rafmacalaba/datause-encoder-data huggingface dataset:
associated with the dependence on agricultural export growth as well as political events. For example, in 1956 a new constitution was drawn up and in 1963 Guatemala was preparing to enter into the Central American Common Market (MCCA).<sup>5</sup> At first sight, Figure 1 suggests that the civil war’s guerilla activities — starting around 1960 — appeared to have an impact only on short-run growth. However, the later growth accounting exercise suggests that, indeed, the trend growth of total factor productivity (TFP) was negatively affected by the civil strife from the beginning.

**Figure 1. Guatemala: Economic Growth, Social Conflict and Politics, 1951-2003** (growth rates in percent)

<!-- Start of picture text --> 10<br>1963: Central American Common Market<br>(MCCA) and coup d‘état<br>8<br>1956: New constitution Since 1977: Explosion of the internal<br>conflict into a full-scale civil war<br>6<br> = Presidential elections<br>4<br>1967: Counterinsurgency<br>guerilla operations<br>2<br>1975: Second oil crisis<br>1954: Military-backed coup d‘état<br>1985: Transition to civilian rule<br>0<br>1964-1965: Urban<br>1957: Murder of C. Armas guerilla activities<br>-2<br>1962: Student revolts “Jornadas<br>1960: Beginning of the civil strife de Marzo y Abril” 1982: Peak of the internal military<br>conflict under General R. Montt<br>-4<br>50 55 60 65 70 75 80 85 90 95 00<br><!-- End of picture text --> _Source:_ Author’s elaboration based on data from Banco de Guatemala. Data for 2002 (2003) is GDP growth preliminary (estimated). Historical events are taken from Luján (2000).

_External shocks and the civil war, 1975-1985._ A second period starts shortly after the deterioration of the terms of trade and the international oil crisis. In 1976 a major earthquake affected Guatemala. After 1977, social tension culminated in a full-scale civil war that reached genocidal proportions in the early 1980s. Consequently, growth declined dramatically. Apart from causing immense human sorrow, these events destroyed human life and physical capital. They also imposed high costs for long-run growth.

> 5 An excellent review of the rise and fall of the Central American integration process for 19501999 can be found in de La Ossa (2000).

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## 3.3.3. **Should payments be proportional to ATP?**

In principle, then, requiring that payment be proportional to ATP has the attraction of providing an answer to the question how progressive payments ought to be on pre-payment income, or equivalently how much narrower or wider income inequalities ought to be post-payment than pre-payment. In practice, however, as has been seen, there is the problem that how one defines ATP-i.e., how one defines the "deductions" _D(x)-_ appears to have an important influence on one's conclusions concerning the fairness of the distribution of health care payments and changes in equity.

Quite aside from this issue, there is the issue of whether policymakers everywhere would endorse the value judgment that health care payments ought to be proportional to ATP. Although the WHO claims that this value judgment seems to be the one that receives majority support in an opinion survey from a convenience sample (Murray et al. 2001), it is obvious that one might argue that-in much the same way as those with zero ATP are _defacto_ exempt from contributing-ceilings or maximum contributions could be set at a certain level of ATP above which payments are not to required to rise any further. Irrespective of the-inevitably arbitrary-choice of a target distribution of payments as a finction of ATP, the framework presented in this section is helpful to unravel the various factors that have an influence on the difference between the actual distribution and desired distribution.

# **4. Vertical vs. horizontal inequity**

So far in the paper the focus has been on vertical issues-how people with different prepayment incomes or different abilities ought to pay for their health care relative to their income. In the case where payments are required to be proportional to ATP, measurement proceeds by searching for departure from proportionality in the vertical relationship between payments and ability to pay (as captured by **_TK),_** or by comparing inequality in income after deductions and before health care payments with inequality in income after deductions _and_ health care payments (as captured by _?r_ **_S_** _)._ In the case where the requirement of proportionality to ATP is not assumed, measurement proceeds by searching for departure from proportionality in the vertical relationship between payments and prepayment income (as captured by _<K ),_ or by comparing inequality in pre-payment income with inequality in post-payment income (as captured by **_;J,RS)._** In each case, the focus is on vertical differences, and, in the case of the ATP approach, on _vertical equity._ _15_
and establishment surveys, which are indispensable instruments in a modern NSS’s toolbox to monitor the latest trends in the economy. Second, the SCI focuses on poorer countries, which limits its relevance and application in an increasingly globalized world. This limitation with country coverage becomes even more noticeable in these modern times, when all NSSs face similar challenges to evolve to adapt to new data sources, data technology, and data partners. A standard tool to measure country statistical capacity in a consistent and comparable manner would be useful for richer and poorer countries alike. Finally, the theoretical principles of the SCI are not clearly formulated. For example, concerns were raised over the lack of a solid foundation behind the index’s aggregation method as well as its practice of placing equal weights on each pillar and indicator (Ngaruko, 2008).<sup>5</sup> In this paper, we aim to improve the current SCI to better suit the changing global data landscape. In particular, we aim to clearly lay out the conceptual foundation behind statistical capacity indices. We identify five key pillars regarding data usage and production in any modern economies—data use, data services, data products, data sources, and data infrastructure—which offer a new way to examine a country’s statistical performance.<sup>6</sup> We subsequently construct an updated set of Statistical Performance Indicators to replace the SCI based on conceptual and practical considerations. The new index constructed from these indicators is hereafter referred to as the Statistical Performance Index (SPI). On the empirical front, we collect new data for the past several years, expand the number of indicators in the SCI by almost twice, and we extend the

> 5 These concerns have practical relevance. For example, the scores of Cameroon and Sudan rose from 55.6 and 51.1 in 2015 to 68.9 and 63.3 in 2016, respectively, indicating a 24-percent improvement of statistical capacity over one year. This stands in sharp contrast with the common knowledge that a country’s statistical capacity often improves incrementally.

> 6 While measuring a country’s statistical capacity is our ultimate goal, this task is difficult, if not impossible to implement at scale for all countries, given the typically unobserved inherent characteristics with an NSS. It is, however, relatively more straightforward to measure a country’s statistical performance through objective and comparable indicators. This challenge is highlighted by a large number of indicators with missing data that we discuss later. Also see Cameron et al. (2021) for a related discussion.

4
# **Table 4: Results with Economic and Social Expenditures (panel OLS and SURE with 3-year averages)**

## **_Dependent variable: Growth rate of GDP per capita_**

||**OLS**|**SURE**|
|---|---|---|
|Initial human capital|0.028<br>(1.764)*|0.022<br>(1.532)|
|Tax revenue|-0.230|-0.165|
|(% of GDP)|(-0.972)|(-1.084)|
|Other revenue<br>(% of GDP)|-0.299<br>(-2.462)**|-0.284<br>(-3.449)***|
|Economic expenditure (-1)|1.083|0.988|
|(% of GDP)|(2.613)**|(3.161)***|
|Social expenditure (-1)|0.194|0.063|
|(% of GDP)|(0.679)|(0.314)|
|Budget surplus<br>(% of GDP)|0.495<br>(3.135)***|0.462<br>(3.707)***|
|Inflation - consumer price index|-0.002<br>(-2.711)**|-0.002<br>(-3.642)***|
|Growth rate of GDP per capita (-1)|0.05<br>(0.192)|0.007<br>(0.045)|
|No. of observations<br>Adjusted R2|35<br>0.279|35<br>0.525|

Note: The estimation method is the ordinary least squares for panel data in the first column and the cross-section seeminly unrelated regression for panel data in the second column. 3-year averages are used. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

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# **4. Econometric Approach**

As a baseline model for the calculations of the pass-through effects, we consider the following simple autoregressive model that explicitly focuses on the short-run correlations between international and domestic food prices: where is the annual percentage change of the domestic food consumer price index, represents lagged annual percentage changes of the domestic food prices, and helps account for domestic factors and expectations, and is the annual percentage change of the World Bank‟s international food price index, which is calculated from food prices measured in current US$. The pass-through from an international food price shock to inflation denominated as _,_ can be obtained by inverting the equation as follows (de Gregorio _et al_ ., 2007): The logic behind equation (4) is to discount for the effects of domestic inflation, including inertia or expectations. For example, in the case of strong domestic factors or expectations driving inflation the role of world food price transmission would be small. On the other hand, if there are insignificant domestic factors then the pass-through can be measured by simply summing up the coefficients.

In addition to world food prices, exchange rate shocks are important in determining inflation. If the domestic currency depreciates (appreciates), international food price increases will have a stronger (weaker) pass-through effect. This is a significant consideration, because some of the inflationary effects could be due to currency movements, rather than changes in world food prices. We also take advantage of findings on food price transmission (Meyer and Cramon-Taubadel 2004; Peltzman, 2000; Vavra and Goodwin, 2005), and consider increases or decreases in commodity prices as separate variables. This allows us to determine whether the transmission is asymmetric and the extent of asymmetry. Therefore, the baseline model in equation (3) is transformed into a threshold regression, which controls for

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– outright discriminate against mature workers.<sup>102</sup> A survey in EU countries found that a majority of respondents thought that being over 55 years of age put job applicants at a disadvantage.<sup>103</sup> An experimental study in Sweden showed significant and independent negative correlation between being older and the chances of being called for an interview, and callback rates began to decline for workers already in their early 40s.<sup>104</sup> A similar approach for the United States provided robust evidence of age discrimination against older women, but not against older men.<sup>105</sup> In Deloitte’s 2018 Global Human Capital Trends report, 20 percent of business leaders viewed older workers as a competitive disadvantage, and in countries such as the Netherlands, Russia, and Singapore, this percentage was far higher; some 15 percent also felt the older employees to be blocking rising (young) talent.<sup>106</sup>

**Firms—particularly small and microenterprises— generally invest little in skills development for their workforces, despite the importance of skills for firm productivity.** Individuals’ low investment in their skills development is thus mirrored in underinvestment by employers. The World Bank’s enterprise surveys show that only one-third of firms offer training to their workforce, and those that do, invest in a minority of their workers (Figure 8a and b). Across country income groups, small and micro-firms are the least likely to offer training, and firms in L/MICs are less likely to offer training compared to firms in HICs, for each size class of firms.

**Figure 8: Access to training through employers is limited, especially in L/MICs**

- a. Average share of firms providing training and b. Average share of firms providing training, by share of workers receiving training (in firms where firm size and income group offered), by income group

<!-- Start of picture text --> 60 80<br>70<br>50 70<br>42 41 43 60<br>60<br>40 35 34 51<br>50 44 43<br>30 25<br>37<br>40 34<br>20 27<br>30<br>20<br>10 20<br>0 10<br>LIC MIC HIC<br>0<br>LIC MIC HIC<br>% firms providing training to workers<br>% workers receciving training Small Medium Large<br>% firms providing training<br>% firms, % of workers<br><!-- End of picture text --> Source: Estimates based on World Bank Enterprise Surveys.

**Several market failures may help explain firms’ underinvestment in skills beyond formal education.**<sup>107</sup> Firms may underinvest because training to some extent is a public good, and they risk

102 OECD 2019b. 103 European Commission 2015.

104 Carlsson and Eriksson 2019.

105 Neumark et al. 2015. 106 Deloitte 2018.

> 107 See the framework presented in Almeida et al. 2012.

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to one if an agreement exists. As a result, we de...ne visa costs as Policy variables, fj and ej, only matter for origin countries that do not have free mobility agreements with country j (when bij = 0). The partial derivatives of v(:) with respect to both of the arguments are negative, vf0<sup><0; v</sup> e0<sup><0,v</sup> ff00<sup>; v</sup> ee00<sup>?0and</sup> 00 vef<sup>(:)>0:theprobabilitythatanindividualreliesonfamilyreunionprogramde-</sup> creases (resp. increases) when economic program becomes more (resp. less) generous or vice versa.

The net e¤ect of human capital level on visa costs is given by The e¤ect of human capital on visa costs also depend on the size of the diaspora. When the diaspora size is bigger, the probability that a migrant relies on an economic migration program declines and the probability he relies on family reunion programs increases. Hence, we have 00 since v<sup>ispositive.Withthesede...nitionsinplace,wecanwrite(1)as</sup> ef<sup>(:)</sup>

# 3.1 Self-Selection

This simple model and the underlying assumptions allow us to analyze major characteristics of diasporas, especially how the existing diaspora in‡uences the size of migrant ‡ows, their composition in terms of human capital and concentration across di¤erent destinations. Before proceeding to these questions, we ...rst analyze how changes in human capital level in‡uence the migration decision of the individual and the overall migration level. From equation (4), we have
in startups in Africa and their importance in startups’ growth in Africa. Fintech is the fastest growing startup industry in Africa. According to Disrupt Africa (2022), Fintech is the power behind the region’s startup revolution. Only in 2022, it raised 43% of the total funding. The second sector in terms of funding was E-commerce and retail tech, with 17% of the region’s total. When we analyze the total number of companies in the African Entrepreneurial Ecosystem, almost 20% of the companies are in Fintech, while 13% are E-commerce firms (Briter Bridges, 2022). The rationale for selecting the IT sector has to do with its characteristics, as it includes technologies like software, IT infrastructure and services, and data storage and management, which are associated with core activities of the digital sector.

For the purpose of this paper, we define startups as digital businesses belonging to these sectors (Fintech, E-Commerce, or IT), younger than ten years of age. The age definition is arbitrary, but follows the convention observed in many regulatory acts aiming to promote innovative and high-growth startups. For example, Tunisia’s Start-Up Act, approved by the parliament in 2018, defines up to eight years as the threshold. This law has been used as a reference by many other African countries (e.g., Senegal). This threshold is used as a reference when comparing the level of disruptiveness between digital startups and older firms.<sup>5</sup> One limitation of the Crunchbase data is that it has only a cross-section. Although it includes very detailed information about the companies, such as the year when the firm was founded, similar firms, the amounts and different rounds of funding, and characteristics of the founders and investors, among others; it is a cross-section and takes the most recent values or attributes of the firm. This does not allow us to analyze the evolution of these firms over time. Therefore, any reference to the life cycle in this paper refers to the comparison of firms across different age groups.

We complement the analysis with data from Pitchbook (https://pitchbook.com/), another VC/PE private repository that provides detailed characteristics and financial and financing history information on more than 3.6 million companies worldwide. Pitchbook focuses on the entire of investments and collects information on life-cycle mostly investment-ready firms.

Figure 1 shows the spatial distribution of the African digital firms analyzed in this paper. As shown, South Africa, Nigeria, and the Arab Republic of Egypt stand out as digital hubs, where most of the firms in our sample are located. Appendix Figure B1 shows the spatial concentration of African digital firms in the main city of each country. In most countries, a large share of the firms is located in the capital city. From the socalled **_Big Four_** countries (Egypt, Nigeria, Kenya, and South Africa), where most of the digital firms operate, it is only in South Africa where the main city concentrates less than 50% of all digital firms operating in the market.

> 5We also test the robustness of our results with a threshold of five years of age for startups.

6
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The survey is representative at the national level, but in this paper we only use the data pertaining to cotton producing regions: the Central, Eastern, Southern and Lusaka provinces. CSO gathers information on land tenure, land usage (allocation), output in physical units, and household characteristics such as demographic composition, age of head, and housing infrastructure. There are also limited data on farm assets and inputs.

Table 1 provides an overview of the relevant sample sizes, by year and by province. Around 600-700 households are interviewed in the Central province, around 1,200, in the Eastern province, around 800 in the Southern province, and around 200 in Lusaka. Table 2, which reports the fraction of farmers involved in cotton production, confirms that these are the major cotton producing areas. Significant percentages of cotton farmers are only observed in the Central, the Eastern, and the Southern provinces. Cotton participation is largest in the Eastern province (39 percent in 2002, for instance), then in the Central province (20 percent in 2002), and finally in the Southern province (12.6 percent). There are some, but not many, cotton producers in the Lusaka region, too. In the remaining provinces, the percentage of households involved in cotton is virtually zero.

Table 2 reveals interesting dynamic patterns that we explore below. During the introduction phase, which spans the years 1997 and 1998, cotton participation is relatively stable in all provinces (although a declining pattern may be discernible). The failure phase, which spans the 1999-2000 period, shows lower participation rates. This is particularly evident in 2000: in the Central province, for instance, cotton participation drops from 22.6 percent in 1998 to 10.3 percent. Similarly, participation declines from 32.7 to 20.4 percent in the Eastern province, from 10.7 percent to 4.3 percent in the Southern provinces, and from 3.3 percent to 0.4 percent in Lusaka. The success phase correlated with entry into cotton: the percentage of cotton growers increases significantly in all provinces.

In Table 3, we report data on the fraction of land allocated to cotton. In 2002, for instance, an average farmer in the Eastern province allocated around 17.2 percent of his land to cotton; in the Central province, the fraction is 10.5 percent. Instead, cotton adoption is less widespread in the Southern province, where only an average of 5.6 percent on land is allocated to cotton. The dynamics of cotton adoption are also revealed in Table 3. The 9
opportunities in metropolitan Haiti. People living in the metropolitan area have access to more jobs, and the self-employed have access to domestically produced and imported goods that they can resell in the metropolitan market or to other urban and rural markets. This is significantly different from conditions in other areas in Haiti, where very few goods originate (see Section 5 for more on income inequality).

Rural households are better off than urban households in the low end of the income distribution. This is a major difference to other countries in the region in the same location in the income distribution where rural dwellers always fare worse in terms of per-capita household income than urban households. The PCHI of rural households is higher than that of urban households for the first 4 deciles in Haiti (Figure 4.1). From the fifth quintile it changes and the average PCHI becomes higher in urban areas than rural areas. In the top deciles (9th and 10th) the PCHI in urban areas is 34 and 66 percent higher than in rural areas respectively.

|Table 4.2<br>|: Average Income<br>|Per Capita byDecile,2001(gourdes)<br><br>|
|---|---|---|
|Decile|Metropolitan|Urban<br>Rural|
|1|910|166<br>199|
|2|1,435|473<br>524|
|3|3,074|862<br>879|
|4|4,360|1,301<br>1,306|
|5|6,177|1,888<br>1,787|
|6|8,690|2,638<br>2,352|
|7|12,312|3,570<br>3,100|
|8|16,915|5,112<br>4,177|
|9|25,624|8,469<br>6,196|
|10|73,430|28,522<br>17,177|

_Source:_ Own calculations based on HLCS 2001.

Self-consumption is part of the explanation for the poorest being better off in rural than in urban areas, as shown in Figure 4.2. Given the difference between PCHI in urban and rural areas for the poorest in the population, it makes one wonder why people move to the urban areas except of course to gain access to public services. (See Section 7 for more on public service provision.) 18