rafmacalaba/datause-encoder-data huggingface dataset:
the migrants have a college or a university degree and have been living in Italy for about 7 years (median). Only 21 percent are never married, but only 5 percent have their spouse with them in Italy. Nearly 68 percent of the respondents are employed as domestic workers, with the other main occupations being housecleaners (8 percent) and nannies (6 percent). The median wage is 900 euros per month. Almost 96 percent have remitted regularly to the most closely connected household in the Philippines in the last 12 months, and 72 percent remit monthly. The median amount of remittances is 380 euros per month. 81 percent report having sent remittances in the past year for the specific purpose of funding the education of someone in the Philippines, with a median of 970 euros per year sent for this purpose.

The most closely connected household to the migrant in the Philippines has an average of 4.45 individuals in it. The head is the migrant’s parents in 29.5 percent of cases, the migrant’s spouse 22.6 percent of the time, and the migrant’s sister or brother in 17.2 percent of cases. 62.9 percent of migrants have one of their own children in this household, and 32.1 percent have a nephew or niece in this household. Communication between the migrant and this household is frequent, with 52 percent communicating daily, and 93 percent at least weekly. 71 percent of migrants declare themselves to be “very well informed” on what happens in this household. However, despite this, 55 percent claimed they would like to have more influence on how the overall budget of the most closely connected household is spent and 37 percent stated that they wish to have more influence on the spending decisions of the remittances. This suggests there may be demand for interventions which provide them with more control over remittances.

# _3.4 The lab-in-the-field experiment_

The experimental intervention was administered to participants face-to-face, immediately after they answered the baseline survey. There were three main objectives of this experiment. The first was to test the responsiveness of total migrant remittances when migrants have the ability to control some of this for education use. The second objective was to attempt to unbundle the different features of the EduPay product, in order to determine whether any positive responsiveness to EduPay was driven by the ability to channel funds directly to schools, or whether EduPay simply provided a way for migrants to label that a transfer was intended for education. The third objective was to examine how use of these features varies with the degree of asymmetric information between the migrant and the recipient.

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In simple words, more than the depth of the agreement, it is its content that determines the choice between vertical integration or foreign outsourcing and that therefore will impact on the structure of trade (intrafirm versus arm's length). As others in the literature have recognized (e.g. Baldwin, 2011, WTO, 2011, Orefice and Rocha, 2013), the depth of a trade agreement is associated to more offshoring. But its relationship with FDI can, in general, be either positive or negative.

Before we move on to the empirical analysis, there are two considerations that concern the specific structure of the model used in this paper. Both considerations have important implications for the empirical strategy that follows. The first relates to an endogeneity problem. In the model, PTA provisions are introduced as exogenous shocks to the institutional environment. However, as a growing literature shows, international trade itself can have an impact on institutional choices, including the decision to sign a trade agreement and the depth and content of such agreement.<sup>11</sup> Specifically, negotiations of deep PTA provisions result from a two-level game, where governments interact strategically with special interests in the domestic arena and with other governments in the international arena, much like the tariff negotiations analyzed in Grossman and Helpman (1995). In this set up, countries that have stronger FDI relationships may have a greater incentive to introduce in a trade agreement language that facilitates vertical integration. The correlations in Predictions i) and ii) are still valid, but we need to recognize that the direction of causality may run in both ways, from the content of a trade agreement to the composition of trade and vice versa. We will come back to this point in Section 4.

The second consideration is that Predictions i) and ii) are specific to the “property rights” theory of the firm (Grossman and Hart , 1986) underlying the AH model. Importantly, prediction i) stands in contrast to the “transactions cost” approach to the boundary of the firm (Williamson, 1975, 1985), which underpins several studies on the international organization of production (e.g. Grossman and Helpman , 2005, and Costinot, Oldenski, and Rauch , 2011). As discussed above, in the property rights approach what matters in the makeor-buy decisions of firms is the relative contractibility of different inputs. This is the deep reason why

> 11 For recent surveys, see Nunn and Trefler (2013) and WTO (2013), chapter C.6.

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constrained by the current total value of its assets. Arrow _et al._ (2012) argue that this may not be the most comprehensive measure of total wealth, but it is arguably a comprehensive measure of the wealth which underpins the consumption of produced goods and services.

As Annex I makes clear, estimating total wealth from the top down depends crucially upon a number of assumptions and parameter choices. World Bank (2011, Chapter 5) and Ferreira and Hamilton (2010) provide some tests of the extent to which the total wealth estimates are reasonable. Figure 1 below shows the distribution of estimated rates of return on total wealth across countries – this builds on a result in Hamilton and Hartwick (2005) and Asheim (2000), which shows that net national income is the return on total wealth. As can be seen, 80% of the implicit rates of return on total wealth across countries fall between 4% and 6% – a reasonable result.

**Figure 1. Distribution of the implicit rate of return on total wealth, 2005**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Implicit rate of return on total wealth<br>Number of countries<br><!-- End of picture text --> Source: World Bank (2011) Ferreira and Hamilton (2010) carry out panel regressions with intangible wealth as the dependent variable to show that human capital, based on the Barro and Lee (2001) data set on educational attainment is a statistically significant component of intangible wealth. They also perform a development accounting test to show that intangible capital includes what might be termed the ‘stock equivalent of TFP’ (total factor productivity).

Table 1 displays one of the key findings from the World Bank wealth accounts. At least in cross section, there is a clear tendency for natural capital to shrink as a share of total wealth as countries become wealthier, while there is a corresponding increase in the share of intangible capital. However, because the World Bank does not have estimates of human capital by country, the result is that intangible capital – the sum of human capital and the stock equivalent of TFP – constitutes 50% to 80% of total wealth. Pulling this intangible capital residual apart, and using the values of human capital in selected countries estimated by the OECD, is one of the primary contributions of this paper.

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educated and uneducated mothers reporting approximately 91⁄2 hours a day. In contrast to the considerable allocation of time to housework, mothers work for pay an average of 40 minutes per day and only 11 percent of mothers report any paid work at all (conditional on reporting any paid work at all, mothers do report just over 6 hours a day on this activity). The average time spent on paid work is slightly higher for uneducated mothers (44 vs. 29 minutes/day) compared to mothers with some education. Time spent “looking after children’s needs” accounts for 11⁄2 hours a day and is again very similar across education levels—93 minutes and 99 minutes for uneducated and educated mothers, respectively. Where we do see some difference across maternal educational levels is in the time spent on children’s educational needs—this is virtually 0 for uneducated mothers and 20 minutes a day for mothers with some education. These numbers from Pakistan accord well with previous work in rural India, where mothers were spending no more than 90 minutes a day on childcare (Desai and Jain, 1994).

Since some paid work is done inside the house and some housework could be outside the house, it is further useful to classify total work done—both paid and unpaid—as inside versus outside the house. We do so using sub-categories such as cooking, cleaning, livestock and unpaid farm work for every main category in our survey. The data present a picture of a mother’s life that is centered largely inside the house. A typical mother’s working day involves spending over 8 hours a day inside the house and an hour and 40 minutes outside. In fact, if anything, mothers with some education spend less time outside the house (an hour vs. two hours) and more time inside (91⁄2 vs. 8 hours) than uneducated mothers. This paucity of paid work and, more generally, the fact that the bulk of work is inside the house has implications for bargaining models of household decision-making that rely, quite literally, on “outside” options.

The comparison with the United States is of interest. The Americans’ Time-Use survey data between 2003 and 2006 show that the average mother spends 13.96 hours a week, or just under 2 hours a day on all types of childcare—a number roughly comparable to our categories of “looking after children’s needs” and “children’s educational needs”. Of this, the bulk of time is spent on “basic child care” - feeding, medical care, putting a child to sleep. 2.1 hours a week, or just less than 20 minutes a day is spent on “educational child care”. The gradient of time spent on childcare with maternal education is positive and significant—Guryan 9
18 nonmarket good that has some of the attributes for parents of a publlc good. For this reason most empirical analyses of intrafamily distribution of resources have focused on human capital investments in children, because such investments are largely produced by **the** family, **are** embodied **In** the children, and hence are subject to their future control, and are quantifiable in a survey. A family's investmnts in children account for a larg" part of a fasily's savings and intergenerational transfers. Three indicators of human capital investment in chil-iren **are** most frequently studied: mortality, anthropometric measures of child nutrition and health, and schooling. However, the number of studies examining gender differences in child mortality, health, and education in low income countries is still quite limited. A **few** econodic studies must suffice to illustrate how gender differences can **be** interpreted within fasilies to **measure** regularities in behavior that should inform policymaking.

Assume that survival rates of girls and boys, or those of women and men, are related directly to the relative contributions of women and men to their familiea' cash and kind incone. In India, where female mortality exceeds male mortality (Visaria, 1971), as more women participate in ethe local labor market and are more productive relative to adult men, the survival of girls compared to boys is higher. This pattern can be observed at the distrLct level in the 1970 **Census** of India among rural populations and **also at** the household level in parallel studies of representative rural **sample** surveys (Rosenzweig and Schultz, 1982a). A study in the Punjab (Anin and Pebley, 1987) suggests, moreover, that public policies that increase access to health services, without affec Ing the relative productivity of sen and women, reduce general mortality, but increase the mortality rate of girls compared to boys after the
## **Appendix Table 3.4: Impact on Firm Survival**

||**First-F**|**ollow-up S**|**urvey**|**Second**|**Follow-up**|**Survey**|
|---|---|---|---|---|---|---|
||Base<br>Specification|PDS<br>Lasso|Cross-Wave<br>Imputation|Base<br>Specification|PDS<br>Lasso|Cross-Wave<br>Imputation|
|Assigned to Insourcing|0.001|0.001|0.000|0.022|0.022|0.015|
||(0.019)|(0.016)|(0.017)|(0.034)|(0.030)|(0.034)|
|Assigned to Outsourcing|0.023*|0.023|0.021*|0.043|0.043|0.043|
||(0.013)|(0.015)|(0.012)|(0.031)|(0.030)|(0.031)|
|Assigned to Training|-0.006|-0.006|-0.006|0.030|0.030|0.030|
||(0.019)|(0.015)|(0.018)|(0.032)|(0.030)|(0.032)|
|Assigned to Consulting|0.016|0.016|0.015|0.042|0.042|0.042|
||(0.015)|(0.016)|(0.014)|(0.031)|(0.030)|(0.031)|
|Mean of Control Group|0.977|0.977|0.979|0.902|0.902|0.902|
|Sample Size|678|678|723|726|726|727|
|P-value: all treatments zero|0.029|0.294|0.029|0.653|0.601|0.605|
|P-value: all treatments equal|0.049|0.215|0.049|0.876|0.880|0.785|

Notes: All regressions control for stratification batch fixed effects. Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.

First follow-up survey conducted 2-9 months post-intervention; second follow-up survey 14-21 months post-interventions. Base specification uses data from current survey round only, and includes no other controls.

PDS Lasso uses post double-selection lasso with baseline variables as inputs. No covariates were selected in either wave. Cross-wave imputation assumes that firms that have missing survival status in round 1, but are observed open in round 2, were also open in round 1; and that firms which are closed in round 1, and that have missing survival status in round 2, remain closed.

# **Appendix 4: Measurement of Key Outcomes**

The survey instruments and data will be made available in the World Bank’s open data library: Nominal Naira were converted into real (January 2018) Naira using the Consumer CPI of the Central Bank of Nigeria.

Key outcomes are measured as follows:

# **Table 4:**

**Index of ten Finance and Accounting practices** , averaging the practices that a firm carries out (coded: ‘1’ if the practice was implemented in the last three months; and ‘0’ if not). This is the average of the following practices:

- a. Records all money in to the business on a daily or weekly basis

- b. Records all money out of the business on a daily or weekly basis

- c. Prepared an income statement (profit and loss statement)

- d. Prepared a balance sheet

- e. Analyzed a statement of cash flow

- f. Analyzed which products/services are the most profitable

- g. Used cost-control methods

- h. Prepared and used a budget

- i. Set financial goals or financial performance targets

- j. Conducted feasibility studies before starting a new venture or investment

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report when interviewing by phone or a change in the group composition of those who answer the survey. For instance, people can be less likely to report a high level of stress on the phone. Likewise, people who are less likely to answer the phone can coincide with those who are less likely to report a high level of stress, regardless of the survey method.

Thus, we examine whether the survey method modifies the composition of the respondents by comparing differences in reporting a high level of stress at the midline and endline surveys. The advantage of using the endline survey as a benchmark is that we are comparing everyone under the same data-collection process. If differences in the phone-based survey method at the midline survey are driven by changes in the composition of respondents, we should observe similar disparity at the endline survey. In other words, by virtue of the randomization, the detection of a difference across groups at the endline will likely provide information about changes in the group composition rather than the survey method used in the midline, 6 to 8 weeks earlier. Unlike columns (1) and (2), in which the phone-based survey method substantially affects the likelihood of reporting a high level of stress, we observe no significant differences in columns (5) and (6). This suggests that differences in columns (1) and (2) are likely to be driven by changes in the likelihood of reporting a high level of stress based on the survey method rather than a change in the composition of respondents.

# **5.4 Effects on Consistency in Reporting Sensitive Outcomes**

To measure the quality of the reporting of sensitive information, such as mental health and physical punishment, the ideal is to collect objective measures of sensitive items and compare them to what respondents are reporting. However, it was not possible to collect these measures of stress or physical punishment in this particular context.<sup>37</sup> In this sense, with the aim of providing a proxy for quality in the collection of sensitive information through different survey modes, we measure consistency in self-reports of stress and perceptions of the effectiveness of physical punishment. To do this, we compare changes in perceptions of

> 37Face-to-face interviews would be necessary to collect objective measures of stress. Also, information on physical punishment is usually under-reported in administrative records.

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**Figure 5 - Selected Plot Area Coefficients w/ 95% Confidence Intervals - Intercropped Plots**

**Notes:** SR, CC, RS, and FP stand for self-reporting, sub-plot crop cutting, remote sensing, and full-plot crop cutting, respectively. MAPS II SR estimates are based on the plot sample net of those subject to full-plot crop cutting. Household and Parcel Panel are defined as in Table A4.3.

**Figure 6. Consistency of Bias Across MAPS I and II**

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- The timing of cost reductions will be influenced by the regulatory timetable (where companies are not allowed to retain savings for a fixed period of time), rather than the firm’s own assessment of its business. Presumably, this will be less efficient than a timetable driven solely consideration of the actual costs and benefits of making an efficiency improvement.

- Variation in incentives encourages “gaming”. Cost savings can be delayed, hidden or transferred between years. If successful, gaming is bad for customers because it results in higher prices than necessary. Even if unsuccessful, it increases the difficulty of carrying out regulatory reviews as it makes the assessment of efficient costs more difficult.

**<mark>Figure 8: Illustration of benefit retained by a regulated company making an efficiency improvement in the final year of a regulatory period</mark>**

<!-- Start of picture text --> Additional profit<br>retained by the<br>company<br>First Second<br>regulatory regulatory<br>review review<br>Allowed revenue<br>Actual costs (including normal profits)<br><!-- End of picture text --> There is some evidence to suggest that regulated companies in a number of sectors have responded to these signals, delaying savings that could be made in the last few years of a regulatory period and making rapid reductions in costs in the years immediately after a review. The table below shows the results of Malmquist productivity analysis undertaken by Frontier Economics. The productivity estimates 26
percent of this list were intermediate goods and 24 percent were consumer goods. Several products were dropped from the earlier list, including bedsheets, gloves, and smartwatches. China responded with its own finalized list of $60 billion of US exports, with tariffs ranging from 5-10 percent, down from the originally presented 5-25 percent. Both sets of tariffs then went into action on the 24th September.

Finally on the 1st December 2018, the US and China agreed to halt the next increase in tariffs that was due in January while they negotiate a solution. At the end of 2018, the US faced retaliation on 8 percent of its total exports whilst covering approximately 12 percent of its total imports with tariffs, largely on intermediate inputs and capital goods.

# **3. Data**

## **3.1 Online job adverts**

This paper uses data collected by Burning Glass Technologies (BGT), a company that scrapes what they identify to be the quasi-universe of US online job postings on a daily basis. BGT estimate that these postings are sourced from approximately 40,000 online job boards and company websites. This data has now been widely used to study US labor markets (e.g. recent examples include Acemoglu et al. (2022) and Bloom et al. (2021)).

Hershbein and Kahn (2018) provide a detailed analysis of the industry-occupation mix of vacancies in BGT relative to other detailed US data sources, such as JOLTS, and how this mix has changed over time. They find that the BGT postings are disproportionately concentrated in occupations and industries that typically require higher skill levels, but that the distributions are relatively stable across time and the aggregate and industry trends in the quantity of vacancies track official sources reasonably closely. Therefore, while online job adverts do not provide a complete picture of the entire labor market, they can provide a useful barometer of labor market demand.

BGT classify the job adverts along a range of dimensions; this paper makes use of the classification by county and Standard Occupational Classification (SOC) codes. They also clean the data and remove duplicate postings.<sup>7</sup> The postings cover 3,228 counties across the US, with 98.6% of total job postings being classified by county.

> 7Duplicates are recorded as a single posting in the first period in which the posting occurs.

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# **I.** **<u>Vulnerability</u>**

Estimating how vulnerable countries are to changes in energy prices is a complicated task, not only methodologically but also due to the lack of publicly available data even for basic analysis as performed here. We have discussed already some of the methodological choices we have made in this paper in, for example, performing a static analysis and shying away from general equilibrium effects. We define energy-intensive industries as per the European Union Emission Trading Scheme list discussed in the previous section. We measure vulnerability as the combination of three main indicators: 1) estimated energy price increase after regulation; 2) the share of employment in energy-intensive industries; and 3) the share of value-added in the same energy-intensive industries<sup>12</sup> . In ECA, a significant share of employment and value added is in energy-intensive industries – around 30 percent of employment and around 40 percent of value added (Figure 3 and Figure 4, respectively).

**Figure 3 – Energy-Intensive Sectors Take Up to 30 Percent of Employment in ECA**

Cumulative percentage of employment, by sector, 2008 <!-- Start of picture text --> 100%<br>energy intensive<br>50%<br>industries<br>19.05%<br>7.95%<br>2.16%<br>1.52%<br>0%<br>Mining Elec, Gas, Water Transport Manuf Finance<br>Hotel & Rest Real Estate Health Public Admin Constr<br>Education Retail Agriculture<br><!-- End of picture text --> Source: Authors’ calculation based on UN Data Note: The figure is a representation of unweighted averages across ECA countries. The names of the sectors had been shortened to fit the axis. The full names are Mining and Quarrying; Electricity, Gas and Water Supply; Transport, Storage and Communications; Manufacturing; Financial Intermediation; Hotels and Restaurants; Real Estate; Health and Social Work; Public Administration and Defense, Construction; Education; Wholesale and Retail Trade; and Agriculture

> 12 In terms of data, the first component we use while calculating vulnerability is the potential energy price hikes as mentioned in the previous section. This data was derived from IEA (2011) and ERRA tariff database. We rely on United Nations Statistics Division (UN) data for both employment (at the ISIC Rev 3 level) and value added statistics (at the SNA93 2.1 level).

8