rafmacalaba/data-use-mentions-extended huggingface dataset:
in large longitudinal studies such as the National Longitudinal Survey of Youth in the United States, the German Socio-Economic Panel and the Household Income and the Labour Dynamics in Australia Survey. Economists researching noncognitive skills (such as Cobb-Clark and Tan (2011); Heineck and Anger (2008)) have relied on these data and the measures.

Our analysis uses data on men’s and women’s self-esteem in 1 _,_ 511 farmer couples in Côte d’Ivoire to determine how relative self-esteem levels in the household affect spouses’ agricultural income-earning. Our findings show that while having higher self-esteem increases one’s likelihood of earning crop income, one’s spouse’s self-esteem reduces it. Own self-esteem emerges as a more important determinant of income-earning for women, who also suffer more from their partners’ self-esteem. A one standard deviation increase in female self-esteem is associated with her husband being 12 percent less likely to earn crop income, and a one standard deviation increase in male self-esteem is associated with his wife being 33 percent less likely to earn crop income.

Conforming to gendered norms about assertiveness, a high self-esteem wife reduces husband’s agricultural income-earning regardless of his own self-esteem; whereas for wives, a high selfesteem husband is not consequential as long as she also has high self-esteem. In line with theory, we find evidence that this channel operates through a higher ability to negotiate access to the fixed inputs in agricultural production functions. Self-esteem levels seem to instead operate as complements in off-farm employment, which relies less on fixed inputs.
the number of men. In terms of trip generation _rates_ – or number of trips made per person<sup>2</sup> per day – men in AMBA are estimated to make an average of 2.63 trips per day each, compared to 2.42 trips among women. The gender imbalance in trip generation rates is consistently observed across all age groups and is the largest in the eldest category, where men make about 18 percent more trips per day per person compared to women.

**_Figure 1_** _: Total trips and trip generation rates on a weekday in October 2021, by gender and age_

<!-- Start of picture text --> A. Total trips per day B. Trip generation rate<br>Men Women Men Women 3.2<br>5,000,000 2.9<br>2.7<br>2.5 2.6<br>4,000,000 2.4 2.3 2.4<br>2.2 2.2<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br><18 18-28 29-44 45-63 >64 <18 18-28 29-44 45-63 >64<br><!-- End of picture text -->

# **Secondly, the CDR analysis also suggests that women have shorter individual trips compared to men.**

About 66 percent of the trips made by women in AMBA according to the October 2021 data are shorter than 5 kilometers, compared to 61 percent of men’s trips. The higher distances traveled by men compared to women persist across the income spectrum although the difference declines somewhat at the higher income deciles. The average trip distance differences between men and women are also relatively consistent across travel modes as reported in the ENMODO 2018 household travel survey although are more distinct in the case of travel by car or by public transport
earners in Romania to be 13% for men and 18% for women. Based on the Structure of Earnings Survey 2018, Eurostat estimated the share of minimum wage earners to be 13%.<sup>18</sup>

> 17 The higher share of part-time workers in tax data may be driven by COVID-related reductions in working time, which are not recorded in the EU-SILC.

> 18 https://ec.europa.eu/eurostat/statistics-

<u>explained/index.php?ttle=File:Proporton_of_employees_earning_less_than_105_%25_of_the_minimum_wage,_2018_(</u>

> <u>%25).png</u>

30
important to note that, after applying the sample restrictions discussed in section 2, we do not eliminate any outliers from the sample, neither in wage levels nor in changes. We let our model of measurement error deal with this problem.

The set of covariates used to estimate the notional wage change distribution varies in the two countries, reflecting differences in data availability. The Brazilian data are more comprehensive and allow us to control for gender, age, tenure and their quadratic terms, educational attainment, occupation and sector of operation, establishment size, type of labor contract, and location of the firm (in Belo Horizonte or outside the state capital). In the case of Uruguay, we control for gender, age, tenure and their quadratic terms, sector of operation, white or blue collar status, establishment size, and the location of the firm (in Montevideo vs. the rest of the country).

Table 2 reports maximum likelihood estimates of the notional wage change equation parameters and their associated z-statistics for Uruguay and Brazil in 1998-1999.<sup>7</sup> The estimated coefficients are highly significant in almost all cases. Some commonalities within the two countries emerge. As expected, individual wage growth declines nonmonotonically with age, is higher in the capital, and tends to increase with establishment size, although wages in medium/large Brazilian establishments (those with the 50 to1,000 employees) display higher wage growth than the largest firms in the sample (those with 1,000 employees or more). Perhaps surprisingly, the effect of education and type of contract on individual wage growth in Brazil is not clear, as suggested by inconsistent signs across years, and often coefficients not significantly different from zero.

Figure 4 presents the wage changes predicted by the model, as well as the observed wage changes in 1997-1998. Panel A displays the corresponding distributions for Uruguay and panel B for Brazil. The predicted histogram is obviously smoother, but the overall fit of the model to the shape of the observed wage
<u>Table 3: Policy intervention/shock-NAFTA liberalization+elimination of PROCAMPO Using the CGE Model Simulation Results for Mexico</u>

|<br>**Impact on**|<br>**North**|<br>**Central**|<br>**Southwest**|**Southeast**|**Rio Bravo**|
|---|---|---|---|---|---|
|Household income<br>(urban)||||||
|Household income<br>(rural)||||||
|**Comments**|This simul<br>with elimin<br>by 10% of<br>impact of a<br>subsidies a<br>reflected in<br>simulated.<br>equally dis<br>not across<br>and Center<br>incomes in|ation combi<br>ating PRO<br>the gross v<br>combined<br>re similar in<br>rural hous<br>The drop in<br>tributed acr<br>regions. Th<br>regions suf<br>creases acro|nes the effect o<br>CAMPO incom<br>alue of their mai<br>NAFTA and re<br>general, the on<br>eholds‘ income<br>rural househol<br>oss household t<br>e RBB region s<br>fer the most. In<br>ss all regions an|f NAFTA trade<br>e transfers to m<br>ze production.<br>moval of ProC<br>ly important dif<br>drop when PR<br>ds‘ income is m<br>ypes (poor, med<br>
>_i_= 1</sup><sup>_,_2</sup><sup>_, ..., p_and</sup><sup>_εt_=</sup><sup>_G−_</sup> 0<sup>1</sup><sup>_ut_.</sup> The dynamic properties of the GVAR model in (8) can then be examined using Structural Generalized Impulse Response Functions (SGIRFs).

# **3 Data and relevant tests**

This section describes the data, model specification, the results of the weak exogeneity test, time varying weights and the contemporaneous effects.

## **3.1 Data and model specification**

I use quarterly data from 1994Q1 to 2016Q4 for China, Japan and ten ASEAN countries (i.e. Brunei, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam). The model is estimated with data on total trade (TRADE), real GDP growth (GROWTH), real effective exchange rate (REER) and short term interest rate (SIRT). If the data were not available in seasonally adjusted form, they were adjusted using the census X-12 method. Though the main objective of this study is to investigate the effects of trade and growth shocks, the short term interest rate is 7
. Based on the 2021 EU-SILC, a household living below the relative poverty line has, on average, 2.51 household members. This allows us to get to the cost of the food basket for a family of this average size or how much is needed to afford a food basket for a typical poor household. Then, we calculate the amount of this household income that needs to come from labor income, given that about 82 percent of overall household income comes from labor income in these families (our estimate based on EU-SILC data). To get the wage per worker estimate, we estimate the number of full-time equivalent workers in these households. These families have, on average, one member working full-time and 0.13 members working part-time (our estimate is based on EU-SILC data) and convert

> 18 These statistics are for households living below the relative poverty line whose labor income is strictly positive, and therefore, are affected by changes in wages via their labor income channel. These exclude households with solo pensioners, or other households that receive other sources of income but not earnings. 67% of poor single-person households are households with pensioners, and 42% of poor households are single-person households.

18
- Doraszelski, U. & Jaumandreu, J. (2019), Using Cost Minimization to Estimate Markups, CEPR Discussion Papers 14114, C.E.P.R. Discussion Papers. **URL:** _https://ideas.repec.org/p/cpr/ceprdp/14114.html_

- Draca, M., Sadun, R. & Van Reenen, J. (2006), Productivity and ICT: A review of the evidence, CEP Discussion Papers dp0749, Centre for Economic Performance, LSE.

- Dutz, M., R.K., A. & Packard, T. (2018), The jobs of tomorrow” technology, productivity and prosperity in Latin America and the Caribbean, Staff report, World Bank.

- Edmond, C., Midrigan, V. & Xu, D. Y. (2015), ‘Competition, markups, and the gains from international trade’, _American Economic Review_ **105** (10), 3183–3221.

- Edmond, C., Midrigan, V. & Xu, D. Y. (2018), How costly are markups?, Technical report, National Bureau of Economic Research.

- Hempell, T. (2005), ‘What’s spurious, what’s real? measuring the productivity impacts of ICT at the firm level’, _Empirical Economics_ **30** , 427–464.

- Hilbert, M. R. & Katz, J. (2003), ‘Building an information society: A Latin American and Caribbean perspective’.

- Iacovone, L., Perreira-Lopez, M. & Schiffbauer, M. (2016), Competition makes IT better: Evidence on when firms use IT more effectively, Policy Research Working Paper 7638, World Bank.

- ITU (2017), ‘The challenge on managing digital content’. Paper for the ITU-TRAI regulatory roundtable.

- ITU (2020), ‘Regulatory tracker’.
adapted to the electricity prices in the respective future electrification intervention.<sup>11</sup> The instrument was validated during pre‐tests and focus group discussions.

**Table 3: Initial price bid for WTP elicitation (randomly assigned)**

|**Burkina Faso**|**Rwanda**|**Senegal**|
|---|---|---|
|10 USD|1.67 USD|6 USD|
|20 USD|8.35 USD|16 USD|
|40 USD|16.7 USD|26 USD|

_Sources_ : Senegal (2011), Burkina Faso (2010), Rwanda (2011).

# _3.3 Balancing across the randomly assigned electricity consumption levels_

Each household was randomly assigned one of three electricity consumption levels. The randomization was done ad hoc by the enumerators in the field. Before entering a household, the enumerators drew one of three envelopes which contained the three electricity consumption levels and noted the drawn level on the questionnaire.

As can be seen in Table 4, the randomization produces three very similar groups, though some significant differences exist. Households that are assigned to the ʺgrid electricityʺ consumption level tend to be slightly better off than those assigned to the ʺSHSʺ or ʺsolar lampʺ consumption level. The size of the differences is not very big in most cases and accordingly economically not significant. Nevertheless, our results should be viewed with acknowledgment of possible biases induced by this slight imbalance.

**Table 4: Balancing across electricity consumption levels**

> 11 Burkina Faso exhibits the highest price structure because the SHS fee-for-service included capital costs, unlike Rwanda and Senegal where the program covered capital costs.

19
OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Using this demarcation, the unorganized sector makes up approximately 75% of total employment in both manufacturing and services. We also show below that our results for the services sector are very similarif we use the full sample of services firms.

Establishments are surveyed by the NSSO with state and four-digit National Industry Classification (NIC) stratification. We use the provided sample weights to construct populationlevel estimates. Much of our analysis employs district-industry variation. Districts are administrative subdivisions of Indian states or territories that provide more meaningful local economic conditions. The male or female business ownership questions that we use are an outcome of the survey and not a factor in the stratification design.We have confirmed that our results hold at the state-industry level, for which the stratification was originally designed.

2Organized manufacturing establishments are not surveyed by the NSSO because they are instead surveyed by the Annual Survey of Industries (ASI). The ASI unfortunately does not collect the gender of the business owner. Ghani et al. (2011b) describe the organized and unorganized sectors in greater detail.

5
restrictions after the sanctions. The raw data confirm that households in these industries experienced the lowest incidence of family income changes after the sanctions. Figure 2 panel (a) displays the real median family income for treated households and their synthetic counterpart in the period 1995-2015. The synthetic control almost exactly reproduces the family income for the treated households during the entire pre-sanctions period (1995-2006). As Figure 2 panle (b) shows, the gap between the actual and counterfactual family income widens from around zero over the pre-sanctions period, to 45% in 2013.

Table 1 reports households’ and children’s characteristics of the synthetic group comparing to treated households in the absence of the sanctions. The variables overall are well balanced between these groups. It is important to emphasize that many economic sectors were affected by the sanction. This paper compare the most affected households with the least affected comparison group. In section 6, I check the validity of the synthetic control for counterfactual by checking the sensitivity of results to the choice of different matching methods (different matching variables, different matching year range, and different methods for selecting weights) and different selected donor industries. Overall, the sensitivity tests verify the robustness on the original results (Table S4).

Table 1: Descriptive Statistics (before the 2006 UN Economic Sanctions)

||Treatment|Synthetic Control|
|---|---|---|
|_Household-level variables_|||
|Ln(Total Family income)|18.59|18.50|
||(0.60)|(0.74)|
|demean|-1.23e-07|-7.66e-07|
||(0.59)|(0.73)|
|Ln(Labor income)|18.15|18.02|
||(0.61)|(0.78)|
|demean|-1.63e-07|4.86e-07|
||(0.61)|(0.
0.000)|(0.000)|(0.000)|(0.000)|
|PM's income from main job (logged)|-0.043***|-0.002|-0.023*|0.010|
||(0.011)|(0.008)|(0.012)|(0.009)|
|Share of underemployment in household|0.058|-0.070|0.084|-0.052|
||(0.076)|(0.080)|(0.080)|(0.085)|
|Respondent non-agricultural income|-0.000***|-0.000|-0.000***|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Household enterprise operating period|-0.000|-0.000|-0.000|0.000|

23
60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Delta Edo Rivers Ekiti Lagos Ogun Ondo Osun Oyo<br>Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17<br><!-- End of picture text -->

# Source: Authors’  estimates based on MICS 2016/17 data

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. For Ebonyi the shares for urban boys and girls are excluded because of too limited sample size. The threshold for exclusion is set at less than 30 observations per demographic group.

29
*|1983|1990|1985|1992|
|**Luxembourg**|1990|1990|1981|1993|
|**Netherlands**|1980|1981|1978|1992|
|**Portugal**|1992|1992|1992|1992|
|**Spain**|1992|1992|1987|1994|
|**United Kingdom**|1979|1979|1979|1993|
|**Source: Buch and Hei**|**nrich(2002).**||||

7. In 1999, the adoption of a five-year financial harmonization program—the Financial Services Action plan (FSAP) —brought about a new level of financial integration. The FSAP‘s main objectives were (i) the development of a single market for wholesale financial services; (ii) the creation of open and secure retail markets; (iii) the establishment of clear, efficient, prudential rules and supervision of financial services; and (iv) the establishment of the conditions for an optimal single financial market. The Lisbon agenda (2000) and the re-launching of the Lisbon strategy (2005) further reinforced the goal of creating a single financial market. These reforms resulted in a fully integrated financial market. Using the Chinn-Ito index which measures openness in capital account transactions the table below shows that countries reached the status of full liberalization after the adoption of the euro in 2002 (Table 2).

4
For the majority of beneficiaries, at or shortly after six months, the wage paid by employers exceeds the sum of the salary and incentive they accepted at the start of the job. By this time, the incentive may thus not be necessary to keep people in their job.

# **3.3 Data sources**

For our main employment outcomes, we use monthly social security data (IMSS). In Mexico, employers are required to register contributions on behalf of formally employed workers. IMSS data are reported monthly and include information on the type of contract the worker holds (temporary or permanent) and the daily salary reported by the employer. The administrative data are comprehensive of private sector employees, but exclude public sector employees since their social security contributions are managed through a different agency. Given that the public sector is small in San Luis Potosi (the study region) and only 2% of the study sample report holding a public sector job in our endline survey, this omission is unlikely to affect our estimated program impacts. We consider the use of administrative data as an important contribution of this paper. Most existing studies from developing countries in this literature rely on self-reported employment data, which are prone to surveyor demand effects and recall biases (McKenzie, 2017). High-frequency administrative data also allow us to accurately estimate cumulative treatment effects on work experience and income required to conduct a cost-benefit analysis.

We complement administrative data with two rounds of in-person data collection: a baseline survey prior to the workshop and a follow-up survey completed in June 2021, two years after the treatment. Both surveys are administered through a professional survey firm.<sup>20</sup> For the endline survey, we successfully surveyed 75% of participants. The overall attrition rate is slightly lower for treatment participants: 23.2% for the treatment and 27.2% for the control group. In addition, we administered an SMS survey in October and November 2021 to collect data on people’s time preferences and
other costs of imperfect input smoothing – such as unnecessary fluctuations in employment (which is costly for the workers and the state) and the higher risks of bankruptcy (as Table 2 showed that firms relying on informal finance were less likely to have gone bankrupt than firms relying on formal finance).

# **5.  Discussion and conclusions**

In this paper, we have analyzed risk coping behavior by firms in Cambodia following the global financial crisis using a panel survey that was implemented before the crisis (August 2007-March 2008) and during the crisis (July – November 2009). The survey shows that firms in Cambodia were hard hit by the economic crisis between 2008 and 2009, with an average fall in demand (sales) of 30% (Table 1). Firms

> 22 (30 x (0.13-0.10)) = 0.9 (column 3) and (30 x (0.13+0.10)) = 6.9 (column 4). 23 (6.9-0.9)2/(0.9)2=44.4.

28
guidance as to which road segments to invest first based on their importance in maintaining decent travel times for many commuters under wet conditions.

Finally, we hope to point out that given climatic uncertainties in the future, the frequency and magnitude of floods in Kinshasa and other regions around the world will likely increase. The 10-year flood scenario has the lowest exposure level among all five flood return periods that we examined in this study. As climate change impacts unfold over time, the potential impact of floods on transportation is likely to increase dramatically. It is possible to imagine that the economic cost we report for the current 50-year flood event, for example, will occur more frequently in the future, thereby increasing the returns from resilient transport infrastructure planning and design in Kinshasa.

A limited number of uncertainty areas deserve some discussion. First, because of the difficulty of surveying the entire public transport network during the narrow window when floods are still affecting travel conditions, only a share (approximately 25%) of routes were mapped under wet conditions. While we implemented strategies to overcome this data incompleteness and were able to draw valuable and reasonable insights from the sampled data, future studies could provide a more complete picture of public transport during heavy rain episodes in developing country cities and could help establish causal chains between floods, vehicle frequencies, rerouting and decreased travel speeds more clearly. Ongoing work on the city of Kigali, Rwanda, which has a smaller population, urban footprint and public transport network, will include a survey of the entire transit system in both dry and wet conditions, and will provide a more complete and precise picture of public transport disruptions from floods. Second, in our study, we used mosaics of the maximum value of each raster pixel from fluvial and pluvial flood map products to approximate flood depth and extent induced by the combination of both types of floods. Although this provides a useful estimate that captures the extremes of both types of floods, such operation lacks considerations of the hydrodynamics of flood processes and may introduce error and uncertainties