rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.2 – Differential attrition by personal and firm characteristics**

||Did not drop<br>from survey|Dropped from<br>survey<br>(attrition)|Difference|N|
|---|---|---|---|---|
|_Business outcomes_|||||
|Ln of typical monthly revenues|12.52|12.51|-0.01|795|
||(0.07)|(0.16)|(0.18)||
|Ln of revenues in previous month|11.81|11.36|-0.45|799|
||(0.14)|(0.40)|(0.43)||
|Ln of revenues last year|14.08|13.60|-0.48|779|
||(0.13)|(0.39)|(0.41)||
|Ln of profit in in previous month|10.74|10.31|-0.43|798|
||(0.14)|(0.39)|(0.42)||
|Ln of profit last year|13.21|12.40|-0.82|791|
||(0.13)|(0.40)|(0.42)||
|Ln of monthly earnings|12.22|12.38|0.16|811|
||(0.06)|(0.11)|(0.13)||
|_Other characteristics_|||||
|Tenure at baseline (years)|6.86|5.95|-0.92|814|
||(0.24)|(0.57)|(0.62)||
|Completed O Level (%)|41%|37%|-0.03|817|
||(0.02)|(0.05)|(0.05)||
|Married (%)|58|56|-0.03|817|
||(0.02)|(0.05)|(0.05)||
|Head of household (%)|35|43|0.08|817|
||(0.02)|(0.05)|(0.05)||
|With at least 1 child under 5 (%)|12<br>(0.01)|15<br>(0.03)|0.02<br>(0.04)|817|
|Access to clean water (%)|81|89|0.078*|817|
||(0.01)|(0.03)|(0.03)||
|Access to electricity (%)|78|78|0.00|817|
||(0.02)|(0.04)|(0.04)||
|Ln of total value of household assets|14.66|14.86|0.20|817|
||(0.06)|(0.13)|(0.14)||

27
**Table 4** – Mediating effects: trust and descriptive norms

|Dependent va|riable: Will|ing to vacc|inate|||||
|---|---|---|---|---|---|---|---|
||||Low||Whole|Low|High|
||Whole|sample|trust|High trust|sample|belief|belief|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Stringency index|0.003***|0.003**|0.004**|0.003***|0.005**|0.005***|0.002**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.001)|
|Stringency index X Trust in govt health authorities||0.002<br>(0.001)||||||
|Stringency index X No. People out of 100 who will vaccinate|||||-0.000<br>(0.000)|||
|Vaccination campaign dummy|0.068***|0.087***|0.084***|0.088***|0.071***|0.103***|0.059***|
||(0.016)|(0.018)|(0.017)|(0.021)|(0.016)|(0.022)|(0.013)|
|Daily deaths per million (7 day avg)|-0.000|0.001|0.002|-0.000|-0.000|-0.001|0.002|
||(0.005)|(0.007)|(0.007)|(0.007)|(0.006)|(0.009)|(0.004)|
|know positive case|0.076***|0.081***|0.091***|0.063***|0.076***|0.101***|0.061***|
||(0.010)|(0.011)|(0.016)|(0.011)|(0.010)|(0.016)|(0.006)|
|_Mediating factors_||||||||
|No. people out of 100 who will vaccinate|0.006***||||0.008***|||
||(0.000)||||(0.002)|||
|trusts govt. health authorities|0.123***|0.041||||||
||(0.016)|(0.085)||||||
|_Individual attributes_||||||||
|male|0.067***|0.081***|0.082***|0.078***|0.066***|0.055***|0.079***|
||(0.012)|(0.014)|(0.017)|(0.011)|(0.012)|(0.018)|(0.014)|
|urban|0.012|0.021*|0.027*|0.009|0.009|0.021|0.003|
||(0.008)|(0.011)|(0.014)|(0.010)|(0.009)|(0.013)|(0.008)|
|age 20-30 yrs|-0.048|-0.095*|-0.110*|-0.066|-0.053|-0.145*|-0.023|
||(0.033)|(0.045)|(0.053)|(0.054)|(0.033)|(0.069)|(0.037)|
|age 31-40 yrs|-0.059*|-0.105**|-0.129**|-0.061|-0.063*|-0.163**|-0.031|
||(0.032)|(0.045)|(0.053)|(0.055)|(0.032)|(0.065)|(0.039)|
|age 41-50 yrs|-0.056*|-0.095**|-0.108*|-0.068|-0.057*|-0.153**|-0.027|
||(0.032)|(0.045)|(0.054)|(0.051)|(0.032)|(0.066)|(0.039)|
|age 51-50yrs|-0.051|-0.079*|-0.089|-0.057|-0.050|-0.149*|-0.018|
||(0.032)|(0.044)|(0.053)|(0.053)|(0.032)|(0.072)|(0.038)|
|age 61-70 yrs|-0.016|-0.034|-0.038|-0.023|-0.016|-0.135*|0.019|
||(0.031)|(0.040)|(0.044)|(0.054)|(0.031)|(0.069)|(0.034)|
|age 71-80 yrs|0.034|0.022|0.027|0.014|0.034|-0.071|0.057|
||(0.029)|(0.033)|(0.034)|(0.051)|(0.029)|(0.071)|(0.034)|
|primary edu or less|-0.046***|-0.046***|-0.061***|<br>-0.020|-0.040**|-0.037|-0.028**|
||(0.016)|(0.015)|(0.014)|(0.015)|(0.017)|(0.025)|(0.013)|
|secondary edu|-0.032***|-0.031***|-0.040***|<br>-0.015|-0.032***|-0.026**|-0.028***|
||(0.008)|(0.008)|(0.009)|(0.009)|(0.008)|(0.009)|(0.008)|
|in good health|-0.015**|-0.006|-0.015|0.015|-0.011|-0.021*|-0.000|
||(0.007)|(0.007)|(0.009)|(0.009)|(0.007)|(0.010)|(0.005)|
|Constant|0.039|0.390***|0.323***|0.505***|-0.080|0.186*|0.556***|
||(0.060)|(0.085)|(0.100)|(0.079)|(0.168)|(0.097)|(0.063)|
|Observations|61760|61760|38009|23751|61760|21941|39819|
|R-squared|0.179|0.086|0.065|0.051|0.165|0.069|0.047|

Standard errors in parentheses clustered at country level. All estimations include country and month fixed effects. * p<0.05 ** p<0.01 *** p<0.001 23
where _F_<sup>_1j_</sup> is the Fisher index for region _j_ with region 1 as the reference area. This procedure is equivalent to taking a geometric mean of every possible combination of two regions. As discussed in Section 2.3, a multilateral price index can also be calculated by the CPD method.<sup>8</sup>

# _Choice of price data_

While the choice of price index calculation methods matters, the quality and types of price data used for the calculation may be even more important. There are three types of data commonly available for spatial price adjustment. The first type of price data is the market price survey data that are typically collected in parallel with an official household budget survey. The second type of price data that can be potentially used for spatial price adjustment is the CPI price data. The third type of price data is unit values that can be calculated based on the consumption module of official household budget surveys. Gaddis (2016) nicely summarizes the general characteristics of these price data from a poverty analysis perspective as in Table 1. We encourage readers to refer to Gaddis (2016) for the discussion, and only highlight here some key factors particularly relevant to spatial price adjustment for poverty measurement below.<sup>9</sup>

> 8 The GEKS procedure can also be applied to the Törnqvist index (Caves, Christensen, and Diewert 1982; Balk 2008). Another common approach to calculate a multilateral index is the Geary-Khamis method, which is used by the Penn World Table (Feenstra, Inklaar, and Timmer 2015) and the regional price parities in the United States (Aten 2017).

> 9 Also see Chapter V of United Nations Statistics Division (2005). Aside from these commonly available price data, there are other types of price data, such as the price data collected for the PPP calculation under the ICP, scanner data collected by private companies, and price inventories of specific goods and services (agricultural products, housing, and so on). For example, recent research that takes advantage of scanner data demonstrates the impacts of quality (or item) variations on the costs of living across cities. Handbury and Weinstein (2015) use detailed barcode data to address heterogeneity bias (stemming from the comparison of different goods in different locations) and variety bias (stemming from not correcting for the fact that some goods are unavailable in some locations), finding that prices are lower (as opposed to higher) in larger cities in the United States. Feenstra, Xu, and Antoniades (2017) analyze China in a similar way and compare it to the United States. Other research with scanner data includes Handbury (2013), which constructs a spatial price index for different income levels (that is, non-homothetic) across the U.S. metropolitan areas. It finds that price levels for high-income households tend to be overestimated when a standard homothetic index is used.

6
_Africa’s Macroeconomic Story_ The real effective exchange rate for the region as a whole has appreciated by 20 percent since 2000 (Figure 22).<sup>21</sup> However, this largely reflects the impact of resource development in resourcerich countries. The real effective exchange rate appreciated by 66 percent in oil exporting countries, but has hardly changed in oil importing countries. There are few examples where the real exchange rate depreciated significantly in countries with large external imbalances.

**Figure 22: Real Effective Exchange Rates, 1990-2012**

# _Index (2000=100)_

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>80<br>2004 2006 2008 2010 2012<br>Africa region Oil exporters Oil importers<br><!-- End of picture text --> Source: IMF _Regional Economic Outlook Sub-Saharan Africa_ May 2013.

# _Assessments of economic management show improvement since 2005_

The World Bank Country Policy and Institutional Assessments (CPIA) for IDA-eligible countries in Africa give economic management the highest rating of the four clusters (Figure 23, left panel). The increase in the economic management cluster average since 2005 stems from improvements in macroeconomic management and debt policy (Figure 23, right panel)<sup>22</sup> 21 Data limitations prevent us from examining real effective exchange rates from a historical perspective. Data for real effective exchange rates are only available for 17 of the 46 countries in the region prior to 2000. 22 Gollwitzer (2010) finds that African countries with better budgetary institutions tend to have lower external debt burdens. The quality of budgetary institutions is measured using an index that takes into account the planning, approval and implementation aspects of the budget process.

31
extra efforts to increase human capital.<sup>2</sup> Furthermore, private tutoring is now widespread in many countries, especially but not solely in East Asia,<sup>3</sup> and evidence indicates that it improves students’ academic performance in some countries, including Germany, Israel, Japan, and Vietnam (Dang and Rogers, 2008).<sup>4</sup> Finally, there has been considerable debate about tutoring among policymakers. One crucial question is whether widespread availability and use of private tutoring exacerbates or helps equalize social and income inequality (Bray, 2009; Bray and Lykins, 2012), a question that is relevant to both developing and developed countries.<sup>5</sup> Here, the link with demography is important: if use of tutoring is correlated with both smaller family size and higher family income, this heightens the risk that it could exacerbate inequality.

Second, more generally, we improve on previous studies by providing a rather comprehensive investigation of different aspects of household investment in private tutoring for each child (i.e., at the child level), most of which have not been used before. These include participation in tutoring, household monetary investment in tutoring, and time spent both in the short term (i.e., frequency of attending tutoring classes in one year) and in the long term (i.e., number of years attending tutoring classes) on tutoring. We also go one step

> 2 Bishop (2006) emphasizes the importance of student effort on learning, and a recent study by MacLeod and Urquiola (2012) finds that it is not simply more school choice but rather student effort that can enhance educational performance.

> 3 Private tutoring (or supplementary education) is a widespread phenomenon, found in countries as diverse economically and geographically as Cambodia, the Arab Republic of Egypt, Japan, Kenya, Romania, Singapore, the United States, and the United Kingdom. A recent survey of the prevalence of tutoring in 22 developed and developing countries finds that in most of these countries, 25–90 percent of students at various levels of education are receiving or recently received private tutoring, and spending by households on private tutoring even rivals public sector education expenditures in some countries such as the Republic of Korea and Turkey (Dang and Rogers, 2008).

> 4 Other recent studies that find tutoring to have positive on different measures of student academic performance include student test scores and academic performance in India (Banerjee et al., 2010) and the United States (Zimmer et al., 2010). But see also Zhang (2013) for recent evidence that tutoring may benefit only certain student groups in China.

5 For example, in a recent opinion piece in the New York Times on the widening inequality in the US, the Nobel laureate Joseph Stiglitz (2013) calls for more “...summer and extracurricular programs that enrich lowincome students’ skills” to help level the playing field between these students and their richer peers.

3
- 21 -

# Tahle 8: RESULTS FROM EARNINGS REGRESSIONS

|Independent<br>Variables|(A)<br>_LOG_WAGEINDEX|Dependent Variable<br>(B)<br>LOG WGINDDEX|(C)<br>LOG WGCONDEX|
|---|---|---|---|
|CONSTANT<br>*|3.1<br>(1.70)*|0.887<br>(1.082)|-0.097<br>(1.845)|
|LOG HHINDEX|0.322<br>(0.348)|0.725<br>(0.237)***|1.002<br>(0.401)***|
|MIXIND|0.004<br>(0.002)*|||
|MIXCON|0.009<br>(0.004)**|||
|MIXTRANS|-0.001<br>(0.002)|||
|MIXTRADE|-0.025<br>(0.012)**|||
|MIXENERGY||0.011<br>(0.001)***||
|MIXMETAL||0.012<br>(0.001)***||
|MIXELEC||0.003<br>(0.001)**||
|MIXCHEM||0.001<br>(0.001)||
|MIXMINERAL||-0.001<br>(0.002)||
|MIXPAPER||0.001<br>(0.002)||
|MIXLIGHT||0.003<br>(0.001)**||
|Adjusted R2|0.47|0.86|0.10|

Standard errors in parentheses

* = Significant at 90% level = Significant at 95% level

= Significant at 99% level
# **I.  Introduction**

The financial crisis is wreaking havoc on the global economy. In the first quarter of 2009, nominal trade fell by 30 percent on average since last year. The world trade volume is estimated to have fallen by over 15 percent during this period ( _World Trade Monitor & League of Nations)_ . The declines have been widespread across countries and products, largely reflecting the sharp drop in global demand.

We examine historical data on global slowdowns to look for similarities that may offer insights into the large decline in trade that has already begun. There are four such events in recent history: 1975, 1982, 1991, and 2001. While these events were on average modest as compared with the current crisis, they may offer some guidance for what to expect in the coming months. We focus on global downturns, as opposed to financial crises, because these share the key characteristic (for international trade) of low global demand with the current environment. In contrast, during regional financial crises, demand in the rest of the world tends to remain strong, limiting the trade impact of the crisis. We also examine a handful of countries, which experienced financial crises during the 1991 global downturn, to determine whether banking crises significantly exacerbate weak trade performance during slowdowns.

The first issue we address is how much does trade contract when the global economy falters? We find that the trade contraction follows the GDP decline and that the trade volume declines by about 1 percent on average in first year of its contraction. The decline in the growth rate of trade (from historical average to trough) is sudden and is on average more than 4 times as large as that of income. On average, trade growth returns 2
# **Figure 7a: Urban households facing affordability problems by country (current US$)**

<!-- Start of picture text --> 80% Bolivia<br>Paraguay<br>70% Nicaragua<br>Honduras<br>60% Colombia<br>Argentina<br>50% Ecuador<br>Guatemala<br>40% Brazil<br>Peru<br>30% Dominican Rep.<br>El Salvador<br>20% Uruguay<br>Chile<br>10% Jamaica<br>Costa Rica<br>0% Venezuela<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Mexico<br>Cost of subsistence consumption (US$/m) Panama<br>Percentage of households spending over 5% of income<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data

**Figure 7b: Urban households facing affordability problems by country (PPP)**

<!-- Start of picture text --> Bolivia<br>80%<br>Honduras<br>70% Ecuador<br>Jamaica<br>60% Guatemala<br>Colombia<br>50% El Salvador<br>Dominican Rep.<br>40% Paraguay<br>Brazil<br>30% Nicaragua<br>Chile<br>20% Costa Rica<br>Peru<br>10%<br>Argentina<br>Uruguay<br>0%<br>Venezuela<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>Mexico<br>Cost of subsistence consumption (US$/m) Panama<br>Percentage of households over 5% of income<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data 18
Table 10: Intensive margin: Existing exports

|**Dependent Variable:**Exports_f pjt _|||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|-0.2179<br>(0.044)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0351|-0.0696|-0.0438|-0.0982|-0.0333|
|||(0.036)|(0.054)|(0.047)|(0.051)*|(0.051)|
|SPS_i_,_j_,_p_,_t_||0.0021|-0.0146|-0.0914|-0.0161|-0.0328|
|||(0.048)|(0.042)|(0.050)*|(0.048)|(0.048)|
|TBT_i_,_j_,_p_,_t_||-0.0971|-0.0811|-0.0968|-0.1387|-0.0650|
|||(0.044)**|(0.028)***|(0.042)**|(0.039)***|(0.044)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0388|-0.0154|0.0094|0.0323|0.0350|
|||(0.053)|(0.046)|(0.049)|(0.047)|(0.056)|
|QuantityControl_i_,_j_,_p_,_t_||-0.5739|-0.4905|-0.2355|-0.3295|-0.2107|
|||(0.158)***|(0.155)***|(0.100)**|(0.115)***|(0.159)|
|PriceControl_i_,_j_,_p_,_t_||-0.0483|-0.0410|-0.1115|-0.0793|0.0420|
|||(0.040)|(0.036)|(0.028)***|(0.035)**|(0.046)|
|OtherNTMs_i_,_j_,_p_,_t_||0.1252|0.1040|0.2088|0.0491|0.0289|
|||(0.101)|(0.097)|(0.100)**|(0.097)|(0.075)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0448|0.0324|0.0251|0.0291|0.0285|0.0340|
||(0.009)***|(0.006)***|(0.006)***|(0.005)***|(0.007)***|(0.007)***|
|Observations<br>|253212|253212|227964|200738|250213|219733|
|Adjusted_R_<sup>2</sup>|0.93|0.96|0.96|0.94|0.96|0.96|
|Fixed Effects<br>−|_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the intensive margin of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
Both graphs above show essentially the same pattern. Between 1994 and 1995 there was a reduction in the level of concentration, but it increased in 1996 and again in 2000. After that there was an upward trend which peaked in 2003 at the highest level in the last 11 years. Although there was a slight reduction in 2004, the HH Index was still higher than in previous years.

One reason for the increased the level of concentration during the last couple of years could be an increase in the relative importance of sectors that are themselves highly concentrated. Alternatively, the internal concentration within all categories may have increased, while the relative weights of the sectors have remained roughly constant. In order to test these alternative hypotheses, we calculate the within-sector HHI for each of the most important sectors in each of the years 1999, 2003 and 2004. The results are presented in Table 3.

**Table 3**

|**Argenti**<br>**Product**|**na: Export Concentration by Sector**<br>**Description**||||
|---|---|---|---|---|
|**Code**||**HHI 1999**|**HHI 2003**|**HHI 2004**|
|27|Mineral fuels, oils & product of their distill|0.66|0.34|0.30|
|23|Residues & waste from the food indust; prepr a|0.78|0.87|0.88|
|15|Animal/veg fats & oils & their cleavage produc|0.38|0.53|0.52|
|10|Cereals|0.38|0.44|0.45|
|87|Vehicles o/t railw/tramw roll-stock, pts & acc|0.15|0.16|0.16|
|12|Oil seed, oleagi fruits; miscell grain, seed,|0.42|0.86|0.90|
|02|Meat and edible meat offal|0.32|0.48|0.48|
|39|Plastics and articles thereof.|0.04|0.06|0.07|
|26|Ores, slag and ash.|0.98|0.99|0.98|
|41|Raw hides and skins (other than furskins) and|0.48|0.42|0.42|
|03|Fish & crustacean, mollusc & other aquatic inv|0.19|0.26|0.21|
|84|Nuclear reactors, boilers, mchy & mech applian|0.03|0.03|0.03|
|04|Dairy prod; birds' eggs; natural honey; edible|0.33|0.32|0.36|
|73|Articles of iron or steel.|0.19|0.23|0.21|
|08|Edible fruit and nuts; peel of citrus fruit or|0.20|0.18|0.15|
|72|Iron and steel.|0.07|0.07|0.08|
|29|Organic chemicals.|0.05|0.06|0.05|

The results show that the degree of concentration increased in the vast majority of sectors during 2003 and 2004 compared with 1999, when overall concentration was relatively low. The only major exception is mineral fuels, where concentration decreased significantly during the last few years. The results confirm that increased concentration within sectors account for the rise in overall concentration rather than changes in the relative shares of exports.

# **V. The productivity level of Argentina’s Exports**

Following the methodology developed in HHR, the “productivity” of each product group is defined as a weighted average of the per capita GDPs of countries that export the product. The formula is as follows: where EXP is the value of exports in US dollars at the 4 digit level, and GDP per capita is the PPP adjusted GDP per capita expressed in 2000 constant terms for each country. Hence, the index 9
and Rubin (1983). The purpose of the paper is to review the logic of this matching method, and illustrate its implementation and the computation of related impact indicators in EViews. PSM is an algorithm that matches treated and nonparticipants on the basis of the conditional probability of participation (the propensity score), given the observable characteristics. If outcomes are independent of participation, conditional on observables, then the use of the matched comparison group would yield an unbiased estimate of the mean impact of treatment.

The paper is organized as follows. Section 2 reviews the structure of two types of matching algorithms. The nearest-neighbor method pairs a given participant with the member of the comparison group with the propensity score that is closest to that of the given participant. Kernel-based methods associate with the outcome of participant **i** a matched outcome computed as a kernel-weighted average of the outcomes of all nonparticipants. This section also provides a brief comparison of PSM with evaluation methods such as double difference (DD), instrumental variable (IV) and Heckman’s method of selection bias correction. A more detailed description of these other methods is presented in Appendix C. Section 3 uses well-known and publicly available data sets to illustrate how to implement in EViews the methods described in section 2. Data on the treated come from the National Supported Work (NSW) Demonstration. The comparison group is drawn from the Population Survey of Income Dynamics (PSID) [Dehejia and Wahba 2002, 1999; Becker and Ichino 2002]. Concluding remarks are made in section 4. Appendix A shows how to estimate the propensity score using the log likelihood object (LOGL) while Appendix B provides the entire computer code for PSM.

# **_2.  Matching Methods_**

# **_The Principle of Matching_**

Matching methods can be framed within the context of _nonparametric estimation_ of the relation between an outcome variable for unit **i** ( **yi** ), a dummy variable indicating participation in the program ( **di** ), and set of other characteristics ( **xi** ). These characteristics are also referred to as covariates and are assumed exogenous in the sense that they are not affected by the intervention. Such a relation can be stated as (Moffit 5