rafmacalaba/datause-encoder-data huggingface dataset:
# 4.5 The message from the principal components46

The multidimensional nature of the intersectoral flow data makes it somewhat difficult to summarize the experience _of_ different countries and identify trends and common features. One way of simr 4<ying this kind of data set is to consider only first one or two principaJ components47 of the data. The first principal component is an artificial data series constructed in such a way that it has a very high correlation with all of the four sectoral surplus series. It is thus a good summary of the entire data set.

The first principal component "P1" of the data matrix consisting of the four surplus vectors (each consisting of 106 data points) is plotted in figure 7. It has no immediate interpretation as an economic variable, but provides the basis for some further analysis. This component "explains' just over 50 per cent of the variation in the four surplus vectors.

Though with only 50 per cent explanatory power of a four-dimensional data set, P1 is not as good a one-dimensional summary as one might hope for, it nevertheless seems worth analysing in its own right. Figure 8 orders the countries by the average value they score on this variable over their sample period. A confidence interval is constructed around each mean. The ranking of countries once again shows no clear pattern by geographical region.

An interesting feature of P1 is the differing degree to which it is correlated with individual surplus series. Its correlation with the 46 This section is based on a somewhat smaller sample, not including Ecuador or Portugal, and with a number of small differences from the main data set. 47 A precise statement of the properties of principal components is contained in, for example, T.W. Anderson, &U Introduction to Multivariate Statistical Analysis, (New York: Wiley), 1958.

48
_Table 4 Summary statistics of outcome variables for the control group_

|VARIABLES|N|mean|sd|min|max|
|---|---|---|---|---|---|
|_Main outcom_<br>|_es (survey‐ba_<br>|_sed)_<br>||||
|Employment index|763|0|1|‐2.314|6.401|
|Consumption index|763|0|1|‐1.580|5.037|
|Savings, investment and debt index|763|0|1|‐4.013|2.984|
|Business skills index|763|0|1|‐2.971|2.569|
|Psychological wellbeing index|763|0|1|‐2.625|3.606|
|Risk index|763|0|1|‐2.789|3.142|
|Trust index|763|0|1|‐2.982|3.147|
|Crime and violence index|763|0|1|‐1.214|5.667|
|Migration index|763|0|1|‐0.838|3.740|
|List experiment cattle index|763|0|1|‐3.360|3.095|
|List experiment argument index|763|0|1|‐3.666|4.163|

_Note: Higher values of all indicators refer to higher scores in the respective outcome. For instance, higher values in the risk index imply a higher preference for risky behavior. Higher values in the list experiment cattle index imply a higher propensity to engage in cattle raiding, while higher values in the list experiment argument index imply a higher propensity to engage in arguments. Higher values of the migration index mark a higher propensity of having, or planning to migrate._ 27
# **_Measuring the long term cycle_**

The multiples or models were shifting due to the then current “hardness” or “softness” of the market. But that is almost tautological: price can only be explained if we know where we are in the cycle. If this is the case we need a measure of where we are in the cycle and a pricing model independent of the cycle. A satisfactory approach might be to try to strip out the cyclic effects and see if the cycle-adjusted price can be explained by expected loss. It might give a more stable relationship. The proposed approach herein is thus first to try to separate out cyclic effects and second to explain adjusted or separated spread by expected loss, then by components of expected loss, i.e., each peril separately.

One measure of cycles in price is demonstrated in Figure 3. It is an index composed of observations of price from various sources. From 1984 to 2000 it is an index of price movements calculated by the now defunct company, Paragon. To the best of our knowledge, it is the only long term index available. More recently Lane Financial LLC has established a new index<sup>10</sup> based on observation of secondary market ILS prices, a pseudo constant expected loss series of original issues from Swiss Re, and observations of ILW prices. This last series combining several markets, the index is deemed to be more robust in the period since 2002. That leaves a gap from 2000 to 2002 where experts’ judgment has been used to complete the longer term view. The resulting index is displayed as the thick lines in Figure 3.

This index<sup>11</sup> is displayed in the lower panel of Figure 3 against the rate on line<sup>12</sup> reported by Guy Carpenter in 2006 for various zones or perils around the world. It seems to capture the essentials. The index exceeds 1 when the market is hard and is less than 1 when the market is soft. Over the period 19962008, the index varies from 0.76 (in 2000) to 1.65 (in mid-year 2006). This index is therefore used as the component to strip out cyclic effect from observed prices.

> 10 Both the longer term index and the shorter term version were presented in the 2007 Annual Review “That was the Year that was” published by Lane Financial LLC. See Annex 2 for the list of long term index of reinsurance prices. 11 We have also cross-checked the analysis herein described using purely Paragon and secondary prices as an alternative cyclic indicator, i.e., without the 2000-2002 “guesstimates”, and using secondary prices in that gap. The main results do not change.

> 12 The “rate on line” of reinsurance is analogous to premium for limit of coverage, i.e., just like the spread premium of an ILS. And while Guy Carpenter, and others in the traditional market, regularly report rates on line they do not report corresponding expected losses.

7
**<u>Table 1. Representativeness of Survey Sample and Response Rates</u>**

||Number of<br>firms in list|Proportion in<br>the universe<br>of freight<br>forwarding<br>firms|Number of<br>firms<br>contacted/<br>screened|Number of<br>firms that<br>complied<br>with<br>screening<br>criteria|Number of<br>firms<br>interviewed|Number of<br>firms<br>successfully<br>interviewed|Response rat<br>(%)|e<br>Proportion in<br>final survey<br>sample|
|---|---|---|---|---|---|---|---|---|
|_Source of Information_|||||||||
|Serbian Freight Forwarding|||||||||
|Association & relevant freight<br>forwarders (small and large<br>firms)|36|3%|36|28|17|17|61%|11%|
|National Bank of Serbia|||||||||
|(medium firms with 50‐250<br>employees and large firms<br>with more than 50 employees)|28|2%|28|13|11|10|77%|7%|
|National Bank of Serbia<br>(small firms with less than 50<br>employees)|1283|95%|720<br>(randomly<br>selected)|206|130|126|61%|82%|
|**Total**|**1347**|100%|**64**|**247**|**158**|**153**|**62%**|100%|

In this study we refer to medium/large firms as those with more than 50 employees (in 2011 and 2012) and small firms as those with less than 50 employees (in 2011 and 2012). The official Serbian government definition of large firms being those with 250 or more employees is not relevant for this sector since such firms are extremely rare.<sup>4</sup> Among the 153 firms in our final survey sample, only six do not report the number of employees in the National Serbian Bank data and for those we use survey data on the number of employees to identify their size category. Our final survey sample is constituted of 92% of small firms and 8% of medium/large firms.<sup>5</sup> Based on the employment data from the Bank of Serbia Table 2 shows that the typical (median) small freight forwarder in the final sample has five employees, while the typical (median) medium/large freight forwarder has 66 employees.

**Table 2. Employment Distribution Overall and within Freight Forwarder Size Categories**

||Number of<br>freight<br>forwarders|Mean number<br>of employees|Standard<br>deviation of<br>number of<br>employees|Median<br>number of<br>employees|
|---|---|---|---|---|
|Small|135|9.4|10.4|5|
|Medium/Large|12|98.6|59.9|66|
|Total|147|16.7|31.1|6|

Source: Employment data from the National Bank of Serbia.

> 4 Only two firms that qualify as international freight forwarders have more than 250 employees. One of them was used during the pilot of the survey questionnaire and is not included in the final sample.

> 5 The information on employment collected by the survey is consistent with that in the Serbian Bank data: 90.5% of firms report in the survey that they have less than 50 employees while 9.5% report that they have more than 50 employees.

6
This version: 15 Feb 2024 41 Equally, while inter-ethnic inequality has declined, polarization and segregation have increased - citizens of different ethnicities do not socialize much less than they used to. In other words, while relative inequality has sharply decreased, social exclusion has become more salient although hidden in official narratives/data on inequality.

This is also consistent with previous empirical research on the downside of economic growth. For instance, Offer’s (2007) work on the US and the UK in the 1940s and 1950s argues that well-being lagged behind affluence in these societies. He describes a range of social and personal disorders, including family breakdown, addiction, mental instability, crime, obesity, inequality, economic insecurity, and declining trust _despite_ rising material abundance, thus hinting at the social and psychological costs of growth.<sup>12</sup> Such literature is limited for developing countries.<sup>13</sup> Some notable quantitative studies include Graham and Pettinato (2006), Graham, Zhou and Zhang (2017) and Devarajan and Ianchovichina (2018). Graham et al. (2017) talk about the “paradox of progress” in China, how those with insufficient rest and leisure are significantly less satisfied. Urban and educated respondents are also more likely to report depression.<sup>14</sup> Graham and Pettinato (2006) report greater frustrations in terms of relative concerns among the upwardly mobile in Peru. Another study by Devarajan and Ianchovichina

> 12 For a more recent work on ill-being and working-class poverty in affluent societies, see Deaton and Case (2020).

> 13 One of the earliest essays is Schneider (1993) who wrote about the underside of the Chilean economic miracle -- high decade-long GDP growth and low inflation by 1990 coinciding with high inequality and poverty in post-Pinochet Chile, leading to OECD membership by 2010.

> 14 In an accompanying essay on the paradox of progress, Graham and Pinto (2018) also share similar evidence on India and the US.

41
- 11 -

increased from 10 percent in 2005 to 20 percent in 2010, as the government has been making efforts toward creating many industrial parks, such as Special Economic Zones and Export Processing Zones (Ministry of Industry and Trade, 2012). Naturally, rail transport has an important role to connect firms to the port.

Our collected firm data show the mixed impacts of railways.<sup>7</sup> As discussed above, rail transport is generally cheaper than road user costs but much less reliable than truck transportation. As a result, rail users can economize on transportation costs but may have to bear more costs on inventory. In theory, the economic order quantity model or ( _S_ , _s_ ) model suggests that a firm determines its inventory level, depending on the size of total demand, ordering and transportation costs, and opportunity costs of inventory (e.g., Arrow et al., 1951). Low rail tariffs allow firms to reduce their inventory, but unreliability in rail operations may add more inventory, therefore, increasing firms’ total costs.

Clearly, in hinterland areas, firms are still more likely to use railways ( **Figure 9** ). Except for Dar es Salaam, current rail users are located in the areas far from Dar es Salaam ( **Figure 10** ). However, these firms seem to tend to hold more inventory ( **Figure 11** ). There is weak correlation between the probability of rail use and the average inventory level ( **Figure 12** ). Note that this may or may not be attributable to the unreliability of railways, yet. Transport costs paid by firms are highly heterogeneous. Transport costs appear low even in some inland areas, such as Kigoma and Mwanza ( **Figure 13** ). They are potential beneficiaries from TRL lines. On the other hand, the average transport cost in Mbeya, which is located along another rail line, TAZARA, is high.

7 See the following sections for detailed discussion.
# **4. Setting an international poverty line**

Using our new compilation of national poverty lines we can now return to the basic idea behind the “$1 a day” poverty line, which was chosen to be representative of the poverty lines found amongst poor countries. We explore a number of ways one might set a new international poverty line consistent with this idea.

The median poverty line across the full sample (n=75) of $60.81 per month is equivalent to almost exactly $2.00 per day; the mean is higher at about $2.90 per day. However, the presence of the marked economic gradient in Figure 2 implies that the mean will be well above the poverty lines found amongst the poorest countries.

The poverty line for Malawi—the lowest PCE per capita in the sample—is $26.11 per month. However, like all specific data points in a sample, this is susceptibly to measurement error and the country-specific error term could be large. It is notable that, although the relationship in Figure 2 is quite flat at low PCE, there is still a sizable variance. No doubt, idiosyncratic differences in the data and methods used for setting national poverty lines are playing a role; there are measurement errors and methodological differences between countries in how poverty lines are constructed, which can be interpreted as noise in the mapping from the underlying welfare space into the income space. Some averaging is clearly called for, as is normal in economic measurement. A better method is to use the expected value of the poverty line in the poorest country, based on how the poverty lines vary with mean consumption. Table 1 gives a number of parametric specifications (including the forms used by RDV and Chen and Ravallion, 2001), and the implied estimates of the poverty line for the poorest country.

Three concerns can be raised about the estimates in Table 1. First, the results may be driven by the specific parametric form, and there are signs of this in Table 1, notably in the fact that the semi-log model (<sup>_Z_</sup> _i_ regressed on a quadratic function of ln _Ci_ ) gives a much higher predicted _Z_ for the poorest country that. But this is deceptive, since the turning point of the quadratic function is above the lowest consumption; this is clearly an artifact of the parametric form, since there is no sign of a negatively sloped segment at low PCE in Figure 2. Ignoring this specification, the results in Table 1 suggest that a poverty line of around $1 a day is defensible if one measures poverty in the world by the standards of the poorest country in the world.

Second, a parametric model need not estimate well at all levels of consumption. For example, the linear regression of<sup>_Z_</sup> _i_ on _Ci_ has a very good overall fit, with a correlation of 12
<!-- Start of picture text --> LG indepence LG legislative election<br>LG executive election LG direct democracy<br>LG expenditure decentralization LG vertical gap<br>LG unconditional transfers LG taxation autonomy<br>LG borrowing<br><!-- End of picture text --> Figure 1: Decentralization variables in 2005 - World Map Note Source: Ivanyna and Shah (2010). Each country is colored according to value of corresponding decentralization variable (see Table ?? for definitions). The lighter is the color of the country the smaller value of decentralization variable in this country is. Shades of the color (4 in each subfigure) correspond to 0-25th, 25-50th, 50-75th, 75-100th percentiles of the world’s distribution.

12
the school or teachers to try to alter the results.

Once the test is completed, students are asked about their personal characteristics (gender, age, educational history) and a set of questions related to their family background. In particular, the questionnaire asks the maximum level of education that each parent attained, the possession of more than a dozen assets, and access to basic infrastructure services. Of these, I will base most of the analysis to a question on the number of books at home as a proxy of family socioeconomic status. Among the family background questions, this is the one with the best response rate. In addition, this variable is highly correlated with parental income and education, and reading skills in various international surveys (Hanushek & Woessmann 2008, Ammermueller & Pischke 2009).

While parental education is generally perceived to be a reasonable measure of overall family background, I chose to exclude it from the main analysis because more than a third of the students in the survey did not report it. This is not unusual in these sorts of surveys; the proportion of missing values is similar to that in PIRLS (Progress in International Reading Literacy Study, for developed countries plus others), and in SERCE (Segundo Estudio Regional Comparativo y Explicativo, for seventeen Latin American countries). Still, the pupils who have missing values in parental education tend to come from relative less well-off families (lower number of books at home, durable assets, and services) and to perform worse than those who has completed information (in terms of test scores and grade repetition). For robustness, I will replicate the analysis using parental education and an index of assets.

Of the total number of schools surveyed, for the purpose of estimation I exclude schools with only one class per school year. This exclusion allow me to identify the peer-group effects in a convincing way.<sup>7</sup> The resulting sample includes over 7,000 schools (half of the schools in the country), 20,000 classes and almost 400,000 students distributed across the country.

Table 2 presents basic summary statistics for all students, according to the socioeconomic background of the students. The classification is based on the reported number of books at home, divided in four groups: lower (ten or less books), lower middle (between 11 and 50 books), upper middle (between 51 and 100 books) and upper (more than 100 books). More than

> 7See section 4 for a detailed explanation of the estimation and identification strategy employed.

7
similar coefficients to our baseline, the AIA policy reduced cloud use for treated firms by between 15-20 percentage points (depending upon estimation period, 2007-2013 or 20072019). Since around 43%-46% of firms were treated by the AIA policy, this implies an aggregate reduction in cloud use of 7-9 percentage points. Over our sample period aggregate cloud use has been increasing roughly 6 percentage points per year (whether measured over 2007-2013 or 2007-2019). Thus, the AIA policy appears to have slowed down cloud diffusion by 1 to 1.2 years.

Again we note the robustness of these results if we define firms’ treatment status using a 2-year average of investment based on the years 2006/07 (regression 2), or a 3-year average based on the years 2005/06/07 (regression 4) (Table A4), or, as we show in Table A5, if we measure the effects of cloud for the years 2007 and 2015 (regression 1), 2017 (regression 2) or 2019 (regression 3). Across these regressions the estimated effect of the AIA on cloud adoption was between -11 and -17 percentage points.

# **5.2 Types of cloud and capital investment and the measurement of the AIA**

The detailed nature of the UK data allows us to further explore how the AIA policy is linked to different types of investment and different types of cloud services. Within Table 4 Panel A, we begin by separating the aggregate investment (shown in the previous Table 3) into hardware (column 1), software (column 2), plant and machinery (column 3), vehicles (column 4), and land and building investment (column 5). Similarly, in Panel B we study the effect of the AIA on the different types of cloud included in the E-Commerce survey. These are cloud databases (column 6), storage (column 7), customer relations management (CRM) software (column 8), office software (column 9) and email (column 10).<sup>34</sup> We find that the AIA incentivized firms to invest only in the types of capital that were upon request.

> 34We report results for finance software and own software in Table A6, along with Eurostat (2018) definitions of low, medium and high-tech types of cloud technologies.

22
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 22 To address the first and second study objectives, we conducted intent-to-treat analyses for all study groups and outcomes of interest. In our main analysis, we estimated OLS regression models including mutually exclusive indicators for the treatment arms (T1 through T4) as independent variables in the same regression, estimating a separate model for each of the outcome variables of interest. We included control variables to improve power and account for baseline differences between treatment arms; the variables were child age and sex, an indicator for whether the mother was the primary caregiver, household size, a household wealth index, primary caregiver depressive symptoms, and community fixed effects. When baseline values of the outcome variable were available, we included them in the regression to maximize statistical power. Cluster-robust standard errors were used to account for the geographic clustering in the random assignment of treatments.

To address the third objective, we estimated the interaction of treatment group assignment and caregiver literacy. We also included an un-interacted indicator for being illiterate, to capture the outcome difference between literate and illiterate caregivers in the control group. All analyses were conducted using Stata 14.2 (StataCorp, L. P., 2016).

# **Results**

# **Descriptive Statistics**

The mean baseline age of children in our follow-up sample was 54.77 months; 52% of children were male; 12% were stunted; and 84% of primary caregivers in the sample are the mothers of the sample children (Table 1). Most children in our sample did not have access to age-appropriate reading materials prior to our study: Only 13% of homes had any children’s books (Table 1). At baseline, no household in our sample owned a children’s storybook in Luo,
20 Table 5: DESCRIPTION OF POLICY EXPERIMENTS

|Base Run|(BR)|Yearly<br>Growth Rate|
|---|---|---|
||**A**<br>H|Money supply<br>102|
||**A**<br>LG|Government employment<br>5X|
||**A**<br>**A**<br>W,WG|Wages (Govt., modern labor)<br>102|
||**A**<br>GE|Government Expenditure (recurrent)<br>52|
||GI|Government Investment<br>102|
||ER|Rate of devaluation<br>52|
||A*<br>Pm|World prices of imports<br>0t|
||i|Foreign interest rate<br>82|
|External|Shock:|Foreign Borrowing, No Adjustment (El)|
||(El):|Same as (BR) but|
|||A*~~<br>PM =10Z; i -<br>16Z; d-<br>152<br>fort-<br>2,...<br>,5<br>(t-6,7: same as BR)|

Adiustment: External Borrowing Reduction (E2) (E2): Same as (El) but ALG = 5.62 a/ t - 2, ... , 7 (Exchange rate endogenous) Adjustment with Cut in Government Expenditures (E3) (E3): Same as (E2) but **A A ~~A** WG = GE - GI = 0 t - 2, ... , 5 (t=6,7: same as BR) Adiustment with Wage Freeze and Credit Squeeze (E4) (EM): Same as (E3) but H - 52; W - 02 t=2,...,5 (t-6,7: same as BR) Adjustment with Targeted Expenditure Cuts and Targeted Subsidies (ES)

- Public Works (WGLG constant); W-0; t-2,...,5 (E5) - (El) + 8I - 8E - 0 t-2,...,5 (ES) -(El) + Raise import tariffs by 5021 t - 2,...,5 Subsidy on sales of agricultural products of 202 t-2,... ,5 For t-6,7 variables have same values as in BR.

a/ Yearly borrowing expressed as a percentage of initial stock of foreign debt.