rafmacalaba/data-use-mentions-extended huggingface dataset:
The shortcomings in terms of cross-country availability, comparability, and quality of individual-level data on time use can in part be traced back to underinvestment in rigorous methodological research for the development of scalable and accurate survey methods (Daum et al., 2019; Kilic et al., 2024). Research has drawn attention to the weaknesses in the availability and quality of individual-level survey data and statistics on time use measurement (Chatzitheochari et al., 2017; Harvey and Taylor, 2000; Juster et al., 2003; Kan and Pudney, 2008; Kes and Swaminathan, 2006). Studies have emphasized the importance of developing improved survey methods for time use (Bauman et al., 2017; Buvinic and King, 2018; Charmes, 2015), particularly in low-income contexts with high prevalence of illiteracy and unfamiliarity with clock-oriented time (Seymour et al., 2020).

> 2 Time use diaries can also be self-administered, and respondents can use a paper diary or an electronic device (e.g., tablet or smartphone) and are instructed to use it to record all their activities during the reference period.

2
# **4. Econometric results**

Controlling for unobserved household characteristics allows us to quantify impacts of share tenancy and compare them to the literature. With a 20% difference in net revenue between owned and sharecropped plots, the size of the Marshallian inefficiency estimated here is high relative to what is found by other studies. Input use is also significantly lower on barga plots, though the size of the effect varies depending among others on the ability to observe inputs. We also find evidence of a negative investment effect, the estimated magnitude of which ranges between 26% for soil conservation and 7% for private irrigation. Combining these implies a conservative estimate of tenancy-induced productivity losses of about 25%.

# **4.1 Cross-sectional evidence from listing data**

As descriptive data point towards differences in observables between owner-cum tenants and others, checking for potential differences in unobservable attributes that may set this group aside and possibly compromise the external validity of results from our plot-level analysis is needed. To do so and at the same time obtain a proxy of overall effects of share tenancy on output and investment, we use listing data to run cross-sectional regressions for all 40,000 agricultural cultivators. Two specifications are used, one with a zero-one indicator each for pure tenants and owner-cum tenants and one with the share of tenanted land in a household’s total cropped area as independent variables on the right hand side.

Results for output from production in the current period (columns 1-4 of table 5) suggest that, irrespective of whether gross or net revenue is used as a dependent variable, tenancy has a statistically significant and economically meaningful impact on productivity. The point estimate for the magnitude of the ‘Marshallian inefficiency’ for pure tenants is close to 20% in both cases. At the same time, with estimates of 11% and 14%, the coefficient for owner-cum tenants is significantly smaller (col. 1 and col. 3); in fact we can reject the hypothesis of the two coefficients being equal at the
in school management - TCHPARTI indicates a Dev7 mean value of -0.2169 compared to 1.6445 for Vietnam. Principals in Dev7 are more likely to say that they communicate and act on school goals (LEADCOM), but there is much higher prevalence of public posting of school achievement data (SCORE ~~P~~ UBLIC) in Vietnam. Interestingly, even Dev7 countries have high levels of achievement tracking data by authorities (80% of schools report this - SCORE ~~A~~ UTHRITS). Finally with regard to the school climate, indices described further in the PISA documentation, STUDCLIM (student climate) is roughly even between Vietnam and Dev7, but TEACCLIM (teacher climate), that includes variables such as teacher absenteeism and teacher expectations of students, is higher for Vietnam.

# **2.10 Preliminary conclusions from comparison of endowments**

In summary, the mean comparisons between Vietnam and Dev7 students finds a number of potentially insightful results. Consider the four-fold classification of factors presented in the conceptual diagram of Figure 3 - students, parents, teachers and the school, the findings are summarized below.

**Students** : Students in Vietnam are more likely to have attended pre-school and less likely to have repeated grades in the past. They are likely to behave more disciplined at school, skip fewer classes, and assume greater responsibility for their own learning. Vietnamese students are less likely to brag about their abilities and experience and yet work harder, especially out of school, in extra classes. They tend to have lower anxiety about mathematics and higher about the usefulness of mathematics in their future.

**Parents** : Parents in Vietnam are likely to be more involved in the school life of their children than parents of students in Dev7 countries. Though time spent on homework help is similar in both groups, Vietnamese parents are more likely to volunteer and take part in fund-raising for the school and help the teachers as classroom assistants. Vietnamese parents are also more likely to seek
6 details on bilateral tariffs (incorporating all significant reciprocal and non-reciprocal preferences) including ad valorem tariff equivalents of specific and compound tariffs and key non-tariff barriers such as tariff rate quotas (TRQs).<sup>2</sup> The new GTAP database has lower tariffs than the previous Version 5 database because of the inclusion for the first time of non-reciprocal trade preferences and because of major reforms between 1997 and 2001 such as China’s progress towards WTO accession (which alone contributed to the ratio of global exports plus imports to GDP rising from 44 to 46 percent over those four years) and the continued implementation of the Uruguay Round agreements (van der Mensbrugghe 2006).

According to this dataset, the average import tariff in high-income countries in 2001 is 20.2 percent for primary agricultural products, and 13.0 percent for processed food, compared with just 0.5 percent for other primary products and 2.1 percent for other manufactures.<sup>3</sup> In developing countries, by contrast, the average tariff for primary agriculture is lower at 17.0 percent, while it is higher for the other three sectors at 19.3 percent for processed food, 2.5 percent for other primary products, and 10.0 for other manufactures. Needless to say, these averages vary considerably within those two country groups, as shown in Table 1(a).

These import tariff averages can be poor indicators of overall assistance to farming, even if there were no farm production or trade subsidies or any exceptional tax

> 2 More information on the MAcMaps database is available in Bouët et al. (2004) and at <u>http://www.cepii.fr/anglaisgraph/bdd/macmap.htm. For details of its incorporation into the GTAP</u> Version 6 dataset, see Dimaranan and McDougall (2005) and <u>www.gtap.org.</u>

> 3 High-income countries include all members of the
|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|Financial services including insurance|49|<br>2.0%|
|Utilities|32|1.3%|
|Other professional, scientific, and technological|20|0.8%|
|<br>Real estate|18|0.7%|
|Hospitality|16|0.7%|
|<br>Telecom|13|0.5%|
|Media|9|0.4%|
|Health|7|0.3%|
|Arts and recreation|6|0.2%|
|Education|5|0.2%|
|Scientific research and development services|5|0.2%|
|Water supply and waste management|3|0.1%|
|<br>Residential care and social work|2|0.1%|
|Machinery and equipment|1|0.0%|
|<br>Metals and metal products|1|0.0%|
|<br>Public administration and defense services|1|0.0%|
|Other or unclassified services|357|14.7%|

_Source_ : Computation based on the 2019 GIC Survey.

_Source country_ : Overall, nearly 80 percent of respondents report having foreign owners from the East Asia & Pacific and Europe & Central Asia regions (see Table 2).<sup>3</sup> The vast majority of respondents (87 percent) report having foreign owners from high-income countries.

**Table 2. Respondents by region of foreign owner**

|**Host**<br>**country**||**Decline**<br>**to**<br>**answer**|**East**
following decomposition (under normality): 2 Since _cov_ � _βf Sf_<sup>_p_</sup> � _≥_ 0 , assuming independence in equation (10) will in general underestimate the role of the intergenerational transmission of schooling. The evidence on intergenerational income mobility in Denmark reported by Bingley and Cappellari (2019) suggests that the relaxation of the independence assumption is especially important; the estimated share of the intergenerational component (father's income) increases substantially as a result.

The decomposition in equation (11) relaxes two important restrictive assumptions in the standard specication (5): homogeneity in _βf_ and independence between _βf_ and _Sf_<sup>_p_,but</sup> it relies on the normality assumption which is rejected by the data in most of the cases. Bingley and Cappellari (2019) nd that imposing normality tends to underestimate the share of intergenerational inuences in sibling correlation. They relax the normality assumption by using an unrestricted form of the intergenerational correlation between the children and parents. In our empirical analysis, we will report estimates from both the classic methods (Bjorklund et al. (2010), Mazumder (2008), Solon (1999)), and the method due to Bingley and Cappellari (2019).

# (4) Data and Estimation Methods

A major hurdle for credible cross-country ranking of inequality of opportunity and intergenerational mobility is that data from dierent surveys may not be comparable. As noted earlier, the survey instruments used for education information by DHS are standardized across countries which makes the data much more comparable.<sup>27</sup> Even when trying to elicit the same information (say education of parents and children), dierent household surveys may contain dierent kinds of data. In the context of intergenerational educational mobility, there are two issues relevant here. First, whether data on educational attainment refer to years of schooling or education categories (primary, secondary etc.). The DHS data we use have
Algadaref State, poverty was one of the five reasons cited for child marriage, but it was not the most common response and in a similar study in neighboring Red Sea State, poverty was not mentioned at all.<sup>15</sup> Four of the five reasons respondents in the Algadaref State study cited had to do with patriarchal customs: control of girls, fertility, protection of girls from immorality, and protection of girls from the stigma of being unmarried. Protection from immorality, which was the most common response, can mean any number of things. In a review of the three related studies conducted in Red Sea State, Kassala State, and Algadaref State, protection from immorality included discouraging, if not outright forbidding, girls from attending school since school uniforms, public transportation, and mixed-gender classrooms challenge patriarchal customs regarding female modesty—the subject of the public order laws—and the idea that the public sphere is the male domain. In some parts of Red Sea State, an estimated 70 percent of girls stopped going to school by the sixth grade (Tonnessen and El Nagar 2018).

The stigmas of being unmarried and not being able to have children, perhaps because one waited too long to get married, also motivate the choice of early marriage over education. That said, the Algadaref State study revealed that this patriarchal custom appears to be weakening. When respondents were asked, “What are the consequences to families of delaying the marriage of their daughters?” 57 percent of the female respondents said, “There is no stigma, as their daughters are educated and can work to support the family.” That view was shared by 50 percent of the male respondents (El Nagar, Eljack, and Tonnessen 2017, 31).

Both Sudanese women and men have internalized Sudan’s patriarchal customs and norms. Kathleen Fincham, who has conducted research on youth, gender, and school dropout across several states in Sudan, notes that, “Particularly important in determining whether girls would continue their
# **1 Introduction**

Can foreign investors trigger an efficiency-enhancing structural change in the recipient countries? While financial openness seems to boost overall investment and economic activity (Henry 2000a; Bekaert, Harvey, and Lundblad 2005; Gupta and Yuan 2009; Levchenko, Ranci`ere, and Thoenig 2009), things get more complicated when looking at the structural impact of foreign investors. Not only do foreign-owned firms affect the productivity and corporate governance of their immediate competitors and suppliers (Javorcik 2004; Albuquerque et al. 2019). The entry of foreign investors also brings economy-wide structural changes with ambiguous impact on economic efficiency. On the one hand, financial liberalization can trigger positive changes in corporate regulations and governance (Stulz 2005; Doidge, Karolyi, and Stulz 2007; Kaminsky and Schmukler 2008). On the other hand, it can decrease incentives for the government to pursue necessary economic reforms (Fern ́andez-Villaverde, Garicano, and Santos 2013; Challe, Lopez and Mengus 2019) and discourage domestic banks from proper monitoring of their corporate borrowers (Alessandria and Qian 2005; Tressel and Verdier 2011). An inflow of foreign capital can improve market efficiency by relaxing financing constraints (Gupta and Yuan 2009), but it can also worsen capital misallocation by benefiting mostly unproductive firms (Reis 2013; Gopinath et al. 2017). Identifying the full structural impact of financial liberalization thus requires data that are both disaggregated and cover the whole economy.

This paper examines whether the entry of foreign investors aligns the export portfolio of the recipient country with its comparative advantage. The use of disaggregated data covering countries’ entire export portfolios allows us to capture the full structural impact of foreign investors, including potential firm-to-firm spillovers and economy-wide externalities. Moreover, the product-level data on international trade are available and comparable across a large set of diverse countries. This makes them a valuable alternative to firm-level census data, which allow for an analysis of structural changes within one particular country (e.g., Varela 2018). Finally, the concept
of the above biases, mostly commodity and outlet substitution bias. Engel’s law arises from the observation that the food budget share in household consumption declines with the increase in real household income. This suggests that movements in the food budget share over time can reveal changes in real incomes, conditional on changes in relative prices and household characteristics. As a corollary, among demographically similar households at the same level of real income, differences in food budget shares at different points in time might signal mismeasurement of the true change in cost of living.

> 1 The current international poverty line stands at $1.90 per person per day in 2011 international dollars. See PovcalNet for the latest international poverty estimates: http://iresearch.worldbank.org/PovcalNet/.

2
women in the workforce, and innovation capacity, that may affect firms’ ability to deal with the COVID-19 pandemic and other such crises. Although there is no formal study, the possibility that these factors have a differential impact on small and large firms cannot be ruled out. Section 3 discusses several of these factors in detail.

# **3. Data and Main Variables**

# 3.1 _Data description_

Our data sources are the World Bank’s Enterprise Surveys (ES) and the World Bank’s Enterprise Surveys COVID-19 follow-up (COV-ES). The ES are firm-level surveys representative of a country’s registered or formal private sector employing 5 or more employees in most manufacturing and select service industries. Informal firms and businesses operating in agriculture, the extractive sector, and service sectors such as banking and finance, health care, and education are excluded from the sampling universe. A standard sampling procedure based on location, sector, and firm size stratifications ensures representativeness at the country, sector, and firm size level, while the use of a common questionnaire guarantees comparability across countries. The ES included in this paper were conducted between 2016 and 2020, covering the pre-pandemic period defined as before March 2020. After the pandemic was declared, the same firms interviewed in the ES were re-contacted with the aim of measuring the effects of the pandemic (COV-ES). These interviews were administered over the phone. Our baseline sample includes all the firms in the developing countries (low- and middle-income countries) that were operational at the time of COV-ES, were successfully interviewed in COV-ES between May and December 2020, and for which data on the main variables are available. There are 5,197 firms (SMEs and large) spread across 19 developing countries in this sample. In Appendix A, Table A1 contains the list of 10
and disadvantages to each of these approaches,<sup>5</sup> there are some distinct advantages to using an existing F2F survey when it comes to counteracting coverage and nonresponse bias. The F2F survey that the phone sample is drawn from will typically contain detailed information across a wide variety of domains not just for interviewed households but also households that were ineligible (i.e. did not have access to a mobile phone) and those that were not successfully interviewed (i.e. those that did not respond or refused). This wealth of information can be harnessed to more effectively reduce bias in the phone survey sample. RDD surveys and surveys utilizing network provider frames often do not have the luxury of having detailed information on the ineligible and nonresponding sample to attempt to reduce the bias and thus any bias adjustments employed for those surveys are potentially inferior to those from representative F2F surveys.<sup>6</sup> The most prominent method to achieve a reduction in bias in telephone surveys is through adjustments to the survey weights. In order to implement these weighting adjustments, additional information is required to understand and attempt to correct for the bias. External information on known demographic composition of the general population (e.g. from population census) can be compared with that of the interviewed sample and the weights adjusted to counteract that balance. Such external information is usually limited to basic demographics. More detailed information from recent representative F2F surveys (e.g. LSMS, Demographic and Health Surveys, Household Budget Surveys, etc.) could similarly be compared with the interviewed sample. However, for RDD and telecom list-based phone surveys, information on these characteristics must also captured in the phone survey which is not always feasible in a phone survey format where the scope (in terms of question complexity) and length (in terms of interview time) are much reduced compared to a typical F2F surveys. For example, it is not feasible to collect all information necessary to construct an aggregate measure of household
_Randomization strategy._ We randomized 1,332 participants to one of two brief psychosocial motivational interventions (“personal initiative” or “interpersonal initiative”, n=666 per group) and 1,296 to a control condition. These randomizations were stratified by timing of the delivery of the main multi-faceted ASP program components (Early: February-March / Late: April), the policy experiment treatment arm (Full: all components / Psychosocial: all components except a one-off cash grant), and participation in the policy experiment baseline survey (Y/N). Randomization of the intervention occurred at the individual level. However, women participants in the multi-faceted ASP program were organized into groups for other program activities (e.g., skills trainings, savings associations). On top of the individual-level randomization, we included a group-level randomization, such that those groups were assigned to have to have 25% (n=36 groups), 50% (n=36 groups), 75% (n=36 groups) of their group members treated with either psychosocial intervention. These groups had between 11 to 33 women participants, with an average of 25.

Randomization produced well-balanced conditions on pre-registered key sociodemographic variables, including a poverty proxy score, age, nomad status, living in a hamlet outside the village versus inside the village, and household head status, as well as randomization strata (see Table S4). Note that randomization was not balanced on the randomization stratum of participation in the policy experiment trial and that all analyses control for this variable, as with other randomization strata variables. Response rates in the endline survey were high overall, with 95% of the sample participating. Attrition was minimal in magnitude with 5% ( _N=_ 135) lost to follow-up (see Fig. S2). However, attrition was marginally differential across conditions: 5.7% (N=74) in the control, 6.0% (N=40) in Personal Initiative, and 3.2
29 happened. Rather autonomization appears to have left the cost structure of Vietnamese hospitals unchanged.

Oddly, given these results and given the apparent positive effects of autonomization on patient volumes seen in Table 4, we see no significant effects – upwards or downwards – of autonomy on total hospital costs or on costs per case for any level of hospital.

Oddly too, despite the positive impacts we saw on inpatient days in provincial and district hospitals, and on outpatient visits in district hospitals, we see a significant impact on total user fee and out-of-pocket fee revenues only in the case of provincial hospitals. However, Table 5 shows that – for both central and provincial hospitals – autonomy increases user fee revenues _per case_ and out-of-pocket revenues _per case_ , after controlling for casemix; oddly, this is not evident for district hospitals.<sup>5</sup> The patterns of the individual-level results in Table 6 are not wholly consistent with the hospital-level results, but they do point to autonomization exerting some upward pressure on out-of-pocket payments. Hospital autonomization in a household‘s district appears to have led to higher out-of-pocket spending on hospital care: there is fairly strong evidence that this reflects higher utilization rates, and a suggestion that it may also reflect higher spending per contact.

# _Quality_

We find no evidence in Table 4 or Table 5 of autonomization leading to changes in hospital death rates or complications. Our results suggest a mixed picture, however, on the

> 5 In the case of the district hospitals, the impact on inpatient days is driven in part by an increase in length of stay; it may be that the extra length of stay has not translated into higher revenues. It may also be that that the additional outpatient visits have not translated into extra revenues. In both cases, though, the finding is a little strange. It may well be a data quality issue – even with the extensive data-cleaning that we did prior to the estimation, the quality of the revenue data may still be inadequate for the task. One