rafmacalaba/data-use-mentions-extended huggingface dataset:
case, since the changes over time are a function of initial conditions (through an endogeneous growth model) that also influence program placement. On correcting for this bias by controlling for the area characteristics that initially attracted the development projects, the authors found significant longer-term impacts while none had been evident in the standard _DD_ estimator.

The second example is the Pitt and Khandker (1998) study of Grameen Bank. Following my interpretation above of their method of assessing the impacts of GB credit, it is clear that the key assumption is that the returns to having land are independent of village-level GB eligibility. A bias will arise if GB tends to select villages that have either unusually high or low returns to land. It seems plausible that the returns to land are lower in villages selected for GB (which may be why they are poor in the first place) and low returns to land would also suggest that such villages have a comparative advantage in the non-farm activities facilitated by GB credit. Then the Pitt-Khandker method will overestimate GB’s impact.

The upshot of these observations is that controlling for initial heterogeneity is crucial to the credibility of DD estimates. Using PSM for selecting the initial comparison group is an obvious corrective, and this will almost certainly reduce the bias in DD estimates. In an example in the context of poor-area development programs, Ravallion and Chen (2005) first used PSM to clean out the initial heterogeneity between targeted villages and comparison villages, before applying DD using longitudinal observations for both sets of villages. When relevant, pipeline comparison groups can also help to reduce bias in DD studies (Galasso and Ravallion, 2004). The DD method can also be combined with a discontinuity design (Jacob and Lefgren, 2004).

These observations point to important synergies between better data and methods for making single difference comparisons (on the one hand) and double-difference (on the other). Longitudinal observations can help reduce bias in single difference comparisons (eliminating the additive time-invariant component of selection bias). And successful efforts to
parallel workplace-orientated training system. In the present climate, it would seem that the chances are slim.

# 5. Summary **and Conclusions**

This paper examines, for the case of Israel, the efficacy (in terms of labor market outcomes) of vocational school education in relation to that of the academic secondary school, for non-postsecondary school attenders. Given the relatively small fraction of youth that attend, and complete, tertiary education in developing countries, the secondary school completer population that does not continue into tertiary education, plays an important role in economic development. Since vocational schooling in Israel is sizeable - with over half of Israeli secondary school pupils attending vocational schools or vocational streams in comprehensive schools, it is quantitatively far more important than in most other countries - the Israeli setting seems to be a fitting one for a case study comparing the outcomes of academic and vocational schooling. It is hoped that the study contributes to a firmer basis for policy decisions concerning the appropriate role to be accorded to vocational education within the schooling system as a whole.

Using data from the 1983 population census, the study shows vocational schooling to be more cost-effective than general academic education. In particular, those vocational school completers that work in occupations related to the course of study pursued at school earn more (by up to about 10 per cent annually) than their counterparts that attended

- 46 -
the population with mobile phone access between the non-agricultural population and the most dependent on agriculture is more than 40 percentage points.

_<mark>Figure 2. Share of population with mobile phone access, by share of total household income from agriculture.</mark>_ <!-- Start of picture text --> Income from agriculture > 30% No income from agriculture<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> _Source: Authors’ calculation based on the FAO Rural Livelihoods Information System (RuLIS) database_ A similar pattern in mobile phone access between agricultural and nonagricultural households is observed in the countries where the World Bank HFPS were implemented. Using HFPS and LSMS-ISA data from Ethiopia, Malawi, Nigeria, and Uganda, Ambel et al. (2021) demonstrate that the sample of households who own or have access to mobile phones are considerably different from the sample without access to a mobile phone. Households without access to a mobile phone tend to be poorer and have lower living standards (across multiple dimensions) relative to the sample of households with access to a mobile phone (Ambel et al., 2021).

This issue of coverage is common to all three of the predominant phone survey sampling approaches. In what follows we discuss possible approaches to mitigate this risk during different stages of survey implementation, and in Section 3.3 we discuss post-survey adjustments that attempt to manage this problem.

# _Nonresponse_

While the issue of coverage is critically important for the representativeness of phone surveys, another important factor is the pattern of nonresponse. In phone surveys in particular, nonresponse can be linked not only to refusals, but to failure of the interviewers to establish contact. Just like refusals, this type of nonresponse can be systematic and therefore introduce bias. For
Monthly inflation expectations for the next year|Central<br>Bank<br>of<br>the<br>Russian Federation|
|TUR|Monthly survey of expectations for future output, inflation,<br>interest rates, and FX rates,|Central Reserve Bank of<br>Turkey|
|THA|Professional forecasts on future inflation and output|Consensus Economics|
|ZAF|Bureau of Economic Research Inflation expectations for current<br>and next years<br>South African Chamber of Commerce and Industry Business<br>Confidence Index|Bureau<br>of<br>Economic<br>Research,<br>South African Chamber of<br>Commerce and Industry|
|KOR|Monthly consumer survey index of inflation expectations|Bank of Korea|
|SWE|Monthly consumer trend survey of the general economic<br>situation and inflation|National<br>Institute<br>of<br>Economic Research|

41
grandparent–grandchild relation. This finding implies that women in Pakistan are more likely to invest in education, particularly girls’ education, when they have more decision-making power.

# **5. Conclusions**

The household is central to most policy initiatives aimed at reducing poverty. A clear understanding of the ways in which households make decisions is critical to the effective implementation of anti-poverty programs. Because a significant portion of economic activity occurs within the household, this is particularly important in developing countries, where there are efforts to seek the most efficient and effective ways to transfer income and other resources to poor households. Few studies have examined women’s bargaining power and human development outcomes in cultures where men’s power dominates and women are often perceived to have limited impacts on household decisions. Using data from Pakistan, this study finds that, similar to other countries, Pakistani households behave as a collective model would predict. Women participate in various decisions. The results that when women have more decisionmaking power, household investments shift from food and transportation to education, medical care, footwear and clothing, and fuel and lighting. The relationship between women’s decisionmaking power and education expenditures appears particularly strong. The relationship between changes in women’s power and changes in household expenditures differs slightly in rural and urban areas. For example, when rural women have more power, they tend to spend more on fuel and lighting, but women in urban households spend more on transportation. This result suggests that when women gain more power over decisions about household spending, they tend to spend more money on the activities they pursue the most. In both urban and rural areas, however, households spend more on education when women have more power.When women have more 15
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income using CHIP data do not suffer from truncation bias, unlike the estimates of intergenerational persistence; whether some of the children were nonresident at the time of the survey is not relevant for this analysis.

> 37The 5 year income data cover from 1998 to 2002 in the CHIP 2002.

> 38The 3 years income data in CHIP 1995 cover 1991, 1993, and 1995.

23
, and the number of homicides fell 5.4 percent, causing Colombia’s murder rate to reach its lowest level since 1979.<sup>19,20</sup> In contrast, the United States excess mortality estimate for 2020 reflects in part a 7.2 percent increase in motor vehicle deaths, a 29.4 percent increase in homicides, and a 30.0 percent spike in drug overdose deaths.<sup>21–23</sup> Official COVID-19 death reports and excess mortality estimates show broadly similar age patterns country-by-country. This finding, along with the fact that direct COVID-19 deaths account for the large share of excess mortality deaths, suggests that the flatter profile of excess mortality in lower income countries is driven principally by direct COVID-19 deaths. To 14
offline flows, in the case of US imports we do have data on freight and insurance costs from USITC. When plotting these costs against distance (see Figure 4) we find that for both online and offline flows, shipping costs are uncorrelated with distance, even though shipping costs seem to be much higher online.<sup>7</sup> This suggests that the introduction of observable shipping costs in the gravity equation, which are often omitted due to lack of data, is not going to explain the importance of distance in the gravity equation. But this is a testable hypothesis at least in the online sample.

Offline trade data and trade cost variables come from the usual sources and are described

> 7Using data on all country pairs online gives a similar picture.

7
and create a LinkedIn profile; or

- a Rp100,000 convenience store shopping voucher.<sup>22</sup>

Choosing the career mentoring course is taken to indicate support for their own (for female respondents) or their wife’s (for male respondents) labor force participation. A comparison of this variable across the control and treatment groups provides an estimate of the interventions’ impacts.<sup>23</sup>

> 21In the first survey we did not collect information on the support of mothers or mothers-in-law for married women with children working outside the home. The information provided on norms in this domain is constructed from 2018 Indonesian World Values Survey data on the proportion of women aged 40 to 60 who disagree or strongly disagree with the statement “When a mother works for pay, the children suffer”. For more information on the World Values Survey, see Haerpfer et al. (2022).

> 22For information on the career mentoring course, see https://skillacademy.com/p/career-mentoringregular-bimbingan-untuk-dapat-kerja?courseType=SingleCourse. The shopping vouchers were redeemable at Indomaret and Alfamart stores which are ubiquitous throughout urban Indonesia.

> 23We also conducted a list experiment which generates an alternative outcome measure. At the end of the survey (just prior to the choice of reward), respondents were asked how many of the following statements they agreed with (in randomized order): a) The minimum wage should be kept at its current

11
|5.163|5.254|-0.760|0.447|
|Male head|0.723|0.777|-2.383|0.017|
|Secondary education (head)|0.150|0.328|-8.016|0.000|
|Age (head)|44.065|43.681|0.468|0.640|
|Observations (households)|658|822|||

_Notes:_ Numbers in columns (1) and (2) are raw means. The means in the rows “Amount paid for electricity”, and “Regular blackouts” are based on households that have electricity in their dwelling.

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

Table 2 presents the results of the DiD regressions specified in equation (1) for three different outcome variables capturing access to electricity, amount paid for electricity, and regular blackouts. In columns (1), (4), and (7), the respective outcome variable is regressed on the time indicator (pre or post program), treatment indicator, and the interaction term between the time and treatment indicators. The specifications reported in columns (2), (5), and (8) include additional control variables. The regressions in columns (3), (6), and (9) further add district fixed effects (FE).

The interaction term ( _Post × Treat)_ is statistically significant in most of the specifications in columns (1) to (3) and (7) to (9) in Table 2, indicating that the project was associated with an increase in electricity access and a decrease in the reported occurrence of regular blackouts. At the same time, the insignificant coefficients of the interaction term in columns (4) to (6) suggest that the project did not cause households who were living in project areas to pay
Parameters and Mixed Logit: Households' Choice of Electricity Supplier, No E00-274, Economics Working Papers, University of California at Berkeley, <u>http://EconPapers.repec.org/RePEc:ucb:calbwp:e00-274.</u> Rights and Resources Initiative. 2014. _What Future for Reform? Progress and Slowdown in Forest Tenure Reform since 2002_ . Washington DC.

Saatchi, S.S, N. L. Harris, S. Brown, M. Lefsky, T. A. Mitchard, W. Salas, B. R. Zutt, W. Buermann, S. L. Lewis, S. Hagen, S. Petrova, L. White, M. Silman and A. Morel. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. _Proceedings of the National Academies of Sciences_ 108(24): 9899-9904.

Smith, K. R., Uma, R., Kishore, V.V.N., Zhang, J., Joshi, V. and Khalil, M.A.K. 2000 Greenhouse Implications of Household Stoves: An Analysis for India. _Annual Review of Energy and the Environment_ , 25: 741–63 Somanathan, E. and R. Bluffstone. 2015. Biogas: Clean Energy Access with Low-Cost Mitigation of Climate Change. _Environmental and Resource Economics_ Vol 62: 265-277.

Springate-Baginski, O. and Blaikie, P. 2007. (Eds.). _Forests, People_ and _Power: The Political Ecology of Reform in South Asia._ EarthScan Forestry Library. London and Sterling: Earthscan.

Strassburg, B., R. K. Turner, B. Fisher, R. Schaeffer. and A. Lovett. 2009. Reducing Emissions from Deforestation – The “Combined Incentives” Mechanism and Empirical Simulations. _Global Environmental Change_ 19: 265-278.

Timilsina, N. P. and Paudel, N. S. 2003. State Versus Community: A Confusing Policy Discourse in Nepals Forest Management.
,113|124.198|2.328|
|Per capita transport expenditure (in SDGs)|5,840|393.269|6,113|361.97|31.299***|
|Per capita personal care expenditure (in SDGs)|5,840|199.746|6,113|182.771|16.975***|
|Per capita house related expenditure (in SDGs)|5,840|218.84|6,113|186.886|31.955***|
|Per capita recreational expenditure (in SDGs)|5,840|47.559|6,113|49.777|−2.218|
|Per capita other expenditure (in SDGs)|5,840|39.972|6,113|34.494|5.478|
|Food Insecurity (=1)|5,840|0.081|6,113|0.103|−0.022***|
|Household size|5,840|5.506|6,113|6.162|−0.656***|
|Household asset index|5,840|26.638|6,113|24.063|2.575***|
|Household tropical livestock units|5,840|4.292|6,113|6.678|−2.385**|

_Source:_ Authors’ calculation using 2014/15 NHBPS data. *** p<0.01, ** p<0.05, * p<0.1

# 2.3 Covariate Shocks

In addition to idiosyncratic shocks, households may also be exposed to covariate shocks that potentially affect all households in a community such as a town or state. In this section, we describe the exposure of Sudanese households to covariate shocks at the state level. We consider deviation of
, similar to other countries, Brazilian law allows small purchases to be performed without competitive auctions (Fazio, 2022). We can also compute measures of competitiveness in tenders, such as the number of participants per tender - the average fluctuates between two and four across the municipalities in our sample. The dataset often contains unique identifiers for each supplier, which in Brazil vary if the supplier is an individual or a firm, allowing us to compute the share of suppliers identified as firms (which vary substantially across states, from less than 60% in CE and PB to over 80% in MG).

Statistics for the budget execution dataset are presented in Table 3. Our budget execution dataset includes over 880 million observations - over 250 million commitments, around 300 million verifications and over 300 million payments. For all three stages of the budget execution, we present the total number of observations and the total number of _distinct_ events – in some cases, we are unable to assess whether two commitment observations, for example, refer to the same commitment or not. In those cases, we set our respective identifier variable to missing to flag to users that we are unsure whether these are unique events that can be tracked across datasets. Using the budget classification discussed previously, we estimate that between 25%-35% of total budget commitment events are related to the procurement of

> 17Municipalities that are in our sample are slightly richer and more educated than those that are not. In 2019, the average GDP per capita (literacy rate) in our sample was 26.8 thousand BRL (87.7%), while in the municipalities that are not in our sample it was 22 thousand BRL (82.5%). Population size, however, does not differ across the samples (37.7 thousand inhabitants in both).

8
**Benchmark model.** In this model, consists of year-on-year changes in log real crude oil prices (global variable), log real natural gas prices (global variable), log real coal prices (global variable), log real GDP tt (country-specific variable), and current account balance as percentage of GDP (country-specific variable). zz We follow a similar ordering of variables as in earlier models, where growth rates of real natural gas prices and real coal prices are, respectively, ordered right after real oil prices growth and right before countryspecific real GDP growth. The motivation behind this ordering is due to the market share of these commodities in global energy exports, although we also consider an alternative ordering of these variables below.<sup>5</sup> All variables can impact other variables after one quarter.

**Robustness #1.** We check the robustness of the benchmark results to an alternative ordering of the energy price variables, where real coal prices are ordered before real natural gas prices. This way, we control for the possibility of real natural gas prices being immediately affected by shocks to real coal prices, although shocks to real natural gas prices can have an impact on real coal prices after one quarter. All other details are the same as in the benchmark model.

**Robustness #2.** As in Section 3.1, we check the robustness of the benchmark results to the inclusion of country-specific real effective exchange rates as an additional endogenous variable. We follow a similar ordering of variables as in the benchmark model, with the new real effective exchange rate variable ordered last, as it can be affected immediately by any changes in other variables. All other details are the same as in the benchmark model. As data for real effective exchange rates are not available for certain countries, this model is estimated individually for 29 EMDEs by using quarterly data.

# **3.3. Disentangling oil price shocks: Demand versus supply**

The
(2009). Measuring microenterprise profits: Must we ask how the sausage is made? Journal of Development Economics 88, 19–31 Fiala, N., and Premand, P. (2018). Social accountability and service delivery: Evidence from a large-scale experiment in Uganda. Mimeo Flores Macias, F., and Lawson,C. (2008). Effects of interviewer gender on survey responses: Findings from a household survey in Mexico. International Journal of Public Opinion Research, vol. 20 (1), 101-109.

Hatchett, S., and Schuman, H. (1975). White respondents and Race-of-Interviewer effects. Public Opinion Quarterly, vol.39 (4), 523-528 Himelein, K. (2016). Interviewer effects in subjective survey questions: Evidence from Timor-Leste. International Journal of Public Opinion Research, vol. 28 (4): 511-533 Huddy, L., Billig, J., Bracciodieta, J., Hoeffler, L., Moynihan, P.J., Pugliani, P. (1997). The effect of interviewer gender on the survey response. Political Behavior, vol. 19: 197-220 Jæger, M.M. (2016). Hello beautiful? The effect of the interviewer physical attractiveness on interview success. Sociological Methods & Research, forthcoming 22
D|0.65|1.24|1.52|2.41|0.95|1.75|0.78|1.30|0.67|1.23|0.63|0.95|
|75–25|0.88|1.74|1.99|3.34|1.43|2.61|1.26|1.94|0.81|1.60|0.82|1.28|
|90–10|1.99|3.25|3.94|5.67|2.89|4.47|2.56|3.67|1.60|3.11|1.59|2.44|
|Cov (TFPQ,TFPR)|0.7|0|0.|85|0.6|9|0.|74|||||
|Reg.Coef|0.4|2|0.|52|0.4|4|0.|53|||||
|N|4146|4146|757|757|1151|1151|4012|4012|41,006|41,006|211,304|211,304|

Note: Log(TFPR) and Log(TFPQ) are demeaned by industry-specific average. Industries are weighted by their value-added shares. The statistics for Cˆote d’Ivoire are calculated by taking the average for the years 2003-2012. The statistics for India and China are taken from Hsieh and Klenow (2009). We compute these statistics for India using the Prowess database and obtain similar values as in Hsieh and Klenow (2009).

# **6.2 Calculating Counterfactual Productivity**

Next, we use our estimates to perform counterfactual liberalization experiments. Specifically, we assess the potential productivity gains associated with equalizing total factor revenue productivity (TFPR) across the existing set of
/sup> Finance companies' profitability also improved during the early 1990s due to (i) an increase in net interest rate mrargins, (ii) a higher share of risk assets, and (iii) an increase in income from trading and brokerage activities. From 1993 to 1996, however, profitability of finance companies decreased quite markedly. Most of this decline was due to a fall in noninterest income from securities trading (which fell 77 percent during 1993-96) and brokerage fees (which decreased by 47 percent over the same period).<sup>4 9</sup>

- 45 ROE in the 90s are most probably overstated as financial institutions were subject to lenient interest accrual and loan loss provisioning guidelines. By the same token their capital is most likely overstated.

- 46 Compared to banks operating in more stable economies such as the UK and Germany (where banks recorded average net interest margins of 2.9 and 3.1 percent respectively over 1990-1997), Thai net interest rate margins (2.8 percent) appear out of line in terms of risk profile. Data Source: Worldscope.

- 47 However, the quality of the loan portfolio differed across banking institutions. Big banks still showed moderate levels of nonperforming loans and a smaller increase in accrued interest while smaller banks were more adversely affected by the slowdown in the economy and the collapse of the real estate sector.

- 48 The tax treatment of provisions also did not provide adequate incentives for commercial banks to provision. Thailand does not allow deductions from taxes that are greater than the lower of .25 percent of loans each year or 20 percent of interest income.

- 49 The stock market peaked in December 1993 and started to decline thereafter.