rafmacalaba/datause-encoder-data huggingface dataset:
- <mark>De Weerdt, J., Gibson, J., & Beegle, K. (2020). What can we learn from experimenting with survey methods?.</mark> _<mark>Annual Review of Resource Economics</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>, 431-447.</mark>

- <mark>Deaton, A., & Zaidi, S. (2002).</mark> _<mark>Guidelines for constructing consumption aggregates for welfare analysis</mark>_ <mark>(Vol. 135). World Bank Publications.</mark>

- <mark>Durazo, J., Herewait, Y., Pattinasarany, G.D.V., & Jolliffe, D. (2017) bridging Surveys: A methodological experiment of food consumption data in the Indonesian Social Economic Survey (Susenas). Contribution presented at the XV EAAE Congress, “Towards Sustainable Agri-food Systems: Balancing Between Markets and Society”, August 29 – September 1, 2017, Parma, Italy</mark>

- <mark>FAO, IFAD, UNICEF, WFP and WHO. (2021). The State of Food Security and Nutrition in the World 2021.</mark> <u><mark>https://data.unicef.org/resources/sofi-2021/</mark></u>

- <mark>Friedman, J., Beegle, K., De Weerdt, J., & Gibson, J. (2017). Decomposing response error in food consumption measurement: Implications for survey design from a randomized survey experiment in Tanzania.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>, 94-111.</mark>

- Government of Tanzania. (2017). Agricultural Sector Development Strategy Phase – “Agricultural Sector for Industrial Development.”

- <mark>Mancini, G., & Vecchi, G. (2022). On the construction of a consumption aggregate for inequality and poverty analysis.</mark> _<mark>World Bank Group, Washington, DC</mark>_ <mark>.</mark>

- Ministry of Finance and Planning - Poverty Eradication Division (MoFP- PED) [Tanzania Mainland], National Bureau of Statistics (NBS) and the World Bank. (2020). Tanzania Mainland Household Budget Survey 2017/18. Final Report Dodoma, Tanzania MoFP-PED, NBS and Washington DC USA, and WB.

- National Bureau of Statistics (NBS). (2022a). _National Accounts of Tanzania Mainland (2015 – 2021)._ NBS, Tanzania

- National Bureau of Statistics (NBS). (2022b). _Integrated Labour Force Survey 2020/21_ . NBS, Tanzania.

- <mark>Sharp, M., Buffiere, B., Himelein, K., & Gibson, J. (2019). Effects of data collection methods on estimated household consumption and poverty, and on survey costs: evidence from an experiment in the Marshall Islands. In</mark> _<mark>IARIW-World Bank Conference on New Approaches to Defining and Measuring Poverty in a Growing World, Washington DC, November</mark>_ <mark>.</mark>

- <mark>UNICEF. (2021). The state of food security and nutrition in the world 2021</mark> . <u>https://data.unicef.org/resources/sofi-2021/</u>

- <mark>Zezza, A., Carletto, C., Fiedler, J. L., Gennari, P., & Jolliffe, D. (2017). Food counts. Measuring food consumption and expenditures in household consumption and expenditure surveys (HCES). Introduction to the special issue.</mark> _<mark>Food Policy</mark>_ <mark>, 72, 1-6.</mark>

26
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(for return migrants) and at the time of the survey in 2012 (for current migrants). This allows us to account for both forced return migration and planned return.

While conflict occurrence in host countries constitutes a strong predictor of return migration, an empirical concern would be if conflicts might have consequences on the Egyptian economy through, for instance, changes in trade flows, foreign direct investments, or remittances. To address this concern, we additionally rely on data from the World Bank Development Indicators in order to control for FDI (% of GDP), remittances (% of GDP), and the exports and imports of goods and services (% of GDP) in Egypt in equation (4). We show in the robustness checks section that our results are robust to these two checks, which provides support to the exclusion restriction of occurrence.

# **5 Empirical findings**

## **5.1 Does the legal status of migrants matter upon return?**

### **5.1.1 Hourly Wages upon Return**

In Table 4, we depart from simple OLS regressions to estimate the effect of return migration on wages in 2012 upon return. We are interested in examining whether the legal status of migrants has a differential effect on their wage premium upon return. In column (1), we focus on the full sample of non-migrants and returnees. In column (2), we focus on the subsample of documented migrants compared to nonmigrants, and in column (3), we compare undocumented migrants to non-migrants. Without accounting for any of the selection issues presented earlier, Table 4 shows, that controlling for individual and job characteristics, return migration is associated with a significant wage premium. However, when we disentangle the effects by legal status, the results show that this positive association between return migration and wages holds only for documented migrants.

Accounting for the triple selection into migration, illegality, and return using a conditional mixed process model, in Table 5, we estimate equation (1a) on the effect of illegality on hourly wages upon return, conditional on return migration. We start by presenting the results from the reduced form equations and then discuss the results from our primary structural equation (1a). First, we find that oil prices are a strong predictor of the probability of migration. In column (1), we find that a one dollar increase in oil prices leads to an increase in the probability of migration by 1.3 percentage points. Diplomatic exchange also plays a crucial role in explaining the legal migrant status. In column (2), we find strong suggestive evidence that negative shocks to the bilateral relations between Egypt and the countries of destination of Egyptian migrants, at the time of migration, drive illegal migration to the destination countries. Indeed, we find that the lack of diplomatic representation of countries of 19
is more than one respondent in a given country-policy-cluster pair and these respondents did not hold their respective public sector positions during the same period of time.<sup>19</sup> **_α_ 2** is a vector of coefficients for our respondent-level controls contained in **X2** _i_ . Here _δn_ denotes unmeasured time-invariant determinants of the World Bank’s policy influence in countrypolicy-cluster _n_ and _rn_ is a country-policy-cluster indicator dummy, with _N_ representing the total number of countries and _k_ representing the total number of policy-clusters in the sample. The term _εi_ is an error term that denotes all unmeasured idiosyncratic factors that affect the World Bank’s reported level of policy influence at the individual respondent level.

In order to measure exposure to the World Bank’s lending and non-lending services, we extract information on the timing, policy focus, and cross-country distribution of ESW products, DPLs, PAs, and investment loans from the organization’s Business Intelligence Database and Development Policy Actions Database. The number of World Bank ESW products received by a country in a given policy cluster and year is our primary variable of interest ( _n_ _~~e~~ sw_ _~~m 1~~ p_ ) and test of _H1_ . The World Bank assigns each of its ESW products to one of four policy clusters: Macroeconomic and Structural Policy (MSP), Human Development (HD), Sustainable Development (SD), and Governance (GOV). Therefore, we cross-walk these policy cluster-categorized ESW data to the 24 policy domains to which _2014 Reform Efforts Survey_ respondents anchored their evaluations of the World Bank (see Table A1 in the Appendix). This approach allows to define exposure to the World Bank ESW “treatment” at the country-policy-cluster-year level, so if a respondent worked in a specific country and policy cluster that received ESW from the World Bank during a year in which he or she evaluated the policy influence of the World Bank, we identify the respondent as having experienced exposure to the World Bank ESW “treatment.” Given that the World Bank provides a bundle of lending and non-lending services to its client countries and virtually all of these services have policy objectives, we also construct four additional variables that measure the World Bank’s lending services. In order to capture

> 19In general, they could vary for any pair that meets these criteria.

15
A closer look at the data for old age and disability pensions shows that male pensioners on average do reach a high net replacement rate. Male pensioners in 1994 on average had 39.3 insured years of service with an average of 1. I personal points per year. Relating the average pension paid to male pensioners in 1994 to the average wage results in a net replacement rate of 72 percent (gross:50 percent). For female pensioners, however, the net replacement rate was considerably lower at about 40 percent due to an average of only 25 insured years and 0.7 personal points per year.7 A pensioner receiving a full pension at or above age 65 may earn unlimited additional income. For cases of early retirement pensions are reduced according to the level of additional income earned.

Pension contributions are subject to income tax. Pension contributions are paid out of after-tax income; but some tax deductions are allowed for insurance contributions.According to German tax laws, only that portion of the pension is taxable which represents the notional capital gain on contributions.

There is no minimum pension in Germany; however, a method is applied to lift very low pensions by increasing the personal points to a maximum of 0.75 points per year, provided that the insured has contributed for 35 years.This increase has benefited predominantly female pensioners of the Workers' Insurance who made very low contributions during their working life. But the most important supplement for lowincome pensioners is social assistance. Approximately half of all social assistance is paid to pensioners, and about one third of all social assistance is spent for long term care. Social assistance is financed at the local level, thus the municipalities' budgets are increasingly burdened with the costs of aging. Therefore, in 1995, Germany introduced a new branch of social insurance for long term care.

Pensioners must contribute to the statutory health insurance unless they are covered by a comparable private health insurance contract. The rate is currently 6.7 percent of the pension. This contribution is matched by an equal share payable by the pension insurance.

# Civil servants' pensions

The approximately 2.5 million civil servants in Germany are not insured in the public pension system. Their pension payments are financed out of the budgets of the respective employing authorities, i.e. by the federal government, states, and municipalities.<sup>8</sup> The beneficiaries do not contribute to the financing of benefits.

> 7Data refers to former West Germany s Only one state so far, Rheinland-Pfalz, has recently decided to set up a separate fund for pension payments in order to move away from a purely budget financed scheme.

10
Breton, Albert (1998). _Competitive Governments: An Economic Theory of Politics and Public Finance_ . New York, NY: Cambridge University Press.

- Carlson, Curtis, Dallas Burtraw, Maureen Cropper, and Karen L. Palmer ( 2000). “Sulfur-Dioxide Control By Electric Utilities: What are the Gains from Trade?” _Journal of Political Economy_ 108 (4): 1292-326.

- Cointreau, Sandra and Constance Hornig (2003).  “The Application of Economic Instruments in Water and Solid Waste Management”.  Regional Policy Dialogue, Inter-American Development Bank.  Washington, D.C.

- Cornes, Richard and Todd Sandler (1986). _The Theory of Externalities, Public Goods, and Club Goods_ , Cambridge University Press.

- Cropper, Maureen and Wallace Oates (1992).  “Environmental Economics: A Survey”, _The Journal of Economic Literature_ 30 (4): 675-740.

- Dalmazzone, Silvana (2006).  “Decentralization and the Environment.”  In Ehtisham Ahmad and Giorgio Brosio (eds.) _Handbook of Fiscal Federalism_ .  Cheltenham, UK:  Edward Elgar, 459-77.

- Dinan, Terry M., Maureen L. Cropper, and Paul R. Portney (1999).   “Environmental Federalism: Welfare Losses from Uniform National Drinking Water Standards”. In Arvind Panagariya et al. (eds.), _Environmental and Public Economics: Essays in Honor of Wallace E. Oates_ .  Cheltenham: Edward Elgar Publishing Co., 13-31.

- Ellerman, A. Denny, Paul L. Joskow, Richard Schmalensee, Juan-Pablo Montero, and Elizabeth M. Bailey (2000). _Markets for Clean Air: The US Acid Rain Program_ . Cambridge: Cambridge University Press.

- Engel, Kirsten H. (1997).  “State Environmental Standard Setting: Is There a ‘Race’ and Is It ‘To the Bottom’?” _Hastings Law Journal_ 48: 271-398.

- Farber, Daniel A. (1997).  “Environmental Federalism in a Global Economy”. _Virginia Law Review_ 83: 1283-1319.

- Fullerton, Don, and Sarah E. West (2002).  “Can Taxes on Cars and on Gasoline Mimic an Unavailable Tax on Emissions?” _Journal of Environmental Economics and Management_ 43 (2): 135-157.

- Gordon, Roger (1983).  “An Optimal Tax Approach to Fiscal Federalism”. _Quarterly Journal of Economics_ 97: 567-586.

- Gramlich, Edward M. (1987).  “Subnational Fiscal Policy”. _Perspectives on Local Public Finance and Policy_ 3 (1): 3-27.

45
in the course of the upswing in consumption expenditures, some households fell behind, while others shifted toward the top, joining the ranks of those whose consumption put them in the top decile in 1991/92.

To summarize these changes, we present in Figure 3 the set of relative polarization indices computed from the GLSS data using Equations(7)-(9).<sup>17</sup> These indices track changes in the shape of the distribution only, and they code the direction as well as the magnitude of the change. The overall index (MRP) rises continuously and the rise is statistically significant from the outset, thus confirming the visual impression from Figure 1(d). Decomposing the MRP into the contributions from the lower and upper tails of the distribution, it also appears that “downgrading” dominated “upgrading” in the – polarization upswing the value of the LRP is indeed always greater than that of the URP.

# **3.2 Temporal decomposition**

To get a more compact picture of the timing and nature of the polarization trend described above, we can break the 21-year period into 3 sub-periods – 1991-98, 1998-2005, and 2005-12 – and highlight the changes that took place _within_ each of them. The top three panels of Figure 4 show the relative distribution for each sub-period. In contrast to the 21-year decile series, which takes 1991/92 as the reference distribution for all waves, each panel here takes the beginning year of the sub-period for the reference distribution and the end year for the comparison. The displays clearly point to the median up-shift in household consumption expenditure as the dominant trend for each sub-period. These are the images of a “rising tide that lifts all boats”, i.e. the effect of a location shift that was the most influential contributor to the overall pattern during all sub-periods. The differences due to the median shift – representing what the relative density would have looked like if there had been no change in distributional shape – are plotted in the middle row panels of Figure 4. As expected, the strongest effects were in the bottom deciles, confirming that more low-consuming households joined the ranks of those whose consumption levels put them in the top half of the reference distributions. However, once changes in location are netted out, there is also an indication of growing polarization that is not evident in the overall relative distributions. The differences explained by the shape changes are presented in the bottom row panels of Figure 4, where the median-adjusted relative distributions take an approximate U-shape. Strong growth occurred in the fraction of households at the top and bottom tails of the period-specific consumption distributions, while sizable declines occurred in the middle. This polarizing trend seems nearly symmetric for the years 2005 to 2012, while throughout the 1990s and up to the mid-2000s the growth in the lower tail of the distribution was noticeably stronger than in the upper tail.

> 17 Since the value of the three indices always equals 0 in the baseline year (Morris et al., 1994, p. 209), polarization summaries for 1991/92 were not included in the graphical display.

16
Estimating the Economic and Distributional Impacts of the RCEP Table 2. Impact on trade by economy. Percentage change relative to ‘Business-as-Usual’ scenario, 2035

|**Region**||**E**|**xports**<br>|||**Im**|**ports**<br>||
|---|---|---|---|---|---|---|---|---|
||**Tariffs**<br>**alone**|**RCEP**|**ROO**<br>**liberaliz-**<br>**ation**|**Productivity**<br>**kick**|**Tariffs**<br>**alone**|**RCEP**|**ROO**<br>**liberaliz-**<br>**ation**|**Producti-**<br>**vity kick**|
|Australia-New|||||||||
|Zealand|0.2|3.6|4.3|4.7|0.2|3.2|4.1|4.6|
|China|0.7|1.7|2.4|4.0|0.6|1.8|2.5|3.9|
|Indonesia|0.3|3.0|3.7|5.5|0.2|2.6|3.6|5.0|
|Japan|3.0|7.6|9.0|8.9|2.6|5.2|6.2|6.4|
|Cambodia|0.4|3.6|4.7|6.5|0.1|2.7|3.6|5.2|
|Korea, Rep.|0.9|2.0|3.2|4.1|0.9|2.0|3.3|4.2|
|Lao PDR|0.2|3.9|4.3|4.5|0.1|3.1|3.8|4.3|
|Malaysia|0.1|1.6|2.7|5.6|0.1|1.7|3.0|5.5|
|Philippines|0.3|5.6|7.1|8.5|0.2|4.6|6.0|7.2|
|Thailand|0.4|1.9|3.1|4.2|0.3|2.2|3.5|4.4|
|Vietnam|-0.3|6.2|7.6|11.4|-0.3|4.7|6.2|9.2|
|**RCEP**|**0.7**|**2.8**|**3.7**|**5.2**|**0.7**|**2.6**|**3.6**|**4.9**|
|Canada|0.0|0.0|-0.1|-0.2|0.0|0.0|0.0|0.1|
|Chile|0.0|-0.1|-0.1|-0.1|-0.1|0.0|-0.1|0.1|
|Europe|0.0|-0.1|-0.2|-0.3|0.0|-0.1|-0.1|0.0|
|Hong Kong SAR,|||||||||
|China|-0.1|-0.1|-0.2|-0.3|0.0|0.1|0.0|0.3|
|India|-0.1|-0.1|-0.2|-0.2|-0.1|-0.1|-0.3|-0.2|
|Mexico|0.0|0.0|0.0|-0.1|0.0|0.0|0.0|0.0|
|Peru|0.0|0.0|-0.1|0.1|0.0|0.0|-0.1|0.2|
|Sub-Saharan|||||||||
|Africa|0.0|0.0|-0.1|0.0|0.0|0.0|0.0|0.2|
|United States|0.0|0.0|0.0|0.4|-0.2|-0.2|-0.5|-0.5|
|Rest of Southeast<br>Asia|-0.2|-0.4|-0.8|-0.6|-0.2|-0.4|-0.8|-0.5|
|Rest of Europe<br>and Central Asia|0.0|0.0|-0.1|0.0|0.0|0.0|0.0|0.3|
|Rest of Latin<br>America|0.0|0.0|-0.1|0.0|0.0|0.0|0.0|0.2|
|**Rest of the World**|**0.0**|**-0.1**|**-0.1**|**-0.1**|**-0.1**|**-0.1**|**-0.2**|**0.0**|

Source: World Bank staff calculations using ENVISAGE model.

Agricultural and manufacturing exports from RCEP countries are expected to increase substantially. Among them, meat products, food and beverages, textiles, chemicals, and wearing apparel are the sectors registering the fastest growth rates of exports (Figure 9 LHS). This is linked to both the reduction of tariffs, which takes place mainly in meat products, motor vehicles, food and beverages, wearing apparel, crops, nonmetallic minerals and textiles, and reductions of non-tariff measures. Exports of some services are also boosted: tourist services, trade, and public
**Table 8.  Sector Export Intensities by Regional Market: Exports of the Sector as a Percent of Production of the Sector** (in percent)

|Good|Central|Far East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|MAR|53|57|57|51|53|50|54|57|51|57|
|AGR|1|1|1|1|1|1|1|1|1|1|
|AIR|46|48|48|44|45|43|43|48|47|48|
|CHM|27|21|17|48|27|37|24|35|40|57|
|CNM|3|3|3|6|3|5|3|4|4|7|
|COL|23|23|23|23|23|23|||||
|CON|3|3|3|3|3|3|3|3|3|3|
|CRU||56|56|56|56|56|||56|56|
|ELE|2|2|2|2|2|2|2|2|2|2|
|FIN|1|1|1|1|1|1|1|1|1|1|
|FME|40|35|34|37|43|37|48|75||37|
|FOO|4|12|8|8|11|4|9|6|3|9|
|GAS||27|27|27|27|27|||27|27|
|HEA|0|0|0|0|0|0|0|0|0|0|
|HOU|1|1|1|1|1|1|1|1|1|1|
|MWO|11|25|14|48|25|18|45|32|12|27|
|NFM|44|45|48|43|58|55|62|67||79|
|OIL|32|32|32|32|32|32|32|32|32||
|OTH|12|12|12|12|12|12|12|12|12|12|
|OTI|15|15|15|15|15|15|15|15|15|15|
|PST|9|9|9|9|9|9|9|9|9|9|
|RLW|2|2|2|2|2|2|2|2|2|2|
|SCI|3|3|3|3|3|3|3|3|3|3|
|TMS|7|7|7|7|7|7|7|7|7|7|
|TPP|7|77|33|48|11|17|5|30|12|33|
|TRD|1|1|1|1|1|1|1|1|1|1|
|TRK|2|2|2|2|2|2|2|2|2|2|
|TRO|5|5|5|5|5|5|5|5|5|5|
|CLI|11|11|11|11|11|11|11|11|11|11|
|Total|364|534|474|545|488|477|397|459|357|510|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

44
# **Table** **<u>3 Continued. Mapping:</u>** **_Legal_** **_<u>Services</u>_**

|**Australian categories**|**Weight**|**Score**|**Weighted**<br>**score**|**The World Bank data**|**Weight**|**Score**|**Weighted**<br>**score**|
|---|---|---|---|---|---|---|---|
|**Temporary movement ofpeople**|**0.01**|||**Temporary movement ofpeople**|**0.01**|||
|No temporary entry|0.01|1||No limitation on the duration of stay initially allowed for IPC|0.01|1||
|Temporary entry of up to 30 days|0.01|0.75||Duration of stay initially allowed for IPC is up to 1 month|0.01|0.75||
|Temporary entry of up to 60 days|0.01|0.5||Duration of stay initially allowed for IPC is up to 2 month|0.01|0.5||
|Temporary entry of up to 90 days|0.01|0.25||Duration of stay initially allowed for IPC is up to 3 month|0.01|0.25||
|Temporary entry over 90 days|0.01|0||Duration of stay initially allowed for IPC is over 3 month|0.01|0||
|**Total**|**1.05**||||**1**|||

29
# Figure 15 QMY and DCPY in 1985

<!-- Start of picture text --> 0.7-<br>0.6-<br>0.5-<br>0.4-<br>~0.3-<br>0.2-<br>0.1-<br>ARO CL GR IDN KOR' MYS 'OAN' PHL' THA' VN<br>BRA CHL IND JOR MEX NGA PAK PRT TUR ZWE<br>country<br>QMY DCPY<br>LZ]<br><!-- End of picture text -->
Table 6: Take-Up and First Stage

||Page S|upport|Online|Training|Mark|ets On|
|---|---|---|---|---|---|---|
||Sent<br>Pictures<br>(1)|Became<br>Owner<br>(2)|Opened<br>Link<br>(3)|Watched<br>1hr+<br>(4)|FB<br>Page<br>(5)|FB<br>Groups<br>(6)|
|_β_1: Treatment (_×_ Low Mobility)|0.26<sup>_∗∗∗_</sup>|0.39<sup>_∗∗∗_</sup>|0.59<sup>_∗∗∗_</sup>|0.14<sup>_∗∗∗_</sup>|0.30<sup>_∗∗∗_</sup>|0.14<sup>_∗∗∗_</sup>|
||(0.04)|(0.03)|(0.03)|(0.02)|(0.04)|(0.04)|
|_β_2: Treatment _×_ High Mobility|0.00|-0.03|-0.01|0.04|-0.04|-0.04|
||(0.06)|(0.04)|(0.04)|(0.03)|(0.05)|(0.05)|
|High Mobility|0.00|0.01<sup>_∗_</sup>|0.00|-0.00|0.05|0.03|
||(0.04)|(0.01)|(0.00)|(0.00)|(0.03)|(0.03)|
|_β_1+_β_2 (High Mobility Efect)|0.26<sup>_∗∗∗_</sup>|0.36<sup>_∗∗∗_</sup>|0.58<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|0.26<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
|_β_1+_β_2 = 0 (_p-value_)|0.00|0.00|0.00|0.00|0.00|0.01|
|RI p-values _β_1|0|0|0|0|0|0|
|RI p-values _β_2|.96|.58|.9|.21|.51|.4|
|Low Mobility Control Mean|.34|0|0|0|.17|.16|
|High Mobility Control Mean|.35|0|0|0|.27|.21|
|Obs|983|981|983|983|968|968|

_Notes_ : High Mobility refers to a binary variable for above median mobility index, which averages the z-scores from mobility variables (a)-(c) in table 1. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust standard errors in parentheses. All regressions have strata fixed effects and control for variables chosen using post-double-selection lasso procedures and baseline level of the outcome when available. RI p-values are from a randomization inference procedure that randomly permutes treatment assignment. Outcomes are defined as follows: outcomes (1) and (2) reflect study participants’ engagement with the logistical support part of the bundled intervention, where (1) is a binary variable for whether the respondent sent the necessary pictures and information to set up a Facebook business page for her, and (2) is a binary variable for whether the respondent became an owner on the page by accepting the invitation to become an administrator. Outcomes (3) and (4) reflect participants’ engagement with the online training part of the bundled intervention, where (3) is whether the respondent opened the training link at all, and (4) is whether she watched more than an hour of the training. Outcomes (5) and (6) reflect the first stage, where (5) is a binary variable for whether the respondent markets on a Facebook business page, and (6) is a binary varibale for whether the respondent markets on Facebook groups.

24