rafmacalaba/datause-encoder-data huggingface dataset:
# living standards.

The first way of reducing the role of transitory events and measurement error is to examine inequality in average incomes (Shorrocks, 1978). For each family, we calculate 4 measures of their living standards: (i) their income in the current month, (ii) the average of their incomes in the current month and 12 months ago, (iii) the average of their incomes in the current month, 12 months ago, and 24 months ago, and (iv) the average of their incomes in the current month, 12, 24 and 36 months ago. More idiosyncratic components of income and measurement error will be averaged out as we average over more periods, but method also averages out some true mobility - movements in the underlying level of material well-being. Nevertheless, the inequality of average income over four periods is likely to be a better approximation of underlying inequality than the one based on income in a single month. Figure 1 shows how inequality, as measured by the Gini coefficient, declines as we average income or expenditure over more periods. In Russia the Gini drops by about 20% to 25% when income or expenditure is averaged over 4 periods. The relative drop in Poland is smaller, <!-- Start of picture text --> Figure 1: Gini Drops when Income or Expenditure is Averaged over Time<br>r Russia Russia Poland Poland<br>Income Gini Expenditure Gini Income Gini Expenditure Gini<br>0 45 0li 0450 l | oo0.50 0.50<br>° 0.4_Q5 0.45 0.45<br>00.40 00.0 0.00<br>E ~~~~-E<br>0.35 _0.35 0 0.35 0.05<br>.E030 ~030 *;0.3<br>0.25 4) ~~~~0.2 x 0~~~~~~~~~~~~~~~~~~~~~0 025*0.25<br>0 a ~0) )<br>l 0.20 is 0.20 0.20 0.20<br>0 c *6~~~~<br>Note: AOmeasuresareforequive adults wheretheeq 1~ 2 3~20 ~ 0.10 1234| e 0.10 205<br>0.05 50.05 0.05 0.5<br>0.00 0.00 ~~~~~~~0.00 0.00<br>1 2 34 1 2 34 1 2 34 1 23 4<br>tkirnber of periods over whi.ch Ir&irrber of'periods over which lbknberof perios overwhich Nimnberof periods over whch<br>irron-e is averaged expenditure is averaged incomB isaveraged expenditure is aveae<br>Note: All measures are for equivalent adults where the equivalent scale equals household size to the power of 0.75. Ginis are<br>calculated such that the difference between I period Ginis and multi-period Ginis is solely due to the averaging and not due to any<br>changes in inequality during the years for which the Ginis arc calculated. Data consist of balanced panels, 1993-1996 for Poland and<br>1994-1998 for Russia. Standard errors on the Ginis are less than 0.010 and standard errors on the differences in Ginis are less than<br>0.005.<br><!-- End of picture text -->

16
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- Denissen, Jaap JA, Ligaya Butalid, Lars Penke, and Marcel AG Van Aken (2008), “The effects of weather on daily mood: a multilevel approach.” _Emotion_ , 8, 662.

- Department of Public Safety, Texas (2019), “Seventeenth report examining reporting compliance to the texas computerized criminal history system.” Technical report.

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38
# **TABLE 1: COMPARISON OF ESTIMATES FOR QUARTERLY ENEU DATA**

# **PANEL A: LOG SPECIFICATION**

Dependent Variable: Log Real Household Income

||(1)<br>OLS|(2)<br>OLS|(3)<br>IV|(4)<br>A-IV|(5)<br>Pseudo<br>Panel|(6)<br>Pseudo<br>Panel|
|---|---|---|---|---|---|---|
|Quarterly Lag of Household Income|0.668|0.598|0.990|-0.062|0.988|0.832|
|T-statistic|955.85|791.23|213.59|-2.36|159.14|45.25|
|[95% confidence interval]|[.666,.669]|[.597,.600]|[.981,.999]|[-.114,-.011]|[.976,1.000]|[.796,.868]|
|P-value|0.00|0.00|0.00|0.02|0.00|0.00|
|Cohort Effects|No|Yes|No|---|No|Yes|
|Household-quarter observations:|1113172|1112464|165275|757561|---|---|
|Cohort-quarter observations:|---|---|---|---|842|842|
|Adjusted R squared:|0.4508|0.4731|||0.9679|0.9703|

# **PANEL B: LEVELS SPECIFICATION**

Dependent Variable: Real Household Income

||(1)<br>OLS|(2)<br>OLS|(3)<br>IV|(4)<br>A-IV|(5)<br>Pseudo<br>Panel|(6)<br>Pseudo<br>Panel|
|---|---|---|---|---|---|---|
|Quarterly Lag of Household Income|0.376|0.316|0.999|0.012|0.973|0.738|
|T-statistic|447.64|366.61|88.83|4.97|103.64|32.86|
|[95% confidence interval]|[.375, .378]|[.315, .318]|[.977, 1.021]|[.007, .017]|[.954,.991]|[.694,.782]|
|P-value|0.00|0.00|0.00|0.00|0.00|0.00|
|Cohort Effects|No|Yes|No|---|No|Yes|
|Household-quarter observations:|1147860|1147127|169193|787094|---|---|
|Cohort-quarter observations:|---|---|---|---|842|842|
|Adjusted R squared:|0.1486|0.1862|||0.9274|0.9362|

# Notes:

IV uses education of the household head and an asset index as instruments for lagged income; only for households observed in the third period.

A-IV denotes the Arellano (1989) instrumental variables estimator, which differences the data and uses Yi,t-2 as an instrument for the lagged first difference.

All cohort-period observations are averages based on at least 100 household observations
# Table **4t** Devaluation of the Official Exchange Rate

Dependent Variables D0UR

|Country<br>DODZRt|Wtl2<br>w|Nt4|PROk.1<br>2|R|2|IDV|
|---|---|---|---|---|---|---|
|Nigh ?resdim comitias<br>|||||||
|Argentina0.45<br>Argzentin<br>(3.04)<br>olvia<br>0.06<br>(0.96)<br>Brasil<br>0.49<br>(5.04)<br>Chile<br>**0.**<br>(206)|-0.26<br>(-1.73)<br>0.09<br>(1.30)<br>-0.19<br>(-1.97)<br>0.17<br>(167)|0.39<br>(4.45)|1. 06<br>(5.91)<br>0.27<br>(11.42)<br>0.'0<br>(5.92)<br>0.02<br>(553)||0.51<br>0.69<br>0.74<br>0.26|1.66<br>2.17<br>2.01<br>2.34|
|.<br>Demnidcan Rep.<br>.0.03<br>(027)|.||.<br>0.12<br>(460)||0. 1)|1.96|
|..<br>Egpt<br>0.19<br>(1.16)<br>hana<br>0.03<br>(026)|||.<br>0t11<br>(2.17)<br>0.01<br>(391)|-0.09<br>(-1.70)|0.06<br>0.10|2.03<br>1.98|
|.<br>Nigeria.<br>0.40<br>(402)|||.<br>0.02<br>(272)||0.21|1.92|
|.<br>Peru<br>0.32<br>412|||.<br>0.24<br>768||0.45|1.7$|
|(.)<br>Tanzaia<br>_,_<br>0.30<br>(247)|||(.)<br>0.09<br>(604)|-0.07<br>(-393)|0.34|1.96|
|.<br>V<br>1^WAOU<br>I<br>.0.05<br>( i030)|||.<br>(273)|.|0.08|2.02|
|..<br>Zambia<br>I0.003<br>1(0.03)|||.<br>0.02<br>(3.41)||0.03|2.03|
|Moderate PrF<br>Comtriest|||||||
|Colombia<br>0.90<br>(2090)|||0.04<br>(223)||0.75|1.96|
|.<br>94uado2||0.221|.<br>0.12||0.37|2.09|
|OeXIC0<br>2<br>0.15||(2.46)|(4.09)<br>0.42|-0.33|0.26|1.48|
|(0.91)<br>Turkey<br>014<br>(1.40)|||(3.64)<br>0.46<br>(7.674)|(-2.65)|0.26|1.71|

Source US. World Curreny Yearbook. Pick's Currency Yearbook. Pain(1991) for Argentina. Data **r** qurterly, nd of period for 1970-1990. ecept for Argentina, Mexco and Ven eula (*ee text for deteils). The period for unified exchange rates to excluded for Chie.

**4Piures** o parentheses arm t-s*atUtice DQUs Dwalatin In the official exchange rate. PI **IWs** Parallel Nate/Offioiel **Pate** - 1.

Is DOI 2. Equaton for Kexio itnlude, a conatant torm with va 0.10(2.57)
7

# **What works in high-income countries?**

A full review of the literature in high-income countries is beyond the scope of this study. However, it may be useful to highlight recent work on in-service teacher PD from the United States – which spends almost $18,000 per teacher and 19 days of teacher time on training each year (TNTP, 2015) – and other high-income countries, in order to ensure that low- and middle-income countries are not ignoring well-established evidence. A recent meta-analysis of 196 randomized field experiments to improve education in the U.S. – that measure student test scores as an outcome – examined the impact of both “general” and “managed” professional development, relative to a wide range of other interventions such as tutoring and teacher incentives (Fryer, 2017). General professional development (PD) – as the name suggests – leaves a fair amount of flexibility, even as it may focus on classroom management or increasing the rigor of teachers’ knowledge. Managed professional development, on the other hand, is much more prescriptive; it prescribes a specific method, with detailed instructions on implementation and follow-up support. On average, managed PD increased student test scores by 2.5 times (0.052 standard deviations) as much as general PD and was at least as effective as the combined average of all school-based interventions. However, the analysis is based on relatively few studies, with just seven general PD studies and two managed PD studies. Nonetheless, this finding in support of specific and practical teacher training aligns with that of Walter & Briggs (2012) who, in a review of 35 evidence-based studies of teacher PD, found that concrete and classroom-based programs make the most difference to teachers.

Another U.S.-focused review found that PD programs with significant contact hours (between 30 and 100 in total) over the course of six to twelve months were more effective at raising
<!-- Start of picture text --> A. Placebo during 1990-91 Recession B. Permanent Transfers Placebo/Lead/Lag<br>Actual 2001 Transfers Actual 2008 Transfers<br>t-stat=2 cutoff<br>-6 -4 -2 0 2 4 6<br>1990Q3 1990Q4 1991Q1 1990Q3 1990Q4 1991Q1 Lag (Quarters)<br>Beta Coeff. (LHS) Absolute t-stat (RHS)<br>Placebo timing first transfer received<br>C. Temp Transfers (WL) - Placebo/Lead/Lag D. Temp Transfers (GDP) - Placebo/Lead/Lag<br>t-stat=2 cutoff<br>t-stat=2 cutoff<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Lag (Quarters) Lag (Quarters)<br>Beta Coeff. (LHS) Absolute t-stat (RHS) Beta Coeff. (LHS) Absolute t-stat (RHS)<br>3 7<br>2 6<br>1.5 1.5 1 5<br>1.0 1.0 0 4<br>0.5 0.5 -1 3<br>0.0 0.0 Beta coeff & CI -2 2 Absolute t-stat<br>Multiplier and 95% CI -3 1<br>-0.5 -0.5<br>-1.0 -1.0 -4 0<br>.5 5 1 6<br>0 4 .5 5<br>0 4<br>-.5 3<br>-.5 3<br>-1 2<br>Beta coeff & CI Absolute t-stat Beta coeff & CI -1 2 Absolute t-stat<br>-1.5 1 -1.5 1<br>-2 0 -2 0<br><!-- End of picture text --> Notes: Panel A: multiplier and 95% condence intervals (CIs) from regression of real labor income growth during the 19901991 recession on actual state-level transfers from 2001 or 2008 using the benchmark specication. Panels BD: Regressions of growth on transfers using the benchmark specication (Permanent Transfers, Temporary Transfers for labor income and Temporary Transfers for GDPPC, respectively), but moving the growth variable backward/forward by up to 6 quarters. Source: BEA, Romer and Romer (2016), author's calculations.

Figure 2. Placebo Tests For temporary transfers (Figure 2 Panels CD), the largest t-statistic is always at t = 0 (when the actual transfers were paid), and there is little increase, and sometimes signicant at the 10% level. Romer and Romer (2016) nd that Social Security increases were typically legislated roughly a quarter in advance of payment, though they nd no evidence of an increase in aggregate consumption at that time. Anticipated demand shocks in NK models often reduce output as rms raise prices and markups in advance, a possible rationale for negative leads. However, I do not want to emphasize those results given their insignicance and general lack of robustness (not reported).

24
# **2 Data**

Our analysis is primarily based on data on refugee stocks compiled by the UNHCR. UNHCR annually publishes the data on refugee stocks by source and destination country pair. The term “refugee” includes both refugees and asylum seekers. Under the 1951 Convention Relating to the Status of Refugees and the 1967 Protocol, a refugee is defined as “a person who has been forced to flee his or her country because of persecution for reasons of race, religion, nationality, political opinion or membership in a particular social group” (Art 1.A.2.).

The UNHCR Population Statistics Reference database contains data for the period 1951 – 2017 (released on June 19, 2019). The data set compiles annual stocks of refugees and asylum seekers at the source-destination level for 197 destination and 223 source countries. The ultimate source of the data is the authorities of each receiving country. While in principle there are observations going back to 1951, coverage prior to the late 1980s is too sparse to be usable. Thus, our analysis covers the period 1987-2017. Overall, we have 112,522 non-zero observations for bilateral stocks over the period 1987-2017.

Since the data are not recorded at the individual level, we cannot reliably calculate refugee flows. Thus, the main variable used in the analysis is the refugee stocks. By definition, the stock of refugees in any particular year mixes individuals that arrived at different times. Since our main object of interest is changes in refugee behavior over time, analyzing stocks will if anything attenuate temporal differences.

To better approximate flows, we restrict the sample to large refugee events. A refugee event begins in the year in which the global stock of refugees from a particular source country first exceeds 25,000. An event ends when the stock falls below 25,000, or 10 years after initiation if the destination is an OECD country, whichever comes first.<sup>2</sup> Capping the termination date of the event also puts earlier and later years in the sample on a more equal footing, as stocks in later years contain earlier vintages of refugees. An added benefit of restricting the sample to large refugee events is that this procedure also removes source countries with small numbers of refugees, who fled not

> 2For OECD countries, UNHCR does not include most individuals who have been in the country for more than 10 years in their published refugee statistics.

5
# Appendix I.A.21 Historical Demographic Data, Mexico 1895-1970

|Year|1895|1910|1921|1940|1950|1960|1970|
|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|-|24.48|-|18.65|16.31|15.20|13.48|
|Population aged 20 to 59/<br>population over 60 years old|13.47|13.08|9.20|8.48|7.70|7.16|6.71|
|Percentage<br>of population<br>over60yearsold|3.30|3.37|4.93|5.12|5.52|5.57|5.62|
|Percentage<br>of population<br>under l5 years old|41.55|42.20|38.79|41.19|41.77|44.39|46.22|
|Women over 60 years old/<br>Men over 60 years old|0.83|0.93|1.02|1.06|1.09|1.04|1.07|

Ratios calculated using data found in Mitchell, B. R. 1983 'Intemational Historical Statistics: The Americas and Australasia".

-4

# Appendix I.A.22 Historical Demographic Data, New Zealand 1886-1971

|**Year**|**1886**|**1896**|**1906**|**1921**|**1936**|1951|**1961**|**1971**|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>populationover65yearsold|26.92|21.35|13.14|12.93|10.36|6.71|6.76|6.98|
|Population aged 20 to 59/<br>populationover60yewsold|12.75|8.92|7.14|6.91|5.29|3.84|3.84|3.72|
|Percentage<br>of population<br>over60yearsold|3.49|5.28|7.21|7.55|10.44|13.19|12.22|12.53|
|Percentage<br>of population<br>under 15 years old|41.54|36.23|31.45|31.42|25.50|29.47|33.10|31.76|
|Women over 60 years old/<br>Men over 60 years old|0.66|0.61|0.68|0.84|0.79|1.07|1.22|1.23|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics: The Americas and Australasia".
- 1.21 -

Table 1L.1 3: UveeWtok mbers and HodersNo a/

||Co|b|**Uwp**<br>|&Go"|PIs||_Po0I_|_41_|
|---|---|---|---|---|---|---|---|---|
|Region|Number|Holderl|Number|Holders|Number|<sup>Holders</sup>|<sup>Number</sup>|<sup>Holders</sup>|
|Ashani<br>Brong Ahafo<br>Cental<br>Efamn<br>Greer Accra<br>Nortwn<br>UpperEast<br>Upper WeN<br>Vora<br>Weeosm<br>Nokth<br>South<br>survey|0<br>0<br>0<br>0<br>12<br>451<br>182<br>342<br>34<br>35<br>975<br>81<br>1066|0<br>0<br>0<br>0<br>7<br>102<br>56<br>61<br>5<br>2<br>221<br>14<br>235|234<br>43<br>324<br>197<br>70<br>455<br>292<br>211<br>295<br>165<br>968<br>1328<br>2286|67<br>18<br>61<br>57<br>20<br>157<br>81<br>69<br>81<br>42<br>207<br>348<br>653|9<br>9<br>10<br>72<br>107<br>145<br>36<br>15<br>35<br>14<br>187<br>187<br>363|2<br>3<br>2<br>1<br>16<br>31<br>13<br>4<br>5<br>3<br>48<br>34<br>82|575<br>227<br>645<br>507<br>84<br>1368<br>1044<br>447<br>1056<br>**600**<br>2659<br>3694<br>6553|58<br>33<br>70<br>64<br>13<br>165<br>89<br>53<br>102<br>67<br>307<br>408<br>715|

a/ Holders may be greater than sample se<sup>due to aggregabon of animal t"yp.</sup>

* PourVy kiudes chckwns, skeys _and_ guWn fowla .

Livestock can be reared intensively by putting them<sup>In kraals, pens or coups and</sup> feeding them with concentrates, gralns, and fodder.<sup>Alternatively, they could be left to</sup> roam In an open space and look for food. Commercial<sup>rearing of cattle may use both</sup> techniques so that at certain times of the day<sup>the ankials are allowed to graze on a</sup> pasture and at other tknes, especially durlng the<sup>dry season, the feed Is supplemented</sup> with concentrates and grains. Commerclal raising of<sup>poultry requires keeping the birds</sup> in coups or houses and feeding them with concentrates.<sup>On the other hand, farmers who</sup> keep a few birds allow them te roam and look for<sup>their food although occasionally they</sup> are given small quantities of grains. Pigs<sup>are normally kept In pens but in the villages</sup> they are allowed to roam about.

One major cost component In livestock maintenance<sup>Is voterinary care and</sup> modIches. The anknals have to be vaccinated<sup>against some common Illnesses and when</sup> they are sick they have to be given drugs. Since the withdrawal of subsidies on veterinary drugs, veterinary care could be expensive.<sup>On the average, the Ghanaian</sup>
|**Table6:Chang**|**esinDistrict-Le**|**velPove**|**rtyTargeting ofVas**|**o deLe**|**cheTransfers,19**|**94-1997**|
|---|---|---|---|---|---|---|
|_Dependent_<br>_variables_||_Ela_|_sticities_||_Marginalef_|_fects_|
|_Year_|_Percapita_<br>_district_<br>_transfers_|_t-stat_|_Shareofdistrict_<br>_population_<br>_participating_|<br>_t-stat_|_Averagevalue_<br>_ofpercapita_<br>_transfersfor_<br>_recipients_|_t-stat_|
|1994|1.21|11.18|**<br>0.577|5.99|**<br>_1.55_|8.51**|
|1997|0.19|2.01|*<br>0.815|9.81|**<br>3.15|18.93**|
|Difference|-1.02|-7.15|**<br>0.238|1.90|+<br>1.60|6.46**|

_Note:_ Independent variable for district level models is the district headcount ratio. For the household model, the independent variable is an indicator of whether the household is poor or not.

** indicates significance at 99% level of confidence, * at 95% level of confidence, and + at 90% level of confidence.

Although the nutrition targeting coefficients presented in Table 4 are positive and significant, they are considerably smaller than those found when using poverty as the targeting criterion. This follows from the degree of leakage being higher for malnutrition than for poverty (i.e., a greater share of healthy children participate in the program relative to the share of nonpoor households benefiting from VL transfers). Nevertheless, coverage rates of all malnourished kids under the age of five exceed the percentage of nonmalnourished benefiting from VL transfers - hence the positive targeting coefficient.

Moreover, it is possible that targeting of children based on _ex-ante_ nutritional needs may have resulted in improved _ex-post_ outcomes. In fact, this is the stated intent of the program - to improve the nutritional status of children. This could explain the low levels of coverage of malnourished children. However, if targeting based on _ex-ante_ needs is persistently effective, then as the nutritional status of participants improves over time, a deterioration in the targeting coefficient should be observed. This appears not to be the case, as the coverage rates for malnourished children rose between 1994 and 1997 (Table 4).

**Table** 7: **Percentage of Children Under Five in VL Program** _(percent)_

||_Quintil_|_esofheight-for-ageZ_|_-scores_|
|---|---|---|---|
|Quintiles|ENNIV<br>1994|DHS<br>1996|ENNIV<br>1997|
|1|42.8|41.6|64.0|
|2|33.8|33.2|49.2|
|3|28.4|26.4|41.8|
|4|25.4|21.7|34.8|
|_5_|20.0|20.8|30.5|
|**Total**|**30.1**|**28.7**|**44.1**|

_Note:_ Domain is the set of children with HAZ scores.

_15_