rafmacalaba/data-use-mentions-extended huggingface dataset:
where superscripts _m_ and _x_ denote the indices for imports and exports respectively. Denote _l_ as the number of workers (in all of Mexico) employed in industry _i_ at time _t_ , but that were also employed in any other industry _j_ during 2008-09. Intuitively, these indices are equal to the weighted average of “related” imports and exports in each time period, and the weights are the share of workers employed in industry _i_ but who also were employed in any other industry _j_ during 2008 and 2009.<sup>7</sup> Since confidentiality concerns preclude us from tracking workers over time when they change firms these industry-level “transition probabilities” were calculated separately by IMSS staff. It is also noteworthy that industries j can include non-tradable industries, in which case the value of imports and exports would be equal to zero (we set the log of exports or imports equal to zero in these cases). Hence the variance of these indices is lower than the variance of industryspecific trade flows. The variance of these indices is also lower because they are averages across numerous sectors. This feature of the indices is important for interpreting the economic magnitude of the coefficients. The augmented employment and wage equations in differences (within industries) in the assembly time specification that take into account the indirect effect of trade shocks on workers via their effects through related industries are:

> 7 Econometric results using employment data from 1997-9 were strikingly similar to those reported below, thus suggesting that endogeneity in labor mobility across industries during the period under examination is not a serious problem.

18
Dillon (2012). Contacting all respondents 10 times by phone would increase the cost of the survey by 54 percent, while calling all respondents 30 times would increase costs by 162 percent. Our particular experiment required 24 calls to cover the complete agricultural season, but this is highly context-specific, and other surveys may be able to achieve gains in accuracy with fewer points of contact.

These numbers suggest that, in practice, the use of high-frequency phone surveys to collect more reliable labor data may be quite expensive. Nonetheless, it may represent a viable option in surveys that already use phone calls to respondents for other purposes, such as to ensure the continued participation of respondents, to keep track of respondents who relocate, or to collect data requiring high-frequency contacts or a quick turnaround (Dillon 2012; Garlick, Orkin, and Quinn 2015).

Given the importance of cognitive burdens in driving mismeasurement in labor data obtained by recall, another approach is to design surveys in ways that minimize these burdens. For instance, where the analytical demands on the data make this possible, questions could be posed in ways that are more intuitive relative to and better aligned with the ways farmers remember and make inferences about their work.<sup>23</sup> Similarly, data collectors can attempt to shorten the recall period so that labor reporting is likelier to be based on memory than on inference.

Another approach involves managing and correcting for known shortcomings in recall survey data. For instance, by assessing the degree of irregularity in farming practices in the survey context, data collectors will be able to anticipate more effectively whether and the extent to which the resulting labor data will be reliable. They may also use high-frequency surveys such as the ones used in this study, which dramatically shorten the traditional season-long recall period, as an approach to large-scale data collection or as a means to create a consistent adjustment factor that can be applied to past and future recall surveys in the traditional vein. Of course, whether the latter is a reasonable approach
the period 2010M7- 2011M6.

Finally, there is evidence that international prices have asymmetric effect on domestic prices (Table 4). Following the approach described in section 4, domestic food price inflation is found to increase by only 5.4% when inflation of FAO food index increases by 10% and by 1.8% when inflation of FAO food index increases by the same magnitude. Using the FAO cereals sub index inflation, the asymmetric effect is less pronounced: an increase of 10% in international food prices would cause a 2.3% increase of domestic food prices, while a decrease of the same magnitude would cause a decline of 1.8%.

**Table 4: Coefficients of International Price Inflation in Step 1 of the 2-step Regression**

||**FAO food index**|**FAO Cereals sub-**<br>**index**|
|---|---|---|
|**Slope of inflation increases**|0,5366|0,2317|
|**Differential slope**|-0,3548|-0,4086|
|**Slope of inflation decreases**|0,1817|-0,1769|

Note: * All coefficients are significant at the 95% level.

15
even if one builds rapport with respondents, given the lack of anonymity, it is still possible that respondents continue to underreport sensitive behaviors.<sup>6</sup>

> 3For example, an endorsement experiment can be used to try and infer support for a particular group, but cannot be used to understand what that support consisted of, the frequency of support, and so on. 4In the mobile phone survey analyzed here, we also included an endorsement experiment that resulted in a likely underestimate of support for parties to the conflict; and in the internet-based survey used here, we performed a series of endorsement and list experiments to evaluate support for parties to the conflict and found inconsistent results. It is difficult to know exactly why the results were not robust. For example, it is possible that some of actions that were endorsed by parties to the conflict were deemed too important to be impacted by partisan views, but regardless of the reason, the results were not robust across wordings, contexts, and modalities.

> 5For example, in Blattman et al. (2016), only a subset of the households that were part of the randomized control trial were able to be interviewed in this way.

> 6There is another approach that is direct and anonymous from the enumerator, where respondents are administered self-interviewing surveys through audio recordings and tablets. However, the respondent is made aware that their responses and identifying information will be accessible to researchers, and thus

6
Being the first quantitative research, based on a large survey data, in this topic in Indonesia, this study contributes to a broader view and understanding of the potential gains and losses from migration for sending households. It also contributes to the limited existing research on the gender dimensions associated with these gains and losses.

Our results suggest important gender differences in the impacts of international migration on sending households. In Indonesia, migration reduces the working hours of remaining household members, but this effect is mainly driven by what happens in households with male migrants. This negative relationship was not observed for households with female migrants. The results about child outcomes are also divided along the gender line. Female migration and their remittances tend to reduce child labor outside the home but not necessarily boost schooling activities. Though not borne out statistically significant, the direction of the estimates suggest that migration may have a slightly positive impact on school enrollment among households with male migrants, but this impact disappears among those with female migrants. The lack of oversight associated with the mother’s absence is likely to make it difficult to ensure sufficient schooling activities for children at home.

The following section reviews the relevant literature. Section 3 describes the IFLS data, and Section 4 provides a descriptive examination of migrants and migrant-sending households in Indonesia. The empirical strategy and results are presented in the subsequent two sections. The last section concludes.

# **2. Literature Review**

While there has not been any quantitative analysis, before this paper, about the development impacts of migration with a gender focus in the Indonesia context, there is a general literature on different country experiences of impacts of migration<sup>3</sup> and some indicative evidence on the differential impact by gender. Most studies find that migration and remittances tend to reduce the labor supply and participation of non-migrating family

> home country include Hildebrandt and McKenzie (2005), Mansuri (2006b,c), Acosta (2006) and Beaudouin (2005).

> 3  See Adams (2010
The estimation results with the pooled OLS method are presented in the first four columns of Table 4. The first column shows annual change in product mix. Firms in industries that were delicensed before 1989 are more innovative than the firms in the remaining industries. They increase their product mix at a rate 1.3 percentage points faster than the rest of the firms. Size and R&D investment significantly increase product creation rate. Higher tariff rates decrease the rate of product growth; however the coefficient of this variable is not significant. The industries that had large decrease in nontariff barriers also have higher product growth rates. The second column in the table repeats the estimation in column one restricting the sample to firms that were in the sample from 1989 to 1995. Since the Prowess data is not balanced and sample size increases from 1989 to 1995, the estimation results could be biased as successful firms are more likely to survive and innovate compared to inefficient firms who are more likely to exit. The results are similar and the impact of licensing reform on innovation is only slightly higher.

Introduction of new varieties to the economy shows a sporadic behavior. As Table 2 showed there are many firms that have zero product growth rates annually. In the third column, I compute product growth rate over a three year period instead of two years. With this definition of product growth rate, firms in delicensed industries grow at 1.8 percentage points faster than firms that are in licensed industries. Although standard errors are clustered at firm level and the estimation equation includes year dummies, serial correlation can still be a concern. To alleviate this concern in the fourth column I include only the data from 1991. The first year that product growth can be computed is 1990 but the sample size is larger in 1991. Although a large group of industries were delicensed towards the end of 1991, the effects of the reform on these firms would not be seen right away. Hence _Delicen89_ will still be able to capture the difference in innovation performance between firms
52 *<br>-0.15<br>-0.55 *<br>-0.45|Fail to reject<br>Null<br>Hypothesis|No|0.38 **||No||86|
|6)|rule<br>Log(patents per capita)<br>oil<br>landlock<br>Sub-Saharan Africa<br>malaria|eurfrac<br>lip|0.50 *<br>0.15 *<br>0.40 *<br>-0.34 *<br>-0.44 *<br>-0.41 **|-0.01<br>0.23 *<br>0.26<br>-0.47 *<br>-0.41 *<br>-0.49|Fail to reject<br>Null<br>Hypothesis|No|rule<br>0.37 **<br>0.04|lpats<br>1.75 *<br>1.26 *|No<br>No|0.14<br>0.17|86|

Notes: Confidence Levels: *5%, **10%. All regressions include a constant, dummy for net exporters of non-oil commodities, absolute value of latitude, and the log of Frankel and Romer's (1999) constructed trade shares.

The results reported in the first two rows show that the results reported in most studies that followed Acemoglu et al. (2001) are reproduced with our data and sample. The impact of rule of law on income per capita is positive and significant in both OLS and IV estimates. Also, the IV coefficient is larger than the OLS coefficient, which might indicate that the OLS estimates suffer from attenuation bias possibly due to measurement error in the rule of law indicator. Furthermore, the Hausmann specification tests suggest that
a Bayesian geostatistical model (P W Gething, Dasgupta, and Andres 2017; Peter W. Gething and Joseph 2017) to yield an interpolated raster grid (one each for sanitation and water risk). Three other geospatial covariates were also derived from the household survey data using the same approach: the percentage of households with soap in their toilet area (as a proxy for handwashing); the mean household size; and the percentage of households with unimproved sanitation. The latter variable was combined with an underlying population map to derive a population density of people without adequate sanitation across the city. Finally, geospatial data was obtained on the percentage of households classified as being in extreme poverty by municipal ward (de la Fuente, Murr, and Rascon 2015) and the location of graveyards across the city which is a potential risk factor for spread of cholera. Both were converted into raster grids.

To assess the association between each covariate and cholera risk, the Pearson's product moment correlation coefficient was calculated.
With preferential tariffs approaching the zero lower bound, the coverage of PTAs in terms of policy areas has widened over time. Analysis based on the new World Bank data set on PTAs content (see Box 2) shows that agreements signed before 1991 included on average 9 provisions whereas agreements signed between 2005 and 2015 included on average 15 provisions (see Figure 15.a). With respect to the provisions included in active agreements, as expected, all agreements include reductions in tariffs on manufacturing goods. At the same time, more than 50 percent of agreements include deeper provisions such as anti‐dumping and countervailing (CVM) measures, rules on competition, movement of capital and intellectual property rights (TRIPS and IPR). Moreover, TBT, investment disciplines and SPS measures are often included in PTAs (see Figure 15.b).

Figure 15: Evolution of depth over time and most common provisions, 1958‐2015

- a. Evolution of depth over time                                                     b. number of agreements per provision

<!-- Start of picture text --> 30 300<br>25<br>250<br>20<br>200<br>15<br>150<br>10<br>5 100<br>0<br>50<br>0<br>Average number of provisions<br>Average number of legally enforceable provisions<br>Source: Calculations based on World Bank PTA content dataset (2016).<br>Number of agreements<br><!-- End of picture text --> Deep PTAs: The Brazilian experience Brazil is a member of only 1 active Preferential Trade Agreement: The Southern Common Market (Mercosur). Mercosur comprises five‐member countries: Argentina, Brazil, Paraguay, Uruguay, and the República Bolivariana de Venezuela.<sup>7</sup> Bolivia is currently in the final stages of the accession to become the sixth member. The group encompasses 290 million people and has a combined GDP
)|
|Intercept|648.0***|1939.7***|705.7***|2173.6***|
||(71.0)|(117.9)|(83.3)|(132.7)|
|No. of Children||-716.8***||-838.0***|
|||(49.7)||(56.8)|
|No. Observations|4326|4326|3632|3632|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
opportunity cost of time. Combes and Lafourcade (2005) have also documented the existence of a strong relationship between GTCs and estimates of optimal travel time derived using GIS techniques for France.

# **4. Estimation of the Wage Equations**

In this section, we discuss in detail the second stage of our methodology, the estimation of the NEG wage equations [3] and [4]. A literature has developed following the seminal work of Hanson (1997, 2005) and Redding and Venables (2004) which consistently finds a strong positive relationship between RMA and nominal wages, and this has been used to provide supporting evidence for NEG models. This includes previous work for China by, _inter alia_ , Au and Henderson (2006), Bosker _et al._ (2010), Hering and Poncet (2010a, 2010b), and Moreno-Monroy (2008), although they typically confine themselves to the use of prefecturalcity data, whereas our sample also includes other types of prefectural-level regions (see Appendix A). Overall, with a sample size of 331 prefectures, this makes for a more comprehensive geographic coverage of China's territory, which is central to our purposes. Furthermore, previous NEG wage equation studies for China have focused on the estimation of wage equations using either aggregate data (Bosker _et al._ , 2010; Moreno-Monroy, 2008) or data for the urban sector only (Au and Henderson, 2006; Hering and Poncet, 2010a). However, our theoretical model predicts that there should be a positive relationship between RMA and nominal wages not only in the urban sector, but also in the rural sector. The estimation of equation [4], therefore, is important for establishing the adequacy of our underlying theoretical framework, and provides an important contribution in its own right.<sup>13</sup> To transform equations [3] and [4] into forms that are suitable for estimation we first assume that (relative) labor efficiency levels in both the urban
in Brazil has been associated with longer reallocation times for workers moving from one formal-sector job to another formal-sector job. Their results are robust to di¤erent levels of exposure to trade, ...rm-level productivity, worker heterogeneity, as well as other general trends that occurred in the country during the period studied–such as skill-biased technological change and labor market reforms.

Kaplan and Verhoogen (2009) use matched employer-employee data from Mexico to examine the wage premia paid by exporters in the Mexican manufacturing sector. Wage premia are 31
Rodriguez-Alvarez, A. Manuel Llorca,M., and Jamasb, Tooraj, 2021. Alleviating energy poverty in Europe: Front-runners and laggards, Energy Economics, Volume 103, 105575, ISSN 0140-9883, <u>https://doi.org/10.1016/j.eneco.2021.105575.</u> Rude, B. L., & Robayo, M. (2024). Statistical Matching for Combining the European Survey on Income and Living Conditions and the Household Budget Surveys: An Evaluation of Energy Expenditures in Bulgaria. Policy Research Working Paper No. WPS 10818, PROSPERITY; RRR. Washington, D.C.: World Bank Group. <u>http://documents.worldbank.org/curated/en/099918006242435645/IDU18642b8e2160eb142ff18e2 41ebb95b21d260</u> Ruggeri Laderchi, Trimble, C, & Olivieri, A. "Balancing Act, Cutting Energy Subsidies While Protecting Affordability," 2013.

Saghir, J. (2005). Energy and poverty: myths, links, and policy issues.

Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., ... Wu, J. (2014). Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sensing, 6, 1705–1724. <u>http://dx.doi.org/10.3390/rs6021705Schuessler, R. (2014). Energy Poverty Indicators: Conceptual</u> Issues-Part I: The Ten-Percent-Rule and Double Median/Mean Indicators. ZEW-Centre for European Economic Research Discussion Paper, (14-037).

Schulte, I., & Heindl, P. (2017). Price and income elasticities of residential energy demand in Germany. Energy Policy, 102, 512-528.

Siksnelyte-Butkiene, I. (2021). A systematic literature
in I2D2 shows that informality measured as a share of informal workers in total does not change much over short periods of time, with any changes visible over the long term. Furthermore, according to the World Bank (2019) informality has remained remarkably stable despite economic growth and changes in the nature of work. For example, in Peru informality has been constant at about 75 percent over the last 30 years. In Sub-Saharan Africa, informality remained, on average, at around 75 percent of total employment from 2000 to 2016 and, in South Asia, it increased from an average of 50 percent in the 2000s to 60 percent over the period 2010-16. Hence, cross-section regressions are more relevant estimation methods here given the low variation in informal employment over time compared to time series or panel data methods.

> 10 Authors’ calculations.

> 11 See Table A.1 in the Annex for the exact data coverage. For over 80 percent of the countries considered, the latest data is between 2011 and 2017.

10
**Figure 19. Net capital flows (quarterly data)**

<!-- Start of picture text --> billion USD % of GDP (right - hand scale) %<br>20 20<br>15<br>15<br>10<br>10<br>5<br>5<br>0<br>0<br>-5<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-5 -10<br><!-- End of picture text --> Note: net capital flows = net financial account. Source: authors’ calculation.

**Figure 21. Structure of gross capital inflows (% of GDP)**

<!-- Start of picture text --> FDI Portfolio Other<br>%<br>8,00<br>7,00<br>6,00<br>5,00<br>4,00<br>3,00<br>2,00<br>1,00<br>0,00<br>-1,00 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-2,00<br>Source: own calculation.<br>Figure 23. Liquidity ratios in banks with assets<br>above 200 million PLN<br>STLR LTLR<br>1,8<br>1,6<br>1,4<br>1,2<br>1<br>0,8<br>0,6<br>0,4<br>0,2<br>0<br>01.200903.200905.200907.200909.200911.200901.201003.201005.201007.201009.201011.2010<br><!-- End of picture text --> Source: own calculation.

**Figure 23. Liquidity ratios in banks
monthly periodicity, and disaggregation according to the country or territory from which the search query is performed. For each country, _Google Trends_ normalizes the search for any given term with respect to the month with the highest volume of searches of such term in that country, setting that month equal to 100. We then use this information to measure, for every year, the average of the _Google Trends_ indicator, allowing us to track the increase over time in the prevalence of those queries. As Figure 6(a) illustrates, there is a noticeable variation across countries and secular increases over the period of interest on the five platforms. In the econometric exercise below, we define countries as “high” adopters of tourism digital platforms when the constructed Google Search Index exceeds 50%.<sup>11</sup>

**Figure 5: Internet use for business‐to‐consumer transactions and digital demand for tourism services, 2017**

In addition to covering a longer time period, an advantage of the second proxy is that it is more directly related to the usage of digital platforms. Nevertheless, in contrast to our first approach, we are unable to distinguish between people using platforms to plan a trip in the origin country — demand for tourism services — and businesses in the destination country offering travel services — the supply side — although Figure 6(b) shows that there is a strong correlation between B2C Internet use and the proxy for digital

> 11 Table A.1 presents the mean Google Trends index by world region; Table A.2 further presents the index for MENA countries.

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|6,481|814|19,939|6,726|33,960|0.19|0.02|0.59|0.20|1|
|Manufactured fertilizers|15,599|0|9,750|2|25,351|0.62|0.00|0.38|0.00|1|
|Petroleum and products|10,512|110|1,889|1,301|13,812|0.76|0.01|0.14|0.09|1|
|<br>Rubber & plastic products|515|20|7,947|957|9,439|0.05|0.00|0.84|0.10|1|
|Glass & cement|3,344|17,199|31,558|26,702|78,802|0.04|0.22|0.40|0.34|1|
|Iron & metal products|5,982|5,387|7,872|10,123|29,365|0.20|0.18|0.27|0.34|1|
|Machinery & transport equipment|25,503|17,369|31,523|21,171|95,565|0.27|0.18|0.33|0.22|1|
|**Source: SITC Revision 3 data from UN**|**COMTRAD**|**E Statistic**|**s.**||||||||

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