rafmacalaba/datause-encoder-data huggingface dataset:
# 3. **ICP Data and an Alternative Regional Price Index**

Data collection for the International Comparisons Project was carried out in CMte d'Ivoire in 1985 (Eurostat, 1989). The localities for the price collection were selected on the basis of the same sampling frame as the CILSS (although of course the localities selected in the final sample were not necessarily the same). Price collection covered both urban aid rural areas in all major regions of the country (East Forest, West Forest, Savannah). In total well over 20,000 prices were collected covering 912 product codes. The first challenge in using these data was to establish a correspondence chart between those product codes and the expenditure categories distinguished in the CILSS. This correspondence chart is shown in Annex I.

A characteristic feature of ICP data collection is the very specific and well defined product descriptions. This is necessary in order to ensure the international comparability needed for the project. The drawback for local use of the data is that not all items will be found in all parts of the country. For the purposes of establishing a within-country regional price index it is clearly necessary that the same product is found in all regions which need to be distinguished. This reduced the number of ICP prices which were available for our purposes. Nevertheless a total of 260 product categories were used to calculate the regional price index and these were matched with 27 food categories and 25 non-food categories derived from the CILSS. The detailed expenditure shares derived from the CILSS over these categories are shown in Annex II.

Two restrictions slightly hampered our use of the ICP data. First, very few non-food items were available for rural Savannah. We made the assumption that in view of the low population density in the Savannah, many non-food items would only be available in the urban centers of the region, and therefore we used the urban Savannah prices to impute values for rural Savannah for these products. Second, there were a few products which we felt were important representatives for a given expenditure category but for which prices were not available in all regions. In this case some imputations were made: for example, 4
# FIGURE 13 Foreign-currency import invoicing and pass-through

A. Share of imports invoiced in foreign currency <!-- Start of picture text --> Percent Interquartile range Median<br>100<br>80<br>60<br>40<br>20<br>0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> B. Share of foreign-currency invoicing and pass-<br>through from monetary policy shocks<br>Ratio<br>0.8<br>Interquartile range Median<br>0.6<br>0.4<br>0.2<br>0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> <!-- Start of picture text --> C. Share of foreign-currency invoicing and pass- D. Share of foreign-currency invoicing and<br>through from monetary policy shocks in EMDEs average exchange rate pass-through in EMDEs<br>100 100<br>90 90<br>80 80<br>0 0.2 0.4 0.6 0.8 -0.2 -0.1 0.0 0.1 0.2<br>Pass-through, ratio Pass-through, ratio<br>percent<br>Foreign currency invoicing,<br>percent<br>Foreign currency invoicing,<br><!-- End of picture text --> Note: Share of imports invoiced in foreign currency based on data for 50 countries calculated by Gopinath (2015). Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; ERPTR = exchange rate pass-through ratio.

B. Low and high share of foreign-currency invoicing are defined as below or above the sample average.

C.D. The sample only includes EMDEs with floating exchange rate regimes according to the IMF classification.

D. Shock-specific pass-throughs are aggregated using shares of currency movements accounted for by each type of shock as weights.

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the tax system. Monetary transfers, meanwhile, leave the Gini coefficient unchanged, according to Figure 2, though some authors find a regressive impact, as discussed in Section 4.

13. **Limited fiscal redistribution in Latin America should not be an excuse for inaction in Colombia.** Instead, Colombia could beneficially link its targets of reducing inequality to its aspirations of joining the OECD, where fiscal redistribution is actively used.<sup>10</sup> Also, given that Colombia‟s level of income inequality is among the highest in the world, there is an argument for taking additional measures, even if this policy is not commonly used in neighboring countries.

**Figure 2. Fiscal Distribution in Colombia, Latin America, the UK and Europe.**

<!-- Start of picture text --> Colombia Latin America UK EU<br>0.60<br>0.54 0.54 0.54<br>0.55 0.520.52 0.53<br>0.51 0.50 0.51<br>0.50<br>0.46<br>0.45<br>0.39<br>0.40<br>0.36 0.36<br>0.34 0.34<br>0.35<br>0.31<br>0.30<br>(I) Market Income (II) Gross Income (III) Disposable (IV) Post Tax<br>(Income before (after public cash Income (after Income (after<br>transfer and taxes) transfers) direct taxes) indirect taxes)<br><!-- End of picture text --> Source: Goñi et al (2011) using (ECV 2003). Note: The Graph shows the gradual development in the Gini coefficient as market incomes are augmented with public cash transfers, reduced by direct taxes and, finally, by indirect taxes. See Annex 1 for details regarding the definition of income concepts.

14. **Inequality of market incomes in Colombia is similar to that observed in countries such as the United Kingdom.** Figure 2 illustrates one of several ways to decompose inequality statistics. Accordingly, income inequality arises from a combination of market outcomes, public monetary transfers, direct and indirect taxation. In addition to fiscal redistribution, one of the reasons why EU countries have a much lower Gini coefficients is that their starting point, i.e. the result of market transactions, is less unequal (0.46 in the EU vs. 0.54 in Colombia). To show that this is not always the case, Figure 2 illustrates the case of the United Kingdom (a relatively unequal country within the EU). The inequality generated by market transactions is quite similar in both countries, but the United Kingdom uses its tax-transfer system much more actively to bring down its Gini coefficient 16 points lower than in Colombia.

# 15. **Finally, it should be emphasized that the very favorable macroeconomic situation that Colombia has been experiencing recently is potentially conducive for a more deliberate redistributive policy.** In the context of solid economic growth (averaging 5 percent in 2010-

> 10 Three quarters of inequality reduction in the OECD countries is due to transfers, while the remaining is explained by progressive taxation.  Source OECD (2012).

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**Table 1: Country** **<u>groups: EU-15, NMS and ACC.</u>**

||**Countries**|
|---|---|
|**EU-15**|Belgium, Greece, Luxembourg, Denmark, Spain, Netherlands, Germany,<br>France,Portugal,Ireland,Italy,United Kingdom,Austria,Finland,Sweden.|
|**NMS (EU New member states)**|Poland, Czech Republic, Cyprus, Latvia, Lithuania, Slovenia, Estonia, Slovakia,<br>Hungary,Malta,Bulgaria and Romania.|
|**ACC(EU Accession countries)**|Albania,Croatia,Macedonia,Montenegro and Turkey|

_Note:_ These groups stay the same throughout the analysis even though some of them have changed their formal country group categories during the time period studied.

To start out we see that the share of service value added as a percentage of GDP is very high in EU-15 and NMS, while it is lower in ACC (Figure 1, see also Figures A1-3 in the Appendix for reference to specific countries). This is expected given their income levels, but many NMS countries have even higher than expected service shares and ACC lower than expected. Figure 2 (and Figures A4-6) shows GDP growth - controlling for initial income levels and initial levels of services value added as a share of GDP - for 127 countries over 2000-07, against service value added growth. EU-15 grew slower than the other groups, both in terms of overall growth and growth in service value added, which partly is explained by a catching up effect in NMS and ACC that start out at lower initial levels. However, most European countries have a higher than expected GDP growth than suggested by their service value added growth. Perhaps there is a higher share of services being exported or the sophistication is higher, resulting in a larger growth impact of their service value added share?

**Figure 1. Share of service value added as a percentage of GDP vs GDP per capita, 2009.**

<!-- Start of picture text --> 6 7 8 9 10 11<br>Log Per capita GDP PPP $2009<br>Note: black - all countries, red - EU-15, blue - NMS, green - Accession<br>80<br>60<br>40<br>20<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors’ calculations.

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**Figure 3: Non-parametric regression mean function of probability of infant death (GWEC winsorized at 95th percentile)**

<!-- Start of picture text --> .08<br>.06<br>.04<br>.02<br>Mean Function (prob. infant death)<br>0<br><!-- End of picture text -->

**Figure 4: Non-parametric regression mean function of probability of neonatal death (GWEC winsorized at 95th percentile)**

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The pattern in the Figure 3 differs from the findings by Deaton (2005), Ravallion (2003) and Karshenas (2003). Deaton and Karshenas both find a negative relationship between the gap and country income levels, with the gap being largest among the richest countries. In contrast, our sample shows that the gap is smaller among high-income countries than among middle income countries. An important part of the explanation for this is that compared to previous studies, our database contains more surveys overall from richer countries, and also more consumption surveys for richer countries and more income surveys for poorer countries. Moreover, we observe a very similar pattern for income surveys and consumption surveys, which Deaton and Ravallion did not. We believe this difference originates from income surveys being more heavily concentrated in richer countries in the Deaton and Ravallion samples, where they observed a larger gap, while consumption surveys were concentrated in poorer countries, where their gap was smaller. This pattern was particularly strong in the case of Deaton who focused the analysis of rich countries on the UK and the US, two high-income countries where the gap is particularly large. We also find relatively larger gaps in the UK and US, but these appear somewhat unique cases among rich countries.

A further possible reason we observe a smaller and narrowing gap among richer countries than previous studies may be linked to evolving practices and consolidation of data sources in these countries. Recently, it has been more common in rich countries to integrate administrative data, such as tax records, into survey data estimates. For example, the EU-SILC surveys, a major source for our income survey data for rich countries, explicitly allow countries to supplement income variables with administrative records, such as tax and payroll data (Jäntti et al., 2013). This practice helps align estimates, reduces underreporting in surveys, and contributes to a reduction in the gap between national accounts measures and survey measures. A caveat to this interpretation is that consumption surveys, which do not typically rely on any such administrative data, also show a similar narrowing of the gap among rich countries. In Panel B of Table 1, we also assess the gap between household surveys and GDP. Overall, survey means are on average 47 percent lower than GDP across countries. While this large discrepancy may not be surprising to national accounts and survey experts, it is worth noting that maybe the most commonly cited indicator summarizing living standards in a country – GDP per capita – and often described as ‘income per person’, is on average twice the size of per capita household 16
Table 3 shows the effects of being assigned to receive a childcare center on the use of childcare for the full sample and for the subsample of households with children under two years of age. Participants in the treatment group are 11.7 percentage points more likely to have a child in the household who attended a childcare center in the past 24 hours compared to a control group rate of 6 percent. This translates to a tripling in current use of childcare centers at the time of the follow up survey, 14 months after the baseline. We see similar effects when we look at the likelihood of any childcare center use over the evaluation period. Participants in the treatment group are 24 percentage points more likely to have had at least one child attend a mobile creche in the past 12 months. These results demonstrate persistent increases in childcare center take up that extend beyond use during the public works program, suggesting that initial exposure to the childcare centers generated lasting demand once the centers reopened following the 8-month COVID-19 related closures.

# **4.3 Effects on Summary Measures**

Table 4 and Figure 4 present treatment effects on our summary measures of outcomes for the full sample and for the subsample of households with children under two years of age. The first row of Column 1 indicates a positive and significant impact on our summary employment measure.<sup>13</sup> The ITT estimates show an increase in our women’s employment measure of 0.08 SD and for the full sample and 0.09 SD for the subsample of respondents in households with children under two years of age respectively. We also see significant and relatively larger positive impacts of being assigned to receive a childcare center on our summary measures of women’s psychological well-being (column 3),<sup>14</sup> women’s financial resilience and savings (column 4),<sup>15</sup> and on child development (column 5),<sup>16</sup> with an increase of 0.175 SD. However, our results indicate no significant impacts on women’s involvement in decision-making or gender attitudes (column 2).<sup>17</sup> None of the estimated effects for the subsample of women with children under age 2 are significantly different from the results we find for the full sample, suggesting that childcare had broadly similar impacts for all women in the sample at an aggregate level. However, we do not have enough statistical power to definitively rule

> 13Aggregate index of the employment outcomes listed in Tables 5, 6 and 7.

> 14Aggregate index of outcomes listed in Table 11.

> 15Aggregate index of outcomes listed in Table 12.

> 16Aggregate index of outcomes listed in Table 13.

> 17Aggregate index of outcomes listed in Tables 8 and 9.

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Primary outcomes _y_ are whether households used a minikit (in 2017 Rainy 1 or in 2017 Dry) and adoption of horticulture. Assigned minikit _i_ is an indicator for whether household _i_ was randomly assigned to receive a minikit, Minikit saturation _i_ is the probability of receiving a minikit for households in the water user group of household _i_ ’s sample plot, and _X_ 1 _is_ includes the stratification variables (Zone fixed effects and O&M treatment status), as well as indicator variables reflecting the probability that a household would receive a minikit,<sup>47</sup> and in some specifications 2016 Dry horticulture adoption. For precision, we restrict to command area plots, and for plot level outcomes we focus on the 2017 and 2018 Dry seasons; these are the plots and seasons in which we expect households receiving minikits to adopt horticulture. As minikit saturation is assigned at the water user group level, robust standard errors are clustered at the water user group level.

We present the results of this analysis in Table 10. First, we find a strong first stage; households assigned to receive a minikit are 40pp more likely to use a minikit than households not assigned to receive a minikit. Almost all non-compliance is driven by households who were assigned to receive a minikit but did not pick it up — 4.8% of households not assigned to received a minikit used one, while 43.8% of households assigned to receive a minikit used one. Second, we find no effects of minikits on horticulture use, and we have sufficient precision to reject estimates from other contexts of the effect of minikits on technology adoption (Emerick et al., 2016; Jones et al., 2018). Third, we find no effects of minikit saturation, although these estimates are less precise than those of the impacts of assignment to receive a minikit; we note that we also fail to reject that the sum of the coefficients on assigned minikit and minikit saturation (the effect on adoption in a fully treated compared to an untreated water user group) is zero. Fourth, we find strong positive selection into using a minikit: farmers who grew horticulture in 2016 Dry, who are 30.6pp more likely to grow horticulture in 2017 and 2018 Dry, are 13.1pp more likely to use a minikit in response to assignment to receieve a minikit receipt.

We interpret these results as corroborating evidence that information and financial constraints are not dominant constraints to adoption of irrigation. Most farmers assigned to receive a minikit do not pick it up and use it, and the farmers who do

> 47After matching names from the lists of water user group members to our baseline survey, we found that 32% of households either had multiple household members on the lists of water user group members or had a single household member listed multiple times; these households are more likely to be assigned to receive a minikit and may differ from other households.

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21 steadily rising since 1995.

Low technology and unskilled labor intensive products are also Turkey’s major source of net foreign currency earnings. Turkey is a huge net exporter of low technology and labor intensive products to the EU and net importer of medium to high tech products. Its exports of the former have been around five times higher in terms of value than imports from the EU generating export earnings to finance other imports. While Turkey is a large net importer of medium to high value products, their exports in terms of their imports significantly increased from 20 percent in 1995 to 26 percent in 2000, 59 percent in 2001 and slightly fell to 56 percent in 2004. So did the imports. They were around three times higher in 2004 than in 1995.

Relative stability of the time profile of ‘technology content’ of Turkey’s imports from the EU contrasts rather sharply with changes in ‘technology content’ of Turkish exports to these markets. Figure 6 presents respective shares of exports and imports from the EU in total ‘tech’ exports and imports excluding resource intensive products. The change in Turkish EU-oriented exports was very extensive, with the share of low tech and unskilled labor intensive products falling steadily from 90 percent in 1990 to 70 percent in 2000 and 56 percent in 2004. Over 19902004, the share of ‘low tech and unskilled labor intensive’ products in Turkish imports from the EU oscillated between 13 percent (2000) and 20 percent (1995) and high and medium tech products between 77 percent and 80 percent.

**Figure 6** : _‘Technology content’ of Turkish trade with the EU in 1990-2004 (in percent)_

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Imports: 'low tech' products Imports:'medium to high tech' products<br>Exports: 'low tech' products Exports:'medium to high tech' products<br><!-- End of picture text --> Source: Computations based on EU25 data from UN COMTRADE Statistics.

Hence, since around 1998 there has been a very strong shift towards products characterized by medium to high technology processes. Since exports of resource intensive products expanded broadly in line with total exports, the growth in the weight of medium to high tech products has been at the expense of low technology products. On the import side, the composition has been stable with medium to high technology products accounting for most of Turkey’s imports from the EU.

EU markets have played a critical role in transformation of Turkish export basket. Table 11 listing 20 top sectors, identified in terms of four-digit SITC products, ranked according to the value of exports to the EU (Table 11) provides a good illustration of it.<sup>9</sup> These sectors drove the 9 Sectors successful in global markets have also been successful in EU markets. Annex Table 1 provides the list of top twenty sectors in terms of the size of their worldwide exports. It is the same for total exports and EU-destined exports.
necessarily monotonic in the correlation between achievements. Allocation x has its outcomes perfectly negatively correlated, but has the highest head-count ratio. The impact that the association between outcomes have on the head-count ratio depends on whether this association pushes individuals above or below the identification threshold. In our example, a positive correlation in extreme deprivation reduces the head-count ratio because an individual is identified as poor as soon as she has one extreme deprivation. In contrast, a positive correlation in moderate deprivations increases the head-count ratio because an individual is identified as poor when she cumulates two moderate deprivations.

Together, these considerations suggest that two separate surveys only provide a very noisy picture of the head-count ratio. There is potentially a large added value in collecting in the same survey the achievements in all relevant dimensions.

# **5.3 Only one marginal distribution**

We consider the case for which only one survey exists. This survey is assumed “specialized” in the sense that it collects information either only on monetary poverty, or only on non-monetary poverty. We ask how to best identify individuals with low well-being from such a specialized survey. More precisely, we ask how to set the weights in order to decrease the (expected) mis-identification errors.

We need to consider a slightly more general framework with m-dimensions. The first l dimensions are market goods and they form the market domain. The remaining m − l dimensions are non-market goods and they form the non-market domain. We assume that l ∈{2, . . . , m − 1}, which implies that both domains are non-trivial. Any bundle x can be decomposed into (x<sup>m</sup> , x<sup>nm</sup> ) where x<sup>m</sup> = (x1, . . . , xl) is the market bundle and x<sup>nm</sup> = (xl+1, . . . , xm) is the non-market bundle. The non-market bundle corresponds to non-market endowment while the market bundle is optimally selected in the individual’s budget set. Individual i’s monetary aggregate corresponds to the monetary value of her market bundle, i.e., to �lj=1<sup>pjxij.</sup> Our main point is that the weights given to the observed dimensions should reflect how their achievements are related to achievements on the non-observed dimensions.

## **Identifying low well-being from monetary data**

We consider the case of a specialized survey covering the monetary domain. We show by means of an example that the weights of an achievement-LIM that minimizes mis-identification errors may deviate from market prices, at least when market behavior is non-separable from non-market endowment. This non-separability arise when the welfare function U cannot be written as U(x) = U(u(x<sup>m</sup> ), x<sup>nm</sup> ), where function u : R<sup>l</sup> +<sup>→Rrepresentsbehavior</sup> 28