rafmacalaba/datause-encoder-data huggingface dataset:
be exposed to world shocks (including the world interest rate shocks) as long as it remains dependent on commodity production and exports.

**Figure 1.5: World Interest Rate and Commodity Prices**

<!-- Start of picture text --> 1994q3 1999q1 2003q3 2008q1 2012q3<br>Reat int. rate, world Terms of trade (Pex/Pim)<br>140 .06<br>.04<br>120<br>.02<br>100<br>0<br>Reat int. rate, world<br>Terms of trade (Pex/Pim) 80<br>-.02<br>60 -.04<br><!-- End of picture text --> _Source_ : authors.

Third, the higher world interest rate may also affect the Paraguayan economy indirectly by weakening economic conditions in its major trade partners – Brazil, Argentina, Chile, Uruguay – who are significantly exposed to the world financial markets through sovereign borrowing. These conditions would spill over into Paraguayan economy through these countries’ lower demand for Paraguayan exports. Higher world interest rates may affect consumption demand by these countries through a number of channels: (i) through intertemporal substitution, by encouraging higher savings by households; (ii) through negative wealth effects to indebted households; (iii) through portfolio reallocation effects. Higher world interest rates may also negatively affect investment by firms by raising their cost of borrowing.

While we tried to control for the effects of foreign demand conditions on the Paraguayan economy by using a trade-weighted output of the Paraguay’s major trade partners, that measure may not fully capture such demand shocks if there are nominal rigidities in the goods or factor markets, or if there exists a significant informal sector, or if supply and demand shocks are correlated in these economies. The world interest rate will again subsume some of these foreign demand effects.

The fourth potential effect of higher world interest rates is a reduction in remittances to Paraguayan households from abroad, which would negatively affect domestic demand. While remittance inflows have increased significantly in Paraguay in recent years, from 0.67 percent of GDP in 1991 to over 2 percent of GDP in 2011, lack of quarterly data does not allows us to explore this hypothesis in detail.<sup>11</sup> We find that the effects of the world interest rate shock tend to be quite long-lived, as Paraguayan GDP shows a persistent decline, lasting about 10 quarters after the shock. While we propose several channels through which world interest rate shocks may spill over into the Paraguayan economy, a detailed investigation of them is left for future work.

Next, we turn to terms of trade. Positive innovations to terms of trade (measured as price of exports to price of imports) lead to a fall in GDP on impact but the effect is not statistically significant.

> 11 These numbers are computed using annual Balance of Payments data from the Banco Central del Paraguay.
# **Tables**

Table 1: Summary statistics

||(1)<br>Mean|(2)<br>SD|(3)<br>N|(4)<br>5th<br>percentile|(5)<br>10th<br>percentile|(6)<br>Median|(7)<br>90th<br>percentile|(8)<br>95th<br>percentile|
|---|---|---|---|---|---|---|---|---|
||Panel A:|Baseline s|urvey|(June to Au|gust 2013)||||
|Male|0.66|0.48|593|0|0|1|1|1|
|Married|0.63|0.48|593|0|0|1|1|1|
|Number of hh members|4.7|2.1|593|2.0|2.0|4.0|7.0|8.0|
|Acres of land|1.5|1.2|593|0.3|0.5|1.2|2.8|3.5|
|Value of non-fxed assets (MK)|177,697|595,306|593|4,590|7,100|40,500|264,000|517,300|
|Asset index|-0.05|3.23|593|-3.10|-2.85|-1.00|3.74|6.28|
|Distance to branch (km)|3.70|1.71|593|1.27|1.63|3.60|6.34|7.10|
|Hyperbolic|0.23|0.42|593|0|0|0|1|1|
|Patient now, impatient later|0.27|0.45|593|0|0|0|1|1|
|Impatience (switching point, out of 6)|2.90|2.03|593|1|1|2|6|6|
||Panel B:|Total of it|emize|d expenditur|es from roun|d 1 surve|y||
|Total|11,500|15,860|593|520|960|5,625|28,950|43,760|
|Food|4,593|5,377|593|220|480|2,780|10,710|16,386|
|non-durables|6,942|12,456|593|100|280|2,440|17,000|32,180|
|Unplanned food|422|815|593|0|0|60|1,300|2,130|
|Unplanned non-durables|114|428|593|0|0|0|200|700|
||Panel C:|Bank tran|sactio|n before rou|nd 1 survey||||
|Any activity 7 days prior|0.07|0.26|593|0|0|0|0|1|
|Any activity 90 days prior|0.33|0.47|593|0|0|0|1|1|
|Value of deposits 90 days prior|13,045|107,024|593|0|0|0|11,500|45,000|
|Value of withdrawals 90 days prior|15,230|171,546|593|40,500|11,440|0|0|0|
|Value of net deposits 90 days prior|-2,184|69,350|593|-2,000|-600|0|774|3,500|

‘Asset Index’ is a principal component index based on 62 asset and seven livestock categories. The impatience measure is based on a series of questions asking whether the respondent would prefer MK 400 tomorrow or a different amount in one month. The choices increased as follows: MK 450, 500, 600, 800, 1000 or more. We report the ordinal number of the question for which the respondent preferred to wait; larger numbers indicate greater impatience. ‘Non-durables’ is the sum of spending on non-food non-durables. ‘Durables’ is the sum of spending on durable goods, assets, livestock and farm inputs. Withdrawals are represented as negative numbers. Exchange rate: MK 420 per USD.

29
Figure 12: Available liquidity - Number of days during which establishment can cover its costs with cash on hand.

<!-- Start of picture text --> Unconditional mean<br>350<br>300<br>250<br>200<br>150<br>99 99 101<br>10050 27 37 41 25 47 45 44 29 52 32 49 38 40 36 42 44 52 27 65 46 54 59 59 61 61 66 36 68 83 48 87 82 68 59<br>8 12 9<br>0<br>XKX GAB TUR SDN RUS GRC ZAF HND GTM TGO ROU MNG SLV ZWE TUN TCD GEO IDN NPL BRA MDA SVN NER ALB PAK ZMB ITA CYP GIN NIC BGR MDG LKA VNM POL BGD KEN TZA CIV SEN<br>Predicted mean<br>350<br>300<br>250<br>200 192<br>168<br>150<br>10050 21 27 28 29 30 31 33 33 33 34 35 37 38 38 39 40 40 41 44 45 49 50 51 51 52 53 55 63 70 73 75 77 84 85 100<br>4 7 8<br>0<br>XKX GAB TUR SDN RUS GRC ZAF HND GTM TGO ROU MNG SLV ZWE TUN TCD GEO IDN NPL BRA MDA SVN NER ALB PAK ZMB ITA CYP GIN NIC BGR MDG LKA VNM POL BGD KEN TZA CIV SEN<br>Mean 10th and 90th percentile<br>with cash available<br>Days the establishment can cover costs<br>with cash available<br>Days the establishment can cover costs<br><!-- End of picture text --> Note: The predicted mean comes from a linear regression that controls for country, size, sector, and weeks before and after the peak of the mobility shock.

24
<!-- Start of picture text --> FIG=U GS TII POSITIV AND AVAILABILITIES MODELS<br>Fix fiscal. metarr nd exchange rate policy<br>. . ~~~~~~1<br>r.,<br>Goods Balance of<br>Froregn<br>Market Ass t Make Payents<br>1⁄2 (C*1) |R c<br>Ir Fp l<br>.Privte - Bp < Domtic Boad Central Bank<br>.Se*ctor lMarket Balance Sheet<br>l l1<br>CR'<br>I.I<br>Bning System CRd< Central Bank<br>lnce Sheet Credit Market<br><!-- End of picture text -->

37
One last variable for which we do observe systematic differences is the magnitude of the CR closing error, although differences are not significantly different across high-bias and non-high bias plots in Ethiopia. In both Ethiopia and Tanzania there is a gradient between plots that are underestimated by a large margin (which have the smallest closing error), plots with error below 10 percent (which have moderate closing error), and plots with large GPS over-estimate (with the largest closing error). In Nigeria the plots with larger over-estimates are also the ones with the largest closing error, but the ranking of the other two groups is inverted. What we conclude from these observations is that for the cases in which we observe substantial deviations between GPS and CR measures, part of the explanation is likely to rest in noise in the CR measures. In that sense the inaccuracy in the GPS measures may be somewhat less serious than if one just looked at the prevalence of high bias cases, and that as observed earlier the gold standard is also bound to be imperfect.

# _3. Compass and Rope vs. Self-Reported Estimations_

With an understanding of the comparability of GPS and compass and rope objective measurements, we now explore the difference in subjective (self-reported) and objective (CR) measurement.<sup>9</sup> Table 7 presents mean plot areas as measured by farmer self-reported estimation and compass and rope for all three methodological experiments. The data is grouped by compass and rope plot size class. While the mean plot areas as measured by GPS and compass and rope differ by only as much as 3% on average, the mean self-reported and compass and rope measurements differ by as much as 143% on average (Tanzania). The mean difference is smaller in Ethiopia and Nigeria, at 23% and 5% respectively, but still considerably larger than the divergence observed between the objective measurements.

Self-reported measures result not only in higher average deviations, but in dramatic systematic error as the size of small plots is overestimated by anywhere from 30% (Nigeria) up to a factor of six (Tanzania), with the over-estimation declining almost monotonically as plot size increases and eventually results in under-estimation in the larger plot size classes in Nigeria and Ethiopia. These results comparing objective and subjective area measures are in line with findings of previous literature (Carletto et al. 2015; de Groote and Traore, 2005). The scatter plots on the right side of Figure 3 convey the same message in graphic form.

> 9 Comparing self-reported to GPS yields exactly the same results, even when analyzed using data from the nationally representative LSMS surveys in Malawi (2010/11) and Tanzania (2010/11), therefore we limit the comparison to self-reported and CR.

22
**1 Introduction**

Policies for refugees like the right to work and freedom of movement are decided by the governments of host countries. The policies vary widely and change over time (Blair et al. 2021a); for instance, while Tanzania prevents refugees from working even within the strictly-enforced camp boundaries, Colombia has extended the right to work to 1.7 million Venezuelans. Social cohesion is one of the many factors influencing governments’ decisions and often used to publicly justify restrictions and containment. Despite anecdotal evidence to the contrary, governments are often worried that more inclusive policies will lead to crowd out of host citizens, reduced social cohesion, and a backlash against the politicians who facilitated refugees’ access.<sup>2</sup> Restrictive policies remain despite the significant costs to refugees and, in many cases, the host communities through missed opportunities for exchange and growth.<sup>3</sup> In this paper, we examine how attitudes toward refugees are affected by the arrival and presence of refugee populations. We first ask how attitudes respond to a large arrival of refugees on average across low- and middle-income countries (LMICs). We then decompose the findings by the characteristics of hosting environments to investigate whether social tension and negative perceptions are reduced in restrictive environments or whether interactions in a more welcoming environment facilitates trust and mutual gains from exchange. We focus on LMICs, where 86% of refugees reside but where relatively little quantitative research has been conducted to date on the determinants of social cohesion (UNHCR 2021b).

We build and combine three main datasets to address our research question. The resulting dataset covers a large proportion of LMICs between 2005 and 2018. The first is a dataset on attitudes. Our main outcome comes from the Gallup World Poll (GWP), due to its extensive spatial and temporal coverage, and we

> 2See Verme and Schuettler (2019) for a review on the minimal evidence on economic effects on host communities.

> 3See Clemens et al. (2018) for a discussion of the literature on the economic effects of policies for both refugees and hosts, and Bahar et al. (2021) for an analysis in Colombia of a recent regularization program.

1
In the absence of distortions, _TFPRsi_ should be equalized across establishments within in each industry.

The actual TFP at industry level can be calculated as where _TFPRs_ is a geometric mean of the average marginal revenue product of capital and labor: When _Asi_ (= _TFPQsi_ ) and _TFPRsi_ are jointly lognormally distributed, HK show that To empirically implement the HK framework, we require information for several parameters. The primary parameter we need to fix is the elasticity of substitution – _σ_ . There is little agreement in the literature on the plausible magnitude of this parameter. We follow Hsieh and Klenow (2009) and set a conservative estimate _σ_ = 3.<sup>4</sup> . Again, following Hsieh and Klenow (2009), we set _R_ = 10% assuming a real interest rate of 5% and depreciation rate of 5 %. There is some evidence that the cost of capital is high in Africa. But the different values of _R_ only affect the average capital distortion but not the differences between firms in a given industry. Thus, it doesn’t affect our calculation of gains from reallocation. For the industry-level factor share, _αs_ , we use NBER Productivity Database. We assume factor intensities are the same as those of the corresponding U.S. industries, which is assumed to be undistorted.<sup>5</sup> After obtaining the capital share at four-digit level, we combine it with our firm-level datasets.<sup>6</sup> Once these parameters are fixed, the wedges can be computed as follows:

> 4Note that this parameter doesn’t affect the basic measure of dispersion but only alters their effect on aggregate productivity

> 5HK point out that the labor share in this dataset underestimates the labor compensation because it doesn’t include fringe benefits and employer social security contribution. Following Hsieh and Klenow (2009), we inflate the labor cost by a factor of 3/2.

> 6Note that industries in Ethiopia, Ghana and Kenya are classified according to ISIC Rev 3.1, ISIC Rev 3 and ISIC Rev 4, respectively. Industries in Cˆote d’Ivoire are classified according to NAEMA (equivalent to ISIC Rev 3) whereas the industrial data for US is reported based on 1987 SIC and 1997 NAICS classifications. We use appropriate concordance tables to match the datasets. We keep firms that correspond with the US data at four-digit levels.

11
# <u>Introduction: Pre-industrial global inequality</u>

The studies of global, and a fortiori, global pre-industrial inequality are a relatively recent phenomenon and they are few in number. Obviously, the reason is the lack of household survey data that are needed to estimate global inequality, that is, income distribution across all individuals in the world. The lack of household surveys and their variable quality are problems that plague even _contemporary_ studies of global inequality. They are much more severe for studies of past inequality. But even the very concept of global inequality—that is, of measuring and comparing incomes of (theoretically) all individuals in the world—is a new one, both because the idea of such a study had to wait for a more advanced stage of globalization to take hold, and because it crucially depends on the availability of purchasing power parity estimates that are needed to convert incomes expressed in national currency into a single global numeraire.

There are only two long-run empirical historical studies of global inequality. The first and seminal work was done by François Bourguignon and Christian Morrisson in their 2002 _American Economic Review_ paper which estimated global inequality from 1820 to 1992. The estimates were made at more or less regular 20-year intervals. The BourguignonMorrisson approach relied on two building blocks. The mean incomes of countries were taken from Maddison (2004 or earlier) while 33 income distributions of uneven quality and coverage were put together by Christian Morrisson to represent various parts of the globe. “Similar” countries were allocated the same income distribution, coming from a country for which such data were available. This has, for obvious reasons, led to many simplifications. In addition, Bourguignon and Morrisson used in many cases the 20<sup>th</sup> century distributions to “interpolate” (backward predict) the 19<sup>th</sup> century distributions for the countries for which 19<sup>th</sup> century data were unavailable. Thus, the number of data points (fractiles of the distributions) which they show for each benchmark year, say, 1820, 1850 etc. (33 “countries” times 11 fractiles<sup>2</sup> ) are not all really independent and

> 2 For 33 “representative” distributions, Bourguignon and Morrisson give 11 data points per country (nine bottom deciles and two top ventiles). However, these are the already “processed” data and it is not clear how many actual independent data points the authors had. For example, if only a Gini is available for a given country/year and the authors assume a lognormal distribution, then there is not a single fractile

2
Figure 5: Gender differences in the attainment of 15 to 19 year olds from poor households.

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**<mark>Table 2.4:  Real GDP, Population, and Poverty in Bahia’s 15 Regions, 1980– 2000</mark>**

||**P**|**200**<br>**opulation**|**0**|**Pove**|**rty**|**Real GDP**<br>**Growth**|
|---|---|---|---|---|---|---|
|**Region/Zone**|**1980**|**2000**|**Annual**<br>**Growth**<br>**1980 -**<br>**2000**<br>**(Percent)**|***Per Capita**<br>**GDP 2000**<br>**(R$)**|**Household**<br>**heads**<br>**Earning**<br>**<1/2**<br>**minimum**<br>**salary**<br>**2001**|<br>**1998–2000**<br>**(percent)**|
|Metropolitana de Salvador|1,766,724|3,021,572|2.72|7,036|14.7|18.1|
|Litoral Norte|393,616|530,898|1.51|4,389|16.9|20.5|
|Recôncavo Sul|588,527|684,550|0.76|1,890|13.2|24.6|
|Litoral Sul|1,122,232|1,360,539|0.97|1,671|14.0|-3.1|
|Extremo Sul|456,456|664,850|1.90|4,050|14.4|29.6|
|Nordeste|880,296|1,176,201|1.46|2,465|15.2|65.8|
|Paraguaçu|963,565|1,250,163|1.31|1,949|13.6|19.7|
|Sudoeste|863,207|1,135,362|1.38|1,708|13.0|2.4|
|Baixo Médio São Francisco|<br>288,228|407,501|1.75|3,318|18.6|88.7|
|Piemonte da Diamantina|450,778|568,708|1.17|1,922|18.1|7.1|
|Irecê|274,750|372,994|1.54|1,568|19.1|14.9|
|Chapada Diamantina|381,725|507,414|1.43|1,483|20.6|19.8|
|Serra Geral|438,544|565,037|1.28|1,884|18.1|20.0|
|Médio São Francisco|249,890|337,507|1.51|1,508|26.2|29.6|
|Oeste|336,854|486,954|1.86|3,392|19.5|36.7|
|**Total**|**9,455,392**<br>|**13,070,250**|**1.63**|**3,376**|**15.6**|**21.1**|

_Source:_ Statistical Yearbook Bahia 2001- SEI and Census 1980 and 2000. IBGE, Census 2000, available from http://www.assistenciasocial.gov.br/censo2000/ _*Per Capita GDP is calculated using preliminary data from SEI._ The large economic and population growth in the capital zone contrasts sharply with the developments in the zones that are mainly in the Sertão and where income opportunities are limited, droughts are frequent, and social protection programs are few. A large share of the population in the zones that are almost completely in the Sertão migrated to the metropolitan area.

Despite the welcome migration from the poorer interior zones, poverty remained rampant among families left in these zones. More than 60 percent of the household heads had incomes of less than one minimum salary in the poor regions, compared to the regional average of 55 percent, or the capital zone’s 34 percent. The share of household heads earning less than half a minimum salary also varies across regions (Table 2.4). The Southwest region is among the poorest in terms of per-capita GDP. However, the headcount poverty is the lowest; only 13 percent earning less than half a minimum wage in 2000.

9
<!-- Start of picture text --> B, B=<br><!-- End of picture text -->

|Din(PPP;,) = Trend + a,Dln(GDPDeflator;,) + a,DIn(GDP;,) + a3DIn(E<br>jz)+BDZit + €ix<br>Dx, =X — Xprevious t<br>t € {1993, 1996, 2005,2011}<br>Zit<br>B|
|---|
more conservative than their stated 95 percent sizes. In addition, in Figures 1 and 2, I graph the corresponding two‐dimensional confidence sets for the coefficients on the two inequality variables of interest in OBT and HOZ, projecting out only the dimension corresponding to the coefficient on lagged income. The corresponding one‐dimensional confidence intervals in Tables 2‐4 are obtained by projecting these sets onto the horizontal and vertical axes. I calculate the confidence sets over a grid of values for the coefficients on inequality ranging from ‐2 to 2, and for the coefficient on lagged income ranging from 0.75 to 0.99.

These weak instrument‐consistent confidence sets for the coefficients on the inequality variables are large. In the case of OBT, the 95 percent weak instrument‐consistent confidence set for the effect of inequality on growth contains the entire grid of values from ‐2 to 2. This holds for both the levels and differenced equations, and for all three test statistics. This implies that weak‐instrument robust inference cannot rule out _any_ value within this wide range of hypothesized effects of inequality on growth. To put the size of this range in perspective, OBT’s point estimate of ‐0.71 implies that a 10 point increase in the Gini coefficient would reduce growth by 1.4 percent per year. In contrast, the weak instrument‐consistent confidence set includes values of the growth effect of inequality of a 10 point increase in the Gini coefficient ranging from ‐4 percent per year to 4 percent per year (i.e. �� �2 � 0.1 �5 ��4 percent per year). The confidence sets for the effects of redistribution on growth are also large, and consist of the entire grid from ‐2 to 2.

These patterns are also quite clear from the two‐dimensional confidence regions for the growth effects of inequality (horizontal axis) and redistribution (vertical axis) displayed in Figure 1, for the levels equation. In this figure, the dark‐shaded region represents the combinations of parameter values that are not rejected. While their shapes differ somewhat, the AR, KJ, and CLR‐based confidence all include a wide range of positive and negative values for the effects of inequality and redistribution on growth. Crucially, these confidence sets are quite different from those based on the conventional Wald statistic, shown for reference in the top‐left panel. This conventional confidence set is a small elliptical region covering only negative values for the growth effect of inequality, and a roughly even mix of positive and negative values for the effects of redistribution on growth. The difference between the Wald‐based confidence sets and the three weak instrument‐consistent confidence sets clearly show that accounting for low instrument strength can lead to very different inferences about the key parameters of interest.

A similar pattern emerges for HOZ. For the levels equation, the weak instrument‐consistent projection‐based confidence sets in Table 3 cover the entire grid of parameter values for the estimated 16