rafmacalaba/data-use-mentions-extended huggingface dataset:
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19 control panel.<sup>14</sup> We aggregated the number of cases at the quarterly and municipal levels. Then, we used population size at the municipal level to estimate municipal COVID-19 incidence rates and included this rate as a control variable. Second, to account for trade across the border as a potential confounder, we also controlled for per capita exports at the municipal level using data from INEGI (Instituto Nacional de Estad ́ıstica y Geograf ́ıa).

## **3.2 A measure of exposure to US unemployment**

Given that we did not know the labor market status of Mexican migrants by municipality of birth and US state of residence, we created a Bartik-style measure of exposure of each municipality _m_ to US state _s_ unemployment of Hispanics of Mexican origin: where _Mms_ corresponds to the share of migrants from municipality _m_ in each state _s_ (fixed over time), and _Ust_ indicates the state-level unemployment rate at time _t_ . To estimate _Ust_ , we use the unemployment of Hispanics of Mexican origin living in the United States.

# **4 Empirical Strategy**

We estimate the following equation: where _Rmt_ is the log of remittances received by municipality _m_ in semester _t_ , and _C_ -19 is a dummy that takes the value of one for every period during 2020. _Distm_ is a measure of distance from municipality _m_ to the nearest border crossing, and _Ecomt_ is our measure of local labor demand shocks in municipality _m_ during our period of analysis, which controls for the potential increase in remittances in municipalities hit hardest by the pandemic (altruism mechanism) that might also be closer to the northern border.

Additionally, _λt_ and _δm_ are time and municipality fixed effects that control for shocks common to all municipalities and time-invariant characteristics of the municipalities, respectively.<sup>15</sup> All models also include municipality trends _γmt_ . This is important given the increase in formal remittances, which is likely

> 14See https://www.coronavirus.gob.
/19 NLSS and imputing into several waves of the GHS. Despite having very different foundations, these two approaches produce remarkably similar results. Far from poverty dropping by 17 percentage points – as making the invalid comparison between the 2009/10 HNLSS and 2018/19 NLSS would imply – it appears that poverty dropped by between 3 and 7 percentage points in the decade before the COVID-19 crisis. The results suggest that the 2010s were initially marked by gradual poverty reduction,

> 28 Similar indicators are calculated using data from the 2018/19 NLSS to calculate the multidimensional poverty measure for Nigeria (World Bank, 2020). The multidimensional poverty measure combines data on access to education, basic infrastructure and monetary welfare to better understand the different dimensions of poverty (see World Bank 2018). These non-monetary indicators are highly correlated with monetary household consumption. In fact, in Sub-Saharan Africa 40 percent of households that are multidimensionally poor are deprived under all three dimensions (World Bank, 2020). Hence, looking at trends in these non-monetary indicators can be highly informative of overall household well-being, especially in absence of frequent data on household monetary consumption.

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Rigobon (2009), Magud, Reinhart and Rogoff (2011), Klein(2012), Forbes et al. (2015), Ben Zeev (2017), to name only a few) or unintended consequences ((Miniane and Rogers (2004), Forbes et al. (2016) among others)) of capital controls, I attempt to identify the empirical relationship between capital control announcements and expectations of fundamentals by looking at high-frequency survey data and by employing a nowcasting model, an empirical methodology new to this literature. Moreover, my model provides possible explanations for some empirical findings in this literature, such as these in Klein (2012) and Forbes et al. (2015).

Last but not least, this paper joins several earlier papers to explore signaling of and learning about fundamentals through macroeconomic policies in general. Most 4
was implemented, and multiple enumeration training sessions were conducted throughout the year.<sup>5</sup> Finally, the sample size was reduced. The 2022 HIES is representative at the national, division, and rural and urban levels as before the 2016 rounds. The 2016 survey was an exception, with a sample representative at the Zila level.

The 2022 microdata provides a more comprehensive picture of Bangladeshi households' consumption levels and patterns but risks monitoring welfare indicators over time. In this setting, accurate and robust methodologies are crucial to address comparability over time, help rebuild trends, and evaluate policies' effectiveness. Survey-to-survey imputation methods have emerged as a promising avenue to address this issue, leveraging data from one survey to fill in gaps in another, thereby providing more frequent and robust estimates of poverty and inequality trends over time (Dang, 2021).

Traditional imputation methods, like regression-based and simple matching techniques, operate under the assumption that the missing data in a survey can be accurately imputed from another data source. However, such assumptions might not always hold, especially in cases where there are significant changes in the country's population structure or survey design (Dang, 2021) or a lack of consistency in collecting survey data over time (Mathiassen and Wold, 2021). Moreover, traditional imputation methods do not necessarily account for complex relationships between variables and could introduce bias when the model is misspecified.

There is a growing consensus in the literature towards adopting more advanced imputation methods, particularly in a data-scarce environment. One approach is implementing hot deck imputation methods bolstered with statistical learning techniques (Budhwani et al., 2022). These methods can handle missing data in large datasets with many variables and have the potential to capture the relationships between variables more accurately. Although not a perfect substitute, these methods can also address the challenges posed by survey costs or implementation and enable back-casting of consumption from a more recent survey for better comparison with older surveys (Dang, 2021; Edochie et al., 2022
reached 90 or more. Total event counts by governorate in Gaza fall within a narrower range, from 30 at the lowest to almost 100 at the highest.<sup>10</sup> Not only does the distribution of outcomes tend to be more condensed in Gaza than in the West Bank, but those outcomes are on average more severe. If the Facebook survey tends to pull estimates toward the more severe end of the scale relative to the PPCS, the measurable differences produced by this process are likely to be smaller if the PPCS estimates are already at that end of the scale.

People in Gaza are also plausibly more likely to be interested in responding to a survey on the effects of the May 2021 violence on Palestinians, suggesting that the sample of respondents who selected into the Facebook survey is likely to be more representative of the underlying population in Gaza than it is in the West Bank. May 2021 saw escalations of violence in both Gaza and the West Bank, but Gaza experienced more severe violence and more extensive damage.<sup>11</sup> This greater exposure may have resulted in broader motivation to complete a World Bank Facebook survey seeking to understand the effects of the escalation. In fact, out of the sample of respondents who began the Facebook questionnaire, people in Gaza were over 25 percent more likely to complete the survey than were people in the West Bank.

Finally, all respondents to the Facebook survey received the conflict exposure questions, while only respondents in Gaza received these questions in the PPCS. Since the conflict exposure module preceded the mental health modules, some of the greater similarity in Gaza for these modules may be due to the regional differences in questionnaire structure.

How robust are the main results to different ways of controlling for observable characteristics?

# Achieved samples

As is often the case with online surveys, the achieved sample for the Facebook survey differed from the population on several observable characteristics (see Table 5). Respondents to the Facebook survey were more likely than the general population
materials, energy, employment, investment, and detailed location and sector affiliation.<sup>9</sup> Plants are classified into 3-digit International Standard Industrial Classification (ISIC) sectors. Our estimating sample covers Chilean plants located in across 13 regions, 45 provinces, and 187 _comunas_ . The unit of analysis in our empirical specifications is a 3- digit sector- _comuna_ cell. Our estimating sample includes 853 sector- _comunas_ which include on average 5 firms as shown in Table 1.<sup>10</sup>

# **2.2 TFP Measures**

Our empirical approach relates long-run TFP growth of a sector-location to the local economic structure in 1992. We proceed in two steps to obtain estimates of TFP growth at the sector-location level. First, we obtain firm-level TFP estimates based on the Chilean dataset. Second, we average these firm-level TFP estimates up to the sectorlocation level and correspondingly compute TFP growth.

To implement the first step, we assume that within each 2-digit ISIC sector, firm _i_ produces output based on a general and flexible translog production function in period _t_ :<sup>11</sup> where Y is real output and the inputs _X z_ are labor, real materials, electricity and the capital stock,  _it_ is a productivity shock known to the firm but unobserved by the

> 9 We use the words plant and firm interchangeably, although plants are the unit on which the ENIA survey collects data. Between 1997 and 2003 only 8.3% of Chilean plants are part of a multi-plant firm (Fernandes and Paunov, 2011).

> 10 Due to a reorganization of the Chilean territory during our sample period, our final sample includes sector- _comunas_ present in the first and last sample years – 1992 and 2004 - as well as in an intermediate sample year 1998.

> 11 Statistical tests based on OLS estimates indicate that the translog functional form is more appropriate than the Cobb-Douglas functional form for the Chilean industries.

8
26 percent (net of shared revenues).<sup>9</sup> Districts are allocated 90 percent of these funds.

The remainder is distributed across provinces.<sup>10</sup>

**Figure 1: Sub-National Revenues in Indonesia (Aggregated by Province)**

<!-- Start of picture text --> 4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>PCOSR PCSDA PCTAX PCDAU PCDAK<br>Jawa BaratJawa TengahBantenJawa TimurNusa Tenggara BaratSumatra UtaraLampungSumatra SelatanYogyakartaKalimantan BaratSulawesi SelatanNusa Tenggara TimurSumatra BaratSulawesi UtaraSulawesi TenggaraBengkuluKalimantan SelatanSulawesi TengahBaliNanggroe Aceh DarussalamBangka BelitungGorontalo JambiMaluku UtaraKalimantan TengahMalukuKalimantan TimurRiauPapua<br>in thousand Rp<br><!-- End of picture text --> Source: Authors’ calculation using MoF 2004 data. The data is aggregated at the Provincial level. DKI Jakarta is omitted from the figure because of it has a different autonomy set up.The 2004 data is based on 2003 definition of provinces (30). The figure does not include special autonomy fund transfer to Papua which accounted for 2% of total DAU pool.

Table 1 summarizes the existing establishments costs, formula-based allocations, and hold-harmless in the overall allocation of the DAU from 2001-2006. Policymakers in Indonesia introduced a formula-based intergovernmental transfer system with the launch of the 2001 Big Bang Decentralization. However, DAU allocations were still driven largely by existing establishment costs. These estimations were based on the sum of previous transfers for deconcentrated structures and staff, which were handed over to local governments, as well as prevailing development transfers to local governments.

Over time, policy makers envisioned increasingly moving to a transparent formula-based allocation (Lewis 2001;World Bank 2003).

> 9 Law 33/2004 over Law 22/1999 stipulates that during the transitional period the DAU pool should be at the minimum 25.5 percent of net national income and should be 26 percent in 2008. For the 2006 budget,
migrate before 2006. The findings suggest transformation of household gender dynamics and reinforcement of traditional gender ideologies by male migrants returning from other Arab countries.

A similar analysis was undertaken for Jordan (Tuccio and Wahba 2018). Controlling for both emigration and return migration selections, the study found that women with a returnee family member are more likely to hold traditional gender norms than women in households with no migration experience. Further analysis showed that results are driven by returnees from more conservative Arab countries, suggesting a transfer of conservative norms from destinations with highly traditional gender roles. In general, return migration from Middle Eastern countries had an adverse impact on female labor force participation, education, fertility, and gender equality.

Although exposure to new ideas and norms during migration and the impact of this exposure on home country conditions after the end of migration has been studied to some extent, no studies have considered the transfer of norms and values by return migrants from the Gulf or the Middle East to any Indian state or other South Asian countries. This paper addresses this gap given the large-scale South Asian migration to Middle Eastern countries and the potential implications of such exposure on future migration and gender equality policies in sending and destination countries alike.

# **3. Data**

This paper uses data from the Kerala Migration Survey (KMS) 2013, the sixth in a series of migration monitoring surveys conducted by the Center of Development Studies since 1998 (Rajan and Zachariah 2013). The KMS 2013 includes a total of 14,575 households, representative at the level of 14 districts and covering all 63 _taluks_ .<sup>5</sup> Interviews were conducted between December 2013 and May 2013. The survey collected demographic information for 63,230 household members, including 29,882 males and 33,341 females.

> 5 An administrative district used for taxation purposes, typically made up of multiple villages.

10
ts, does separate somewhat from the control group at the top of the distribution, suggesting some increases in capital for some ...rms as a result of the cash treatment.

Next we examine where the grants are going if not into the business. Beginning in wave 4, ...rm owners were asked “During the past three months, did you make any payments in cash or goods to people living outside your household?”and if so, the value of such transfers. Columns 3 and 4 show that women who received the cash grant were more likely to have made such a transfer, and to have given more. On average they are estimated to have given 8 cedis more a quarter over the last 3 quarters of the survey. This does not account for any transfers out made in the ...rst quarter after treatment by ...rms treated after wave 2, since the wave 3 survey did not collect transfers data. However, restricting the analysis to the control group and ...rms treated after wave 3 only marginally increases the coe¢ cient on the cash treatment, raising it to 8.9 cedis.

The remaining columns report the estimated impacts on household expenditure, which was 28
<br> genous regressor|essors<br>s|15.45<br>0.000442|28.69<br>5.89e-07|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01, ** p<0.05, * p<0.1.Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.
. We then exclude self-employed individuals. Furthermore, we use parameters estimated for men only. Lastly, estimates could be sensitive to the extent of non-employment, which varies across countries and by level of development. This could be because workers with certain characteristics might be more likely to be unemployed or out of the labor force. To deal with this, we repeat the estimation using country-year-samples with unemployment or non-employment rates below the 25th percentile (7% and 35%, respectively) or 50th percentile (10% and 35%, respectively) in the sample, and separately for country-year samples above each of these thresholds.<sup>53</sup>

Note that the returns to education are not causal. They are nonetheless consistent

> 53We cannot use historical information on unemployment/participation to adjust the estimates of potential returns to experience to equal actual returns. Since we focus on workers aged 18-67 in samples from 1990-2016, we study individuals who worked in 1941-2016. Unfortunately, data on unemployment and participation does not exist for _all_ countries in _all_ years. Unemployment data is typically only available from the 1990s. Also, since unemployment rates are higher in HICS and participation is lower in MICs (Table 1), we may under-estimate their returns, which should, if anything, reinforce our results.
domestic currency. To correct for this problem, we corrected the constant dollar values for changes in relative prices (IE), as follows: X **t -** (EXR t / EXR t ) / IE t+ where t is 1993 and i is year 0, 1, 2. After that we compute net investment and first difference of sales and then we average all the variables over the period 1993 - 1995 (see the first two columns of Table A2. 1). Table A 2.1. illustrates clearly the importance of correcting for real exchange rate fluctuations. This is especially true for the investment series. In current dollar prices both mean and median investmant are negative, while this is no longer the case once investment is expressed in real dollar prices.

**Table A2.1 Bulgarian Company Data, AMADEUS, USD million**

||_Real Dollar Prices_|_Con_|_stant Dollar Pri_|_ces_<br>_Curr_|_ent Dollar Prices_||
|---|---|---|---|---|---|---|
||MEAN<br>MED|IAN<br>ME|AN<br>ME|DIAN<br>ME|AN<br>ME|DIAN|
|NEMPL|709|286|709|286|709|286|
|K|14.51|2.41|12.42|2.07|7.896|1.33|
|I|3.88|0.35|1.34|-0.03|-2.409|-0.52|
|IB|4.67|0.53|2.01|0.09|-1.999|-0.438|
|LTDEBT|1.62|0.00|1.42|0.00|0.911|0.000|
|CLIAB|
**Figure 5: Mortality between 1975 and 1979 in Cambodia by birth cohort and education level of the sister. From siblings' mortality data, DHS Cambodia 2000**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>30-34 25-29 20-24 15-19 10 to 14 5 to 9 0 to 4<br>Age in January 1975<br>Individuals with a sister with at least secondary education before 1975<br>Individuals with a sister with less than secondary education before 1975<br>1979<br>Probability to die between January 1975 and December<br><!-- End of picture text --> 25
From the created “census” in each of the areas, 20 percent of the observations are sampled using simple random sampling without replacement;<sup>18</sup> this yields our “survey” data. Thus, in this experiment, survey weights are all equal. This generation process is repeated _L_ = 10 _,_ 000 times. In each simulation, the following quantities are computed for the poverty rates and gaps in each area:

1. True poverty indicators _τc_ , using the “census”.

2. Direct estimators _τ_ ˆ _c_<sup>_DIR_</sup> using the “survey”, defined as the sample versions of _τc_ .

3. Original EB estimators ˆ _τc_<sup>_EB_</sup> by MR (2010) with REML fitting as implemented in the sae R package, with _M_ = 50 MC replicates.

4. Traditional ELL estimators _τ_ ˆ _c_<sup>_ELL_</sup> and their estimated standard errors, without location means and also including the location means of the considered auxiliary variables, where _M_ = 50.

5. Census EB (CEB) estimators ˆ _τc_<sup>_CEB_</sup> , obtained as in the MC procedure of MR (2010), but generating full census vectors from the conditional distribution and without appending the survey welfares, using REML fitting and with _M_ = 50.

6. Extended CEB estimators using the MC and parametric bootstrap methods described in Section 6, using either H3 or ELL fitting methods with _M_ = 50. Since in this simulation experiment model errors are homoscedastic and sampling weights are constant, here the extended CEB estimators only differ in the above CEB ones in the model fitting method.

7. Estimators based on the CB-EB approach from Section 4, _τ_ ˆ _c_<sup>_CB−EB_</sup> using either H3 or ELL fitting methods.<sup>19</sup> Since clusters are originally supposed to be nested within the areas and the clustered bootstrap method
_Table 1. OLS Regression Results: Explaining Wage Growth_

|||Model A|||Model B|||ModelC||
|---|---|---|---|---|---|---|---|---|---|
||Total|Unskilled|Skilled|Total|Unskilled|Skilled|Total|Unskilled|Skilled|
|∆Export Index|.012<br>(.0098)|.030**<br>(.013)|-.0007<br>(.0088)|.017**<br>(.0079)|.028***<br>(.011)|.0011<br>(.0079)||||
|∆Export US Index|||||||.019<br>(.013)|.041**<br>(.017)|-.010<br>(.012)|
|∆Skilled/unskilled status|-.054|.11|-.026|-.086|.055|-.071|-.085|.055|-.069|
||(.062)|(.094)|(.063)|(.065)|(.092)|(.078)|(.065)|(.092)|(.078)|
|∆Marital status|-.12*|-.15*|-.014|-.10*|-.14**|.0025|-.10|-.14*|.0016|
||(.072)|(.079)|(.093)|(.063)|(.069)|(.091)|(.063)|(.069)|(.
. Table A9 presents descriptive statistics for the subsample of the 92 surveyed programs that match to individual-level data and shows t-tests for mean differences between these programs and the 153 unmatched ones with 2+ students. Some differences are statistically significant. Matched programs are more likely to update the curriculum based on government standards and HEI labor market perceptions, require a professional test for graduation, or have agreements with firms to hire their graduates. They have more workshops for practice, charge a higher tuition, and have more faculty members. They are more likely to be taught by private, large HEIs, and have high-quality accreditation.

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