rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 13: Investment Indicators**

A. Public and Private Expenditure on Infrastructure (% <u>of GDP), 2003 (Excludes spending on other urban)</u> B. Private and Public Investment since 1996, Percent <u>of GDP</u> <!-- Start of picture text --> 16 45<br>40<br>35<br>12<br>30<br>25<br>8<br>20<br>15<br>4 10<br>5<br>0 0<br>Thailand China Indonesia Philippines 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Transportation Telecommunication Water and Sanitation Power Private Investment (Percent of GDP) Public Investment (Percent of GDP)<br>C. Industrial Capacity utilization D. Private Investment Indicators<br>55,000 550,000<br>100 50,000 500,000<br>95 45,000 450,000<br>90 40,000 400,000<br>85 35,000 350,000<br>80 30,000 300,000<br>75 25,000 250,000<br>70 20,000 200,000<br>65 15,000 150,000<br>60 10,000 100,000<br>55 5,000 50,000<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>50 Domestic cement sales (1,000 tons) (LHS)<br>95Q1 95Q3 96Q1 96Q3 97Q1 97Q3 98Q1 98Q3 99Q1 99Q3 00Q1 00Q3 01Q1 01Q3 02Q1 02Q3 03Q1 03Q3 04Q1 04Q3 Construction areas permitted in municipal zone (1,000 sq. mets.) (LHS)<br>Domestic commercial car sales (units)<br>Import of capital goods at 1995 prices (Million Baht)<br>E. Promotional privileges from Board of Investment F. Capital investment of business registered at<br>(Billion Baht 1988 Prices) Ministry of Commerce (Million Baht 1988 Prices)<br>300,000<br>290,000<br>700 280,000270,000<br>260,000<br>250,000<br>600 240,000<br>230,000<br>220,000<br>500 210,000200,000<br>190,000<br>180,000<br>400 170,000<br>160,000<br>150,000<br>300 140,000<br>130,000<br>120,000<br>200 110,000100,000<br>90,000<br>80,000<br>100 70,000<br>60,000<br>50,000<br>0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Net applications received Applications approved Newly registered Capital increase<br>Issues of promotion certificates Start of operations of promoted firms<br><!-- End of picture text --> 34
# **Box 5:  The case of Guatemala**

Since the creation of the business registry of Guatemala in 1971, its structure remained almost unchanged for two decades. An average of seven employees and a couple of mechanical typewriters composed its organizational structure until 1995. In 1996, under a new administration, the Business Registry undertook an ambitious modernization plan. The initial program, divided into four phases, would be accomplished with the implementation of e-registry and e-distribution in 1999 (Figure 15).

**Figure 15: EBR Implementation in Guatemala**

<!-- Start of picture text --> 40000<br>EBR implementation<br>35000<br>30000<br>25000<br>20000<br>Active Modernization plan<br>15000<br>Inactive Modernization plan<br>10000<br>5000<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: World Bank Entrepreneurship Group Database (2007).

The plan included not only the modernization of the business registry, but also the entrustment of the registry as the central actor for the new private sector development strategy. The registry would gain an active role in the promotion of entrepreneurship through activities such as training of entrepreneurs, investment promotion, dissemination, etc. As shown in Figure 2, the modernization of the business registry and the new economic policies had a direct impact in the number of new business registered, with an increase of 40% on new registrations. In comparison, the three years period (2000-2003) during which the modernization strategy was paralyzed due to a change in the administration, resulted in a sharp 11% decline in the number of new business registered.

In 2003, the new administration reactivated the second modernization plan for the business registry. A number of new and ambitious goals were defined, such as increasing the number of registration locations, the reduction of necessary steps for business incorporation, and the promotion of foreign investment. This second stage had a remarkable impact in the number of new business incorporated, and increased the number of new business registered per year by almost 25%. Moreover, the number of monthly electronic transactions – including incorporations, closures, re registrations and consultations - has climbed over 3400, representing more than 50% of the total number of monthly transactions.

31
Devlin, R. and G. Moguillansky. 2011. _Breeding Latin American Tigers: Operational Principles for Rehabilitating Industrial Policies_ . A copublication of the United Nations Economic Commission for Latin America and the Caribbean and the World Bank. Washington DC: World Bank.

———. 2012. ―What‘s New in the New Industrial Policy in Latin America?‖ Presentation for the _New Thinking on Industrial Policy_ roundtable _._ Organized by the International Economic Association and the World Bank. May 22–23, 2012, Washington DC.

Dargay, J., D. Gately, and M. Sommer. 2007. ―Vehicle Ownership and Income Growth, Worldwide: 1960-2030.‖ _Energy Journal_ , 28(4).

ECLAC. 2008. _Structural Change and Productivity Growth Twenty Years Later: Old Problems, New Opportunities_ , Santiago: ECLAC.

Engerman, S. L., and K. L. Sokoloff. 1997. ―Factor Endowments, Institutions, and Differential Paths of Growth among New World Economies: A View from the Economic Historians of the United States.‖ In S. Haber (ed), _How Latin America Fell Behind_ , 260–304 _._ Stanford: Stanford University Press.

Evans, P. 1996. "Using Cross-Country Variances to Evaluate Growth Theories." _Journal of Economic Dynamics and Control,_ 20(6–7): 1027–49.

Farrell, D., U. A. Gersch, E. Stephenson. 2006. ―The Value of China‘s Emerging Middle Class.‖ _McKinsey Quarterly,_ June.

Foster, V. and C. Briceño-Garmendia (eds). 2010. _Africa’s Infrastructure: A Time for Transformation_ . Washington DC: World Bank.

Friedman, T. 2005. _The World is Flat: A Brief History of the Twenty-first Century._ New York: Farrar, Straus and Giroux.

Freund, C. and C. Ozden. 2006. ―The effect of China‘s exports on Latin American trade with the world.‖ Mimeo, Washington DC: World Bank.

Gallagher, K. P., and R. Porcencanski, 2010. _The Dragon in the Room: China and the Future of Latin American Industrialization_ , Palo Alto: Stanford University Press.

Garnaut, R. 2012. ―The contemporary China resources boom.‖ _The Australian Journal of Agricultural and Resource Economics_ 56: 223–43.

Gerschenkron, A., 1962. _Economic Backwardness in Historical Perspective: A book of essays_ . Cambridge: Belknap Press.

Gill, I. and H. Kharas. 2007. _An East Asian Renaissance: Ideas for Economic Growth_ . Washington DC: World Bank.

56
<!-- Start of picture text --> wok 2<br>20 ‘ Aa Yas% ” :a °<br>10 mee LY i) t 2<br>IN o> Myi " ; x<br>5 Y Ne me / s] H o<br>Wi *<br>0 \yWrn / 8<br>¥ \U<br>5 8<br>1995 2000 2005 2010 2015<br>—— Total Debt Inflows<br>—-—- Private Debt Inflows<br><!-- End of picture text --> <!-- Start of picture text --> 10 a °<br>8 ‘ :a ‘{ on iwet °<br>6 we eee i: Pd 2<br>~ i\: x<br>4 7. Wa H : o<br>\ 7 i Xk +<br>2 \ a / iVyi UN 8<br>0 ‘7 SV i \ /<br>7 3<br>1995 2000 2005 2010 2015<br>—— Total Debt Inflows<br>——- Private Debt Inflows<br><!-- End of picture text -->
# **1 Introduction**

The long-term trend of shrinking agriculture employment and expanding employment in the secondary or tertiary sectors has been associated with economic development. And, since moving out of agriculture and getting a job in the non farm segment normally means earning higher incomes, this type of intersectoral migration should also lead to lower poverty rates. In recent years, Brazil, as many other developing countries, has experienced a significant reduction of its agricultural employment and there is indirect evidence that this migration out of agriculture is poverty reducing. Establishing more direct evidence is difficult mainly because of lack of data reporting the characteristics of migrants at the time of moving, a limitation that affects (sometimes with intractable endogeneity issues) a lot of migration models. This paper, relying on a special section of the Brazilian household survey that chronicles individuals’ past employment, offers, for the case of Brazil, a direct assessment of how much poverty reduction can be attributed to these intersectoral labor movements. By estimating a mover-stayer model using Brazilian micro data, we can identify the main factors affecting the propensity of moving out of agriculture and rank individuals accordingly. The estimations show that individuals that are most likely to migrate are found among the poorer, often landless heads of households, but also among the better educated, and thus less poor, non-head household members. Overall though, we observe, and this is one key finding of the paper, that movers experience the highest poverty reduction when compared to both agricultural and non-agricultural stayers.

This confirms that changes in sectoral employment can significantly contribute to poverty reduction. Chronic poverty is often linked to the difficulty of finding employment in a better paid job in a growing sector. There is mounting evidence of the existence of poverty traps characterized by situations where occupational or technology choices are discrete, and where choosing a higher return occupation or technology implies large sunk or fixed costs (Barrett 2004). Moving out of agriculture, where poverty rates are often much higher than in other sectors, is one example of such choices.

Our intersectoral migration estimations rely on past data, however by embedding the mover-stayer model within a more complete income generation microsimulation approach we can model occupational dynamics and test (or simulate) some relevant hypotheses (scenarios). Our focus is on global trade liberalization. Recent studies show that a major effect of global liberalization is to raise prices of agricultural commodities 3
<!-- Start of picture text --> 3<br>¢<br>fa)<br>7)<br>=)<br>(=<br>Ke)<br>€<br>oO<br>N<br>oO<br>2021<br>excludes outside values<br><!-- End of picture text --> <!-- Start of picture text --> High income Low income<br>8=<br>8<br>a a<br>$3 3"<br>§ §<br>2° z-<br>&<br>o o<br>excludes outside values: excludes outside values<br>Lower middle income Upper middle income<br>O =<br>ay Be<br>=! =)<br>8 8<br>—% ee<br>o o<br>excludes outside values excludes outside values<br><!-- End of picture text -->
**Figure 5: Log real earnings of teachers and non-teachers with college education in Indonesia, by age group, 2002-2008**

<!-- Start of picture text --> 2002 2004<br>2006 2008<br>20-29 30-39 40-49 50-5920-29 30-39 40-49 50-59<br>age group<br>log real earnings of teachers log real earnings of non-teachers<br>Graphs by year<br>14.5<br>14<br>13.5<br>13<br>14.5<br>14<br>13.5<br>13<br><!-- End of picture text --> Figure 6 shows the trend of relative earnings (ratio) of teachers, and the share of labor force with college education that are teachers. Between 2001 and 2005, the share of college graduates shows an growing trend, reaching over a quarter of total workers with college education. Between 2005 and 2008, this trend seems to experience a reverse. By 2008, the share of college graduates on the labor market and who are teaching came back down to 19 percent.

For college graduates, even though the premium of non-teaching job is eroding over time, there were periods that show a reversed trend, such as between 2005 and 2008.

17
# **Table 7: Donor Interaction: IV Estimates**

|**Dependent variable: Pr(D**|**onor ****_j _provide**<br>|**s aid after disas**<br>|**ter ****_i_ =1)**<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||US|Japan|Germany|UK|Norway|
|Number of major donors|0.192**|0.155**|0.336***|0.331***|0.254***|
||(0.077)|(0.068)|(0.102)|(0.074)|(0.069)|
|_Impact measures_||||||
|Log(Nr.affected)|0.013|-0.014|-0.015|0.001|0.016|
||(0.019)|(0.016)|(0.021)|(0.016)|(0.016)|
|Log(Nr. Killed)|-0.006|0.052**|-0.014|-0.014|-0.023|
||(0.027)|(0.026)|(0.035)|(0.026)|(0.029)|
|_Socio economic indicators:_||||||
|Log(Population)|0.046|-0.042|0.065|-0.023|-0.076*|
||(0.041)|(0.039)|(0.051)|(0.036)|(0.041)|
|Pop. density|0.000|-0.001***|0.000|0.000|0.001*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(GDP per capita)|-0.003|0.037|0.008|-0.026|0.005|
||(0.057)|(0.052)|(0.062)|(0.054)|(0.058)|
|Oil dummy|0.118|-0.273***|-0.080|0.043|0.149*|
||(0.090)|(0.086)|(0.116)|(0.091)|(0.086)|
|Trade openness|0.002|0.001|0.000|-0.002|-0.003*|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|ELF index|0.002|0.010|0.015|0.246|-0.231|
||(0.145)|(0.146)|(0.171)|(0.160)|(0.166)|
|Gastil index|-0.036***|0.008|-0.011|-0.004|0.034**|
||(0.013)|(0.011)|(0.014)|(0.011)|(0.014)|
|_Bilateral Variab_|_les_|||||
|Geograph. Distance|0.032|0.006|-0.122***|0.009|-0.004|
||(0.024)|(0.036)|(0.039)|(0.032)|(0.033)|
|Affinity index|-0.027|-0.091|0.079|-0.104|-0.359|
||(0.166)|(0.432)|(0.394)|(0.226)|(0.429)|
|Former colony||||0.043<br>(0.112)||
|Observations|269|269|269|269|269|
|Centered R-squared|0.31|0.42|0.13|0.43|0.29|
|Hansen OID Test|0.14|0.39|0.42|0.98|0.36|
|Cragg-Donald F-Stat|7.21|8.20|4.56|5.47|5.66|

_Notes:_ 2SLS estimates. Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1% All specifications include year, regional and disaster type fixed effects.

30
# **4. Labor productivity of the formal firms that compete against the informal firms**

# 4.1 _Empirical specification and control variables_

Recall that regression results for the relationship between labor productivity and whether a firm faces competition from informal firms (informal competition) are based on a sample of 45,966 formal (registered) firms. The estimation exercise involves two steps. In the first step, we regress labor productivity on the dummy for informal competition along with basic controls for firm and country characteristics. The equation we estimate is as follows: where the subscript i denotes the firm, Y is the dependent variable (labor productivity as defined above), X is the main explanatory variable that equals 1 if the firm reports competing against informal sector firms and 0 otherwise, � Z������ is a set of firm-level controls, CFE is country fixed effects, and ε is the error term. The equation is estimated using the ordinary least squares (OLS) method with Huber-White robust standard errors.

Since we now focus only on the formal firms, factors such as exporting activity and foreign ownership of firms may be important. Further, cross-country comparable data are available for these variables. Thus, the control variables included in Equation (2) are dummy variables indicating the country where the firm is located (country fixed effects); size of the firm measured by the (log of) total number of workers employed at the firm at the end of the last fiscal year; (log of) age of the firm; industry fixed effects captured by two dummy variables indicating if the firm belongs to the manufacturing sector or the retail sector (omitted category is other services sector); 16
**Figure 1. LCOE range for the maximum and minimum values of capital costs when other input variables are standardized ($/MWh)**

<!-- Start of picture text --> 200<br>Median values of LCOE<br>180<br>160<br>129 130<br>140<br>120<br>94<br>100 82 81<br>75<br>67<br>80 56<br>51 52 48<br>60<br>40<br>20<br>0<br>LCOE ($/MWh)<br>Coal<br>Solar PV (Utility) solar cycle Hydro Nuclear Biomass<br>Concentrated Wind Onshore Wind Offshore Gas combine Gas turbine Geothermal<br><!-- End of picture text --> The values of an LCOE can change along with the values of all seven variables used for its calculation (discount rate, overnight construction costs, O&M costs, fuel prices, heat rate, capacity availability factor, and economic life). One approach to confirm the range of an LCOE is to recalculate it considering alternative values for all these input variables. Table 3 presents the values for variables we considered under this case. We calculate 4,104 values of LCOEs. The LCOEs for each technology for selected values of the variables are shown in Figure 2.

While calculating the different values for LCOEs, we pay attention to some critical issues. First, it would be misleading to use the extreme values, both low and high, in a global study. We therefore selected values that fall in a reasonable range. For example, NREL (2019) reports very high CAPEX for some geothermal technologies (US$33,365/kW), which is exceptionally high; it would result in an LCOE of around US$700/MWh. It is possible that some experimental technologies could be that costly; but they should not be included in a globally representative analysis. Similarly, IRENA (2020) shows that some hydropower capacity factors could exceed 80%, but there are few hydropower projects around the world with such high capacity factors.

For the capital costs, we started with the lowest values available from the five sources mentioned earlier (Lazard, IEA, EIA, IRENA, and NERL). When the lowest value represents an 13
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Similarly, a very low rate of project completion often provides a robust indication that gatekeeping functions that prioritize public investment project selection consistent with resource availability are not effectively enforced. Poor project planning and management and procurement delays may also contribute to this outcome.

5. Using this approach in the case study countries, we suggest a diagnostic “gapanalysis” of the actual system relative to this basic system so as to identify structural aspects of the public investment decision and management process that may be weak and in need of attention. The gap analysis will be supplemented by diagnostic indicators to identify the particular areas of weakness that are likely to contribute to low public investment efficiency. As with any diagnostic, good judgment regarding the underlying incentive and capacity problems will be necessary to supplement the gap analysis and diagnostic indicators. The recent Ukraine Public Expenditure Review (PER) provides a good example of a case study that applies many of the suggestions in this note embedded in a broader understanding of public finance management practices.<sup>3</sup> 6. In principle, a good diagnostic would allow reforms to focus scarce managerial and technical resources where they will yield the greatest impact. The approach is based on a clearly defined institutional framework and recognition of the role of institutions, capacity, and incentives. It is broadly consistent with the approach taken in the Public Expenditure and Financial Accountability (PEFA) initiative which addresses broader issues of public expenditure management. Like the PEFA framework, the diagnostic makes use of welldefined symptomatic indicators which can be objectively assessed and which provide information that can be used to identify problematic areas. This identification will enable (and typically require) more detailed assessment to develop institutional remedies to the identified problems. Thus they can motivate governments to undertake periodic selfassessments of public investment efficiency and design reforms to improve government systems.

7. This framework acknowledges that adequate data collection capacity and systems of accounting, auditing, and oversight underscore most of the identified ‘ _de minimis_ ’ features of an efficient public investment management system.

8. Section II below provides a schematic description of the “must-have” features of the public investment system. Section III then proposes some diagnostic questions and indicators that would help assess the functioning of the existing system. Section IV concludes and indicates the next steps to implementing this indicator-based approach in particular country case studies.

> 3 See chapter 5, “Capital Budgeting in Ukraine”, in World Bank (2006).

2
**Table 3: Total Factor Productivity Growth Regressions (Instrumental Variable)**

Dependent Variable: TFPG Method: Instrumental Variable Sample(adjusted): 1971 1997 Included observations: 27

|Variable|Coefficient|t-Statistic|Coefficient|t-Statistic|
|---|---|---|---|---|
|C|0.032|3.531|0.027|2.491|
|DI (1974-75)|_-0.045_|-2.283|-0.045|-2.175|
|D2 (1971-73, 76-84)|0.041|2.604|0.041|2.501|
|D3 (1985-86)|0.016|0.625|0.020|0.750|
|LOANG*|-0.381|-3.303|-0.380|-2.984|
|FDIG|0.035|2.519|||
|MKG|||0.069|2.037|
|LOGBB|0.340|2.646|0.316|2.390|
|R-squared|0.618||0.591||
|Adjusted R-squared|0.503||0.468||
|S.E. of regression|0.023||0.024||
|F-statistic|8.160||7.572||
|Prob(F-statistic)|0.000||0.000||
|Mean dependent var|0.000||0.000||
|<br>S.D. dependent var|0.032||0.032||
|Sum squared resid|0.010||0.011||
|Durbin-Watson stat|2.336||2.110||

* Lagged capital-output ratio is used as an instrument.

TFPG: Total Factor Productivity Growth LOANG: Growth in (loans/GDP) ratio. FDIG: Growth in (FDI/GDP) ratio MKG: Growth in (Imported capital goods/GDP) ratio BB: Budget balance/GDP 16
of the contract, implying the enterprises made fixed payments of δI / �T�3� each month after the third month.

- b) Revenue sharing: the microenterprise pays a share m of relevant revenues each month. This provides the return to the investor as well as the share (1- _δ_ ) of the invested capital not repaid through fixed payments. We denote the share of base-year revenues excluded from the calculation as B, with enterprises paying an amount max�m�R� �BR<sup>�</sup> �, 0� where Rt is revenue for the month, R<sup>�</sup> is monthly revenue for that month in the year before the contract was signed, and 0 �B�1 is the share of prior revenue shielded from revenue sharing.

The proportion of revenues shared, m, varies with the choice of _δ_ and _B_ . The share increases as more of the capital is repaid through revenue sharing (that is, as δ becomes smaller), and as the share of excluded base-year revenues increases (that is, as _B_ increases). As a result, contracts that provide the most insurance to the entrepreneur against downside risk (when δ�0 and _B=1_ ) will also require them to share more of their upside gains with the investor. These more equity-like contracts may increase concerns with moral hazard.

Once δ and _B_ are fixed, the investor and entrepreneur must agree on a value for the share of revenue to be received by the investor. This will be a function of the size of the investment, the expected revenue (including revenue growth) subject to the royalty rate, and the targeted return on the investment. The entrepreneur will have an incentive to overestimate future revenue growth, since a higher projected revenue implies a lower _m_ for any given investment and expected return. There are several approaches that can be used to determine m. In practice, we used a spreadsheet that allowed us to consider payment scenarios for varying δ and _B,_ expected revenue growth rates and investor returns, showing each scenario to the entrepreneur.

The revenue share _m_ paid to the investor is a function of seven parameters: _I, T_ , δ, _B,_ R<sup>�</sup> _, g_ , and the targeted return on the investment ( _r_ ). We based the targeted return on a loan-equivalent return, in the aim of both simplicity and transparency with the entrepreneur. That is, we started by determining the monthly payment for a loan amount _I_ over a term _T_ (in months) and an (annual) interest rate _r._<sup>_9_</sup> We summed this monthly payment over the term of the investment _T_ to determine total (nominal) loan payments. From this, we subtracted the amount of the equity investment to be

> 9 We use T in months and r in years, and hence use the Excel commend PMT([r/12],T,I).

10