rafmacalaba/datause-encoder-data huggingface dataset:
interaction) also did not have a significant effect on adoption, except in the case where a higher subsidy level of 50% to 75% is interacted with information, which was significant at the 5% level. This suggests that a high level of subsidy along with information could increase adoption. Without a significant subsidy, providing information about the private and environmental benefits of this technology cannot provide enough incentives to significantly change behavior. These results are robust to inclusion or exclusion of control variables (Table 6).

In Tables 5 and 6, we also considered the effect of control variables on adoption. The results show that households that are connected to grid electricity are less likely to adopt the solar lantern, suggesting that the former is a preferred substitute for lighting. In contrast, households that used kerosene for lighting were more likely to adopt the solar lantern, suggesting that solar is a preferred substitute. Moreover, households that lived farther away from the market were more likely to adopt the solar lantern, suggesting that better access to solar lanterns increases adoption; but this is weakly significant. We also find that villages that are connected to the grid are more likely to have a higher adoption rate, which was not expected, but the coefficient is weakly significant.

# **_5.2  Analysis of WTP_**

Table 7 presents the effect of information provision on individuals’ WTP for the solar lantern. We find that information treatment does not have a significant effect on WTP. Similarly to the results for adoption, households that are connected to the grid have a lower willingness to pay compared with those not connected, which is expected. Moreover, households that have better access to credit have a higher willingness to pay for the solar lantern, suggesting that credit relaxes the liquidity constraint households face. Households that used fuel wood for lighting and are farther away from the market have a higher willingness to pay but these results are weakly significant.

We also briefly discuss below the sensitivity of the analysis of WTP to the following three issues, in order: effect of extremely large WTP; effects of difference in WTP between those who collected the solar lantern after paying immediately and those who paid and also collected the solar lantern a month later; and the effect of removing respondents who did not purchase the solar lantern although they were winners (decliners).

27
In the absence of distortions, TFPR should not vary across firms within each sector. In other words, in a frictionless environment, more capital and labor should be allocated to firms with higher physical productivity (TFPQ) up to the point where their higher output results in a lower price, Psi, which translates in the TFPRsi equalizing across firms _i_ . As such, any dispersions of TFPR across firms within a sector imply distortions. A firm with TFPR higher than the sector average suffers disproportionately more from the effects of distortions. On the contrary, it is common for TFPQ to vary across firms because different firms may have different productivity levels.

From the revenue data, we can also derive TFPQsi as: This shows that TFPQ is calculated from Psi, which contains elements of distortions, and κ is normalized to 1.<sup>9</sup> Hsieh and Klenow (2009) choose the elasticity of substitution, σ=3,<sup>10</sup> and R=10, assuming a real interest rate of 5% and a depreciation rate of 5%. Capital share, αs, and labor share, (1 −αs), are taken from the United States manufacturing sectors, where firms are assumed to operate in an environment of minimal distortions. Therefore, the shares of capital and labor of firms in the United States represent an efficient utilization of resources. Any deviation from the US capitallabor shares suggests distortions.

Distortions represented by the output and capital wedges can be measured by: where firm _i'_ s wage bill is represented by wsiLsi , and PsiYsi represents the firm's value added. Both values are taken from the data. Rewriting this equation shows that the relative utilization of 1−αs factors will be affected by distortions in the capital market and ~~,~~ the labor-capital ratio in the αs less distorted (United States) environment.

If firm _i_ 's actual labor-capital ratio wsiLsi is higher than the less distorted labor-capital ratio, this RKsi implies that firm i may be facing difficulties in accessing capital (relative to hiring labor), and thus

> 9 See Hsieh and Klenow (2009) for a detailed explanation.

> 10 Elasticity of substitution between products is related to the markups σsσ−1s = 1 + μs, where μs is the markup. An elasticity substitution of 3 corresponds to a markup of 50%.

28
respectively. According to the results, the number of households that used electricity as the main source of energy for lighting increased by around 4.7 percentage points more in project areas than in areas without project components. The coefficients in columns (1) to (3) are statistically significant at the 1-percent significance level (the coefficients in columns (2) to (3) remain significant at the 10-percent level when standard errors are clustered at the stratum level). This suggest that there may have been co-benefits to the program, including the above-mentioned link between health improvements and a reduction in the use of traditional fuels for lighting. At the same time, the results in columns (4) to (6) indicate that the association between project locations and the use of electricity for cooking was negative. It thus remains unclear whether or to what extent the program contributed to an overall reduction in the use of traditional fuels and switch to electricity in Malawi.

**Table 3.** Difference-in-difference estimation (household level) between 2013 and 2019 (continued)

||Use El<br>|ectricityfor Li<br>|ghting<br>|Use<br>|Electricityfor Co<br>|oking<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Post × Treat**|**0.048*****<br>**(0.001)**|**0.049*****<br>**(0.001)**|**0.047*****<br>**(0.001)**|**-0.019****<br>**(0.016)**|**-0.021*****<br>**(0.009)**|**-0.022*****<br>**(0.008)**|
|Post|0.023***|0.017*|0.019**|0.005*|0.007**|0.008**|
||(0.003)|(0.061)|(0.039)|(0.083)|(0.027)|(0.017)|
|Treat|0.156***|-0.024**|-0.085**|0.043***|0.009*|0.021*|
||(0.000)|(0.046)|(0.031)|(0.000)|(0.055)|(0.092)|
|Urban||0.299***|0.266***||0.059***|0.056***|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|Distance to||-0.001|-0.002**||0.000|-0.000|
|Road||(0.310)|(0.011)||(0.724)|(0.953)|
|Grass roof||0.017|0.025*||0.022***|0.028***|
|||(0.238)|(0.084)||(0.001)|(0.000)|
|Dwelling||-0.055***|-0.051**||-0.011|-0.009|
|Owned||(0.005)|(0.010)||(0.253)|(0.374)|
|Male head||0.004|0.003||-0.001|-0.001|
|||(0.731)|(0.805)||(0.844)|(0.800)|
|Age (head)||0.000|0.000||0.000|0.000|
|||(0.265)|(0.205)||(0.476)|(0.448)|
|Secondary||0.152***|0.147***||0.046***|0.046***|
|educ. (head)||(0.000)|(0.000)||(0.000)|(0.000)|
|HH size dummies||yes|Yes||yes|Yes|
|Material dummies||Yes|Yes||Yes|Yes|
|District FE|||yes|||yes|
|Observations|2,960|2,943|2,943|2,959|2,942|2,942|
|R-squared (within)|0.037|0.027|0.028|0.005|0.005|0.005|
|R-squared (overall)|0.072|0.375|0.394|0.015|0.094|0.107|

_Notes_ : Difference-in-difference regressions with robust standard errors. * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Full sets of control dummies are included for household size and the type of construction material used for the dwelling (permanent, semi-permanent or traditional).

_Source:_ Authors’ analysis using data from the Living Standards Measurement Study (LSMS).

12
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- Executive. 2009. South Sudan connection comes calling. Executive Magazine, Issue 115, February 2009. http://executive-magazine.com/getarticle.php?article=11532

- GOSS (Government of South Sudan) Ministry of Telecommunication and Postal Services. ―Foreword from Minister.‖

- www.motps.goss.org/index.php?option=com_content&view=article&id=47%3Aforeword-fromminister&catid=34%3Asite&lang=en.

- Infosaid. 2011. Infoasaid Sudan media and telecoms landscape guide. Infoasaid Sudan media and telecoms landscape guide

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

- Ministry of Telecommunication and Postal Services. 2008. ―Southern Sudan to Own Communication Gateway.‖ _News_ , November 12.

- Reuters. 2009. ―Mobile Phone Network Launched in South Sudan.‖ February 23.

- http://uk.reuters.com/article/2009/02/23/oukin-uk-sudan-south-telecomsidUKTRE51M5YL20090223.

- Wafula, Walter. 2010. ―Libyan Firm Acquires South Sudan’s Gemtel.‖ _Daily Monitor_ , February 15. <u>www.monitor.co.ug/Business/-/688322/861854/-/view/printVersion/-/an77e4z/-/index.html.</u>

# Irrigation

- Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Briceño-Garmendia and Shkaratan. 2010. Power Tariffs: Caught between Cost Recovery and Affordability. AICD working paper, draft version. Washington DC 50
|_(Table C.4 continued.)_|
|---|

|Negotiation (Index)|standardized index following Anderson (2008) and O’Brien (1984)<br>of questions measuring negotiation behavior<br>• 2 questions with vignettes where women were asked in an<br>open-ended way to describe what the woman in the vignette<br>should have done to convince her husband. These are coded as<br>follows:<br>1=She should have behaved as she did,<br>2=She should have apologised to the husband,<br>3=She should have asked nicely again, in a soft tone,<br>4=She should have explained to him why she wants to make<br>this decision,<br>5=She should have explained to him why she wants to make<br>this decision, and acknowledged his concerns,<br>6=She should have explained to him why she wants to make<br>this decision and acknowledged his concerns. She should then<br>try to fnd a solution that allows for a compromise.<br>• 1 forced choice question, recoded such that =1 if respondent<br>chose "If I really want something, I can easily convince my<br>family." over "At times, I fnd it hard to explain what I want."|
|---|---|
|Gender attitudes (Index)|standardized index following Anderson (2008) and O’Brien (1984)<br>of questions measuring attitudes about gender. 3 vignettes describing<br>situations in which women exercised mobility and independence in<br>purchases. Respondents were asked to state their personal opinion.<br>Questions will be recoded such that<br>1=Socially very inappropriate,<br>2=Socially somewhat inappropriate,<br>3=Socially somewhat appropriate,<br>4=Sociallyveryappropriate.|
|Aspirations (Dummy)|Dummy variable =1 if respondent agrees more with "There is still<br>much more I want to achieve in my work career." over "I am looking<br>forward to enjoying a quieter life back home as soon as I have enough<br>money."|
|Overall self-beliefs (In-<br>dex)|standardized index following Anderson (2008) and O’Brien (1984)<br>of Confdence (Index), Negotiation (Index), Gender attitudes (Index),<br>Aspirations(Dummy)|

# **Panel B: Demographic and control variables from surveys**

|Age|Age in completedyears|
|---|---|
|Married|Dummyvariable =1 if married|

59
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|---|---|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|BR DGM for Indigenous People and Traditional Communities|Maria Bernadete Ribas Lange|
|GT: Urban Infrastructure and Violence Prevention|Luis Miguel Triveno Chan Jan|
|GT: Urban Infrastructure and Violence Prevention|Luis Miguel Triveno Chan Jan|
|GT: Urban Infrastructure and Violence Prevention|Luis Miguel Triveno Chan Jan|
|GT: Urban Infrastructure and Violence Prevention|Luis Miguel Triveno Chan Jan|
|Legal Services for Development and International Finance|Aristeidis Panou|
|CapacityBldg. of MMDA to Formulate & Implement the Green Prin|tYan F. Zhang|
|Capacitybuildingof evaluation of China's health system reformpilo|Shuo Zhang|
|Capacitybuildingof evaluation of China's health system reformpilo|Shuo Zhang|
|Zambezi River Basin Management Project|Anna Cestari|
|Zambezi River Basin Management Project|Anna Cestari|
|Zambezi River Basin Management Project|Anna Cestari|
|Establishment of a Transport RegulatoryAuthorityin Greater Cairo|Sami Mohamed Ali|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|EnergyEfficiencyProject|Rome Chavapricha|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
|Sierra Leone SafetyNets Project|Junko Onishi|
# **Table 9: Unorganized Sector Log (Real Output)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||_Other-sector Ou_|_tput Spillovers_||
|Horizontal Output Spillovers|||||
|from Organized Sector|0.1793***|0.1621***|0.1629***|0.0536***|
||(0.0225)|(0.0213)|(0.0214)|(0.0197)|
|Forward Output Spillovers from|||||
|Organized Sector|3.6859***|2.1427***|1.9212**|-14.6519***|
||(0.7183)|(0.7114)|(0.7528)|(2.1219)|
|Backward Output Spillovers|||||
|from Organized Sector|2.6649***|2.9723***|2.6609***|13.4094***|
||(0.4557)|(0.4568)|(0.4791)|(1.6483)|
||_Oth_|_er-sector Produ_|_ctivity Spillove_|_rs_|
|Average TFP of Horizontal-|||||
|linked Organized Plants||0.1672|0.1636|0.1768|
|||(0.1592)|(0.1589)|(0.1466)|
|Average TFP of Forward-linked|||||
|Organized Plants||1.5690***<br>(0.3698)|1.3165***<br>(0.4003)|7.5149***<br>(0.9809)|
|Average TFP of Backward-|||||
|linked Organized Plants||0.6165*|0.5523+|2.9682***|
|||(0.3606)|(0.3621)|(0.8707)|
|||_Own-sector Out_|_put Spillovers_||
|Forward Output Spillovers from|||||
|Unorganized Sector|||0.2965<br>(0.3167)|-2.1341***<br>(0.7546)|
|Backward Output Spillovers|||||
|from Unorganized Sector|||0.4615**<br>(0.2264)|-1.3021***<br>(0.4499)|
|Constant|-142.3842***|-120.8439***|-123.2268***|75.0451**|
||(18.6009)|(17.4443)|(17.3951)|(29.1958)|
|Observations|5888|5888|5888|5888|
|R-squared|0.0752|0.0844|0.0860|0.2831|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Column 4 also includes state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 38
Nonetheless, it is still possible that positive trade shocks make the signing and depth of RTAs more likely, creating reverse causation.

The panel structure of our data helps to mitigate concerns about the endogeneity of trade policy, as transitory trade shocks should not affect the likelihood of permanent changes in trade arrangements and permanent trade shocks are well captured by pair-specific fixed effects. Additionally, the inclusion of the<sup>L2015</sup> _t_ =2001 _Globijt_ -terms also helps to reduce endogeneity concerns as they account for all time-varying policy covariates that affect all international trade partners in the same way such as trends in trade policy (i.e., increased probability of successfully signing RTAs in the first place). Moreover, since WTO law obliges RTAs to cover “substantially all trade”, uncorrelated sector-specific shocks are unlikely to trigger signing an RTA that covers all sectors. Having said all this, we are aware that some endogeneity bias may still bias our estimates upwards. Given that there are no convincing instruments for RTAs, our (almost) saturated model eliminates that concern as far as possible.

Furthermore, identification relies on the assumption that RTAs do not change deep preferences such that consumers prefer sourcing from an alternative trade partner even if trade costs have not changed. As preferences are unobservable, this assumption cannot be properly tested but it is crucial for the validity of the “indirect” approach to measuring changes in NTBs.

Tariffs are set at the tariff-line level, which corresponds in most countries to eight- or even ten-digit-level products. Unfortunately, trade data are only available for higher levels of aggregation.<sup>4</sup> Therefore, in virtually all gravity settings, tariffs need to be aggregated to match the level of aggregation that can be found in the trade data. One might be worried about aggregation bias of the tariff variable. Particularly in cases when tariffs are only reduced for a subset of tariff lines, the aggregated tariff variable might be measured very noisily—in the extreme, it might be impossible to separate the effects of a reduction in tariffs and NTBs. Hence, the RTA-dummy catches both yielding upward-biased estimates for the NTB effect.

Recall that we are differentiating between trade agreements notified under Article XXIV and under the enabling clause; information about the depth of trade agreements is only available for the former. Given the fact that all trade agreements notified under Article XXIV must liberalize “substantially all trade”, we are not particularly worried about aggregating tariffs. Instead, for agreements notified under the enabling clause (partial scope agreements and nonreciprocal trade arrangements) we expect a stronger upward bias for the coefficient of interest,

> 4 Although for some countries trade data can be found at the same level, it is impossible to gather global coverage.

10
Policy Research Working Paper 8958

# **Abstract**

In recent years, there has been renewed interest in access to jobs in relation to transport connectivity. In Sub-Saharan Africa, about 14 million working age people are added to the labor market every year. Ensuring sustained access to jobs seems to be a prerequisite for inclusive and robust economic growth. The paper examines the impact of public transit connectivity on access to jobs, especially focusing on wages. Using data from Antananarivo, Madagascar, it is shown that the wages earned by commuters are systematically higher than the wages earned by those who decided not to commute and are self-employed or engaged with family businesses around their neighborhood. Proximity to public transport, especially taxi-be, is important to promote people’s access to jobs. It is also found that there is a substantial gender inequality in wages in the country: Women are more likely to use buses to commute, and yet, they earn less than men. In addition, the poor tend to benefit less from public transportation. Public bus services are affordable, however, the quality of the services may remain low.

This paper is a product of the Transport and Digital Development 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 author may be contacted at aiimi@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
Annex 4. The instrumental variable fixed effects model: the FAIS impact on monetary and nonmonetary poverty

- A. Poverty

|||_Poverty_<br>||
|---|---|---|---|
||_Food_|_Capabilities_|_Assets_|
|FAIS ∞|−0.998|−0.617|0.849|
||(0.921)|(0.918)|(0.824)|
|Own revenues ∞|−0.339|−0.258|0.0452|
||(0.289)|(0.273)|(0.205)|
|Unconditional revenue ∞|−0.427|−0.895|−2.059***|
||(0.803)|(0.801)|(0.710)|
|Conditional revenue ∞|0.846**|0.962**|0.981***|
||(0.410)|(0.415)|(0.378)|
|Direct workers ‡|−12.67*|−13.78*|−10.68|
||(7.116)|(7.757)|(7.875)|
|Indirect workers ‡|10.10|9.447|6.082|
||(11.34)|(12.80)|(12.75)|
|Productivity per occupied ‡‡|0.488|0.559|0.612**|
||(0.354)|(0.353)|(0.303)|
|Private business assets ‡‡|−0.310|−0.331|−0.330|
||(0.294)|(0.292)|(0.248)|
|Semiurban|−2.824|−3.386|−3.933**|
||(2.244)|(2.241)|(1.919)|
|City|−4.110*|−4.655*|−4.999**|
||(2.433)|(2.440)|(2.107)|
|Year 2010|0.879***|1.444***|2.745***|
||(0.337)|(0.343)|(0.307)|
|Year 2014|−1.441***|−1.457***|−1.349***|
||(0.390)|(0.397)|(0.353)|
|N|6,107|6,107|6,107|
|R-2|0.024|0.033|0.070|
|Municipalities|2,118|2,118|2,118|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 32