rafmacalaba/datause-encoder-data huggingface dataset:
three focal sets of interrelated issues: (i) the country’s public investment management capacity; (ii) the public financial management environment where these investments take place; and (iii) the dynamic impacts of public investment on private sector gross fixed capital formation.

**The parameters captured by the general equilibrium model, the PIMa index, and the PRISM tool are important because they reflect the country’s institutional setting and the country’s long-term growth potential** . This analysis is warranted given the scale of the investment effort being made by developing countries. Plans to increase debt financing of capital investments would need to be reviewed from a broader perspective. In particular, they need to satisfy two basic tests: what is needed to meet the public investment management challenge (e.g., absorption capacity), and what is the impact of these incremental investments on the private sector. To sharpen the understanding of these challenges and the growth impact of public investments, the paper attempts to unbundle the social and economic benefits from the cost reduction in infrastructure services induced by these investments and to measure the impact of public investment on the private sector’s fixed capital formation. The paper gauges the dynamic effects above and beyond a comparative static analysis to measure the increased impact of public investment on the private sector’s fixed capital formation. In doing so, future extensions attempt to extend the existing research in two directions:

- To use dynamic general equilibrium simulations to explore the relationship between public investment policies and debt sustainability.

- To use data from PIMa and PRISM to carry out an empirical analysis of the relationship between public investment productivity and the management and quality of the projects.

# **Three main conclusions** emerge:

- **The simple dynamic general equilibrium model** illustrates how actions designed to reduce the depreciation rate for public capital, due for instance to better maintenance, affect both the overall rate of return on public capital and, therefore, the optimal level of public investment-to-output ratio.  More importantly, this action can help to make compatible the implementation of an optimal public investment program with a debt sustainability policy.

- **The PIMa index** highlights the country’s position in the project management cycle, finding that the degree of centralization and quality of the project screening and appraisal responsibilities, as well as the connection between budgeting capital and recurrent expenditures once these projects are launched, emerge as the most important factors for the performance of the project.

- **The PRISM tool** underscores the parallel between the discipline in the public investment management and in the management of public finances.  Countries committed and able to harmonize capital and recurrent expenditures tend to build more productive public capital assets.  Countries that better manage public investments appear to complement better the private investment inductive to growth (no crowding out effect) **.**

22
|Loayza et al.<br>(2013)|Mining production|Household consumption,<br>literacy<br>Povertyrate<br>Consumption inequality|+<br>-<br>+|Peru,<br>district level|Matching and<br>propensity score|
|---|---|---|---|---|---|
|Marchand<br>(2012)|Indicator of having<br>10% or more of<br>their total earnings<br>from the energy<br>extraction<br>sector.|Employment and earnings|+|Canada,<br>province level|Difference-in-<br>differences|
||Indicator of whether<br>thecounty is located|Employment share of<br>mining|+|||
|Michaels<br>(2011)|<br>above an oil field or<br>part of an oil field<br>(or multiple oil<br>fields) that contains<br>at least 100 million<br>barrels of oil before<br>any oil was<br>extracted|Employment share of<br>agriculture<br>Employment share of<br>manufacturing<br>Stock of educated workers<br>Nominal income|-<br>0<br>+<br>+|U.S., county level|Fixed effect panel<br>model|

_Note_ : + = positive relation; - = negative relation; 0 = statistically insignificant. OLS = ordinary least squares.

55
**Figure 3: Plot of transformed data with overlayed eigenvectors**

**PANEL A: OECD and Sub-Saharan African Countries (South Africa and Mauritius excluded)**

<!-- Start of picture text --> 9<br>6<br>3<br>OECD<br>0 SSA<br>-9 -6 -3 0 3 6 9 Linear (Eiegenvector1)<br>-3 Linear (Eiegenvector2)<br>-6<br>-9<br>KEI<br>ln of GDP per capita<br><!-- End of picture text -->

**PANEL B: OECD and Sub-Saharan African Countries (South Africa and Mauritius included)**

<!-- Start of picture text --> 9<br>6<br>3<br>OECD<br>SSA<br>0<br>Linear (Eiegenvector1)<br>-9 -6 -3 0 3 6 9<br>Linear (Eiegenvector2)<br>-3<br>-6<br>-9<br>KEI<br>ln of GDP per capita<br><!-- End of picture text --> 14
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# _11. Debt Obligations_

Contrary to expectations, the results indicate that central budget infrastructure expenditures are insulated from cuts induced by debt service obligations. In fact, the evidence indicates larger debt service obligations induce slightly greater infrastructure expenditures, especially when commercial bank loans and direct foreign investment are relatively large components in current foreign savings flows. Both the significance level and the estimated magnitude of the impact are greater in the regressions including the size of current net resource flows from foreign commercial banks and the magnitude of direct foreign investment relative to GDP, as can be seen in Tables 6, 7, BI, B2, B3, B4, B5 and B7. Table 6 indicates that when direct foreign investment as a percentage of GDP is included in the regression, the estimated coefficient for DSGDP indicates that for every one percentage point increase in debt service relative to GDP, central budget per capita infrastructure expenditures increase by (1980) US $ 0.58. This figure increases to (1980) US $ 0.70 when net commercial bank flows as a percentage of GDP are included in the regression. The corresponding elasticity values at the sample mean values of DSGDP and BTACCAPK are 0.22 and 0.27. (All figures calculated on the basis of the FCINFPA base regressions.) This finding is not fully robust. Significance levels are low and frequently fail to reach the 15 percent level when the total magnitude of foreign savings alone or the relative magnitude of official net transfers, relative to GDP are included in the regressions. In the regressions considering alternative measures of sectoral balance (B2), the coefficient on DSGDP is only significant when foreign flows are entirely excluded from the regression or the importance of commercial bank flows is included. It is seldom significant with the alternative poverty indicators are included in the regression (Table B8). Further, findings are sensitive to how debt service obligations are measured. As Table B6 shows, if debt obligations are measured as the debt service ratio (TDS), the coefficients on TDS are insignificant in the regressions including total net transfers alone and the magnitude of official transfers. Further, if debt obligations are measured as the total long term debt as a percentage of GDP (DODGDP1), the estimated coefficients are negative and insignificant in all five regressions.

# _12. Institutional Development_

The level of institutional development is a significant and important determinant of per capita central budget expenditures on infrastructure. The coefficient for GASTIL is negative indicating that the greater the degree of institutional development, the greater are expenditures on infrastructure. Significance levels are, with very few exceptions, at the one percent level or better. The only contrary evidence occurs when the altemative measures of sectoral balance are used or when incidence of poverty measures are substituted for poverty commitment. In both these cases, however, the substitutions appear to result in specification bias.
Table C.2. Impacts of Extremism on Educational Attainment Excluding Student and Employment Status from Control Variables

||Education Index|Literate [=1]|Some Education [=1]|Primary [=1]|Secondary [=1]|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A. Taliban Exposure_||||||
|_ETi_|-0.142***|-0.069*|-0.182***|-0.033|-0.284***|
||(0.037)|(0.046)|(0.058)|(0.059)|(0.084)|
|FDR q-values|[0.001]|[0.071]|[0.003]|[0.196]|[0.003]|
|R-squared|0.180|0.150|0.148|0.043|0.068|
|_Panel B. Contested Territories Exposure_||||||
|_ECi_|-0.063***|-0.035**|-0.074***|-0.021*|-0.124***|
||(0.014)|(0.018)|(0.022)|(0.023)|(0.033)|
|FDR q-values|[0.001]|[0.024]|[0.002]|[0.081]|[0.001]|
|R-squared|0.181|0.150|0.148|0.043|0.068|
|_Panel C. U.S. - Allies Exposure_||||||
|_EAi_|0.050***|0.027**|0.060***|0.015*|0.099***|
||(0.011)|(0.014)|(0.017)|(0.018)|(0.025)|
|FDR q-values|[0.001]|[0.024]|[0.001]|[0.088]|[0.001]|
|R-squared|0.181|0.150|0.148|0.043|0.068|
|Mean Dependent Variable|0.599|0.911|0.848|0.141|0.495|
|Observations|5,592|5,592|5,592|5,592|5,592|
|_Controls in All Panels_||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Education Index is constructed using the average of the variables in columns (ii) to (v). Individual controls include: age, gender, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
New, M., D. Lister, M. Hulme & I. Makin (2002) “A high-resolution data set of surface climate over global land areas”, _Climate Research_ , Vol 21, pg 1-25.

- Smith, T. M., & R. W. Reynolds (2005) “A global merged land and sea surface temperature reconstruction based on historical observations (1880–1997)” _Journal of Climate_ **18** : 2021–2036.

- Soto, R. & A. Torche (2004) “Spatial Inequality, Migration, and Economic Growth in Chile” _Cuadernos de Economía_ , **41** : 401-424.

- Tol, R. S. J. (2005) “Emission abatement versus development as strategies to reduce vulnerability to climate change: an application of FUND.” _Environment and Development Economics_ , **10** : 615-629.

- Trenberth, K.E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F. Rahimzadeh, J.A. Renwick, M. Rusticucci, B. Soden and P. Zhai (2007) “Observations: Surface and Atmospheric Climate Change.” In: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.) **Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth  assessment Report of the Intergovernmental Panel on Climate Change.** Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

- Quiggin, J. & J. K. Horowitz (1999) “The Impact of Global Warming on Agriculture: A Ricardian Analysis: Comment,” _American Economic Review_ , **89** (4): 1044-45.

- UNDP Perú (2006) Informe sobre Desarrollo Humano Peru 2006. Hacia una descentralización con ciudadanía. Gobierno de Perú & PNUD Perú.

- Woodman, R. (1998) “El Fenómeno El Niño y el Clima en el Perú.” Documento publicado por el Congreso de la República en "El Perú en los Albores del Siglo XXI/2 ̈; Ciclo de Conferencias 1997-1998", Ediciones del Congreso del Perú, Lima-Perú, 201-242.

26
growing households, and comparing yield and cost elements between the private and public sector. Finally, we use the full sample to compare economic welfare between households engaged in the sector and those who are not.

# **Data**

Our empirical analysis is based on the QBST, a baseline survey conducted in 2004. It is part of a planned series of surveys designed to monitor the productivity and the living standard of the populations engaged in the tea sector. It is important to keep in mind the survey was taken before the implementation of the privatization. That is why we speak of potential impact. The survey provides information on three basic dimensions of interest: (1) productivity indicators such as yield, use and cost of fertilizer; (2) living standard as indicated by income and expenditure; and (3) access and use of social services.

The available sample includes about 2, 064 households representing the 102,812 households living in parts of the country where tea is grown. Thus each household in the sample stands for about 50 households for a total population of 515,217 inhabitants in tea-producing provinces. The average household size is about 5 people. It is estimated that only 30 percent of members of tea households are engaged in the tea sector. The rest is employed in non tea activities.

Table 1 Average Characteristics of Tea Growers

|Characteristics|Private<sup>16</sup>|Public|All|
|---|---|---|---|
|Age|51.93|47.69|48.20|
|Female|0.22|0.30|0.29|
|Land|18.40|30.80|29.30|
|Livestock|0.37|0.48|0.47|
|Bicycle|0.16|0.06|0.07|
|Water30|0.42|0.28|0.30|
|Market30|0.20|0.15|0.15|
|Road30|0.40|0.22|0.24|
|PerCapita Expenditure|1640.39|2377.36|2288.20|
|Sample Size|83|603|686|

Source: Authors’ calculations

> 16 These households  are SORWATHE supported tea growers.

12
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Figure 6: Changes in Contractual Savings and Market Capitalization, 1990 - 1996 <!-- Start of picture text --> D(MC/GDP)<br>1-<br>AUS<br>SGP<br>.5 j<br>0<br>KOR<br>JPN<br>-.2 0 .2 .4 .6<br>D(CSIGDP)<br>The fitted line is given by i, = 0.161 + 0.849x, with a t statistics of 2.593 for the slope. See Table<br>12 in Appendix 2 for the list of countries.<br>1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.<br>Figure 7: Changes in Contractual Savings and Value Traded,<br>1990- 1996<br>D(VT/GDP)<br>NLD<br>USA<br>.5SAAU<br>.6 ~~~~~~~~ESP CAN g -B<br>O~~~~~~~TJ DNK CHIN ZA<br>0<br>JPN<br>-.2 ~~~~0 .2.4 .<br>D(CSAGDP)<br><!-- End of picture text --> Notes: The fitted line is given by **i,** = 0.161 + 0.849x, with a t statistics of 2.593 for the slope. See Table 12 in Appendix 2 for the list of countries.

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Notes: The fitted line is given by y, = 0.044 + 0.968x, with a **t** statistics of 3.289 for the slope. See Table 12 in Appendix 2 for the list of countries. **Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.
effects approach—which imposes time-invariant productivity—and directly deals with the simultaneity issue, we shall mainly rely on the LP productivity estimates in our comparative analysis. However, we note that the correlation between the LP (tfpLP) and the fixed-effects productivity is quite high: 0.73. In sensitivity checks, we also use log labor productivity (measured as log value added per employees) as our productivity measure. The correlation of TFP and log labor productivity is also quite high at 0.61.

We use value added as our output measure since material inputs are the non-state input that the Levinsohn-Petrin procedure uses to correct for simultaneity biases in estimating the CobbDouglas production function. As it turns out, in some firms, the reported sales are less than expenditures on intermediate inputs, resulting in negative value added. There are many reasons for this. Our sample period happens to cover a global recession after the bursting of the technology bubble. Some of our sample firms (in particular, exporters and firms that face import competition) could be experiencing recession induced distress. Our sample also includes young firms and some of the young firms could still be learning how to do business. There is also the possibility that some firms simply underreported their cash sales for the purpose of evading taxes.

It is tempting to simply discard firm-year observations with negative value-added. Doing so, however, could bias the comparative analysis of firm-level productivity between China and India since we would have to drop more Indian firms than Chinese firms from the survey samples. Instead, we drop 7.7 percent of firms on the bottom end of the distribution of value added per employees from each country’s sample and use the remaining sample to estimate the production function of each industry.<sup>9</sup> To the extent that the dropped Chinese firms are more efficient than Indian firms, our estimates would under-estimate China’s advantage. But we believe the difference should be minor because both sets of firms are the worst performers.

# **_Performance Differences_**

We present productivity estimates for the two countries in table 2. Relative to Indian firms, Chinese firms, on average, have a much higher level of TFP. The median firm’s TFP in the China sample exceeds that in the India sample by 1.27 or 127 log points, which means that the median

> 9 The 7.7 percent city cut-off point ensures that all Indian firms with positive value added are included, but this means that some Chinese firms had to be dropped even when they had positive value added.

8
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underruns. About two thirds of projects either had cost underruns or were within +10% of the estimated cost, a finding supported by the analysis of 1984 supervision reports carried out by the Energy Department. More projects approved in the 1980s had substantial cost uriderruns than those approved In the second half of the 1970s because of the world recession during the early 1980s. Bqth International and local competitive bids came In lower than expected be4ause of fierce competitlon among contractors.

The above results Indicate that projects approved before and completed after the 1973 oil crisis were subject to serious cost overruns largely due to the completely unanticipated Inflation, particularly since some projects approved In the late 1960s did not have a provision for price contingencies In the cost estimates. However, It appears that once Bank staff had adjusted to the new Inflationary conditions, actual project costs were more In line with estimated costs. Apart from those projects Implemented during the oil crisis, the level of cost overruns appears to have been reduced In the 19709 and 1980s compared with those In the 19508 and 1960s. Available cost data in the OED surey (whlch Is given for each project component, not for the project as a whole) Indicates an average cost overrun of 13% compared to a negilgible overrun for projects approved for the decade from 1974. A significant part of these cost overruns were for power projects In Mexico which had very high local cost overruns - these overruns were especially significant because local costs made up 80% of total costs, a far higher proportlon than for other countries.

An analysis of cost overruns by region shows that those for Latin American countries have Increased over time. Durlng the 19506 and 1960s, average cost overruns were 18% compared with 42% for projects approved after 1967. LAC had the highest cost overruns for projects approved between 1967-78, as shown In the table below. Even with the 1973 oil crisis, South Asia and EMENA had cost underruns on average, while East Asia kept to within +1 0% of estimated project costs.

Reglon EAP WAP ASP AEP LCP EMP Percent 18.6 16.8 -8.4 9.3 42.4 -9.3 Overruns
14

**Table 5 Djibouti: Annual Private Returns to Education in Public and Private Sector**

||**Selectivity-Corrected**<br>**Regression**|**OLS Reg**|**ression**|
|---|---|---|---|
||**Public**<br>**Private**|**Public**|**Private**|
|Primary|2.4<br>6.7|3.9|4.7|
|Middle|5.8<br>8.9|8.0|8.3|
|Secondary|9.9<br>6.6|10.2|4.6|
|Vocational|9.5<br>2.0|10.6|1.8|
|University|9.0<br>0.8|9.4|2.4|

_Note. Estimates for primary category is the earnings premium over no-formal education. Similarly, middle school category shows earning differential over primary school. Secondary category shows differential over middle school, vocational shows differential over middle school and university is the differential over secondary school. Source: Authors' calculations based on wage regressions from Table 3._ Workers in the public sector earn larger annual private rates of return to schooling than do private sector workers with secondary, vocational and university education. For wage earners in the private sector, the annual private return to education peaks at 8.9 percent after completion of middle schooling, compared with 5.8 percent for those in the public sector. The annual private return to secondary schooling is 6.6 per year of schooling for wage earners in the private sector relative to 9.9 percent for those in the public sector **.** The annual returns for private sector wage earners with vocational training drops to 2 percent, compared with 9.5 percent for public sector workers with the same qualification. Returns to education for workers with university level education are much higher in the public sector than in the private sector. The annual private returns of moving from high school to university education 0.8 percent compared to 9 percent for graduates working in the public sector.

As illustrated earlier, higher private returns do not, however, signal that productivity is higher (and hence better rewarded) in the public sector. The higher returns to education in the public sector are explained by the distorting government pay policies, and the legacy of rewarding educated labor for patronage and loyalty to the ruling elite and the lack of viable job opportunities in the private sector for educated workers.

Average returns to education in Djibouti can also be examined for all workers and for males and females separately. Separate wage regressions corrected for selectivity are shown in Table 6 while Table 7 provides results using the approach in (7) for the working population engaged in various sectors correcting for selectivity bias.
<!-- Start of picture text --> 16 A. Share of total income (%) 91 B. Labor share of entire population (%)<br>14 90<br>12<br>89<br>10<br>88<br>8<br>87<br>6 L<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>14 C. Share of factor income (%) 60 96 D. Alignment coefficient (%) 85<br>a ge<br>12 Li 50 94g A. me f [80<br>oat” oS ° @ gee? Sei<br>10 P 40 92 Sag " 6<br>8 o-e-aee CFB, di :x;<br>See gy 30 90 wy $ 70<br>Beige q Q<br>6 | Bad<br>K-20 ssql Q K-65<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 1965 1970 1975 1980 1985 1990 1995 2000 2005<br>——®—- Labor —©— Capital<br>Separate axes: labor (L) on left, capital (K) on right. Excluding capital gains and negative observations.<br>Capital includes S-corporation profits. Self-employment income split between labor (2/3) and capital (1/3).<br><!-- End of picture text -->