rafmacalaba/datause-encoder-data huggingface dataset:
# **Table Al (cntinued)**

|Conmtry,<br>econoWy or<br>territory|Total population, mid-1965|Total fortility, 195-90|Life expectancy, 1M5-9|
|---|---|---|---|
|Surirnm|Official est., Central Sureau of<br>Civil Affairs|**laced**on officiaL CM, Central<br>Bureau of Civit Affairs|aosd on official CDR, Central<br>Bureau of Civil Affairs|
|Swaziland<br>S-eden|U.N. 1990 revision<br>UNPVSh 4/87(official<br>est.)|U.N. 1990 revision<br>acsedon CER, UNPVSR 10/89<br>(officiat et.)|U.N. 1990 revision<br>U.N. 1990 revision|
|Switzerland|UNPYS 4/87 (official ect.)|**Based**on CSR, UNPVSR 10/89<br>(official est.)|U.N. 1990 revision|
|Syrian ArabRep.<br>Taiwan, China|U.N. 1990 revision<br>**_Base_**on offieial<br>est.,Stat.<br>Data**ook**1967|U.N. 1990 revision<br>Baeed onCR,Taiwan Stat.<br>**DOta**<br>look 1957|U.N. 1990 revision<br>**Based on**CDR**Taiwn Stat. Data**<br>Book 1967|
|Tan4znia|**Bank**projection from 1978 and<br>19"<br>censs|Sank ast.|**Bank**est.|
|Thailand|Official**est.,**<br>National Econ.<br>**and So. Devlopment**Board.1985|U.N. 190 revision<br>|Based on government projection,<br>1965|
|Togo<br>Toketou Islands|<br>**Bank**projection<br>from 1961 census<br>BSed on UNPVSR 4/90|OHS<br>ON<br>7|U.N. 1990 revision|
|Tongs|Bank projection from 1984<br>|USIOC 1968|Sank**est.**|
||mini -csnsu4|||
|Trinidd<br>nd<br>Tobao|U.N. 1990 revision|lasedon OHS1967|U.N. 1990 revisian|
|Tunisca|UNPSMt 1/88 (official<br>cst.)|Based on OHS 1966|U.N. 1990 revision|
|Turkey|Official est.|Based on Population and Health<br>Survey,<br>1968|U.N. 1990 revision|
|Turks**and**Caicos<br>**Istands**|UNPVS4 1/87 (Official est.)|||
|Tuvalu|Based on UNPVSR 4/90|||
|Ugand|Official<br>est.|OHS 1988/69|lankest.|
|United Arab<br>Emirates|U.N. 1990 revision|U.N. 1990 revision|U.N. 1990 revision|
|United Kingdom|Eurostat 1989|Eurostat 1989|U.N. 1990 revision|
|United States of<br>America|UNPVSR 7/86 (official est.)|lased on CBR (official est.)|U.N. 190 revision|
|Uruguay|Official**est.**of 198S census|**lasee**on official est. from 198S<br>cenus|**Based**on 1965 census|
|U.S.S.R.<br>Vanuatu|Sank projection from 1989 census<br>Official set.,**Stat.**Office,<br>1988|lasedonNonnier 1988<br>USBOC 1988|U.N. 1990 revision<br>Bank**est.**|
|Venezuela|UNPVSR 1/87 (official est.)|U.N. 1990 revision|U.N. 1990 revision|
|VietaM|U.N. 1990 revision|U.N. 1990 revision|**Eased**on Sank report|
|Virgin Islands<br>(U.S.)|USIOC 1905|Basedon CBR from USIOC 1987|Based on CDR, UHPVSR 10/89<br>(officiat est.)|
|Wake Island|**Bsed**on UNPVSR 4/90|||
|<br>Wallis**and**|<br>**Based**on UNPVSR 4/90|||
|Futuna||||
|West Bank|**lankest.**|**Based**onCURfrom West Bank Data<br>|Sank est.|
|||**Base**Project 1987||
|Western Sahara|Based**on**UWPVSR4/90|||
(Allen et al., 2005), but a firm-level survey suggests that bank financing has spurred firm growth (Ayyagari et al., 2010).<sup>11</sup> Evidence at the provincial level indicates that capital market depth is positively and significantly associated with provincial growth, though bank depth is usually not (Hasan et al., 2009). Our findings complement the existing papers and provide evidence on the positive association between the use of capital markets and firm performance.

Third, a separate strand of the literature studies the Gibrat’s law, which states that firm size and growth are independent and that the FSD is stable over time and approximately log-normal. This view has been challenged over time. Although the growth of large firms seems independent of their size, including smaller firms in the analysis typically introduces a negative relation between growth and firm size (Lotti and Santarelli, 2004, Coad, 2009). Moreover, the distribution of young firms is skewed to the right (most of the mass is on small firms) and the skewness tends to diminish monotonically as firms age and become larger (Cabral and Mata, 2003, Angelini and Generale, 2008). Our findings suggest that even among the publicly listed firms, which consist of the largest firms within a country, there is some heterogeneity: firms that use capital market financing are larger to begin with and grow faster than non-users. In fact, our results indicate that there is no convergence in firm size; if anything, the distributions seem to diverge. The results seem consistent with the rapid growth within large plants in India (Bollard et al., 2013). Moreover, a misallocation of capital in China and India (Hsieh and Klenow, 2009, 2012) might have kept large, highly productive firms artificially small, which might explain why they are the ones that grow the most when financing becomes available.

Fourth, a related strand of the literature studies financial constraints by analyzing whether measures of financial performance affect firm investment in fixed capital, inventories, and

> 11 A number of others papers also find evidence of a positive relation between financial development and economic growth in China. See, for example, Liang (2005), Chen (2006), Zhang et al. (2007), Guariglia and Poncet (2008), Cheng and Degryse (2010), and Zhang et al. (2012).

6
32 III.1 From Belaunde to Garcia<sup>22</sup> In the 1950s and 1960s Peru experienced significant<sup>growth of</sup> real per capita income and moderate inflation.<sup>Table 3 shows that the</sup> problems of declining per capita income and<sup>high inflation date from the</sup> second half of the 1970s.

Table 3 Growth and Inflation (percent per year)

||1950-60|1960-7|0 1970-75|1975-80|1980-85|
|---|---|---|---|---|---|
|Growth Per Capita|2.8|2.6|1.6|-1.0|-4.2|
|Inflation|8.0|9.3|12.6|50.0|102.1|

Source: Kuczynski (1977), Central Bank and<sup>Ministry of Finance</sup> In the past 25 years, three major stop-go<sup>phases can be</sup> discerned. The first was the Belaunde<sup>expansion which crashed in 1967-68.</sup> The growth program faltered because it ran<sup>into external constraints.</sup> Budget correction and real depreciation were<sup>undertaken and prepared the</sup> ground for the economic expansion of the military<sup>government in the early</sup> 1970s. The extremely favorable world environment<sup>permitted a massive</sup> expansion until 1974, with an average growth<sup>rate of per capita income of</sup> 3.7 percent per year in the 1969-74 period. Figure<sup>3 shows the rapid</sup> increase in real income during this period.

2 2 See R. Thorp "Trends and Cycles in the Peruvian Economy." in P.Bardhan et al (eds.) International Trade. Investment. Macro<sup>Policies and History. North Holla</sup> 1987, R.Thorp and G.Bertram Peru 1890-1977,<sup>Columbia University Press, 1978; an</sup> P.P. Kuczynski Peruvian Democracy under Economic Stress, Princeton University P 1977 for history and extensive references.
**Table 12. Dynamic Short-Run Sector Growth Model for Zimbabwe**

|Explanatory variable|Equation||Explanatory|variable|Equation||
|---|---|---|---|---|---|---|
||Industry|Agriculture|||Industry|Agriculture|
|Industry,-,<br>Agriculturet-.|0.009<br>(0.043)<br>0.023|0.027<br>(0.073)<br>0.372|**Clt**l<br>Constant||-0.105<br>(-0.827)<br>-2.084|1.049<br>(4.459)<br>21.408|
|Servicest|(0.217)<br>0.212|(1.943)<br>-0.355|Trend||(-0.798)<br>-0.007|_(4.445)_<br>0.042|
|Services,.-|(0.885)<br>0.179<br>(0.774)|(-0.802)<br>1.152<br>(2.706)|||(-1.436)|(4.422)|
|_Diagnostics_|||||||
|Single equation analysis|||F.r(1,23)|x<sup>**2**</sup>**nor**<br>**0 **<sup>**1**</sup>|**m**<br><sup>**ity(2)**</sup><br>F17|h(1, 22)|
|Industry||0.083|0.033|0.369|0.0|48|
|Agriculture||0.072|0.748|2.210|5.7|75*|
|Vector analysis||Fr(4, 42)=|0.279 X2no|,,Gty(4)<br>=5.|230||

* Rejection at a 5 percent level of significance.

_Note:_ Values in parentheses are t-statistics. Subscripts denotes the time period, i.e. t: current period, t-1: lagged one period, CI denotes the restricted co-integrating relation. _Source:_ Authors' calculations.

**Table 13. Dynamic Short-Run Sector Growth Model for Ghana**

|Explanatory variable|Equation||Explanatory variable|Equation||
|---|---|---|---|---|---|
||Manufactu|ring Mining||Manufacturing|Mining|
|Manufacturingt1.|0.947|1.435|Construction,-,|-0.341|-0.819|
||(4.818)|(3.193)||(-2.145)|(-2.254)|
|Agriculturet|0.151|-0.675|Gas and watert|0.101|0.172|
||(0.556)|(-1.088)||(1.054)|(0.786)|
|Agriculture,-|0.708|0.536|Gas and watert.,|0.037|-0.246|
||(3.032)|(1.004)||(0.527)|(-1.523)|
|Services,|-0.663|-1.347|**CI_11-**|-1.374|-0.337|
||(-2.551)|(-2.266)||(-7.461)|(-0.801)|
|Servicest-.|0.010|-1.142|CI_21|0.020|0.425|
||(0.032)|(-1.636)||(0.765)|(6.966)|
|Miningt<br>1 <sup>t</sup>|0.148|0.746|Constant|21.245|7.627|
||(1.682)|(3.707)||(7.617)|(1.196)|
|Constructiont|0.457|0.750|Trend|0.008|-0.037|
||(3.671)|(2.631)||(1.974)|(-3.880)|
|_Diagnostics_||||||
|Single equation analysis|a||F.(1, 15)<br>X nor.a|Gity(2) Fa.ch( 1,|4)|
|Manufacturing|0.081||0.275<br>3.935|0.085||
|Mining|0.186||0.135<br>1.639<br>|0.711||
|Vector analysis|Far(4, 26)|0.091,**X**<sup>**2**</sup>**.0n,,**|**witi(4)**= 5.230|||

_Note:_ Values in parentheses are t-statistics. Subscripts denotes the time period, i.e. t: current period, t- 1: lagged one period, CI denotes the restricted co-integrating relation. _Source:_ Authors' calculations.

18
relative political stability as a peaceful democratic transition took place from Jean-Bertrand Aristide to René Préval.

Although schooling levels in Haiti today remain low, school attainment improved substantially during the last half-century. Among those born 1950-1954, only 35 percent completed even one year of education and just 10 percent finished primary school. Completed schooling levels are much higher among those in later cohorts. The proportion of people who attended at least some school increased to 61 percent for those born 1965-1969, and 88 percent for those born 1980-1984.

A useful way to visualize differences in school attainment by group and changes by cohorts is through survival plots, which indicate the percentage of people who completed _at least_ each level of education. Figure 2 presents these survival estimates using the DHS data from 2005 for our three cohorts. The figure shows a large improvement between our first two cohorts, chiefly in primary education (up to six years of education.). The fraction who completed at least first grade increased by 26 percentage points between the 1950-1954 and 1965-1969 cohorts, but primary school dropout rates remained high, as can be seen by the steep decline of the survival curve over the first few years of schooling. Among those in the second cohort (born 1965-69), only one-third finished primary education. In contrast, in the most recent cohort (born 1980-84), two-thirds completed primary education. Despite the poor state of Haiti’s education system, primary enrollment has taken great strides since the 1950s.

Two relatively sharp drop-offs—following fifth grade and sixth grade—are visible in the profiles for the most recent cohort. The drop-off following sixth grade is common to many countries, corresponding to the transition from primary to secondary. However, the sharp decline after _fifth_ grade is unusual. In the Haitian school system, at the end of the sixth grade, students have the option of taking a national exam. Passing this national exam permits the student to continue to secondary education. Two possibilities exist to explain the drop-off after fifth grade in the data. If the sixth grade education is largely focused on the exam and the value of completing sixth grade without taking the exam is low, students not intending to take the exam may be more inclined to drop out in fifth grade. An alternative possibility is that students who completed sixth grade but failed the national exam may not consider themselves having “completed” sixth grade and might report fifth grade as their highest grade completed when reporting their education level in the DHS.

6
**Figure 7: Foreign ownership and governance structures**

towards suppliers towards customers <!-- Start of picture text -->
1.00 0.02 0.02 1.00 0.05 0.11<br>0.15<br>0.80 0.27 0.80 0.23<br>0.16<br>0.40<br>0.15<br>0.60 0.22 0.60<br>0.40 0.40 0.09<br>0.67<br>0.50 0.58<br>0.20 0.20 0.41<br>0.00 0.00<br>Domestic owned Foreign owned Domestic owned Foreign owned<br>B BP BL SC B BW BP WC<br><!-- End of picture text -->

**Figure 8: Exporting status and governance structures**

towards suppliers <!-- Start of picture text -->
1.00 0.01 0.05<br>0.16<br>0.20<br>0.80<br>0.16<br>0.60 0.26<br>0.40<br>0.67<br>0.49<br>0.20<br>0.00<br>Non exporter Exporter<br>B BP BL SC<br><!-- End of picture text -->

towards customers <!-- Start of picture text -->
1.00 0.05<br>0.11<br>0.80 0.24<br>0.34<br>0.15<br>0.60<br>0.06<br>0.40<br>0.57<br>0.49<br>0.20<br>0.00<br>Non exporter Exporter<br>B BW BP WC<br><!-- End of picture text -->

**Figure 9: Firm size and governance structures** towards suppliers

towards customers <!-- Start of picture text -->
1.00 0.05 0.06 0.03<br>0.12<br>0.90<br>0.80 0.24 0.22 0.32<br>0.70 0.40<br>0.60 0.14 0.18 0.10<br>0.50<br>0.40 0.10<br>0.30 0.58 0.54 0.55<br>0.20 0.38<br>0.10<br>0.00<br>Small (5‐19) Medium (20‐ Large (100‐ Very large<br>99) 399) (400+)<br>B BW BP WC<br><!-- End of picture text -->

<!-- Start of picture text -->
1.00 0.01 0.02 0.03 0.02<br>0.90 0.15 0.15<br>0.20<br>0.80 0.34<br>0.16 0.17<br>0.70 0.18<br>0.60<br>0.50 0.25<br>0.40<br>0.30 0.67 0.66 0.59<br>0.20 0.39<br>0.10<br>0.00<br>Small (5‐ Medium Large (100‐ Very large<br>19) (20‐99) 399) (400+)<br>B BP BL SC<br><!-- End of picture text -->

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workers use existing technologies for analysis, creation, management, and design, for example engineers using modeling software and architects using design software. The advanced level refers to specialized ICT skills such as software design, managing cybersecurity, and data science using big data analytics and artificial intelligence.

**Table 1. Definitions of digital skills levels**

|**Level**|**Definition**|**Examples of tasks**|
|---|---|---|
|Basic|Ability to access and use<br>digital technologies to<br>perform basic tasks|1. Functional use of digital devices<br>2. Online communication via emails<br>3. Using software for presentations, basic spreadsheet use<br>4. Finding, managing and storing digital information and content (e.g. social media)|
|Intermediate|Ability to use professional<br>software for analysis,<br>creation, management, and<br>design|1. Using professional software for analytics, accounting, project management<br>2. Digital marketing, social media analytics<br>3. Web design, graphic design|
|Advanced|Ability to perform<br>specialized ICT tasks|1. Computer programming<br>2. Cloud computing, network management<br>3. Artificial intelligence<br>4. Data science, big data analytics<br>5. Cyber security<br>6. Web development, search engine optimization|

_Source_ : adapted from UNESCO (2017) and IFC (2019).

Cognitive and socioemotional skills are commonly identified as two fundamental broad skills categories that people use in their work and life. Cognitive skills are our mental abilities to think, learn, and solve problems (Almlund et al. 2011). They range from basic academic knowledge (literacy and numeracy) to more demanding tasks, such as critical thinking, problem-solving, and time management. Socioemotional skills are the attitudes and behaviors upon which we manage personal and social situations (Guerra, Modecki, and Cunningham 2014; Weissberg et al. 2015). Technical skills are commonly identified as a separate skillset, though they may also be thought of as a subset of cognitive and socioemotional skills that are the know-how to carry out one’s specific job, which includes digital skills. These three broad categories of skills are used on the job (World Bank 2018).

We identify three subsets of cognitive skills and three subsets of socioemotional skills. Using lists of skills that employers commonly demand identified in previous work, we select three cognitive subsets: communication, organization, and thinking, which summarize 21 individual skills (Table 2) (Cunningham and Villaseñor 2016; Muller and Safir 2019). We also define three socioemotional 7
roles:<sup>8</sup> the level and growth rate of income per capita, both of which tend to be positively related to saving rates. The role of the level of income per capita may arise because of the pressures of subsistence needs at low levels of income, such as would emerge under a StoneGeary specification of utility. This interpretation is supported by the fact that the positive effect of income levels on private saving rates tends to taper off at higher levels of income (see Masson, Bayoumi, and Samiei 1998, as well as Loayza, Schmidt-Hebbel and Serven 2000a,b). In turn, the effect of growth on the saving rate may be due to a growth-associated shifting of income from low-saving older cohorts to high-saving young cohorts within countries, or to the influence of habit formation on consumption, which causes changes in consumption to lag those in income (Deaton 1999 offers a comprehensive discussion). In any case, almost all studies of international differences in saving rates include these variables, and they are often found to have statistically significant explanatory power.

Aside from the basic roles of income per capita and growth rates, the large cross-country differences in saving rates has caused researchers to examine the roles of a large number of other explanatory factors suggested by theory. These include structural features of economies such as demographic composition, the presence of mandatory saving schemes, uncertainty in the macroeconomic environment, financial depth and the importance of borrowing constraints; short-run policy variables such as fiscal policy (because of potential Ricardian equivalence effects), interest rates, and tax incentives; and exogenous shocks such as inflows of foreign aid and fluctuations in the terms of trade. Results can be summarized briefly: increases in the young-age and old-age dependency ratios both tend to lower private saving rates, in line with life-cycle theories of consumption. Research on countries with particularly high saving rates, such as Singapore, or that have secured a dramatic increase in saving over a short period of time, such as Chile, has emphasized the role of mandatory saving schemes such as Singapore<sup>’</sup> s Central Provident Fund (e.g., World Bank 1994), finding that such schemes have had a significant effect on the domestic saving rate. Uncertainty in the macroeconomic environment, arising either from the structure of production (e.g., agricultural income tends to be more uncertain than non-agricultural income) or from poor

> 8 See, for example, the overview by Schmidt-Hebbel, Serven, and Solimano (1996).

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- 52 -

of construction; (c) cost of administration, estimated at 20% of (a); (d) contingencies, estimated at 5-10% of (a) plus (b); (e) interest on the value of (a) and (b) during the construction period; and (f) honoraria for representatives.

57. In principle, there is notning to stop authorities from attempting to recapture some or all of the project (net) benefits, but in practice only full recovery of the costs lit ted above is sought. Care in selection is exercised to make sure that projects yield benefits in excess of costs; in practice the rapid development of most cities plus inflation has left a sizable margin for error. In addition, consideration is given to problems that the ;oor or persons with few liquid assets might have in paying the tax. Low income persons can be exempted altogether and others are allowed to stretch out the valorization tax payments over several years if otherwise it would absorb a significant fraction of their annual income.

58. The valorization tax as practiced obviously has a number of desirable features--the benefit connection; the efforts to select economically desirable projects, and the flexibility with which it has been adopted in differing circumstances. As a tax on the unimproved value of land, the tax would have no adverse incentive effects, and in fact the combination of the tax and the improvement serve to raise income and increase tht potential profitability of investments which should have a desirable incentive effect.

59. On the other hand, valorization does have some drawbacks, primarily centered around collections problems.<sup>4 5</sup> Firse, quite aside from 45/ See World Bank, World Development ReRort 1988 (Oxford University Press; June 1988), p. 160.
in and out of the monetized sector could affect the coefficients in the money demand function. Moreover, most Ethiopians have very limited options when they wish to shift out of money to other assets.

We find considerable inflation inertia in the cereal price models but none in the non-food price models. The inflation inertia found in the model for CPI is thus due to the grain markets. Inertia is usually attributed to various factors: official and unofficial price indexation and expectations are the usual culprits. However, agricultural prices in Ethiopia are market determined and there is no indexation. Hence, the inertia is likely to be due to expectations about changes in prices after large agricultural shocks, such as drought or large increases in world food prices, or related to foreign exchange shortages and supply of imports.

The price dynamics between the food and non-food prices also indicate a difference in price flexibility. Since the error correction models determine the price level, as well as inflation, we can see that a permanent exogenous price increase leads to some overshooting of food prices, due to the inflation inertia, while non-food prices only increase slowly. This means that food prices are more flexible than non-food prices.

We use dummy variables to capture the explosive price increases in the cereal market in mid-2008. Our interpretation is that once it passed a certain threshold, the current food-price hike also raised forward-looking expectations. News about prices, future harvests and rainfall expectations can set a self-enforcing cycle in motion where farmers withhold supply in expectation of higher prices, thus causing prices to rise until harvesting begins (Ng and Ruge-Murcia, 2000; Osborne, 2004).

We failed to find direct evidence that world energy price inflation has a direct effect on domestic inflation. This is probably because we indirectly capture oil price inflation through its effect on world food and non-food prices. Another explanation is that the authorities subsidized fuel until October 2008, keeping domestic prices low.<sup>23</sup> There is little evidence that structural changes in the agricultural sector have had a substantial impact on food price inflation. This finding is based on the stability of the empirical models and their coefficients. The test is very indirect, but microeconomic survey data support our result (IFPRI, 2008). In our view, this finding

> 23 In October 2008, Ethiopia fully removed fuel subsidies to relieve growing pressure on the balance of payments.

42