rafmacalaba/datause-encoder-data huggingface dataset:
Table 14A. (Part 1) 8-sector level labor remuneration in the original IFPRI SAM (in current billions of Shillings)

||Child|||Females|<br>||||Males||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(age 10<br>to 14)|No<br>formal<br>education|Not<br>finished<br>primary<br>school|Not<br>finished<br>secondary<br>school|Secondary<br>or higher<br>education|TOTAL|No formal<br>education|Not<br>finished<br>primary<br>school|Not<br>finished<br>secondary<br>school|Secondary<br>or higher<br>education|No formal<br>education|Total labor<br>remuneration|
|Agriculture/Forestry/Fishing|21.403|62.824|68.947|400.732|4.535|537.038|69.767|207.563|232.014|12.839|522.182|1080.623|
|Mining&quarrying|0.093|0.069|0.005|0|0.120|0.242|0.000|0.071|1.466|0.067|1.603|1.938|
|Manufacture|0.550|2.539|3.739|48.443|18.958|73.679|3.752|14.688|119.544|41.637|179.621|253.850|
|Electricity. Gas and water|0|0|0.000|1|0.471|1.147|1.431|1.002|10.473|12.836|25.742|26.889|
|Construction|0.663|0.208|0.000|1.109|2.337|3.654|4.617|25.263|159.069|39.160|228.108|232.425|
|Trade|0.180|3.279|5.419|28.131|4.753|41.581|1.459|4.668|37.328|24.255|67.711|109.472|
|Transport &<br>Communication|0.000|0.000|0.000|2.691|5.105|7.796|0.399|1.311|18.749|27.602|48.061|55.857|
|Rest of services|0.210|0.843|2.899|49.844|119.614|173.199|1.602|11.866|100.142|262.837|376.446|549.856|
|ALL SECTORS|23.098|69.762|81.008|531.673|155.894|838.337|83.026|266.431|678.785|421.232|1449.474|2310.909|

Table 14A. (part 2) 8-sector level factors' remuneration in the original IFPRI SAM for Tanzania in 2001 (in percentage)

||Child<br>(age<br>10 to<br>14)|No formal<br>education|Not<br>finished<br>primary<br>school|Females<br>Not finished<br>secondary<br>school|Secondary or<br>higher<br>education|TOTAL|No formal<br>education|Not<br>finished<br>primary<br>school|Males<br>Not finished<br>secondary<br>school|Secondary or<br>higher<br>education|No formal<br>education|Total labor<br>remuneration|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture/Forestry/Fishing|0.93|2.72|2.98|17.34|0.20|23.24|3.02|8.98|10.04|0.56|22.60|46.76|
|Mining&quarrying|0.00|0.00|0.00|0.00|0.01|0.01|0.00|0.00|0.06|0.00|0.07|0.08|
|Manufacture|0.02|0.11|0.16|2.10|0.82|3.19|0.16|0.64|5.17|1.80|7.77|10.98|
|Electricity. Gas and water|0.00|0.00|0.00|0.03|0.02|0.05|0.06|0.04|0.45|0.56|1.11|1.16|
|Construction|0.03|0.01|0.00|0.05|0.10|0.16|0.20|1.09|6.88|1.69|9.87|10.06|
|Trade|0.01|0.14|0.23|1.22|0.21|1.80|0.06|0.20|1.62|1.05|2.93|4.74|
|Transport & Communication|0.00|0.00|0.00|0.12|0.22|0.34|0.02|0.06|0.81|1.19|2.08|2.42|
|Rest of services|0.01|0.04|0.13|2.16|5.18|7.49|0.07|0.51|4.33|11.37|16.29|23.79|
|ALL SECTORS|1.00|3.02|3.51|23.01|6.75|36.28|3.59|11.53|29.37|18.23|62.72|100.00|

Source: Thurlow and Wobst (2003). See note on Table 7 in this Appendix.

45
of Uganda introduced a performance contract with NWSC to increase accountability for results and to provide the utility with incentives for good performance (see Box 10).

# **<mark>Box 10</mark>**

# **<mark>Performance agreement for NWSC (Uganda)</mark>**

<mark>From February 1999 onwards the management of NWSC has sequentially implemented a number of programs. More autonomy was transferred to the Area Service Providers (‘Areas”), along with defined performance targets and accountability for results. The “100‐Days Program” and the “Service and Revenue Enhancement Programs” resulted in better specification of targets for the Area Service Providers. These programs also increased commitment from the Head Office to provide logistics to enable different Areas to implement programs. In 2002, automatic tariff indexation was introduced. In addition the “Stretch‐Out Program” resulted in a higher level of staff commitment by improving internal communication and setting tougher performance targets and corresponding incentives. A “One‐Minute Management” was introduced to further enhance individual staff’s accountability for achieving targets. In 2000, the Government of Uganda (GoU) introduced a three‐year Performance Contract (PC1) with NWSC. NWSC’s debt service obligations were suspended in return for a commitment to operational and financial improvements and an increase in coverage which reflected GoU policy objectives. A 2003 second Performance Contract (PC2) continued suspension of debt service and specified that NWSC’s debt would be restructured to a sustainable level. The implementation of the agreement was monitored by a review committee. The main sanction of the agreements is in the forms of bonuses for managers and staff.</mark>

**_<mark>Source:</mark>_** <mark>Baietti et al (2006).</mark>

It is worth to put a cautionary note on the adoption of performance based reforms in developing countries that have not first implemented basic budgetary and public expenditure management reforms.<sup>14</sup> In terms of individual performance agreements, the most important performance criteria used in OECD countries include achievement of objectives; competencies and technical skills; interpersonal skills and teamwork; leadership and management skills; and inputs.<sup>15</sup> Box 11 reports the informative example of performance agreements for senior civil servants in the United Kingdom. The UK system has been successful in that it allowed the introduction of powerful incentives for senior civil servants, based on objective criteria as in private enterprises, where good performers are rewarded by performance related pay, whereas poor performers can be dismissed if the poor performance is persistent, even after an agreed improvement plan.

> 14  For an interesting discussion of the virtues of “best practice” vis‐à‐vis “basic first” approach see Andrews (2006).

> 15 See Whitford (2006) for a comprehensive review of the issue of incentives and tournaments in public administrations.

19
World IDvif opmetLRw ts 1980-1990. World Bank.

YAGCI, Fahrettin, Steven KAMIN and Vicki ROSENBAUM. "Structural Adjustment Lending: An Evalua.ion of Program Design." World Bank Staff Working Paper No. 735. 1985.

ZUCKERMAN, Elaine. "Adjustment Programs and Social Welfare." Country Economics Department, World Bank. September, 1988.

ZYMELMAN, Manuel and Toseph DESTEFANO. "Primary School Teachers' Salaries in Sub-Saharan Africa." World Bank Discussion Paper No. 45. 1989.

68
Likewise, the moment conditions used to estimate the parameters are where **Y** _it_ incorporates lagged materials current capital and labor, and their higher-order polynomials and interaction terms as well as lagged output prices, lagged market shares and their appropriate interactions (see De Loecker et al. (2016) for further details). Finally, a translog specification of the production function represented by _fit_ ( ** ̃x** _it_ ; **_β_** ) in expression (A.12) is used. The translog expression is from which we can compute output elasticities of the inputs as

# **A.3 Output Elasticities**

Table A.1 reports results from the production function estimation outlined in the previous subsection. The rows show results by sector. Columns (2), (3), and (4) report output elasticity for capital, labor, and materials, respectively. Column (5) reports returns to scale for each sector. Panel A gives average output elasticities and panel B median output elasticities.

From panel A it is seen that the food and wood sectors had the lowest output elasticities for capital input, 0 _._ 02 and 0 _._ 08, respectively. Another characteristic of the estimation methodology concern the output elasticity of labor, which seems to be small. In the original application of the methodology in India, De Loecker et al. (2016) reported average output elasticities for labor in various sectors within the range 0.09-0.25. Therefore, the results in column (3) of table A.1 are in line with the expected outcome. In addition, it can be noted from column (5) that all sectors have increasing returns to scale.

To cross-check whether the average output elasticities are affected by outliers, panel B of table A.1 reports median elasticities for all inputs and returns to scale. The results show that there are no substantial differences between mean and the median output elasticities across sectors. A slight increase in the capital output elasticities for food and metal sectors can be noted.

As a further robustness checks, sources of variations in the markups are tested for: _<u>it it</u> μ_<sup>_m_</sup> _it_<sup>=</sup> _α_<sup>_θmm_</sup> _it_<sup>onmaterialsand</sup><sup>_μl_</sup> _it_<sup>=</sup> _α_<sup>_θll_</sup> _it_<sup>onlabor.Thesampleisdividedinto</sup><sup>_exante_and</sup><sup>_ex_</sup> _post_ trade liberalization periods, and a statistical test is performed on whether the output elasticities reported in table A.1 for the two periods are statistically different. Results 35
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**Table** **<u>S6. Prevalence of false beliefs about COVID-19 in Uganda versus Malawi</u>**

||Africans immune|Not affect<br>children|Survive warm<br>weather|Common flu|
|---|---|---|---|---|
|Uganda|-0.057***|-0.056***|0.007|-0.293***|
||(0.022)|(0.017)|(0.027)|(0.024)|
|Observations|3,662|3,651|3,152|3,621|
|R-squared|0.005|0.008|0.000|0.150|

_<mark>Note</mark>_ <mark>: Each column reports results from a single regression of a binary variable equal to 1 if the</mark> respondent stated that they thought a false belief was in fact true and 0 if they knew it was false. The binary variable is then regressed on an indicator for Uganda, with Malawi as the base case. The questions about false beliefs were only asked in the first phone survey round in Uganda and only asked in the second round in Malawi. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

33
found in CEE (Central and Eastern Europe) rather than CIS countries (i.e., Commonwealth of Independent States, countries formerly affiliated with the Soviet Union). A thorough understanding of privatization effects thus requires us to account for the selectivity of privatization, and the political and economic contexts in which privatizations take place.

Surveys agree that a glaring shortcoming in the literature surrounding the aftermath of privatization is a lack of convincing studies on the causal effect of privatization on firm performance in China. Most studies on privatization focus on CEE and CIS countries, with occasional exceptions on other large countries such as the United States, Mexico, and India (Megginson and Netter 2001; Djankov and Murrell 2002; Estrin et al. 2009). Much less work is done on privatization in China. Estrin et al. (2009) concludes, “There are as yet no TFP studies using data from China that employ robust methodologies and, perhaps because of this, the available papers find diverse results, with the effect of nonstate ownership being mostly positive but sometimes statistically insignificant and sometimes negative” (Estrin et al. 2009, p. 702).

This deficiency has not been successfully addressed in the past decade, even though this period has witnessed strong interest in, and more studies on, privatizations in China. In the most recent survey on privatization around the world, Megginson (2017) finds that in the new literature on privatization, papers on China accounted for the largest share among all regions. However, most of the papers are on share issue privatizations (SIPs), that is, partial privatization for publicly listed SOEs with the government retaining strong control,<sup>2</sup> and these account for a tiny share of privatizations in China. Under the slogan of “grabbing the big and letting go the small,” the vast majority of the privatized SOEs under the SOE restructuring program near the turn of this century were much smaller and non-listed, and they were afterwards truly private firms (Xu et al. 2005). Since the literature has suggested that the effects of full privatization are much more pronounced than partial privatization (Li and Xu 2004, Megginson 2005), the effects of SIPs are not likely to be representative of full privatization effects in China. Convincing identification of the privatization effect in China remains rare.

> 2 See Sun and Tong (2003), Chen et al. (2008), Berkman, Cole and Fu (2010), Li et al. (2011), Tan et al. (2015), Liao, Liu and Wang (2014). See also additional references on SIPs in China in Megginson (2017). `3`
**Table 4: Marginal effects on trip demand**

||**_HOUS_**<br>|**_EHOLD: ZINB_**<br>|**_ON-SITE: T _**<br>|**_RNBES_**<br>|
|---|---|---|---|---|
|**_Visits_**|**_Coefficient_**|**_% ∆ trips_**|**_Coefficient_**|**_% ∆ trips_**|
|_Count participation equation_|||||
|Travel costs ($USD)|-0.0153***|-1.52|-0.0519***|-5.06|
|Income ($USD)|0.00015***|0.00|0.000013|0.00|
|Age (years)|0.0035|0.35|-0.0263***|-2.59|
|Household size (number)|0.0974***|10.23|0.2711***|31.13|
|Education (years)|-0.0686***|-6.63|-0.0926***|-8.85|
|**_Participation_**||%**_∆_Pr****_(participation)_**|||
|_Binary participation equation_|||||
|Travel costs ($USD)|-0.0109|-1.10|||
|Income ($USD)|0.0012***|0.12|||
|Age (years)|-0.0903***|-9.45|||
|Household size (number)|-0.0313|-3.18|||
|Education (years)|0.2768***|75.82|||
|Yerevan(1=lives in Yerevan)|-0.8631***|-137.06|||

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level

For on-site trip demand, unitary increases in travel costs, age and education decrease the number of trips by 5.1%, 2.6% and 8.9%, respectively, and an increase in household size significantly increases trip frequency by 31%. With the exception of age, each impact has a similar interpretation as in the household model, but the effects are much larger. In the case of age, older individuals are significantly and negatively correlated with higher visitation.

# _(iii) Estimated trip demand and welfare measures_

Using the parameter estimates from the four models in Table 3, the expected number of trips, _E_ ( _yi_ | _X_ _ ) , and consumers surplus (CS) measures were calculated (Table 5).<sup>9</sup> The expected number of trips was estimated for each model using sample means of the independent variables. Comparing the NB with the ZINB, note that the expected number of trips falls once we account for the inflation of zeros (participation). Indeed, since the NB model is treating every zero as being a part of the quantity decision, this

> 9 Although the CV and EV measures are not formally reported above, as the estimated coefficient on income, β _i_ , in both the ZINB and TRNBES models is small, CS is tightly bounded by CV and EV; for the ZINB model CV= $8.7984, EV=$8.8478 and for TRNBES model CV=$8.2137, EV=$8.2123.

15
# **Table A.5. Alternative poverty thresholds and corresponding poverty rates for Russia by household size in 2017**

|**Hosehold Te**|**Poverty**|**Line (in ru**|**bles)**|**Poverty Hea**|**dcount (in**|**percent)**|
|---|---|---|---|---|---|---|
|**u yp**|**Pooled OL**|**BUC**|**Official**|**Pooled OL**|**BUC**|**Official**|
||<br>_Hous_|<br>_eholds with_|<br>_out children_|<br>66|<br>66|<br>66|
|One adult, no children|9,607|9,607|9,607|.<br>_(1.41)_|.<br>_(1.41)_|.<br>_(1.41)_|
||14,891|12,777||3.7|2.0|9.1|
|Two adults, no children|_(343)_|_(784)_|19,214|_(0.76)_|_(0.46)_|_(1.58)_|
|Three adults, no children|19,310<br>_(735)_|14,987<br>_(1,618)_|28,821|1.8<br>_(0.61)_|0.8<br>_(0.40)_|12.7<br>_(2.93)_|
||23,153|16,908||2.7|0.7|10.0|
|Four adults, no children|_(1,127)_|_(2,304)_|38,428|_(1.25)_|_(0.70)_|_(2.56)_|
|Five adults, no children|26,707<br>_(1,470)_|18,542<br>_(2,990)_|48,035|0.0|0.0|7.0<br>_(3.70)_|
||<br>_Ho_|<br>_usehold wit_|_h children_||||
|O dl hild|14,122|12,297|19532|8.7|7.2|27.5|
|ne aut, one c|_(196)_|_(539)_|,|_(3.21)_|_(2.97)_|_(6.45)_|
||17,773|14,218||2.7|0.9|16.9|
|Two adults, one child|_(392)_|_(1,029)_|29,139|_(1.14)_|_(0.52)_|_(2.91)_|
|dl hild|18,734|14,795|4|4.1|0.4|30.8|
|Two auts, two cren|_(196)_|_(784)_|39,06|_(1.10)_|_(0.39)_|_(4.47)_|
|Thr dlt n hild|20,847|15,755|38746|4.1|0.9|21.0|
|ee aus, oe c|_(588)_|_(1,519)_|,|_(1.47)_|_(0.60)_|_(3.49)_|
||20,847|15,852||5.6|1.5|32.0|
|Three adults, two children|_(245)_|_(980)_|48,671|_(2.61)_|_(1.48)_|_(6.41)_|
|Four adults, one child|23,537<br>_(784)_|17,100<br>_(1,912)_|48,353|1.0<br>_(1.02)_|1.0<br>_(1.02)_|14.7<br>_(3.74)_|
|Four adults, two children|<br>22,673<br>_(343)_|<br>16,812<br>_(1,127)_|58,278|<br>0.0|<br>0.0|<br>20.0<br>_(7.61)_|
|Five adults, one child|26,131<br>_(931)_|18,253<br>_(2,255)_|57,960|0.0|0.0|11.0<br>_(5.31)_|

**Note:** Population weights are applied. Standard errors for poverty rates are adjusted for complex survey design. Poverty line for reference “one adult” is defined as an average of minimum subsistence levels for working-age individual and pensioners in 2017. The level of absolute poverty line is 10899 rubles per month for working-age individual, is 8315 rubles per month for pensioner and is 9925 rubles per month for child in 2017. Absolute poverty line of reference adult is adjusted with weights in Table 2 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048) and using Pooled Ordered Logit model (where baseline elasticity equals 0.636 and every child has a weight 0.078).

40
black male workers, who also benefit from a smaller wage premium when employed in the mining industry. These patterns are robust to the inclusion of controls for individual attributes and occupational categories.

While suggestive of the importance of coercion and rent-sharing in the resource industry, the differential income path of former homeland communities in democratic South Africa could at least in part reflect alternative explanations that operate at the communitylevel. First, they might be explained by government interventions aimed at addressing the historically uneven access to infrastructure.<sup>3</sup> In the South African context, Dinkelman (2011) shows that mass roll-out of the electricity grid to rural households in KwaZuluNatal generated sizable employment gains among females and led to pay rises among males. If improvements in access to infrastructure are correlated with initial employment in the mining industry, our estimates would be biased. Second, since the homelands were spread across the country, the effects of their progressive integration in the South African economy might be expected to depend on relative changes in the degree of proximity to markets and connectivity (Harris, 1954; Krugman, 1991; Hanson, 1996; Fujita et al., 2001; Redding and Sturn, 2008). Third, the end of apartheid might lead to migration flows from the former homelands. Heterogeneity in the demographic composition of these flows across locations might challenge our interpretation of the results.

To further strengthen the credibility of our empirical findings, and to assess the role of these alternative explanations, we use the community-level data to provide several additional pieces of evidence. First, we show that our results remain unaltered when accounting for changes in access to several infrastructure items, notably electricity, piped water, refuse and phone. Second, we show that the main findings hold when accounting for the role of market access and connectivity, while also providing evidence that greater proximity to markets was associated with stronger income growth. Third, we find little evidence that population growth differed systematically across local communities from former homelands with varying degrees of exposure to resource rents; and show that our results remain robust when accounting for changes in the size and attributes of the local population. Finally, we perform a placebo test where we find that initial shares of employment in other sectors, where the collective bargaining mechanism is less prevalent, are not associated with stronger income growth.

This paper relates to different strands of existing research. In a recent survey to the literature on historical development, Nunn (2014) emphasizes that while most studies have established how the influence of specific historical episodes persists over long periods of time through their effects on domestic institutions or cultural traits, there is comparatively

> 3At the time of the 1994 democratic elections, over two thirds of black households did not have access to electricity (Dinkelman, 2011).

4
**Figure 5** : Ghana - Gross Fixed Capital Formation, total, private and public sectors, 2004-2011 (% of GDP)

<!-- Start of picture text --> 30.0% Gross fixed capital formation (% of GDP)<br>Gross private capital formation (% of GDP)<br>25.0%<br>Gross public capital formation (% of GDP)<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source_ : World Bank.

**These findings are important because, while the general equilibrium model outlined above underscores the importance of sound fiscal management for debt sustainability, the impact of new borrowing to finance public investments on debt sustainability depends first and foremost on their impact on growth** . A number of empirical studies find a positive impact of public investment on growth both through a direct impact on economic activity and through spillover effects on private investment.<sup>34</sup> A summary way to measure this impact is to calibrate the general equilibrium model with partial equilibrium estimates.  For instance, Buffie et al. (2012) find that, assuming good policy implementation, an increase by 1 percentage of GDP in capital budgeting can raise real GDP growth by about 0.5 percentage points.<sup>35</sup> Gupta et al. (2011), on the other hand, estimate that the unadjusted output elasticity of public capital is reported at 0.25.<sup>36</sup>

# **IV. Conclusions**

**This paper presents a combination of analytical tools, specifically a dynamic general equilibrium framework, a PIMa index, and a PRISM tool aimed at better assessing whether countries meet the necessary and sufficient conditions for optimizing the benefits of debt-financed public investments.** The dynamic general equilibrium model is designed to illustrate the impact that changes in parameters related to public investment management (e.g., changes in public capital depreciation rates due to variations in the level of maintenance, and the share of output devoted to public services) on the optimal rate of public investment-to-GDP ratio. The other tools (the PIMa and the PRISM) are complementary in illustrating the impact of

> 34 Romp, W. and J. de Haan (2007), “Public Capital and Economic Growth: A Critical Survey,” Perspektiven der Wirtschaftspolitik 8, 1–140 presents a survey on this topic.

> 35 Buffie, E., A. Berg, C. Pattillo, Portillo, and L. Zanna (2012), _“Public Investment, Growth, and Debt Sustainability: Putting Together the Pieces_ ”, IMF Working Paper No. 12/144.

> 36 The authors present an alternative measure of efficiency-adjusted capital for which the output elasticity would be around 0.15 (IMF, Working Paper No. 217, 2011).

21