rafmacalaba/datause-encoder-data huggingface dataset:
in the economic analysis of inflation, which tends to be centered around the macroeconomic causes and consequences of fast price increases. Likewise, the varying incidence of inflation is frequently not captured in household welfare analysis that is carried out using “headline” rates of inflation to proxy for the changes in prices faced by the poor.

We estimate decile inflation rates for Turkey –a country battling high inflation in recent years– for the period 2011-2020 using consumer expenditure survey data and item-group specific price data from the official Turkish Statistical Institute. The results show that inflation is typically higher for vulnerable populations, especially when inflation is propelled by fast increases in food prices and housing costs, the most prevalent items in the consumption basket of a typical poor household. Differences in cumulative inflation between 2001 and 2020 show that the consumption bundle of the poorest decile has become 5 percentage points more expensive than the richest decile’s basket, on average a yearly gap of 0.23 percentage point. However, this estimate is most likely a lower bound of the real gap owing to the fact that lower-income households tend to pay higher prices within the same categories of goods –for instance, due to liquidity constraints that prevent them from buying larger quantities and taking advantage of bulk discounts, an issue that we do not investigate in this paper but that is documented in the literature (for instance, Kaplan and Schulhofer-Wohl 2017; Orhun and Palazzolo 2018).

Measuring the inflation realizations of vulnerable groups provides valuable information for policy for a number of reasons. First of all, monetary policy is often guided by macro models built around the welfare maximization of a representative household that is assumed to face average inflation. Therefore, the optimal monetary policy resulting from solving such optimization problem may result in larger welfare losses for households that have to deal with higher inflation. Moreover, also from a macro perspective, heterogeneity in inflation experience may anchor higher inflation expectations across some groups of the population, undermining policy efforts to curb the overall inflation rate (Johannsen 2014).

There are also important policy insights on the micro side. Since low-income households tend to spend a large share of their incomes on food, inflation episodes driven by substantial changes in food prices make these households more vulnerable. Understanding the factors contributing to fast price increases well in advance, for instance through early warning systems 22
additional equilibrium condition besides the one provided by the monetary authority’s balance sheet. Actually, relying on only one equilibrium condition led Bernanke and Mihov [6], who initially consider both the fed funds rate and the discount rate, to make the simplifying assumption that the innovation to the discount rate is zero.<sup>4</sup> For its crucial relevance in the determination of the exchange rate in general and its decisive influence on the monetary conditions faced by the economy in the particular case under study, the external sector conservation condition seems the most appropriate identity to overcome this obstacle. Therefore, the additional equilibrium condition proposed here is the balance of payments.

Note, however, that while the balance of payments summarizes the _flows_ of funds between the country and the rest of the world, the balance sheet of the monetary authority, as any other balance sheet, provides information on _stocks_ . Considering the first difference of the above balance-sheet items, then equation (10) turns into: On the other hand, turning to the balance of payments, the following conservation condition holds for the residuals of a VAR that includes the external accounts of any given country: where _u_ ∆ _nfa_ is the forecast error of the change in net foreign assets also included in equation (11), _uca_ is the residual corresponding to the current account, and _uka_ is the error attached to capital and financial accounts’ transactions _excluding_ the flows affecting the central bank’s foreign-currency liabilities,<sup>5</sup> since the latter are included within net foreign assets. In other words, the claim is that fluctuations in international reserves are relevant for the behaviour of the other variables of the VAR to the extent that they do not have as an exact counterpart adjustments in items denominated in foreign currency in the central bank’s balance sheet. In the event of a positive shock to the current account, this could imply, for instance, an increase in seignoriage affecting _u_ ∆ _cp_ , later possibly sterilized through _u_ ∆ _mp_ .

It is essential to rely on a structural VAR system to know in which precise sense the observable VAR innovations in equations (11)-(12) relate to the “primitive” orthogonalized shocks _v_ , and therefore be able to recover (i.e., _observe_ ) the latter to trace their effects on the economy. The following simple model parsimoniously accounts for the behaviour of the external sector of the economy and the markets for the items in the

> 4In the 1995 NBER version of their article, Bernanke and Mihov [5] explore the effects of considering nonzero discount rate innovations by including an additional reaction function for the Federal Reserve. Insofar as neither the reference short-term interest rate nor the exchange rate are variables unilaterally decided by the policy maker, such a shortcut is not at hand in the present study.

> 5In the Argentine case below, for example, checking accounts in foreign currency and obligations with IFIs.

7
Africans. They are, however, less likely to know about or be willing to utilize the CCMA, a widely used labor arbitration institution available to employees to take employers to court.

In sum, foreigners receive fewer profile clicks despite positive statistical discrimination and lower risks of being taken to court, pointing to the importance of taste discrimination. Consistent with this interpretation, I find that discrimination increases in the intimacy of the employer-worker relationship: it is highest in the nanny and lowest in the gardening sector.

One limitation of this research design is that, similar to most audit studies, we only observe the first step in the hiring process. Faced with a large number of applications, firms may commonly use heuristics in screening applicants and stop reading once they see a certain signal, in our case foreign nationality (Bertrand and Mullainathan (2004)). In fact, a review by Riach and Rich (2002) concludes around 90% of discrimination occurs at this first selection stage. Kuhn and Shen (2013) develop a screening cost model and show that narrow search strategies that pre-screen applicants are more commonly used in low-skilled sectors with large applicant pools. A coarse job screening strategy is thus highly relevant for the high unemployment, low-skill sectors context of this study (Bartos et al. (2016)).<sup>8</sup> A second limitation is that while we observe the same information as employers, we may fail to notice subtle differences between profiles that differ between natives and immigrants. I try to address this by controlling for a rich set of profile characteristics including the number of words, spelling and grammar mistakes, the time and day of the posting, and a set of variables for information such as experience, age, and qualification. I also find that results are robust to controlling for job seeker characteristics _unobservable_ to employers at the screening stage.

This study adds to a growing literature on online labor markets. These markets, which are predicted to increase rapidly in the future (Horton (2010)), are particularly prone to information asymmetries since, especially at an early stage in the hiring process, employers have very limited information of the job seeker (Pager (2007)). The study is most similar to Kuhn and Shen (2013, 2014) who use data from an online labor market in Xiamen, China to test how employer call back rates differ for job seekers with varying observable characteristics like age, gender, or education. This study also contibutes to a larger literature that uses data from online markets to estimate preferences. For example, Pope and Sydnor (2011) analyze data from propsper.com, a peer-to-peer lending website

> 8In addition, social-psychology theories of _unconscious_ bias predict that discrimination is most pronounced when firms have limited information on job applicants (Arrow (1998); Pager (2007)).

4
determined by the proxy means, was above the pre-defined threshold. This process resulted in the identification of 3,002 households to participate in the program.<sup>9</sup> In communities randomly selected to participate in the _Atención a Crisis_ program, the primary child caregiver, who in the vast majority of cases was a woman, was invited to a registration assembly where the program objectives and various components were explained. At the end of the assembly, a lottery took place in each community in which the three packages described above were randomly allocated among the eligible households. Participation in the assemblies and lotteries was close to 100 percent. Note that, within treatment communities, one third of eligible households were assigned to each of the three treatment packages, and all of them received the CCT. In control communities, households did not receive any of the treatment packages.

# _B. Data_

Baseline data for the evaluation were collected in April-May 2005. A follow-up survey, including a large number of tests to assess cognitive development, was collected in July-August 2006 (9 months after the households had started receiving payments). The sample includes the 3,002 eligible households in the treatment group, and a random sample of 1,019 eligible households in the communities that were randomly assigned to the control group. Attrition between the two surveys was minimal, less than 1.3 percent. Attrition is uncorrelated with treatment status, and the baseline characteristics of the full sample of children and those that could be located at follow-up are very similar—see Appendix 1.

Program take-up in the treatment group was more than 95 percent, and contamination of the control was negligible (one household).<sup>10</sup> As discussed above (footnote 9), the main reason households did not takeup the program was due to the fact that some originally eligible households were deemed ineligible by local

> 9 The weights in the proxy means were based on estimates from the national household data from 2001 (EMNV). Additional discussions with local leaders from each intervention community were conducted to identify possible exclusion or inclusions errors.  Based on the discussions with leaders, 3.72 percent of all the households considered were re-assigned from non-eligible to eligible, and 3.65 percent from eligible to non-eligible.  To avoid selection bias, we use the original eligibility, based on the same proxy means test for both treated and control households, as the intentto-treat.

> 10 A small fraction of households, less than 5 percent, did not collect the full amount of the transfer they were eligible for because they had not complied with the school enrollment and attendance requirements.

6
5 recognition and appreciation from superiors and colleagues as well as patients. Furthermore, this valuation did not differ by the type of institution (private, public, or NGO).

Finally, and perhaps most directly relevantly, in a field experiment run in Zambia in collaboration with a public health organization, Ashraf, Bandiera, and Jack (2014) randomized 800 community agents hired to sell condoms in urban compounds into four monetary and nonmonetary performance contingent reward treatments. Agents who were assigned to the nonmonetary reward treatment—namely, stars for performance plus a public ceremony for top performers—sold twice as many condoms as agents who were offered a modest financial margin on each pack sold. The public posting of a prominent number of “stars” to quantify and reward performance, as used in that experiment, served as the inspiration for the specific social recognition intervention in the present study.

# _External validity_

The first time an intervention is shown to be effective (or not) in any given setting has been of substantially greater interest to academic audiences—and has been accompanied by substantially greater rewards for academic authors—than is any subsequent attempt to replicate the finding (Galiani, Gertler, and Romero 2018). For policy makers, however, information about when and where the intervention works, and for whom, is just as important as the initial finding. Indeed, this point applies more broadly than just to field experiments or program evaluation per se. A prominent recent example of different treatment effects in otherwise similar settings is that of the intergenerational effects of incarceration, examined in Sweden and in Norway (see Dobbie et al. 2018 and Bhuller et al. 2018, respectively).

In our context, as in most situations, there are different possibilities regarding the exact mechanism underlying social recognition, some of which can be informed by sufficiently rich data on covariates. We have a more complete set of accompanying administrative data than is typical, though it is not complete (especially in terms of what would be needed for many behavioral models), and we use that to empirically ascertain three things: first: whether these covariates are predictive of the levels of the outcome in the absence of the intervention; second, whether these covariates are predictive of the impact of the intervention; and third, most crucially, whether the estimation of heterogeneous treatment effects allows us to predict the treatment effect across state boundaries. We are able to perform this last step, which appears
|Distance to flood-prone area (DD)|-0.115|<0.001|-<br>0.123|-0.106|1|Yes|
|---|---|---|---|---|---|---|
|Groundwater vulnerability|-0.098|<0.001|-<br>0.107|<sup>-0.089</sup>|1|Yes|
|Number of complaints - sewerage|-0.074|<0.001|-<br>0.086|<sup>-0.062</sup>|1|Yes|
|Number of complaints - water supply|0.059|<0.001|0.047|0.071|1|Yes|
|Prevalence of E. coli contamination (LWSC<br>sources)|0.147|<0.001|0.138|0.156|1|Yes|
|Population density with unimproved<br>sanitation (per 100m2)|0.249|<0.001|0.241|0.258|1|Yes|
|Water risk index|0.087|<0.001|0.079|0.096|0.999|Yes|
|Sanitation risk index|0.139|<0.001|0.130|0.147|0.999|Yes|
|Distance to sewer (DD)|-0.06|<0.001|-<br>0.069|<sup>-0.051</sup>|0.990|Yes|
|Distance to stream (DD)|-0.042|<0.001|-<br>0.051|<sup>-0.034</sup>|0.990|Yes|
|% HHs with unimproved sanitation|0.142|<0.001|0.134|0.151|0.853|Yes|
|Distance to graveyard (DD)|-0.044|<0.001|-<br>0.052|<sup>-0.035</sup>|0.683|Yes|
|Combined water-sanitation risk index|0.15|<0.001|0.141|0.159|0.29|No|
|Prevalence of E. coli contamination (non-<br>LWSC sources)|-0.068|<0.001|-<br>0.076|<sup>-0.059</sup>|0.245|No|
|Distance to piped network (DD)|-0.088|<0.001|-<br>0.097|<sup>-0.08</sup>|0.231|No|
|Mean size of HH|-0.001|0.773|-<br>0.010|<sup>0.007</sup>|0.102|No|
|% HHs with soap in toilet|0.090|<0.001|0.081|0.099|0.043|No|
|Number of complaints - water quality|0.012|0.052|0.000|0.023|0.032|No|
|% HHs in extreme poverty|0.076|<0.001|0.067|0.085|0.002|No|

# _A high resolution map of cholera risk in Lusaka_

Figure 3 shows the 100m X 100m predicted map of cholera risk across the study area, along with the prediction uncertainty per pixel. The geographical pattern of risk is heterogeneous and predicted annual incidence rates vary from zero to in excess of five cases per 1,000 people per annum. The greatest concentration of elevated risk lies in the western Wards of Kanyama and Harry Nkumbula, which both
percent in 1995 to 8.7 percent in 2009 – but also a steady and offsetting increase in NTMs. NTMs primarily included barriers to imports such as exclusive licenses requirements, rules of origin, or quality controls (which leave scope for discretion) for imported products. As a result, Egypt had one of the highest NTM frequencies in the world in 2010 (Malouche et al., 2013).

**Figure 2: The evolution of average (weighted) tariffs and NTMs on imports since 1**

<!-- Start of picture text --> 17 60<br>16 55<br>15<br>50<br>14<br>45<br>13<br>40<br>12<br>35<br>11<br>30<br>10<br>9 25<br>8 20<br>1995 1998 2001 2004 2007<br>Average tariffs (weighted) Year latest revision of NTMs on imports<br><!-- End of picture text --> Source: WITS. Rate reflects most-favored nation tariffs. The NTMs data provides either the year when an NTM has been introduced or the latest year in which it is has been substantially revised.

Of the 53 different NTMs in place in Egypt in 2009, almost half (24) were introduced or amended around 2000 and 21 percent between 2005 and 2009. The first episode coincides with the period when several prominent businessmen started to head directorates for economics and business in the policy committee of the NDP which is the source for new economic policies and regulations of the government (Demmelhuber and Roll, 2007; Roll, 2013). NTM protection during the second episode were issued by the Ministry of Industry and Trade, which was headed at the time by a prominent businessman.

To test if these NTMs disproportionally benefitted politically connected firms, we match data on NTMs (at the 6-digit product level harmonized system classification) with census data (at the 4- digit industry level) that contains information on all establishments in Egypt (about 2.5 million).<sup>18</sup> The NTM measures are available for tradable goods, corresponding broadly to manufacturing and mining industries, or 147 sectors. Among the politically connected firms (broadly-defined) in our larger sample, 200 operate in at least one of these sectors.<sup>19</sup>

> 18 We convert the NTM data from 6-digit (HS Rev. 2002) product classification to the 4-digit industry ISIC Rev. 4 classification by using concordance tables from HS Rev. 2002 to ISIC Rev. 3 to ISIC Rev. 3.1 to ISIC Rev. 4, respectively. Several firms are operating in more than one 4-digit industry so that in total we have 230 industry-firm observations in manufacturing or mining.

> 19 Three politically connected firms are in two 4-digit ISIC Rev. 4 industries, _Casting of non-ferrous metals_ and _Forging, pressing,_

12
**Table A.7. E-commerce development and various categories of household expenditure per capita by household consumption level (Extended model 3)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||All|bottom 25%|25%-75%|top 25%|
|Total household expenditure|0.210***|0.289***|0.114**|0.047|
||(0.061)|(0.105)|(0.046)|(0.043)|
||10,171|2,770|5,287|2,088|
||0.366|0.427|0.449|0.458|
|Cosmetics and beauty|0.328***|0.555**|0.176|0.128*|
||(0.110)|(0.251)|(0.137)|(0.071)|
|Observations|10,069|2,743|5,239|2,061|
|Adjusted R square|0.349|0.332|0.365|0.436|
|Food|0.268***|0.466***|0.227***|0.094**|
||(0.059)|(0.131)|(0.055)|(0.044)|
|Observations|10,034|2,720|5,225|2,063|
|Adjusted R square|0.399|0.464|0.406|0.400|
|Of which: Food at home|0.289***|0.492***|0.264***|0.144**|
||(0.063)|(0.167)|(0.069)|(0.055)|
|Observations|10,034|2,018|5,116|2,877|
|Adjusted R square|0.399|0.464|0.366|0.410|
|Of which: Dining out|0.330**|0.141|0.081|0.061|
||(0.134)|(0.220)|(0.135)|(0.123)|
|Observations|9,924|2,712|5,144|2,042|
|Adjusted R square|0.338|0.329|0.393|0.376|
|Clothes|0.137|0.165|-0.002|-0.066|
||(0.087)|(0.248)|(0.090)|(0.075)|
|Observations|9,924|2,061|5,178|2,911|
|Adjusted R square|0.338|0.479|0.362|0.343|
|Utilities|0.130***|0.279**|0.111***|0.023|
||(0.034)|(0.126)|(0.040)|(0.030)|
|Observations|9,972|2,743|5,198|2,005|
|Adjusted R square|0.305|0.330|0.313|0.328|
|Communications|0.156***|0.033|0.106**|0.081***|
||(0.041)|(0.131)|(0.049)|(0.029)|
|Observations|9,868|2,704|5,148|1,991|
|Adjusted R square|0.348|0.294|0.398|0.436|
|Local transport|0.292***|0.030|0.121|0.220**|
||(0.075)|(0.243)|(0.082)|(0.110)|
|Observations|9,812|2,703|5,105|1,978|
|Adjusted R square|0.319|0.344|0.335|0.325|
|Travel|0.579***|0.771**|0.299**|0.322*|
||(0.164)|(0.327)|(0.126)|(0.170)|

·· 41
# **4. Empirical Evidence on the Channels through Which High-skilled Emigration Affects the Sending Country**

We now turn to measuring the economic impacts of migration on the sending countries, attempting where possible to quantify these impacts and evaluate them relative to a counterfactual of what the individual would have been doing had they not migrated.

# **4.1 Remittances**

Remittances are the most salient and researched contribution of emigrants to their home countries. However, there is debate as to the extent to which highly-skilled emigrants remit. Cross-country studies based on macro data have been used to claim the high-skilled remit less (Faini, 2007) whilst recent microeconomic evidence based on surveys of immigrants in a number of destination countries suggests that more educated individuals remit more, with tertiaryeducated migrants from poorer countries being more likely to remit than those from richer developing countries (Bollard et al, 2009).

Panels A and B of Table 5 show the incidence and level of monetary and goods remittances that the non-student migrants in our sample are sending to their home countries. Our survey data show a high incidence of remitting among the migrants from developing countries, with migrants from New Zealand being much less likely to remit. For the Ghanaian sample we can compare our results to the remitting patterns of all Ghanaian migrants in the OECD (Bollard, McKenzie and Morten, 2010). 86 percent of all Ghanaian current migrants are remitting, which increases to 93 percent if we exclude current students. This can be compared to a remitting rate of 66 percent among all Ghanaian migrants in the OECD. The mean annual amount remitted in monetary remittances conditional on remitting for Ghanaians in our sample is US$4,314, compared to US$3,614 for all Ghanaian migrants in the OECD. Thus the high-skilled migrants in our sample are remitting more frequently and sending more when they do remit than average migrants, even if the amount remitted as a share of income is lower.

The unconditional mean and median amount remitted then include the zero remittances for those not remitting. The appropriate counterfactual here is that these individuals would not be remitting if they had not migrated. So the net effect of migration on remittances is simply the unconditional mean. Adding together the monetary and goods remittances gives a total impact of 15