rafmacalaba/data-use-mentions-extended huggingface dataset:
unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

The findings suggest that in a situation of abundant global liquidity and strong flows to developing countries but comparative caution among investors given the adverse experiences of the crisis, the relative perception of sovereign risk (i.e. how investors perceive a country’s creditworthiness relative to its peers) played a more important role than the sovereign rating in influencing capital flows in the post-crisis period. These findings for capital flows in the post16
to successful entrepreneurship (and business training and mentoring add little value), we might expect the impacts of the franchise treatment to be smaller than the impacts of the grant treatment (because much of the program spending paid for training that, by assumption, would not be the relevant barrier to entrepreneurship for these individuals). On the other hand, the training and mentoring provided through the franchise program might impact participants’ productivity, increasing the fraction of high productivity types. If this were the case, we would expect the impacts of the franchise treatment to be more persistent than those of the grant treatment — though they might initially be smaller in magnitude, depending on the initial mix of types in the population and the value of the capital transferred to franchise program participants.

We test these predictions using data from two rounds of surveys: midline surveys that were conducted between 7 and 10 months after the interventions and endline surveys that were conducted 14 to 22 months after the interventions. Both the midline and endline surveys contain detailed data on involvement in income-generating activities. The endline survey also includes a range of measures of consumption, expenditure, and well-being — which might be impacted by treatment if participants saved or consumed the value of the capital they received without launching a small business.

# **4.1 Estimation Strategy**

In our main analysis, we report intent-to-treat (ITT) estimates of the impacts of the franchise treatment and the cash grant treatment on women assigned to each treatment group. Treatment assignment was random within strata, so the impacts of the interventions on any outcome _Yi_ can be estimated via the OLS regression specification: where _Franchisei_ and _Granti_ are indicators for, respectively, random assignment to the franchise treatment or the grant treatment, _δstratum_ is a randomization stratum fixed effect, _φenumerator_ is a survey enumerator fixed effect, _ζmonth_ is a fixed effect for the month the survey was administered, _Xi_ is a vector of individual controls, and _εi_ is a conditionallymean-zero error term.<sup>14</sup><
trend toward the use of consumption measures (not the least through the efforts of the World Bank and the influence it exerted on the choice of survey instruments in the former communist countries and Africa), we are still far from unanimity on this issue. In the study of global

> 18 For example, in 1990 Chinese statistical office changed the valuation of grain output produced by rural households from state-mandated to market prices. This generated a large change in the calculated poverty rates and a break in the rural mean income series (see Ravallion and Chen, 2005).

> 19 For example, the US Current Population Survey top-codes all very high wage and capital incomes. Similarly, the maximum capital gain that can be recorded in the survey is $149,999 per household annually.

11
limitations in the firm-level panel, in the third key comparative static of the paper the analysis fails to reject that outages have no effect on within-firm worker hourly wages, consistent with reductions in both wages and hours for skilled workers and inconsistent with the simplest version of this alternative interpretation. **?** provides complementary evidence in this regard, using the World Bank Enterprise and Afrobarometer surveys to show that unreliable electricity supplies reduce both employment and the share of high-skilled jobs in the local labor market.

Another interpretation issue to consider is whether increases in worker labor inputs result from hiring or from shift changes among unskilled labor in the existing workforce. Again, data limitations in the firm-level panel preclude a direct test. However, total blackout exposure reported by each firm owner during the panel does not predict the changes in employment seen between February 2014 and March and April 2015, limited but suggestive evidence that extensive margin labor may be less important in the short-term than intensive margin staffing changes.<sup>19</sup> This preliminary conclusion is also consistent with evidence from **?** that small firms in Ghanaian artisan manufacturing are labor constrained.

Finally, although the data includes no direct information on capital utilization by firm owners and skilled and unskilled workers, evidence from Table 1 on available production technologies shows that the vast majority of firms with and without workers have access to manual sewing machines that can

> 18We thank two anonymous referees for this insight.

> 19See Appendix Table 8.

13
that it is restricted to women of childbearing age (and in some countries, it covers only women who have married or live in a consensual union). WVS, though not available for all countries in the region, was selected due to the availability of gender attitude variables that could serve as a proxy for relevant norms (see Table A1 in the Annex for more details).

Following the survey identification, we proceeded to review the indicators in the identified surveys that were related to norms and FLFP. This was done in an iterative process, similar to the one Malhotra, Shuler and Boender (2002) follow for identifying empowerment indicators. Building on the literature on agency and empowerment, and publications on social and gender norms, we identified a set of indicators and variables repeatedly appearing in the literature as norms-related indicators.<sup>8</sup> We then looked at how

> 7  This included the Demographic and Health Survey (DHS), World Values Survey (WVS), Multiple Indicator Cluster Surveys (MICS), Gallup Poll, Gender and Adolescence: Global Evidence (GAGE) (see Table A1 in the Annex for more detail on these data sources). Upon careful review, the latter three were dropped due to limited data availability (GAGE) and duplication of variables already available in the DHS and WVS surveys (MICS and Gallup) .

> 8 For the review we assume social and gender norms to be conditional preference for following behavioral rules based on expectations of other’s approval and/or sanctions for conformity or transgression of that rule (Bicchieri, 2006); that are embedded in formal and informal institutions, as well as individuals’ minds, and specifically define

6
# 8. References

Alongi, D.M. (2002). Present state and future of the world’s mangrove forests. Environ. Conserv. 29, 331–349.

Awal, M.A.; Islam, A.F.M. (2020). Water Logging in south-western coastal region of Bangladesh: Causes and consequences and people’s response. Asian J. Geogr. Res, 3, 9–28.

Best, Ü.S.N., Van der Wegen, M., Dijkstra, J., Willemsen, P.W.J.M., Borsje, B.W., Roelvink, D.J.A., (2018). Do salt marshes survive sea level rise? Modelling wave action, morphodynamics and vegetation dynamics, Environmental Modelling & Software 109: 152-166, Bao, T. (2011). Effect of mangrove forest structures on wave attenuation in coastal Vietnam. Oceanologia. 76. 10.5697/oc.53-3.807.

Bhowmik, A., Cabral, P. (2013). Cyclone Sidr Impacts on the Sundarbans Floristic Diversity. Earth Science Research. 2. 62-79. 10.5539/esr.v2n2p62.

Bricheno, L.M., Wolf, J., Islam, S., (2016). Tidal intrusion within a mega delta: An unstructured grid modelling approach. Estuarine, Coastal and Shelf Science 182, 12-26.

Carrol, C.A., Bunting, P., Hardy, A., Bell, G. (2019). Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region. Remote Sens. 2019, 11(23), 2833; <u>https://doi.org/10.3390/rs11232833</u> Dasgupta, S., Huq, M., Khan, Z.H., Zahid Ahmed, M.M, Mukherjee, N.,
ensure that households did not believe that participation would render any material benefits. Principal investigators personally conducted recruitment to begin with and households were informed that the purpose of the survey was to ― _gather data and information on health and health seeking behavior among households in Delhi_ ‖.

Given the lack of data on medical facilities that are not registered (the majority) in Delhi, it is difficult to ascertain whether the neighborhoods chosen had more medical facilities or households were more aware of health problems than others in the city. As a check on the external validity of this sample, Das and Sánchez (2002) compared the ISERDD sample to two representative samples from Delhi—the (Delhi) National Family Health (1993) and National Sample Survey (2000). Their comparison suggests that the ISERDD sample is, on average, slightly younger (with a one year difference in the mean age) with a greater density of households in the upper-income groups. On a host of variables including education levels, marital status, characteristics of the household-head, household size and availability of different types of facilities they find few significant differences. The income differences do not appear to be large (they could mirror the different survey years in a growing economy), and assuming a positive relationship between income and health, should actually bias our results towards less morbidity than what is representative for Delhi as a whole.

Recall periods varied for the same household through the two years of the survey. The 7 localities were divided into two ―waves‖ composed of 4 and 3 localities respectively. Between August 2000 and December 2000, every household in Wave 1 (4 localities) was visited weekly by a member of the ISERDD team and asked about health problems during the previous week, whether treatment was pursued, and how much treatment (if any) cost. Care was taken to ensure visits to the households were always on the same day of the week, which became the focal reference-point for the weekly recall questions. This design was followed to avoid the possibility
or provision of income (Baranov et al., 2021; Kerr-Wilson et al., 2020). Our effects are over six times lower-cost relative to cash transfer programs among husbands and wives in Kenya (Haushofer et al., 2019) and about three times lower-cost than a monthly transfer program in Ecuador (Hidrobo, Peterman and Heise, 2016), both of which have also quantified causal decreases in IPV. In terms of other educational and attitudes-shifting programs for adolescents, _Dating Matters_ in the United States costs over three times more per student on average than our interventions, for relatively smaller decreases in violence (Luo, DeGue and Le, 2020).

# **2 Background and Study Design**

# **2.1 Setting and Sample**

This study was implemented in three regions of Tanzania—Dodoma, Iringa, and Mbeya— in partnership with BRAC Maendeleo. BRAC opened a network of 150 adolescent female clubs (Empowerment and Livelihoods for Adolescents ( _ELA_ ) clubs) in the second half of 2009 across these regions. Mbeya is the largest of the three regions in terms of population at 2.7 million people as of the 2012 census, with Dodoma having a population of 2.2 million and Iringa just under 1 million people (National Bureau of Statistics et al., 2012). The average population size of study communities is about 3,000, and these are rural areas.

ELA clubs served as the basis for identifying the female study population. ELA is an education-based intervention designed to empower adolescent females by providing a safe social space, life-skills training, and support in adolescent development. Female adolescents and youth are invited to participate in ELA. Participation is voluntary but members are expected to attend five days per week from 3-6PM. Each club averages 20 members and has a mentor who runs the programs. This program was started by BRAC in Bangladesh and is also implemented in Uganda, Sierra Leone, South Sudan and Liberia. While the evidence on ELA from Uganda and Sierra Leone
**Figure 1: Median Values of the Sources of Vulnerability**

<!-- Start of picture text --> Private Sector Credit Growth (%) REER Overvaluation<br>60 .04<br>50 .02<br>40 0<br>30 -.02<br>20 -.04<br>10 -.06<br>0 -.08<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Fiscal Deficit (% of GDP) External Debt (% of GDP)<br>10<br>100<br>5 80<br>0 60<br>-5 40<br>-10 20<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Loan to Deposit Ratio (%) Short-Term Debt (% of Foreign Reserves)<br>220 400<br>200<br>180 300<br>160<br>200<br>140<br>120 100<br>100<br>80 0<br>2000 2002 2004 2006 2008 2000 2002 2004 2006 2008<br>Cluster L Cluster V Cluster G Cluster H<br><!-- End of picture text --> Sources: IMF’s World Economic Outlook database and authors’ calculations. Notes: The lower growth and lower vulnerability is identified by L, the lower growth and higher vulnerability is identified by V, the higher growth and lower vulnerability is identified by G, and the higher growth and higher vulnerability is identified by H. The absence of higher growth and higher vulnerability countries in 2000 and 2008 reflects the lack of data in such cluster for those years.

Is there a correlation between the growth and vulnerability dimensions? As Figure 2 shows, high credit growth rates and loan-to-deposit ratios are typically associated with higher growth rates. In contrast, higher fiscal deficits, debt-to-GDP ratios, real exchange rate overvaluation, and short-term debt (
thresholds for these targeting rates are presented in Table A 2. Once the pseudo-PMT is implemented in the CLCI beneficiary database, one can use the information provided in this subsection to: (i) calculate the chances of being eligible for the MoLSA program for each beneficiary; and (ii) choose the optimal level of referral threshold, by referring to the graphs and tables presented in this subsection. MM _Table 5: PPMT cut-off thresholds for referral to MoLSA program targeting the poorest 18 percent_ WW _Table 5: PPMT cut-off thresholds for referral to MoLSA program targeting the poorest 18 percent_

|**Referral confidence**|**level =**|**90%**|**80%**|**70%**|**60%**|**50%**|
|---|---|---|---|---|---|---|
|**North**|VM|4.2|4.5|4.6|4.7|4.7|
||SEVAT|4.3|4.4|4.6|4.6|4.8|
|**Center-South**|VM|4.0|4.5|4.6|4.7|4.8|
||SEVAT|4.0|4.0|4.5|4.7|4.8|
|**Kurdistan**|VM|--|--|--|--|4.6|
||SEVAT|--|--|--|--|--|

_Source_ : Authors’ calculation using the Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

As with any targeting methodology, we also propose performance criteria for the PPMT models. Figure 7 illustrates the ways to calculate these referral statistics or the performance criteria
15.3|19.6|2.8|3.6|
|Phase 2|-5.5|8.8|17.3|21.0|2.0|2.4|
|Phase 3|-11.0|14.1|22.5|32.0|1.6|2.3|
|All|-8.7|13.1|21.5|30.2|1.6|2.3|
|**Doubled**<br>|||||||
|Phase 1|-2.9|4.0|12.2|12.2|3.0|3.0|
|Phase 2|-6.3|8.8|13.9|14.3|1.6|1.6|
|Phase 3|-12.7|14.1|16.3|22.4|1.2|1.6|
|All|-10.0|13.1|15.9|21.0|1.2|1.6|

Capital transformation elasticities determine the flexibility of shifting production across different subsectors within a sector. In our central case, the capital transformation elasticity is set at 3 for different subsectors within a sector and 1.5 for different sectors, based on estimates from the GTAP database (Aguiar, 2019). This indicates that capital incurs adjustment costs when reallocating across subsectors and sectors, and the adjustment cost is lower for capital between firms producing the same product (subsectors within a sector) than between firms producing different products (sectors).

We explore the uncertainties related to this parameter. Higher capital transformation elasticity leads to lower abatement costs by reducing the capital adjustment cost of shifting from plants with higher emission intensity to plants with lower emission intensity. However, its influence on the cost gap between DEPS-NO and CAT-NO
negligible effect on Roraima’s fiscal balance and is not only incurring additional social assistance expenditure but also may be contributing through indirect taxation, the labor market is finding it challenging to absorb the excess supply. It finds that in the immediate short-run, the influx of Venezuelans has led to an increase in unemployment and decrease in employment among female and low skilled workers, and the effect on earnings seems to vary by sectors, with the highly skilled male workers are experiencing an increase in income in construction and information technology, while the low skilled worker are experiencing an increase in earnings in education, health and transport, suggesting heterogenous impacts of the displaced Venezuelans on the labor market. These asymmetrical impacts on earnings are in line with previous literature, which finds that low-skilled and female workers tend to experience negative impacts, while more-educated workers benefit from increased immigration. It should be noted here that this paper focuses on short-run effects and previous literature like Card (1990) finds that the initial effects of “boatlift” or influx of about 125,000 Cubans in Miami were decline in employment rates and wages, but about three years after the huge influx, wages and employment rates in Miami showed no effect.

The main caveats in the analysis are the unavailability of nationality information in PNADC and consequently, the results discussed in the paper show the effect on the overall market. Another point to emphasize here is that RD estimates are local treatment effects and cannot be generalized. As a result further research needs to be conducted to identify the actual effect on the host community.

However, results of this paper suggest certain policy implications that may mitigate the negative impacts of the influx of the displaced Venezuelan on the labor market and facilitate their fiscal contribution. Policies may focus on supporting the most vulnerable host populations like women 31
of exposure to refugees. We standardize all three measures to have a mean of 0 and standard deviation (SD) of 1 for ease of interpretation. For parishes within the 150km cutoff, Figure 4 displays their raw exposure measures (jittered) by year and the mean values (blue). The figure illustrates that there is a marked increase in all measures of exposure in 2016, which we would expect given the post-2014 influx of refugees. Figures S5 and S6 in the SI also show the distribution of our exposure measures. Following Maystadt and Verwimp (2014) and Maystadt and Duranton (2019), our measures of refugee presence are continuous, consider both the size of and distance to the proximate settlements, allow a locality to be affected by multiple settlements, and are more directly related to theory.

# **4.3 Outcomes: Public Goods Access and Utilization, Attitudes toward Migrants, and Insecurity**

The main outcomes of interest for public goods and development include newly constructed geocoded data on primary schools, secondary schools, health clinics and hospitals, health utilization, and road density. First, our primary school data on over 19,500 schools comes from the Uganda Education Management Information Systems, and is supplemented by about 2,700 additional schools manually collected by a Ugandan education consultant we hired to do manual checks. For each primary school, we have their geographic coordinates, founding year, and whether they 14
Reports that land prices begin to rise sharply as soon as there is a credible announcement that construction has been sanctioned are commonplace. The subject came up almost always in Bell’s (2009a) discussions with villagers in Assam, Himachal Pradesh and Orissa, in which quite startling claims, often of many-fold increases, were usually followed by remarks to the effect that there had been very few, if any, actual sales. Quite some time must elapse after construction before settled valuations based on transactions displace speculative guessing. One hopes that enough panel surveys will be carried out to reveal how things actually develop over the whole course of events, from _before_ any announcement to full maturity many years afterwards. For such evidence would provide an invaluable check on the estimates derived from the approach pursued in this paper.

# **8 Conclusions**

The three big questions posed in the Introduction are addressed _seriatim_ . _Preserving and enhancing current benefits_ The benefits generated by past investments will continue undiminished only if the PMGSY roads are properly maintained. The associated outlays will be very modest in comparison, and the consequences of neglecting to make them can be readily experienced by taking a ride at random on the district and state highways to which the PMGSY roads are connected.

The technical capacity to keep this extension of the network up to scratch appears to be adequate, but the outlook for actually financing the work is poor. Although the five-year provision in the contracts is helpful, it is seen to be just a stop-gap measure when viewed against the roads’ economic lifetime. Many engineers claimed that the funds allocated to the maintenance of the P.W.D. network are but a fraction of the – amount needed to keep it in good order one-third at best, often a quarter or less.

The villagers in Assam, H.P. and Orissa with whom I spoke on my field-trip had not only a firm opinion of where the responsibility lies, but also a blithely optimistic assessment of the states’ finances. In fact, each link is
We investigate in this paper patterns of welfare dynamics in India over the quarter-century between 1987 and 2012. In doing so, we build on an earlier study by Dang and Lanjouw (2018) that focuses on mobility during the 2000s. Similar to this study, we attempt to overcome a set of empirical and methodological challenges that bedevil analysis of this kind in the Indian setting. Recognizing that the debate about the evolution of welfare outcomes such as poverty in India has been overwhelmingly centred on consumption as the preferred indicator of economic well-being, we base our study on the nationally representative National Sample Survey (NSS) consumption surveys that are regularly fielded by the Indian National Sample Survey Office. This contrasts with most extant studies of mobility in India, which draw on income data from the Indian Human Development Surveys (IHDS) fielded by the National Council of Applied Economic Research (NCAER) and the University of Maryland.<sup>1</sup> We tackle the formidable analytical hurdle posed by the fact that India’s NSS surveys are cross-section surveys rather than panel surveys, by implementing a procedure to construct synthetic panels from the NSS surveys.

Yet, our study significantly extends the scope of analysis by Dang and Lanjouw (2018) in both breadth and depth. In terms of breadth, we expand the time window of analysis to the preceding two decades by comparing mobility in the 2000s against mobility that can be observed during the late 1980s and 1990s. In particular, we analyze five ‘thick’ (large-sample) rounds of the NSS for 1987/88, 1993/94, 2004/05, 2009/10, and 2011/12. In doing so, we comment not just on the extent of consumption mobility in India, but also on changes in mobility over time—a question of considerable interest, since this quarter-century has seen the introduction of a host of economic reforms, significant transformation of the Indian economy, and a dramatic acceleration of economic growth and poverty decline. This much longer time

> 1 We