rafmacalaba/datause-encoder-data huggingface dataset:
Again, if parents value sons more than daughters, then the demand for girls' human capital (schooling and health) will be more Income elastic than that for boys. But in the framework above, <!-- Start of picture text --> Y .as Y .S<br>_ -- m_ 1, and<br>Y .aH Y aH<br>0n f<br>H aY ayH BY '-<br><!-- End of picture text --> Again, no gender bias is revealed for investments in children's human capital. Comparative statics with respect to sector shares in time allocation (t L) yield: <!-- Start of picture text -->
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For a rlse in tima allocation to an activity i, _holdlng constant the time allocatlons to other sectors,_ to raise the demand for schooling and health, aS /Ot > 0 and aH _/at_ > 0, respectlvely for i- 1,2,3; j -u,f. The condition that parents value their own consumption more than investment 15
appears that this module was never implemented in LSMS surveys. Yet labor market dynamics are central to labor economics. Depending on the need of data users, one could reconsider to experiment with retrospective questions.

# 5.2. Medium‐term objectives

Develop and refine methods to capture time use in unpaid domestic and care work and time‐ related underemployment. Improving time use data collection is of paramount importance. First, collecting time use data are needed in order to refine measurement of work and employment. For instance, looking at time use helps to capture atypical employment, such as casual employment, that are likely to be underreported (Mueller & Chan, 2015). Second, studying how time is allocated to unpaid work is important for understanding whether unpaid work prevents women in moving into paid work. Third, studying women’s agency over their time use requires time use data.

However, of the 8 LSMS‐ISA surveys, only the survey in Uganda includes a question on time spent on domestic work. Even in this survey, unpaid domestic and care work is arguably poorly measured. The other LSMS‐ISA surveys only include questions on time spent fetching water and collecting wood. To capture domestic and care work, LSMS‐ISA surveys may benefit from introducing a time diary. One example, that could be adapted and integrated in LSMS‐ISA surveys, is the module developed for the Women’s Empowerment in Agriculture Index (WEAI) (Figure 7). Women are asked to report all activities undertaken in the last 24 hours in time intervals of 15 minutes. The activities include farming and wage employment, but also sleeping, domestic work and cooking. By including a time diary, LSMS‐ISA surveys would also contribute to filling data gaps on time use, particularly in SSA (ILO, 2018e). If including a time diary is considered too onerous a new stylized question (e.g. _In the last 7 days, how many hours did you spend on unpaid domestic and care work such as cooking, cleaning, preparing meals, caring for children or the elderly, ...?)_ could be added to the labor module of all LSMS(‐ISA) surveys.

In accordance with the 19<sup>th</sup> ICLS Resolution, we highlight the need for testing time diary modular approaches that are suitable for LSMS‐ISA surveys, are cost‐effective and minimize potential sources of measurement error.

Another approach that might be considered is smartphone‐augmented Time Use Surveys (TUS). The use of photos can help in capturing time use, but does not necessarily help to capture simultaneous activities, especially with respect to secondary care activities (Lentz et al., 2018).

27
program was designed and publicized as conditional. Administrative data show that the average monthly amounts transferred to our sample (US$18.50) were significantly smaller than the US$30 maximum for which households were eligible, which may be indicative of conditions being at least partially enforced. Even if program conditions were not consistently enforced during the first years of the pilot stage program, beneficiaries could not have known with certainty whether conditions would be enforced. Hence, non-compliance would have entailed the risk of loss of benefits in the minds of the study subjects. Finally, as Benhassine et al. (2015) show in Morocco, even a “nudge” or an unenforced condition can be enough to induce beneficiaries to comply.

# _The evaluation design_

A village-randomized evaluation was designed by the World Bank in conjunction with the Philippines Department of Social Welfare and Development. In October 2008, 130 villages were randomly allocated to treatment and control arms of 65 villages each stratified by 8 municipalities. The number of villages was chosen based on power calculations for three primary outcomes: school attendance of children aged 6 to 14, household consumption, and health facility visits. Data for the PMT were collected in all 130 villages from October 2008 to January 2009, and in April 2009 eligible households in the treatment villages began receiving transfers.

Since our conceptual framework suggests that the poorest households are the most likely to exhibit increases in child labor _and_ education in response to a partial schooling subsidy, it is useful to note that these experimental villages represent the poorest villages in the poorest municipalities in the country: the PMT eligibility threshold of US$2.15 per capita per day is barely above the World Bank’s US$1.90 a day poverty line. Indeed, eligible households in the 130 evaluation villages had an average per capita income of approximately $1.50 per capita per day, 11 percent lower than the average per capita income in the other program areas from this period.

9
# **4 Data**

We employ an unbalanced panel of 35 European countries where annual wind installation and incentive policies for wind investment are observed during 1991-2010.<sup>18</sup> The information on renewable policy design was obtained mostly from IEA’s renewable policy database, and cross-checked with government websites, legislative texts and other related publications. Our review indicates a great deal of variation and complexity over the design of FiT policies<sup>19</sup> . First, FiT rates can be defined very specifically according to the installation location and size. For instance, Greece offers separate rates for mainland vs.island wind installations. Many countries restrict FiTs to installations below a certain size (such as below 20MW), or have size-specific rates. Given the nonuniformity of FiT rates, we collect generator-level wind installation data from a global wind farm dataset<sup>20</sup> to match wind installation with specific policies.

Second, policies sometimes have built-in digression rates so that FiT rates for existing contracts decrease over time. Four countries in our sample, i.e. Austria, France, Germany and Switzerland, offer stepped reductions in tariff for existing contracts but not until at least after 10 years in the contract. For instance, in France, wind tariffs will be reduced by 2 percent annually starting from year 11. We did not specifically estimate the effect of rate digression on current investment, recognizing that ignoring future rate reduction may inflate actual FiT remuneration. However, since price adjustment occurs far into the future, the present value of the price reduction is likely to be small. We also exclude these four countries from the sample for robustness check. The main results do not change.

Third, although in most cases FiT rates are fixed over the contract length (standard FiT), rates can also vary annually according to electricity market price changes or other factors (variable FiT). For example, the first type of FiT enacted in Germany in 1991 involves an energy regulator releasing a new FiT rate each year. Several countries offer a

> 18The 35 countries include 23 European Union member states, Switzerland, Norway, and 10 countries in Eastern Europe.

> 19See Fischer and Preonas (2012) for a thorough review of the various design of FiT policies. 20http://www.thewindpower.net/

15
-25-

The sharp differences in the area each ditchrider is responsible for reflects the average size of farms (2.6 ha in Morocco versus 16.2 in Coello and 5.7 ha in RUT).

The reason for the small difference between the number of maintenance staff in Coello and RUT districts is probably related to the lengch of irrigation canals, drains, and service roads that need maintaining (see below) and to the effects of scale.

|Length of|Co<br>(km)|ello<br>(m/ha)|R<br>(km)|UT<br>(m/ha)|
|---|---|---|---|---|
|Irrigation canals|250|9.2|127|13.0|
|Drains|21.5|0.8|144|14.8|
|Service roads|177|6.5|350|35.9|

# Equipment

Maintenance equipment available in Coello and RUT is listed below:

||COELLO|RUT|
|---|---|---|
|Excavators/Dragliners|5|7|
|Tractors|4|2|
|Motorgraders|2|2|
|Loaders|1|4|
|Dump Trucks|8|7|
|Various|1|7|
|TOTAL|21|29|

Again, RUT district's relatively greater need for equipment reflects the greater length canals, drains, and roads to be maintained.
# **Table 4b. Banks' Buffering Role: Recession**

||Log of # of<br>Exporters|Log of Export Value<br>per Exporter|Exporter Entry<br>Rate|Exporter Exit<br>Rate|Exporter<br>Turnover Rate|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||_Panel A:_|_Banks vs. Stock Ma_|_rket_||
|(Bank Credit / (Bank Credit + Stock))*ExtFin|0.251*|0.023|-0.042*|-0.070**|-0.070|
||(0.129)|(0.271)|(0.024)|(0.028)|(0.044)|
|(Bank Credit / (Bank Credit + Stock))*ExtFin*Recess.|0.495*|-1.111**|0.029|0.049|0.023|
||(0.299)|(0.562)|(0.064)|(0.060)|(0.103)|
|Observations|17,870|17,561|15,685|15,639|15,548|
|R-squared|0.883|0.550|0.528|0.498|0.551|
|||_Panel B: Forei_<br>|_gn Banks vs. Dome_<br>|_stic Banks_<br>||
|(Foreign Bank / Total Bank Assets)*ExtFin|-0.240*|-0.765***|0.047**|0.062***|0.125***|
||(0.126)|(0.231)|(0.022)|(0.019)|(0.033)|
|(Foreign Bank / Total Bank Assets)*ExtFin*Recess.|0.350*|0.734|-0.053|-0.046|-0.129|
||(0.209)|(0.488)|(0.047)|(0.048)|(0.085)|
|Observations|17,653|17,233|15,919|15,904|15,766|
|R-squared|0.876|0.542|0.467|0.459|0.503|

Note: This table reports the effects of financial structures on the number, size and dynamics of exporters in 2-digit ISIC industries differentiating between normal times and recessions. The financial structure is captured by the ratio of bank credit to the sum of stock market capitalization and bank credit (panel A) and the share of foreign bank assets in the total bank assets of the country (panel B). The dependent variables are the number of exporters (column 1); the average sales of exporters (column 2); the entry rate of exporters (column 3); the exit rate of exporters (column 4); the turnover (entry+exit) rate of exporters (column 5). Recess. is a dummy equal to 1 if in the country a recession occurs during the year (see text for details). All regressions include FinDev*ExtFin, ExtFin*Recess., FinDev*ExtFin* Recess., country-year fixed effects and industry-year fixed effects, where FinDev is measured as all credit over GDP to control for total financial development. Regressions in panel B also include Bank Concentration*ExtFin and Bank Concentration*ExtFin*Recess. to control for the concentration of the banking sector. Robust standard errors, clustered at the country-year level, are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
Policy Research Working Paper 10557

# **Abstract**

This paper studies the effect of Russia’s invasion of Ukraine on the performance of firms in Central Asia. It uses unique data from the Business Pulse Survey run by the World Bank in the Kyrgyz Republic, Tajikistan, and Uzbekistan, which tracks the sales and employment—along with other main characteristics—of about 1,200 to 1,800 firms in a panel structure. The survey contains two waves before and one wave after Russia’s invasion of Ukraine. Using the difference-in-differences methodology in a regression setup, the analysis finds that Central Asian firms with pre-invasion trade links to Russia suffered greater drops in sales and employment after the invasion—even though exporters to Russia may have experienced, on average, higher sales during the studied period. Considering the pre-invasion digitization of firms, the findings show that digitization helped firms increase their average employment during the studied period. However, the analysis does not find any significant mitigating effect of digitalization associated with the impact of the invasion.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at miootty@worldbank.org; mmelecky@worldbank.org; ndalvit@worldbank.org; nsrinivasan5@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
Learning from Developing Country Power Market Experiences: The Case of Philippines PEMC. (2016a). _10 Years of WESM Works_ . Retrieved from http://www.wesm.ph/download.php?download=RExEQjIwMDYtMjAxNl9JZ25pdGluZ19UcmFuc2Zv cm1hdGlvbl9TcGVjaWFsXzEwdGhfQW5uaXZlcnNhcnlfRWRpdGlvbi5wZGY= PEMC. (2016b). _WESM Works 10th Anniversary Special Edition_ .

PEMC. (2016c). _WESM Works Market Report (26 June 2015‐ 25 June 2016)_ .

- PEMC. (2017a). _Electricity trading and pricing in the Philippine WESM_ . Retrieved from http://www.upecon.org.ph/epdp/wp‐content/uploads/2017/04/EPDP_MO.pdf

PEMC. (2017b). _WESM Works 2016‐2017 Annual Report_ .

- Roxas, F., & Santiago, A. (2010). Broken dreams: Unmet expectations of investors in the Philippine electricity restructuring and privatization. _Energy Policy_ , _38_ (11), 7269–7277. https://doi.org/10.1016/j.enpol.2010.08.003

The Lantau Group. (2011). _The Philippine WESM_ . Retrieved from http://www.lantaugroup.com/files/powergen_manu_mt.pdf

- The Lantau Group. (2013). _Philippines Natural Gas Master Plan Phase One Report: Assessment of the role of LNG within the Philippines energy market_ . Retrieved from https://www.doe.gov.ph/sites/default/files/pdf/downstream_natgas/first_report_naturalgasmast erplan.pdf

- Toba, N. (2007). Welfare impacts of electricity generation sector reform in the Philippines. _Energy Policy_ , _35_ (12), 6145–6162. https://doi.org/10.1016/j.enpol.2007.07.018

USAID. (2013). _Challenges in pricing electric power services in selected ASEAN countries_ . Retrieved from http://www.catif.org/wp‐content/uploads/2013/10/Challenges‐in‐Pricing‐Electric‐Power‐ Services.pdf 45
for five western regions and just one eastern region. Thus while poverty has reduced and consumption improved on the average in the eastern part of the country from 2000 to 2005, the western regions have fallen further behind.

# **V.  Conclusion**

The poor in Bangladesh are more likely to belong to households with a larger number of dependents, lower education among household members, and with the household head engaged in daily wage labor. Poor households are also more likely to be landless or functionally landless and less likely to receive domestic or foreign remittances. Where a household is located geographically has a strong influence on its economic status. This poverty profile for 2005 is similar to the mid-eighties and hence at first glance it would appear that little has changed for the poor in Bangladesh.

A closer look at national household survey data suggests a more nuanced story. For a start the proportion of people below the poverty line has fallen sharply from close to 60% in 1990 to 40% in 2005. We show that the reduction in consumption poverty in Bangladesh during 2000-2005 was also mirrored by substantial improvements in living conditions – including housing characteristics, and access to sanitation facilities, electricity, and communications.

We find that the drivers of poverty reduction between 2000 and 2005 were to an extent similar to earlier decades, but in other key features they were not. Key factors contributing to poverty reduction which are consistent across the past decades were changes in some household characteristics – most prominently, a smaller number of dependents and improvements in education – and an increase in returns to some 20
attributable to coefficients is small is itself interesting, because it is evidence of convergence: the relationship between characteristics and financing is similar in TEs and DMEs.

Table V.6 applies the decomposition to privatized and state-owned firms in TEs in 2005. The big difference in financing source in the raw means in the “Total” row is that privatized firms rely much more on bank financing (10.8 percentage points) and less on state financing (-11.7 percentage points) than SOEs. Interestingly, this is not driven by autonomous or unexplained changes – rather, it is attributable to sector and size, and in particular to coefficient rather than endowment effects. Thus the size-bank borrowing relationship is steeper for privatized firms than for SOEs, and inspection of the regressions on which the decomposition is based (not reported here) shows that bigger privatized firms borrow more from banks, but bigger SOEs do not. Most of the difference in state financing, meanwhile, is a coefficient effect driven by the greater likelihood of state-owned manufacturing firms to get state financing than privatized manufacturing firms.

Privatized and new private firms in TEs in 2005 are compared in Table V.7. Here, the main finding is a null result: the structure of financing before and after decomposition is very similar for privatized and new private firms. The biggest difference, not surprisingly, is in the impact of size: privatized firms are, on average, considerably larger than the new private firms in the sample, with a positive impact on their bank financing and a negative impact on financing from retained earnings (both relating to new private firms). The surprise in the table is the absence of a difference in the use of informal financing: privatized and new private firms are roughly equally likely to use it.

The last three tables, V.8-10, show how the structure of financing has evolved over time for the three ownership categories of firms in TEs, and allow us to address directly the issue raised above about the apparent increasing reliance of firms in TEs on retained earnings. In Table V.8, we see that privatized firms have increased financing from banks and decreased it from suppliers, in both cases in ways largely unrelated to observed 34
# 1 Introduction

On June 23, 2016, UK voters stunned the world by deciding to leave the EU. The news sent the British pound to a 30 year low, stock markets plummeted as much as 8 percent around the world, and David Cameron resigned as the Prime Minister. The vote result went against the advice of many experts. In fact, most economists thought that Brexit was a bad idea (see Baldwin, 2016). Some actually predicted a big short-term drop in trade and income per capita post-Brexit (see Dhingra, Ottaviano, Sampson and Van Reenen, 2016). More than a year has passed, contrary to these experts’advise, the exports of UK to the EU have in fact increased by 2 percent since July 2016.<sup>1</sup> While the full impact of Brexit is yet to show, particularly because the UK has not yet o¢ cially left the EU and thus currently still enjoys the tari¤ free access to the EU market, there are reasons to be optimistic even after the divorce is ...nalized.

This paper analyzes the short-term fallout of trade in goods from Brexit, through potential changes in the trade policies of its main trading partners. We construct the Overall Trade Restrictiveness Index (OTRI) of the UK’s major trading partners.<sup>2</sup> Our analysis shows that in the absence of any trade agreement between the UK and the EU post-Brexit, facing the EU’s Most Favored Nation (MFN) tari¤s could cause the UK’s export of goods to the EU to drop by 2 percent. The impact is not larger because the higher tari¤s are placed on the less elastic products that the UK exports, while the lower tari¤s are placed on the more elastic products that the UK exports. The negative relationship between the MFN tari¤s and demand elasticity softens the potential collapse in the UK’s exports of goods to the EU, in the event the UK and the EU fail to strike any trade agreement post-Brexit. We assume

> 1Data source: Eurostat.

> 2The OTRI was developed in Kee, Nicita, Olarreaga (2008 and 2009) based on the theoretical foundation of a series of seminal work by Anderson and Neary (1992, 1994, 1996, 2003 and 2007). More recently, Kee, Neagu and Nicita (2013) has used the OTRI framework to quantify the minimal impact of trade policies in causing the global collapse in trade in the aftermath of the ...nancial crisis in 2008.

1
# _<u>Rainforest stations</u>_

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