been mixed and sensitive to time factor and variable definition (Steiner, 2001; Hattori and Tsutsui, 2003); the effect in developing countries is not significant (Zhang, et al., 2002).

- Competition and efficiency: In developing countries, competition has resulted in improved operating and capital efficiency (Zhang et al., 2002).

- Competition and prices: The evidence of the effect of competition on cost reflective pricing (i.e. lower industrial tariffs and higher residential tariffs) in developing countries is weak (Zhang, et al. 2002). The price effect of competition in developed countries is mixed and sensitive to time factor and variable definition (Steiner, 2001; Hattori and Tsutsui, 2003).

- Regulation and efficiency: Independent regulation alone has not led to efficiency improvement in developing or developed countries (Zhang, 2002; Steiner, 2001).

- Regulation and prices: In developing countries, the effect of independent regulation on cost reflective pricing is insignificant (Zhang et al, 2002).

- Market opening, access, and prices: In developed countries, TPA and retail access tend to result in lower industrial prices and ratios to residential users (Steiner, 2001; Hattori, 2003).

- Investments and institutions: Private investment in the sector is positively correlated with credible institutional factors such as protection of property

38
# **Gender, Geography and Generations:**

# **Intergenerational Educational Mobility in Post-reform India**

# M. Shahe Emran<sup>1</sup>

IPD, Columbia University Forhad Shilpi DECRG, World Bank

**JEL Codes** : O12, J62

**Key Words** : Intergenerational Mobility, Education, Equality of Opportunity, Sibling Correlation, Intergenerational Correlation, Economic Liberalization, Rural-Urban Inequality, Gender Gap, India

> 1 We would like to thank Will Martin, Giovanna Prennushi and Debdulal Mallick for comments on an earlier draft. Emails for correspondence: shahe.emran@gmail.com (M. Shahe Emran) and fshilpi@worldbank.org (Forhad Shilpi).
# **Table 13.  Impact of WTO Accession on Regional Markets, including decomposition of CO2 emissions (Results are Percentage Change from Base Year)**

||**Overall**||**St. **||**North-**||||||**Far **|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average M**|**oscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare (EVas % ofconsumption)|8.6|7.7|11.5|13.7|12.0|10.7|8.8|9.2|7.1|8.3|10.7|
|Welfare (EVas % ofGDP)|4.0|4.2|5.2|2.9|5.6|4.4|4.1|4.4|3.2|3.9|4.8|
|**Carbon Dioxide Emissions and Decom**|**position**|||||||||||
|CO2price (ruble per tonofCO2)||0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|CO2emissions, decomposedinto:|4.3|2.4|8.9|3.7|8.2|5.2|6.1|4.4|3.3|4.6|4.9|
|Outputeffect(% ofCO2)|4.9|4.9|7.0|5.1|7.4|6.5|5.0|5.4|3.7|4.8|6.7|
|Compositioneffect(% ofCO2)|1.0|-0.9|4.4|0.1|2.7|0.4|2.6|0.7|1.0|1.3|0.4|
|Technique effect(% ofCO2)|-1.6|-1.5|-2.7|-1.4|-2.0|-1.6|-1.5|-1.6|-1.3|-1.5|-2.1|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|6.2|4.5|28.8|3.5|10.1|7.7|11.2|10.6|9.2|4.1|4.7|
|NitrogenOxide|2.8|1.7|4.6|2.8|6.8|3.7|3.0|3.6|2.4|2.8|2.9|
|Hydrocarbons|1.7|-2.0|4.5|1.9|4.8|4.1|2.6|1.8|-0.1|2.7|8.6|
|ParticulateMatter|3.0|1.5|5.9|2.8|5.0|4.0|2.6|2.9|3.1|2.9|2.5|
|Volatile Organic Components|2.7|3.8|1.8|2.8|13.3|2.9|6.2|3.5|1.8|3.0|4.5|
|Carbon Monoxide|4.5|9.9|13.6|2.7|9.8|4.8|17.0|5.8|4.4|3.4|3.5|
|**Aggregate trade**||||||||||||
|Regional terms of trade (% change)|2.9|16.3|17.9|16.2|17.8|16.9|16.3|15.8|15.2|15.1|17.0|
|Regionalexports (% change)|3.0|3.3|3.8|2.9|3.6|3.7|3.1|2.7|2.6|2.8|3.9|
|Realexchangerate (% change)|1.8|1.9|2.7|2.2|2.2|2.2|2.1|2.1|1.2|1.4|2.5|
|Internationalexports (% change)|8.1|11.1|17.6|2.6|17.9|7.5|19.5|8.6|8.7|7.4|9.5|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|3.7|3.9|6.0|3.5|5.6|5.0|3.7|4.5|2.3|3.6|5.6|
|Skilledlabor|3.7|2.5|6.2|4.8|6.1|5.5|4.2|4.3|2.2|3.8|6.2|
|Nationalcapital|3.8|3.9|4.6|4.1|4.2|4.2|4.0|4.0|3.1|3.3|4.4|
|Regional mobile capital|5.8|6.0|9.5|4.3|9.5|7.1|6.5|6.0|4.9|5.5|7.5|
|Crude oil resources|4.4|||5.1|3.7|4.9||4.6|2.5|2.7|5.5|
|Naturalgasresources|7.0|||8.1|-10.1|-3.3||-2.0|-5.2|-6.1|-3.6|
|Coal resources|9.7|||||12.4|10.9|11.2|8.8|9.0|12.5|
|Specific capital indomesticfirms||-29.9|-22.5|-47.7|-20.8|-24.5|-16.9|-24.1|-16.3|-19.3|-26.3|
|Specific capital in multinational firms||60.2|47.7|215.9|79.1|140.9|114.3|125.9|138.8|159.5|113.0|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|2.0|1.9|3.1|1.9|3.8|2.0|2.1|1.5|2.0|1.9|2.3|
|Skilledlabor(% changing sector)|2.6|2.6|3.9|2.7|4.1|2.9|2.7|2.1|2.3|2.5|3.3|
|**Source: Authors' estimates**||||||||||||

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