rafmacalaba/data-use-mentions-extended huggingface dataset:
deployment in alternative contexts. The app illustrations and translations can be updated with ease, and the fixed costs of smartphone procurement can be managed by procuring a limited pool of smartphones that can redeployed throughout the fieldwork. The app pop-ups can also be tailored to collect additional policy-relevant contextual information regarding reported activities, including passive childcare provision, as demonstrated in this study. At the same time, there are key issues that need to be addressed prior to proposing concrete recommendations for scaling up real-time time use data collection in low- and middle-income countries. First, it would be essential to replicate our study in alternative contexts, including with additional recall variants that have emerged in recent literature (Field et al., 2023; McCullough et al., 2023), to verify the external validity of our findings and strengthen the evidence base regarding the utility and requirements of improved approaches to time use measurement. Second, future research should provide guidance on whether the duration of smartphone data collection can be reduced, for example from nine to three full days of reporting, to save on costs without losing the analytical utility of the resulting data. Finally, survey practitioners could benefit from guidance on whether real-time, smartphone-based data collection can be limited to a sub-sample with the idea of relying on model-based within-survey imputation of time use measures – again as a cost saving mechanism while improving the quality of time use data collected in large-scale surveys.

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Observational studies provide insights into determinants of service payment behavior such as households strategically delaying payment as a form of credit (Violette, 2020) or the perceived ease of payment and social pressure being associated with prompt payment (Mugabi et al., 2010). While experimental work on improving utility revenue collection has started to grow, these papers mostly rely on administrative billing and basic demographic – data to assess impacts and typically limit their attention to two dimensions payment and consumption behavior (Allcott, 2011; Jack and Smith, 2020; Szabó and Ujhelyi, 2015; Rockenbach et al., 2019). Our study combines five years of daily administrative billing data with rich primary survey data from property owners and tenants over the course of three years on a range of outcomes that helps present a more comprehensive assessment of the potential welfare implications of utility interventions.

Our study also contributes to the high-stakes contract enforcement approaches to improving regulatory compliance. The evidence on high-stakes contract enforcement is particularly limited, especially when compared to lighter-touch information/engagement interventions. The existing evidence on enforcement is mostly limited to developed country settings, most prominently in the tax evasion literature (Slemrod et al., 2001; Kleven et al., 2011), and to a lesser extent in environmental protection (Telle, 2013; Duflo et al., 2018). The small number of studies exploring high-stakes enforcement in developing countries find significant impacts. In Brazil, de Andrade et al. (2016) randomize inspections and fine firms if they are found to be operating without a business license and find that the intervention increases business registrations. In Costa Rica, Brockmeyer et al. (2019) find significant increases in tax payments from credible enforcement emails. In Kenya, Bedoya et al. (2021) randomize a regime of high-intensive inspections of health facilities with enforcement of warnings and sanctions, including the risk of closure if they do not have a license to operate. These inspections successfully increase compliance with minimum patient safety standards for all types of facilities without increasing
current levels of migration in the community, I calculate the shares of migrants in 2001 and 2011 to a particular destination country from a VDC using the 2001 and 2011 Nepal Censuses. Then, for each destination country, I regress the share of migrants to the destination from a VDC in 2011 on the share of migrants from potential treatment and _z_ be an instrument, then under the assumptions of independence, exclusion and monotonicity IV is a LATE. Independence assumption states that dierence in outcome ( _y_ ) and dierence in treatment ( _d_ ) between _zi_ = 0 and _zi_ = 1 should capture the causal eect of the instrument on outcome and treatment. This is satised when as good as randomly assigned condition in IV is fullled. The exclusion assumption, _y_ ( _di, zi_ = 0) = _y_ ( _di, zi_ = 1) for _di_ = 0 _,_ 1 , is the same as the exclusion criteria condition in IV. Whereas, the monotonicity assumption states that the instrument, if it has any eect, should aect everyone in the same direction i.e. _d_ 1 _i ≥ d_ 0 _i_ or _d_ 1 _i ≤ d_ 0 _i_ . Given these assumptions, _IV_ = _LATE_ = _E_<sup>_E_</sup> (<sup><u>(</u></sup> _d_<sup>_<u>yi</u>_</sup> _i_<sup>_<u>|</u>_</sup> _|_<sup>_z_</sup> _z_<sup>_<u>i</u>_</sup> _i_<sup>=1</sup> =1)<sup><u>)</u></sup><sup>_−_</sup> _−_<sup>_E_</sup> _E_<sup>
.2|
|Electric, Elec-|||||||
|tronic Equipment|34,845|28,480|-<br>508|657|28,331|81.3|
|Motor Vehicles &|||||||
|Equipment|21,466|21,654|-<br>277|1,058|20.873|97.2|
|Other Transportation|||||||
|<br>Equipment<br> <br>|<br>23,871|18,415|-1,401|118|19,698|82.5|
|Instrument,<br>Related|||||||
|<br>Goods<br> <br>|14,158|9,548|-<br>177|214|9,511|67.2|
|Miscellaneous<br>Manu-|||||||
|facturing<br>Goods|7,580|6,058|-<br>67|144|5,981|78.9|
|**Totala**|442,485|358,152|-4,780|23,778|339,154|76.6|

a) excludes USS 19,014.9 million payrolls of head office and auxiliary personnel.

Source: Annual Survey of Manufactures 1975-1976. Bureau of Census, US Dept. of Commerce, May 1979, and information supplied by Robert Parker, Associate Director of National Economic Accounts in July 1985.
compared to 48% at the country level.

Similarly to what we found at the country level, city volume per capita increases with city incomes (Appx. Section C2.-C4. and Appx. Fig. C.3).<sup>6</sup> Using the same regression analyses as before, heights explain between two-thirds and three-fourths of the global city volume-income relation (we found 60%-65% at the country level) (Appx. Section C5. and Appx. Fig. C.4-C.6 for the scatterplots). Low-rises and high-rises account for _∼_ 90% and _∼_ 10% of the upward volumeincome relation, hence 65% and 10% of the total volume-income relation (25% for areas). At the country level, lower contributions of 60% were found for low-rises and 5% for high-rises. Lastly, we find very similar patterns and differences considering developing economies only.

Hence, upward construction appears relatively more important for cities than for rural areas. Combined with the fact that more developed economies are also more urbanized, this could

> 6We use the 2015 city GDP data from Chen et al. (2022) who provide high-resolution “real GDP” data (in 2017 dollars) based on (not top-coded) night lights data as reported by the NPP/VIIRS satellite series. While night lights data could capture infrastructure (including electrification) and past economic development in addition to current economic development as infrastructure and urban structures are durable, we use this data in the absence of better data on city incomes for the whole world. Nonetheless, the city-level results should be taken with caution.
in government, including by entering themselves as contenders for leadership.”<sup>75</sup> Effective accountability occurs when transparency and political engagement are mobilized to select better quality leaders or to motivate existing leaders to follow policies that are in the public interest— i.e., have technical merit and address key development objectives.

The quality of political engagement also matters. Government failures may occur when there is “unhealthy” political engagement; that is, “...when leaders are selected and sanctioned on the basis of their provision of private benefits rather than public goods.” The historical survey of Argentina in this paper established that, in the macroeconomic realm, the lack of central bank independence was combined with fiscal policies that often led to “buyouts” for political benefits. This was complemented at the microeconomic level by regulatory policies that were also influenced by organized special interests, either business, labor or populist movements.

The nature of political institutions also enters into how political engagement makes a difference in policy making. The institutional features of the Argentine policy-making process were discussed above. In addition, the relationship between political leaders and citizens has a role in reshaping the policy making process. In this regard, the informal beliefs and modes of behavior of citizens towards the public sector can evolve as policy actions are taken and access to information improved. Over time, the relationship can evolve from personalized paternalism and conflict to a more informed debate on what are the technically sound policies that are in the broader public interest. The transparency of information can play a critical role in supporting this process.

There is the possibility that the elections of late 2015 may have been the first step in establishing a greater degree of credibility in policy making via greater political accountability and “healthier” forms of citizen engagement. An emerging literature points to the scope for bottomup (popular movements) or top-down (leadership) shifts that bring in a new approach to policy making.<sup>76</sup> It is not
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# **IV. CONCLUSION**

Transport connectivity is essential to sustain inclusive growth in developing countries. Many rural populations are still considered to be unconnected to the domestic, regional, or global market. In the context of the SDGs, the RAI has an important role to play as a global indicator to measure people’s transport accessibility in rural areas where the majority of the poor live.

With sustainability of data updates, consistency across countries, and operational relevance taken into account, a new method to calculate the RAI was developed using spatial data and techniques. The resultant RAI estimates vary significantly across countries and across districts in a given country. The estimates are already useful for road planning and prioritization purposes. But an important policy question may be how the RAI could be improved. In theory, there are only two ways: (i) improving the road condition of the existing road network, and (ii) expanding the road network through new road construction or reclassification.

The paper investigated the characteristics of the subnational RAI estimates in eight countries. It was shown that road density and road condition systematically influence the RAI. For South Asia, improvement of road condition is particularly important, possibly because road density is already high. In Africa, road network expansion and improvement are both important to raise the RAI. In some African countries, in fact, road classification is still imperfect and the current road network is defined narrowly. In such a case, new road construction and/or reclassification of unofficial feeder or local paths is needed. All the indications are that significant resources are likely to be required to achieve “universal access.”
# _Entrepreneurship_

The program had modest success in promoting entrepreneurship among youth (appendix A, table A2). The likelihood that participants became first‐time entrepreneurs, that is, operated their own businesses, including family businesses, for the first time, increased by 4.1 percentage points relative to nonparticipants (15.8 percent). The impacts were larger and statistically significant only among men, although the equality of effects by gender cannot be rejected. These findings are consistent with the self‐employment results and the fact that men in Sierra Leone engage less in nonfarm self‐employment as a primary job than women, allowing men more margin for entry into this area.

The program had a positive and significant impact on volatility, as proxied by the lowest monthly profits derived by household enterprises in the previous 12 months. Treatment increased profits in the worst month by 24.7 percent; indeed, several studies have emphasized that training programs may have the strongest influence on profits during bad months, thereby reducing profit volatility (McKenzie and Woodruff 2014). However, this result appears to be explained by a greater understanding of enterprise finances among participants because the program also raised the likelihood that financial records would be kept, and the improved recordkeeping seems to have influenced how profits were measured (see below). Following the approach used by de Mel, McKenzie, and Woodruff (2014), financial recordkeeping was included as a control in the treatment regression; this showed that financial recordkeeping was positively associated with profits and, once this control is included, the impact observed on profits loses its significance .

The program also shifted the types of businesses in which youth are engaged, resulting in a distribution that resembles more closely that of the general population. According to the latest nationally representative labor force survey, most household entrepreneurs in Sierra Leone are traders or shopkeepers (84.0 percent), compared with 75.4 percent of the youth in the control group. The program increased the probability of involvement in trading or shopkeeping businesses
br>PAKCIVLAOTGOUGAMDGSENPNGMOZ PAKCIVLAOTGOUGAMDGSENPNGMOZ<br>KHM KHM<br>CMRSDNGI NMLIETHGNB CMRSDNGI NMLIETHGNB<br>GA B MRTCAFHTISLE GA B MRTCAFHTISLE<br>BFA DJI BFA DJI<br>AGO AGO<br>COG COG<br>NGAZARNERTCD NGAZARNERTCD<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI Daat Source: World Bank WDI<br>(b.1) (b.2)<br>80 80<br>70 70<br>60 60<br>50 50<br>Life Expectancy at Birth Life Expectancy at Birth<br>40 40<br>100 100<br>80 80<br>60 60<br>Immunization DPT Immunization DPT<br>40 40<br>20 20<br><!-- End of picture text --> 17
Up to this point, the disaggregated analysis of the poverty impacts has been based on sectoral affiliation and thus it has been possible to link it directly to the sectoral results generated by the CGE model. However, additional policy relevant criteria can be used to identify other groups of households and to evaluate their specific trade induced poverty effects. In particular, we conduct impact analyses for rural and urban areas, by regions, by land ownership, by educational level and by occupation. Obviously, all these criteria are somehow correlated to basic categories included in the CGE model, for example the educational level is linked to the skilled/unskilled factor types, or the region to the prevalence of agricultural employment, but strict correlation should not limit the micro analysis. Using the full household survey information provides maximum flexibility and allows extracting ex-post additional information. Instead of roughly inferring the poverty impact on certain groups from correlations between the rigidly CGE embedded categories and micro characteristics, the generated counterfactual income distributions contain all the information needed and hence directly provide a quantitative assessment of the poverty impact on specific groups.

_<u>Table 18: Poverty and inequality impact of trade, urban and rural</u>_

||2001 levels <sup>BaU</sup><br>15 c|<sup>2001-</sup><br>hanges<br>Urb|Doha % of<br>BaU<br>change<br>an|Full % of<br>BaU<br>change|2001 levels|<sup>BaU 2001-</sup><br>15 changes<br>Ru|Doha % of<br>BaU<br>change<br>ral|Full % of<br>BaU<br>change|
|---|---|---|---|---|---|---|---|---|
|P0|19.
Regulatory Index`|<br>`-0.156**`<br>`(0.0669)`|<br>`0.00445`<br>`(0.00268)`|<br>`0.00459`<br>`(0.00490)`|<br>`0.717`<br>`(0.430)`|
|`Private Monitoring Index`|`0.131**`<br>`(0.0630)`|`-0.0131**`<br>`(0.00531)`|`-0.0123`<br>`(0.00873)`|`0.0971`<br>`(0.390)`|
|`Official Supervisory Power Index`|<br>`-0.0375`<br>`(0.0361)`|`-0.00173`<br>`(0.00289)`|`-0.00180`<br>`(0.00509)`|`0.0512`<br>`(0.272)`|
|`Entry Requirements Index`|`0.111`<br>`(0.198)`|`0.00570`<br>`(0.00772)`|`0.00281`<br>`(0.0163)`|`0.752`<br>`(1.723)`|
|`Restrictions on Activities Index`<br>`------------------------------------`|<br>`-0.0347`<br>`(0.0503)`<br>`---
|
|_R_<sup>2</sup>|0.394|0.401|0.413|0.298|0.408|0.401|

**Notes:** This table reports a set of cross-country regressions where the dependent variable is the average growth rate of real annual GDP per capita and the explanatory variables are: net FDI inflows as a percentage of GDP; credit to the private sector by deposit money banks as a percentage of GDP (this variables is scaled by 100); the log of average years of secondary schooling in adult population; the log of initial GDP per capita; the log inflation; the log of export plus import over GDP; the log of government expenditure over GDP; the ICRG investment risk index; the log of the black market premium; and a dummy that takes value one for countries located in SubSaharan Africa. Columns (1)-(3) focus on developing and emerging economies for which we have data starting in 1970. Columns (4)-(6) focus on developing and emerging economies for which we have data starting in 1975. Robust standard errors are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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**Figure 43: Returns to Education in Togo (Mincerian Analysis) (LHS), and Wages vs Primary Education Rate in Togo and benchmark countries (RHS).**

<!-- Start of picture text --> 0.6 700<br>Education and Wages in WAEMU Countries BEN<br>0.5 600 COT<br>0.4 500 SEN<br>0.3 400<br>0.2 300 BUR MAL TOGO<br>0.1 200 NIG<br>0.0 100 GNB<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>0<br>Years of Education 0 10 20 30 40 50 60 70<br>Primary Completion Rate (%)<br>E a rn in g s<br>% C h a n g e in<br>Avg.Monthly Salary (USD) .<br><!-- End of picture text --> Source: Authors’ calculations, LSMS data, and IMF (2009b).

**Finally, the ICA data reveals that none of the interviewed firms had any concerns regarding skills of the labor force.** In fact very low proportion of interviewed enterprises revealed much concern regarding the availability of skilled workers or labor regulations in Togo. In addition to this it is worth mentioning that over the long run, as the economy of Togo deteriorated, human capital has improved – suggesting a low correlation between human capital and growth for Togo at this point in time. In light of this, we cannot conclude that education is currently binding constraint to growth in Togo. That, of course, does not exclude a need for specific skills or a need for reforms given expected future demand as the economy develops.

# **6.6 Macroeconomic Stability**

**Togo has been affected by the financial crisis, but its impact has been moderate.
of that in Delhi, so no more than 27,949 people in other cities have been significantly affected..

For the estimate of disadvantaged beneficiaries, we assume the distribution of these illness episodes follows the distribution of asthma in the general population. WHO data show

> 8 - - <u>http://cpcb.nic.in/upload/NewItems/NewItem_104_airquality17cities package .pdf</u>

32
the imagery was collected in 2019. Indeed, the WSF2019 settlement mask, although including additional settlement areas that arose between 2012 and 2019, still shows a higher accuracy compared to the settlement mask derived from the coarse 30 m Landsat imagery. Moreover, the imperviousness estimation at 10 m based on NDVI ~~m~~ ax from 2019 S2 data led to a more precise masking of urban green spaces compared to the results obtained from Landsat.
on the covariates included in the base specification, including the female plot management dummy in the pooled regression, are stable subsequent to the incorporation of additional covariates, they are less likely to change if we are able to take into account potentially missing omitted variables.

To perform this analysis, we consider the following sets of variables: (i) district fixed effects, (ii) plot geospatial characteristics informed by GIS data, (iii) other plot characteristics solicited by the IHS3, (iv) additional household characteristics, and (v) additional community characteristics. Table A.2 in the Appendix includes the detailed list of the variables included in each set. Tables 5, 6 and 7 present the base regression results and the estimates from the regressions including the additional controls for the pooled, male-managed, and female-managed plot samples, respectively. An overwhelming majority of the coefficients, with respect to the base specification, are stable across the specifications and the plot samples, and do not change sign or significance. This suggests that the assumptions of ignorability and zero conditional mean might not be unfounded.

> 32 The Appendix including the results from the sensitivity analysis referenced in Sections 5.3 and 7.2 are available upon request from the authors or could be downloaded at http://tinyurl.com/mwgenderandagriculture.

19
export subsidies, and tax breaks for foreign corporations, they suggest focusing on programs and grants to help particular clusters by increasing the supply of skilled workers, encouraging technology adoption, and improving regulation and infrastructure, for example. While ―hard‖ industrial policy is easier to implement than ―soft‖ industrial policy measures, tariffs and subsidies more easily become entrenched and are more subject to manipulation by special interest groups.

Lin (2010) and Lin and Monga (2010) argue that policies that target activities which are ―comparative advantage facilitating‖ (CAF) type are the most successful in contributing to structural transformation. Governments should not target production activities that are not aligned with the country‘s comparative advantage. Public policies should be of the ―comparative advantage facilitating‖ (CAF) type, helping to solve and overcome the problems of coordination and externalities that beset processes of structural transformation and innovation.

# **Broadening Opportunities**

There is also a need to better understand how to broaden access to economic opportunities to ensure rapid poverty reduction and promote human development. Inequalities of opportunities lie at the heart of inequitable growth processes. Atkinson, Piketty, and Saez (2011) have documented the evolution of inequality of opportunities by looking at the top income shares over the long run for more than 20 countries using income tax statistics. They found that in the last 30 years top income shares increased substantially in English speaking countries, China, and India (Figures 8a and 8b). They attribute this increase in part to an unprecedented surge in top wage incomes. Broadening opportunities is as relevant for developing countries as it is for highincome countries.

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