rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Table 1 Turning Points: The Start of Tightening and Easing Phases

||Start of|Phase||Start of|Phase||Start of|Phase|
|---|---|---|---|---|---|---|---|---|
||Tightening|Easing||Tightening|Easing||Tightening|Easing|
|Australia|1973m2|1982m9|New<br>Zealand|1976m2|1985m4|Euro area|1999m11|2001m5|
||1984m2<br>|1986m1<br>||1994m1<br>|1998m4<br>||2005m12<br>|2008m10|
||1988m2|1989m12||1999m2|2003m1||2022m7||
||1994m8|1996m7||2004m1|2008m7||||
||2002m5<br>|2008m9<br>||2021m10||Austria|1980m1<br>|1982m9<br>|
||2009m10|2011m11|||||1988m7|1992m9|
||2022m5||||||1975m4||
|Canada|1973m4<br>|1976m11<br>|Norway||1982m6<br>|Belgium|1972m11<br>|1981m4<br>|
||1978m3|1981m9||1986m1|1987m1||1983m11|1985m5|
||1987m4|1990m6||1997m7|1999m1||1988m7|1993m9|
||1997m6|2001m1||2005m7|2008m10||||
||2004m9|2007m12||2009m10|2011m12|Finland|1972m11|1974m7|
||2010m6|2015m1||2021m9|||1976m1|1981m10|
||2017m7<br>2022m3|2020m3|||||1988m8|1990m1|
|Denmark|1973m7|1977m1|Sweden|1974m4|1978m2|France|1972m11|1974m7|
||1979m6|1980m9||1979m7|1981m10||1976m1|1981m10|
||1990m3|1992m12||1987m1|1992m11||1988m8|1990m4|
||1999m11|2000m10||1999m11|2002m11||||
||2005m12|2008m11||2006m1|2008m10|Germany|1972m10|1974m10|
||2022m7|||2010m7|2011m12||1979m1|1980m9|
|||||2019m1|2020m6||1983m9|1985m4|
|||||2022m4|||1988m6|1992m9|
|Israel|1993m11|1996m8|Switzerland|1973m1|1975m3||1997m10||
||2002m2|2002m12||1979m11|1982m8||||
||2005m9|2006m10||1988m8|1992m9|Greece||1982m10|
||<br>2009m8|<br>2011m10||<br>2000m1|<br>2001m3||1984m2|<br>1986m7|
||2022m4|||2004m6|2008m10||1987m10|1994m7|
|||||2022m6|||1997m6||
|Japan|1973m4|1975m4|United<br>Kingdom|1972m6|1976m11|Ireland||1986m3|
||1979m4|1980m8||1977m11|1980m7||1988m6|1992m12|
||1989m5|1991m7||1984m5|1985m3||||
||2006m7|2008m10||1988m6<br>|1990m10|Italy|1973m9<br>|1977m6|
|||||1994m9|1998m10||1979m10|1982m8|
|||||2003m11|2007m12||1987m8|1990m5|
|||||2021m12|||1994m8|1996m7|
|Korea,||1993m5|United|1972m1|1974m7|Netherlands|1972m11|1974m10|
|Rep.|||States||||||
||1997m12|2001m2||1977m1|1981m1||1977m11|1980m6|
||2005m10|2008m10||1983m3|1984m8||1983m5|1985m8|
||2010m7<br>|2012m7||1987m1<br>|1989m6<br>||1988m7<br>|1992m9|
||2021m8|||1994m2|1995m7||1997m3||
|||||1999m6|2001m1||||
|||||2004m6<br>|2007m9<br>|Portugal|1977m3<br>|1985m8<br>|
|||||2015m12|2019m8||1989m3|1993m5|
|||||20223|||||
|||||m||Spain|1973m7<br>|1983m9<br>|
||||||||1988m9|1991m2|
||||||||1995m1|1995m12|

Sources: Authors’ calculations, based on identification of rate cycles described in Section II, with data from January 1970 – May 2024.Notes: Phases for individual countries in the euro area and the euro area as a whole are in the column on the right. Members of the euro area can have national cycles through 1998, after which phases are reported for the euro area as a whole.

80
7 <!-- Start of picture text --> Figure 3: Mettalic Mercury Imports to Brazil<br>240<br>UMD<br>Mo<br>:to<br>*00<br>Mo~~~~~~~~~O<br>40in 174 lw Ion lm 1 106 1_ lo<br>z=-<br>S-u Fwok, & Appel (0,1s. M<br><!-- End of picture text -->

**I.** ~ ~ ~ ~ **.**

Table 1: Mercury Contamination in Rondonia <!-- Start of picture text --> Samples |Standard Values |Rondonia Values<br>Water 0.002 ,ug.1-1 |< 0. 1 to 8.6 pg.1-1<br>Sediments 0.1 itg 0.06 to 157 Ag.g-l<br>Fishes 0.5 jig.g-1 (max.) 0.04 to 2.1 Ag.g-1<br>Air 0.02 jig.m-3 0.02 to 292 ;ig.m-3<br>Hair 0.7 to 6.4 1g.g6 1 Ig.g-U 0.22 to 40<br><!-- End of picture text -->

Source: Boischio (October, 1989), Pfeiffer et. al.(1989), and Malm et. al. (February, 1990), WHO (1989), Hacon (1990).
ouster of Taylor. However, row 1 of table 1 reports only three isolated years of civil war, 1990, 1992, and 2003. (Furthermore, all of these years for Liberia are reported as type 3 conflicts despite the involvement of other African countries in the dynamic of this violence; see the next section on the regional question.) 4. This problem of spotty coverage is mitigated by a more lenient coding rule that is presented in row 2 of table 1 based on fewer deaths (PRIO/Uppsala intensities 1–3) and is the mainstay of the MGB studies. However, a new problem appears. There are country-years that are reported as having instances of so-called civil war under the MGB rules but are unknown to us: Burkina Faso (1987), Burundi (1965), Cameroon (1984), Equatorial Guinea (1979), Ethiopia (1960), Gabon (1964), Gambia (1981), Ghana (1966, 1981, and 1983), Guinea (1970), Lesotho (1998), Liberia (1980), Kenya (1982), Nigeria (2004), Somalia (1978), Togo (1986 and 1991), Tunisia (1980), and Uganda (1971, 1972, and 1977). Sixteen of these 22 instances are denoted as “military faction” in the PRIO/Uppsala documentation “Armed Conflicts 1946–2000,” and 17 instances appear in the list of 188 coups by McGowan (2003, appendix A). An 18th, Tunisia, is outside of McGowan’s sub-Saharan database. The data thus seem to report other types of isolated violent episodes. Moreover, anything termed a civil war with a duration of only one year seems suspicious. For example, the violent episode in northern Nigeria that produced the 2004 entry was one of many in Nigeria’s violent post-1970 history. This conflict appears in the data as an intensity 1–3 event (25 or more deaths) and thereby assumes the same civil war status in an MGB-type coding as the years when Biafra attempted to secede from the Federation. Other instances in northern Nigeria are the Maitatsine risings in the early 1980s. None of these many violent episodes threatened the effective monopoly of force of the Nigerian state, which repressed them brutally.

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Table B5: Rider audits: platform observations

|**Variable**|**Defnition**|
|---|---|
|Percent of passengers who are standing||
|High crowding||
|Diference in presence of male riders be-|Share of reserved space rider who are male - Share of reserved|
|tween spaces|space rider who are male|
|Share of reserved space rider who are male||
|Share of public space riders who are male||

Table B6: Administrative data

|**Variable**|**Defnition**|
|---|---|
|Load factor|Data from Supervia indicates the share of maximum allowed ca-<br>pacity between every two stations by month, hour of the day, line<br>and direction of travel.|
|Crimes against property rate at home sta-<br>tion<br>Crime rate at home station|Monthly data on crime and population from Instituto de Segu-<br>ran ̧ca P ́ublica do Rio de Janeiro, spatially matched to riders’<br>|
|Violent crime rate at home station|home station|

Table B7: Platform survey

|**Variable**|**Defnition**|
|---|---|
|Finished the platform survey|= 1 if respondent did not leave before the end of the interview|
|Number of platform where survey was con-<br>ducted||
|Responded to IAT|= 1 if respondent accepted to take the IAT|
|Was invited to take the IAT|= 1 if respondent was randomly selected to take the IAT<br>_Respondent profle_|
|Age|Median age in years of the respondent’s age category|
|Female family members usually choose re-<br>served space|= 1 if answer to “Which of the Supervia spaces do the women in<br>your family usually ride during their usual rush hour commute?”<br>was either “Always reserved space” or “Mostly reserved”. Asked<br>only to male respondents.|
|Female respondent|= 1 is respondent is female|
|Low education (middle school or less)|= 1 if respondent’s highest degree is middle school or lower|
|Male respondent|= 1 is respondent is male|
|Number of Supervia rides in a typical week|Answered only by female respondents|
|Years of schooling|Number of years equivalent to respondent’s highest degree|
|Young (18-25 years-old)|= 1 if respondent is between 18 and 25 years-old|
|Respondent is employed|= 1 if respondent has part-time or full-time job|
|Usually chooses reserved space|= 1 if answer to “Which of the Supervia spaces do you ride during<br>your usual rush hour commute?” was either “Always reserved<br>space” or “Mostly reserved”. Asked only to female respondents.|
conomic characteristics. The data suggests that both groups were comparable and that those referred by forced migrant organizations were not more vulnerable before migration. Of 15 characteristics analyzed, only the time of settlement in Colombia was statistically different between groups. While this difference is mechanical (because RAMV migrants migrated earlier in general and likely referred other forced migrants who migrated around the same time), it is also small (less than one month). Moreover, in Figure C.1, we show that date of arrival was uncorrelated with an index constructed with baseline socioeconomic characteristics of migrants in our sample during our period of analysis.

We address concerns related to biases introduced the characteristics of sam- by migrants pled through different sources by estimating the local effects for RAMV and non-RAMV migrants who migrated around the RAMV cutoff date. First, we checked the internal validity of this empirical strategy by showing that RAMV and non-RAMV migrants who arrived around the cutoff date were comparable based on a rich set of baseline observables (Table 1). Second, we checked for the comparability of RAMV and non-RAMV referrals from organizations and the comparability of RAMV and non-RAMV referrals from other migrants (Tables B.2– B.3). All the exercises confirm the internal validity of the empirical exercise as the vast majority of tests point to no statistical significant differences between groups.

# **III. B Survey and data collection**

The survey was administered over the telephone between October 2020 and January 2021. Originally, we planned in-person data collection but shifted to a telephone mode because of the Covid-19 pandemic. To ensure the quality of the responses during phone interviews, the overall survey and some specific modules were shortened, and key modules (including labor and health ones) were administered only to the household head and partner. Absent a partner, these modules were administered to another adult member randomly selected from the household roster.

The questionnaire had five main modules. The first posed standard sociodemographic questions to all household members. The second module elicited information on the registration 13
**Figure 3 Uganda employment distribution, actual and projection, 1992-2020**

<!-- Start of picture text --> 100%<br>90% 5% 8% 11% 13% 15%<br>7%<br>80%70% 3% 15% 4% 15% 6% 16% 5% 17% Wage employment - government<br>60% 5%<br>50% Private wage employment - non-<br>40%30% 81% 70% 65% 64% 61% agricultureNonfarm enterprises<br>20%<br>10% Private wage employment -<br>0% agriculture<br>1992 2005 2010 2015 2020 Family farmers<br>Actual Projection<br>Employment Distribution (%)<br><!-- End of picture text --> _Source: see Annex Table 1 and Authors‟ projections._ Generalizing this trend to the subcontinent, Figure 4 shows projections of the share of the labor force working in private wage jobs ten years on, based on increasing annual growth rates in private wage jobs, given initial conditions. The simulation assumes a labor force growth of 3 percent per annum (roughly the average for all of SSA). If a country starts with about 10 percent of the labor force in private wage jobs in 2010 (about the average for the 12 countries shown in figure 2, and indicated by the middle line on the graph in figure 4), even with labor intensive growth and the creation of new private sector jobs at the rate of _10 percent per year for 10 years_ , the country could expect at most 20 percent of the labor force in private wage jobs by 2020. This would still leave the largest share of the labor force in agriculture or operating NFEs. Countries such as Malawi, Burkina Faso and Sierra Leone with a smaller share of the labor force in private wage jobs (illustrated by the lower trajectory in figure 4) would most likely not even reach Uganda‟s share in ten years – even with significantly higher private investment in labor-intensive medium and large businesses. The number of people entering the labor force will swamp the capability of the private sector to respond in even the most optimistic scenarios.<sup>6</sup>

**Figure 4 Wage jobs as share of future labor force: projections**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25% Lower starting point<br>20%<br>15% Mean starting point<br>10%<br>Higher starting point<br>5%<br>0%<br>0 2 4 6 8 10 12 14<br>Annual growth rate in wage jobs<br>wage jobs in 10 years<br>Share of labor force in private<br><!-- End of picture text --> _Source: Authors‟ calculations._ There are other reasons why non-wage employment in HEs and MEs is growing even in very dynamic economies. In urban areas, where the majority of private non-agricultural wage 6 Gollin, (2008), also makes this argument from a macroeconomic perspective, using a growth model with heterogeneity in productivity across firms.

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3 determinants that affect the global response to the emergency needs of<sup>countries</sup> in Africa.

# I. TRANSITORY FOOD INSECURITY: THE STYLIZED FACTS

Unexpected transitory reductions in food production<sup>can have a dramatic</sup> effect on food consumption. Several mechanisms are available<sup>to stabilize food</sup> consumption, including commercial food supplies anaa emergency imports of food aid. Both options have been used to stabilize food<sup>consumption in Sub-Saharan</sup> Africa, where food supplies have been unstable over the<sup>last decade.</sup> Table I summarizes the coefficients of variation (net of trend) of cereal food production averaged over 1970-87. The countries<sup>of the Sahel (Mauritania,</sup> Mali, Burkina Faso, Niger, Sudan, Ethiopia, and Somalia)<sup>had the largest annual</sup> fluctuations, followed by Botswana, Madagascar, and<sup>Tanzania. Note that the</sup> variability in the latter years is much higher, reflecting<sup>the 1983-85 drought.</sup> Output for even the most stable food producers in Africa<sup>fluctuates more sharply</sup> than in most other developing countries.1 The variability of cereal production parallels a declining<sup>trend in per</sup> capita cereal production. Table 2 summarizes annual<sup>growth rates for grain</sup> production and population from 1970 to 1982. East and<sup>West Africa had the</sup> highest rates of population growth and the lowest rates<sup>of cereal production</sup> growth. As a result per capita cereal production<sup>declined by -2.2 and -0.8</sup> percent a year in East and West Africa respectively.<sup>Cereal production was up</sup> 1.8 percent in East Asia and the Pacific Rim, and 1.6 percent<sup>in the industrial</sup> economies, however.

1"The Challenge of Hunger in Africa: A Call to Action." 1988. World Bank, Washington D.C.
too it should be noted that (as measured in practice) private consumption in the NA includes sizeable and rapidly growing components that are typically missing from surveys (Deaton, 2005). However, aside from differences in what is being measured, surveys encounter problems of under-reporting (particularly for incomes; the problem appears to be less serious for consumptions) and selective non-response (whereby the rich are less likely to respond).<sup>19</sup> Survey measurement errors can to some extent be dealt with by the same methods used for addressing selection bias. For example, if the measurement problem affects the outcomes for treatment and comparison units identically (and additively) and is uncorrelated with the control variables then it will not be a problem for estimating _ATE_ . This again points to the importance of the controls. But even if there are obvious omitted variables correlated with the measurement error, more reliable estimates may be possible using the double-difference estimators discussed further in section 7. This still requires that the measurement problem can be treated as a common (additive) error component, affecting measured outcomes for treatment and comparison units identically. These may, however, be overly strong assumptions in some applications.

It is sometimes desirable to collect panel data (also called longitudinal data), in which both participants and non-participants are surveyed repeatedly over time, spanning a period of expansion in program coverage and over which impacts are expected. Panel data raise new problems, including respondent attrition (another form of selection bias). Some of the methods described in section 7 do not strictly require panel data, but only observations of both outcomes and treatment status over multiple time periods, but not necessarily for the same observation units; these methods are thus more robust to the problems in collecting panel data.

As the above comments suggest, NX evaluations can be data demanding as well as methodologically difficult. One might be tempted to rely instead on “short cuts” including less formal, unstructured, interviews with participants. The problem in practice is that it is quite difficult to ask counter-factual questions in interviews or focus groups; try asking someone participating in a program: “what would you be doing now if this program did not exist?” Talking to participants (and non-participants) can be a valuable complement to quantitative surveys data, but it is unlikely to provide a credible impact evaluation on its own.

> 19 On the implications of such selective survey compliance for measures of poverty and inequality and some evidence (for the US) see Korinek et al. (2006).

15
The effect is strongest for the computer and related activities (NACE 72) and post and telecommunications (NACE 64) industries. We also find that, on average, the effect is larger (i.e., DD is more negative) for industries with higher natural entry (as indicated by the plotted regression line in Figure 1), but the effect is not linear across countries (not all observations are on the regression line).

Next, we repeat the exercise for countries, i.e., we divide industries into low entry (LE) and high entry (HE), and order countries from Lowest to Highest entry regulation. In figure 2, we plot the result against the ordered countries. Again, we find that the average effect is consistent with our main results, although the effect is not present in all countries. The effect is strongest for Norway and the United Kingdom. What is reassuring is that no industry or country appear to be driving the results.

# **3.3. Alternative Measures**

In Table 6B we examine alternatives to U.S. entry rates as measures of the natural propensity to enter. In columns (i-ii) we use other measures of mobility. We calculate ExitUS, which is the share of firms that exit in the U.S. Dun and Bradstreet data. It is calculated as the number of firms that exited in year t (because of closure or acquisition) as a percentage of all firms in year t-1. This measure is averaged for the industry over the period 1998-99. Prior literature (Dunne et al. 1988) finds that exit rates and entry rates are strongly correlated – the more there is creation through young firms, the more destruction there also is. So ExitUS should serve as a proxy for “natural entry” and when we replace EntryUS with it in the regression, the interaction has the appropriate negative sign and is significant.

In column (ii) we also use the Dunn and Bradstreet data to calculate SME, the ratio of Small and Medium Enterprises (SMEs), which we define as businesses with less than 250 employees. Since new firms are generally also small, we expect greater entry into industries with larger shares of smaller firms. Indeed, we find a significantly negative coefficient, suggesting that higher entry costs discourage entry to industries with larger shares of SMEs.

21
O.A.M.D.G.

||Public order<br>problem|Guerrilla<br>attack rate|Homicide<br>rate|Capture<br>rate|People in 2<br>lowest income<br>strata (%)|Quality of<br>Life<br>Index|
|---|---|---|---|---|---|---|
|Public order<br>problem|1||||||
|Guerrilla attack rate|0.16|1|||||
|Homicide rate|0.24|0.43|1||||
|Capture rate|-0.09|-0.17|-0.12|1|||
|People in 2 lowest<br>income strata (%)|0.28|0.28|0.11|-0.19|1||
|Quality of Life Index|-0.31|-0.15|-0.10|0.31|-0.40|1|

Table 1: Correlation matrix of violence and welfare We also assess to what extent violence is linked to other community characteristics. Table 1 shows that correlations between welfare and violence indicators are small, but significantly different from zero and with signs as expected. There may be a weak positive link between inequality and violence, and a negative one between Quality of Life and violence. Likewise, better access to basic services (measured in the Quality of Life indicator) goes hand in hand with a higher capture rate.

# **Measuring household assets**

Household variables from the _Familias_ survey cover ownership of assets such as house, land, animals, household goods and vehicles. The survey only asks whether a certain asset is owned, not how much it is worth. We have therefore additionally collected prices for the assets in question through a survey among people belonging to the three lowest income sixtiles (SISBEN 1-3) in a rural province near Bogotá (Boyacá). These prices are estimates and not representative, but constitute a useful approximation in the absence of official price statistics.

We divide the surveyed range of household assets into _fixed_ , i.e. impossible or difficult to move, and _mobile_ assets. Empirically, more than 2/3 of displaced people flee more than 15
region. Relative employment growth was significant at the 95% level in India, Bhutan, Bangladesh, and Sri Lanka. It was insignificant in Nepal and Afghanistan<sup>10</sup> . Notably, the figures below are averages. This indicates that firms with very high growth rates shape the overall picture. At the same time, the median growth rate is often zero, and if not, it is typically higher in urban areas.

**Employment growth was higher in urban areas.** On an annual average, firms in urban areas in South Asia grew at approximately 10.5%, which is significantly higher than 9% in non urban areas. Similarly, the median relative growth rate is zero in non-urban areas, and is significantly higher in urban regions (1.4%). 45% of companies in urban areas invested into physical assets, which is significantly higher than the 41% in non-urban areas.

**Firms located in urban areas were significantly more likely to invest, were on average larger, yet younger.** The median age of 23 years in urban areas is much higher than in non-urban areas of 15 years. However, on average, some very old firms - statistical outliers – which are located in non-urban areas render the average company in urban areas significantly younger than in non-urban areas.

**Table 4: Firm growth in urban areas at the country level**

|**Country**|**Size of**<br>**locality**|**Employment**<br>**(Median)**|**Age**<br>**(median)**|**Share of**<br>**firms**<br>**investing**|**Annual relative**<br>**employment**<br>**growth**<br>**(median)**|**Annual relative**<br>**employment**<br>**growth(mean)**|
|---|---|---|---|---|---|---|
|Afghanistan|Non-urban|12|6|53%|0.0%|8.5%|
||Urban|10|6|44%|0.0%|10.9%|
|Bangladesh|Non-urban|25|14|45%|1.6%|3.9%|
||Urban|30|12|40%|1.3%|9.9%|
|Bhutan|Non-urban|15.5|13|39%|6.3%|29.9%|
||Urban|25|14|74%|14.5%|53.2%|
|India|Non-urban|50|6|30%|0.0%|2.9%|
||Urban|25|14|49%|6.3%|8.7%|
|Nepal|Non-urban|12|12|42%|0.0%|6.6%|
||Urban|20|12|41%|1.4%|7.3%|
|Pakistan|Urban|20|20|41%|0.0%|2.8%|
|Sri Lanka|Non-urban|12|17|28%|0.0%|0.2%|
||Urban|80|22|48%|0.0%|7.1%|
|SAR|Non-urban|15|11|41%|0.0%|9.1%|
||Urban|23|14|45%|1.4%|10.5%|

_Source: Interim Enterprise Survey, 2009/10; Own calculations_

> 10 The sample for Pakistan only consists of firms located in urban areas.

15