rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix A: Additional Tables

Table A.1: Pre-intervention SLM training across treatment status (recalled)

|Variables|Treated mean|Control mean|Dierence in mean|
|---|---|---|---|
|Contact Farmers: before 2010||||
|Learned mulching|0.620|0.762|-0.141*|
|Learned strip-tillage|0.321|0.429|-0.107|
|Learned pit planting|0.504|0.524|-0.020|
|Learned contour farming|0.307|0.381|-0.074|
|Learned crop rotation|0.591|0.690|-0.099|
|Learned row planting|0.285|0.238|0.047|
|Learned improved fallowing|0.212|0.262|-0.050|
|Number of observations|137|42|179|
|Other Farmers: before 2010||||
|Learned mulching|0.306|0.337|-0.031|
|Learned strip-tillage|0.182|0.227|-0.045|
|Learned pit planting|0.145|0.113|0.032|
|Learned contour farming|0.039|0.048|-0.009|
|Learned crop rotation|0.360|0.360|0.000|
|Learned row planting|0.104|0.114|-0.010|
|Learned improved fallowing|0.101|0.104|-0.003|
|Number of observations|4,385|1,499|5,884|

Source: Household survey, 2012. Notes: T-test inferences are based on standard errors clustered at the community level. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level for t statistics. SLM=sustainable land management.

44
14 the second investments to enhance efficiency. The fear has been that Workers' Council-dominated SOEs would lack the will to take either remedial measure. Above, evidence has been presented on high labor shedding by the sample firms. This section discusses wage behavior and decapitalization.

A general perception of SOEs has been that, being workercontrolled, they would quickly pay out all surpluses as wages, run the company into the ground and then approach the government for a bailout. It has also been felt that the only way to eliminate this possibility would be to rapidly privatize. The excess wage tax, PPWW, which was <!-- Start of picture text --> Interest Burden<br>(cumulative)<br>250.0x-<br>200.0%-<br>150.0%-<br>100.0%-<br>0<br>0.0% =;<br>N 89 19II 0 90IV90 191 1 91 1191 IV 91 192 n 92<br>S.. tedxt fo dfNito<br>[AAA +AAIf I A<br><!-- End of picture text -->

**Figure** 7

originally imposed to control inflation, soon became a penalty on wage increases in SOEs. Thus, the private sector was exempted from the PPWW in 1991. At the same time, the basis for determining norm wages was changed from the wage bill to average wages, which meant that firms shedding labor could start paying more excess wage taxes. More recent proposals in the context of the Pact on SOEs (discussed below) focus on the distribution of enterprise surplus among wages, retained earnings and taxes. Throughout, the guiding principle has been that SOEs, being myopic, would rather increase wages than invest and protect the long-run interests of the firm.

This section asks two questions: who pays PPWW? And: are these also the most decapitalized firms? The answer is that the most profitable firms pay the most PPWW but are the least decapitalized. In fact, these firms (the AAA) are also the ones which have been investing the most. The results demonstrate that the image of SOEs as short-sighted decapitalizers is highly exaggerated.

Table 12 on average wages for selected months shows that all three groups were level at the beginning. Thereafter, a comparison of the AAA and A firms shows that AAA wages were about 25% ahead by December 1991; but the gap narrowed to 17% by June, with AAA firms actually freezing nominal wages, while these rose for AA and A firms. Notably, the AAA wage in June 1992 was significantly below the national average of zloty 2.4 million reported by GUS.

Table 12: Average Wages For Selected Months (Thousands of zlotys per worker)

|_______J__D|ec-89|June-90|tec-90|June-91|Dec-91|June-92|
|---|---|---|---|---|---|---|
|AAA|658|918|1568|1573|2178|2169|
|| AA|765|1014|1763|1334|1885|2017|
|A|603|852|1395|1440|1737|1858|
**Table 6: Average wages on MGNREGS and in casual labor 2009/10**

||Average wage rate<br>on MGNREGS<br>(Rs/day)|Aver<br>Overall|age casual wag<br>(Rs/day)<br>Male|e rate<br>Female|
|---|---|---|---|---|
|Andhra Pradesh|91.9|98.5|115.4|75.7|
|Assam|87.0|90.1|94.4|74.9|
|Bihar|97.5|79.4|81.0|65.8|
|Chhattisgarh|82.3|68.8|70.8|65.5|
|Gujarat|89.3|83.3|87.3|71.0|
|Haryana|150.9|139.6|146.1|99.1|
|Himachal Pradesh|109.5|139.6|141.4|110.2|
|Jammu & Kashmir|93.3|158.3|157.5|n.a.|
|Jharkhand|97.7|101.2|103.6|82.2|
|Karnataka|86.0|84.5|96.9|62.8|
|Kerala|120.6|206.5|226.6|119.3|
|Madhya Pradesh|83.7|69.0|74.5|58.1|
|Maharashtra|94.3|75.2|86.0|58.2|
|Orissa|105.9|75.6|81.0|59.1|
|Punjab|123.5|130.4|133.5|91.8|
|Rajasthan|87.4|125.7|132.3|94.3|
|Tamil Nadu|71.6|110.8|132.1|72.6|
|Uttar Pradesh|99.5|94.3|97.0|69.2|
|Uttarakhand|99.0|118.7|122.1|96.7|
|West Bengal|90.4|85.3|87.8|65.9|
|All India|90.2|93.1|101.5|68.9|

_Notes:_ MGNREGS wage rates estimated as total expenditure on wages (excluding skilled or semi-skilled) divided by total number of person days of employment for FY 2009/10 (April 2009 to March 2010). Casual wages for June 2009 to July 2010 period, based on NSS 66<sup>th</sup> round survey. ―All India‖ includes smaller states not reported. Note that we do not report the female wage rate for Jammu & Kashmir, as we found it was based on a sample of only 7 observations and was not reliable. (The sample estimate of 206.5 was also implausibly high relative to the male wage). _Sources_ : Casual wages from _Key Indicators of Employment and Unemployment in India, 2009/10_ , NSSO, Govt. of India (June 2011). MGNREGS expenditure and employment data are from the state-wise Monthly Progress Reports (www.nrega.nic.in).

29
with: where _φ_ ( _x_ ; _m, v_ ) is a normal density function with mean _m_ and variance _v_ evaluated at ̃ the value _x_ . Note that when the variance of the error term tends to zero (i.e. _σ_<sup>2</sup> _→_ 0), the conditional mean _E_ [log _yi|_ log ̃ _xi_ ] will tend to log ̃ _xi_ while the conditional variance will tend to zero, as they should.

A practical way to proceed is to draw an observation of NSS log consumption from the conditional distribution _Fy|x_ for each household, and evaluate the welfare measures of interest. We draw 50 observations of NSS-type log consumption for each household in the CPHS sample, and then compute the aggregate welfare indicator (i.e. poverty and inequality) for each _k_ = 1 _, . . . ,_ 50. The mean and standard deviation evaluated over the _K_ realizations will serve as the point estimate and standard error of the welfare indicator.

Alternatively, when measuring head-count poverty for example, one could evaluate for each household the probability that their NSS log consumption is below the poverty line conditional on the observation of their CPHS log consumption value -- and then compute the mean value of these probabilities across all households in the sample. Let the poverty line for log consumption be denoted by _z_ . The probability that household _i_ is poor equals: where Φ is the standard normal distribution function. Head-count poverty can then be estimated by: where _wi_ denote survey weights that are assumed to sum up to 1.

# _Implementation_

The assumed model (see eq. 2) contains three parameters: _a_ , _b_ , and _σ_<sup>2</sup> . As described above, a minimum of three moments (for both the NSS and CPHS log consumption data) are required to estimate all three of these parameters. All three moments of the CPHS log consumption distribution can readily be estimated using the observed CPHS consumption data. Estimation of the moments from the NSS consumption distributed is complicated by the fact that there is no NSS survey for the same moment in time for which we have CPHS. We have established however that the third moment of NSS 36
-48-

reponse in these circumstances would seem to one that combined demand stimulation and measures designed to raise 1.oour mobility so as to increase job opportunities to outsiders [OECD, 1987b].

While this may seem to reinforce the case for a demand-led upswing, a note of caution is, nonetheless, required. The initial impact of an increase in demand may not translate itself in an increase in employment, particularly if labour hoarding is still widespread. While, as mentioned above, the presence of such labour hoarding would help the cyclical recovery in productivity and profits, it would do little to reduce the power of insiders and this, in turn, could generate inflationary pressures. These would be further increased if, in addition, particular skills were in short supply, as is often claimed to be the case, or the labour market was riddled with a whole host of rigidities (low mobility, inflexible wage differentials, job security provisions, etc.).

It is difficult to know how far these fears are justified. Many of the rigidities that are often cited stem more from the recession itself than from other causes and may well diminish, if not disappear, if recovery occurred. Thus, the much publicized decline in labour mobility, for instance, may well reflect, rather than be a cause of, declines in the demand for labour [Flanagan, 19871. It also appears that relative wages are less important as an allocative mechanism than commonly thought, with job allocation being primarily sensitive to vacancies rather than to pay differentials (Reichlin, 1987J. As for job security, this does raise labour costs and therefore reduces employment, but it also increases the flexibility of internal labour markets (as seen in the case of Japan). Skill shortages may be more real, at least in some countries, but are unlikely to have become more pronounced than earlier.
# **4.3 Counterfactuals**

We calibrate the model to assess the welfare and spatial impacts of new infrastructure investments. The counterfactual exercise is done in three steps. First, we calibrate the model to obtain the underlying parameters of the model for the baseline situation, without the new investments. Second, we update the trade costs and sectoral productivity parameters based on the new assumptions. Third, we use the model to obtain the new employment shares given the new transport costs and productivities, the wage per location, and therefore the real wage given the new equilibrium goods and housing prices.

We assume that electrification increases productivity in modern sectors and that transport investments increase market access by reducing transport times. To build the electrification counterfactual, we assume that access to electricity increases productivity in modern sectors, being manufacturing and services (see Moneke (2020)). To build the transport counterfactuals, we use the available road networks for each country, making assumptions about speed along the networks based on the type and condition of roads that are registered. Investments are assumed to increase the speed at which vehicles can travel along segments that are improved or build new links between locations. We assume trade costs to be iceberg costs, such that the costs between the origin location _o_ and destination _d_ are given by _dod_ = _max_ (1 _,time_<sup>_τ_</sup> ). Border costs are also added to travel costs, as detailed below.

We calibrate the model using spatial data for land, population, and sectoral shares from the sources used earlier. Because of the complexity of a three-sector model to converge in order to recover the initial sectoral productivities, we reduce the spatial disaggregation to fewer locations. Such aggregation also smoothed measurement issues of sectoral employment based on the DHS data. We divide the Lake Chad region into 24 regions, including 8 first-order administrative divisions in Cameroon, 6 in Nigeria, 8 in Chad. Figure A23 shows the share of agricultural employment and population for each of these subnational regions.

## **4.3.1 Productivity shocks from rural electrification**

The counterfactual consists in providing access to electricity for all in each region based on the current coverage measured by Night Time Lights as described in the Data section. We calibrate the increase in productivity using the estimated productivity increase of 28.4 percent for Ethiopia from Moneke (2020) and the current coverage of electricity per location. The lower the existing coverage, the higher the productivity increase in the counterfactual. The new productivity levels _A_<sup>∗</sup> are given by : _A_<sup>_i_</sup> _n_<sup>∗=</sup><sup>_Ai_</sup> _n_<sup>+</sup><sup>_Ai_</sup> _n_<sup>×(1−</sup><sup>_Elecn_)×0</sup><sup>_._284</sup><sup>_,_</sup> _n_ = _M,S_ with _Elecn_ the current ratio of electrified population. Map 5 shows the percent of productivity increase per location.

24
# **5. The World Bank’s Forestry Portfolio**

To test the PRIME framework and the extent to which the five pathways are covered in forestry investments, we undertook a portfolio review of World Bank forestry projects. Our goal was to assess where forest investments were being made, the extent of the investments and the types of activities undertaken and, ultimately, how they addressed PRIME aspects towards poverty reduction in forest landscapes.

Since the adoption of the WBG Forest Strategy in 2002, 88 of the 309 projects with a forest component went through a full project cycle from fiscal year 2002 to 2015, i.e. they were approved, fully implemented, and brought to closure. These projects accounted for around US$ 1.2 billion in loans, credits, and grants specifically allocated to forests.<sup>22</sup> For our analysis, we examine a subset of this portfolio, focusing on 40 projects that made significant interventions in the forestry sector. Our sample included a subset of forestry projects from the World Bank’s public project database that met four criteria: a) relatively recently undertaken projects (approved after 2002); b) completed projects with a results assessment available; c) WBG funding for the forestry-related components amounted to at least US$ 0.5 million and was thus sufficiently large to include hands-on interventions; and d) at least 20 percent<sup>23</sup> of each intervention was in the forestry sector, ensuring the project’s relevance for the sector.

Project information was extracted from two standard documents prepared at the beginning and end of each WBG project: The Project Appraisal Document and Implementation Completion Report. For each project, the summary descriptions of project components were reviewed along the main results indicators. If this summary information was insufficient to clarify the content of a project component or its beneficiaries, then the PAD and ICR were reviewed in greater depth.

To determine whether and where project components aligned with PRIME, the decision criteria listed in Table 1 were applied. For example, for an intervention to be counted under “R”, rights had to be strengthened either informally or formally through a law or regulation. However, the mere inclusion of community members in the design, implementation or monitoring process of a project component did not count, even if it may have _de facto_ strengthened their rights over forests and land. Explicit efforts to make forest decision-making bodies, e.g. forest groups, more inclusive and effective, were included here. Under “I”, projects counted only if the regional complementary investment was supporting people’s livelihoods in forest landscapes, for example through investments in improved telecommunication services to access markets, by improving access roads or through lowering the regulatory burden for small-scale forest enterprises. However, interventions were excluded if they supported macro-level reforms, such as the development of a national forest sector strategy. “M” interventions included actions aimed at enhancing market access, e.g. through marketing and logistics support, and value-addition activities. Market 15
- manufacturing: Spillovers from firm management practices to energy efficiency. _J. Environ. Econ. Manage._ , 68(3):467–479.

- Delmas, M. A. and Toffel, M. W. (2008). Organizational Responses to Environmental Demands: Opening the Black Box. _Strategic Management Journal Strat. Mgmt. J_ .

- Distelhorst, G., Hainmueller, J., and Locke, R. M. (2017). Does Lean Improve Labor Standards? Management and Social Performance in the Nike Supply Chain. _Management Science_ , 63(3):707–728.

- Gillingham, K. and Palmer, K. (2014). Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence. _Review of Environmental Economics and Policy_ , 8(1):18–38.

- Grover, A., Iacovone, L., and Chakraborty, P. (2019). Management Practices in Croatia: Drivers and Consequences for Firm Performance. Technical Report 9067, The World Bank. Policy Research Working Papers.

- Grover, A. and Torre, I. (2019). Management Capabilities and Performance of Firms in the Russian Federation. Technical Report 8996, The World Bank. Policy Research Working Papers.

- Ichniowski, C. and Shaw, K. (1999). The Effects of Human Resource Management Systems on Economic Performance: An International Comparison of U.S. and Japanese Plants. _Management Science_ , 45(5):704–721.

- Karplus, V. and Zhang, D. (2020). Management practices and energy efficiency in industrial firms: Experimental evidence from China. Unpublished manuscript.

- Lyubich, E., Shapiro, J. S., and Walker, R. (2018). Regulating Mismeasured Pollution: Implications of Firm Heterogeneity for Environmental Policy. _AEA Papers and Proceedings_ , 108:136–142.

- Martin, R., Muˆuls, M., de Preuxe, L. B., and Wagner, U. J. (2012). Anatomy of a paradox: Management practices, organizational structure and energy efficiency. _J. Environ. Econ. Manage._ , 63(2):208–223.

- Ryan, N. (2018). Energy productivity and energy demand: Experimental evidence from Indian manufacturing plants. _NBER Working Paper 24619_ .

- Schweiger, H. and Stepanov, A. (2019). Energy Subsidies, Energy Intensity and Management Practices. _SSRN Electronic Journal_ .

- Syverson, C. (2004). Market structure and productivity: A concrete example. _J. Polit. Econ._ , 112(6):1181–1222.

- Syverson, C. (2011). What determines productivity? _J. Econ. Lit._ , 49(2):326–365.

- Taylor, M. (2020). Energy Subsidies: Evolution in the Global Energy Transformation to 2050. Technical report, International Renewable Energy Agency, Abu Dhabi.

- U.S. Census Bureau (2015). Management and Organizational Practices Survey (MOPS). `https://www.census.gov/programs-surveys/mops.html` .

- World Bank (2020a). Enterprise Surveys. `https://www.enterprisesurveys.org/en/ enterprisesurvey` .

- World Bank (2020b). Exchange Rate. https://databank.worldbank.org/source/worlddevelopment-indicators.

- World Bank Data (2020). World Bank Country and Lending Groups Classification. `https://datahelpdesk.worldbank.org/knowledgebase/articles/ 906519-world-bank-country-and-lending-groups` .

33
Learning from Developing Country Power Market Experiences: The Case of Philippines Supply has been secure for Luzon and Visayas in recent years bar occurrence of force majeure events (e.g. major typhoons and earthquakes). Mindanao has entered a period of supply security starting 2017 with the influx of new generating capacities in the region. Indeed, the breakdown of the capacity margin (dependable generation capacity divided by peak demand) for each of the major Philippine power grids highlights the downward trend in Luzon and Visayas until 2014, down from a situation of high margins inherited from the IPPs program of the 1990s. From 2015 to 2017 the reserve margin rose, particularly in Mindanao reaching 75% in 2017, following aggressive investments in (mostly coal‐fired) generation capacity (see Figure 5‐6 **Error! Reference source not found.** ).

<!-- Start of picture text --> Capacity margin<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Luzon Visayas Mindanao<br><!-- End of picture text --> Figure 5‐6 Evolution of capacity margin in Philippine’s grids.

Source: Department of Energy.

However, tight supply conditions have been reflected in episodes of price hikes (EIA, 2015). Tight supply conditions are common in Philippines in particular when Malampaya gas‐fired power plant is scheduled for maintenance or faces gas shortage; as well as due to facilities outage (planned and unscheduled), and hydro availability as an effect of El Niño meteorological phenomena (PEMC, 2016b).

Capacity expansion of generation in Philippines has been driven by coal over the past decade (see Figure 5‐7 **Error! Reference source not found.** ). Indeed, coal operational capacity grew by 64%, from 3.4 GW in 2005 to 7.7 GW in 2017 (CAGR of 6.9%), largely surpassing capacity additions from every other technology. Moreover, the pipeline of committed generation investments is dominated by 6.3 GW of coal‐fired projects (about 73% of on‐grid committed generation projects) to be commissioned over the next four years. The trend is consistent with an outlook of low coal prices, and the regulatory review of contracts for regulated customers (focused on costs rather than economic efficiency).

38
_12_

**_Price_** _Support at Any_ **_Price?_**

products are linked in the model to the world prices, the income from farming activities is linked to them as well. If the real appreciation of zloty takes place, the level of agricultural income remains almost unchanged in dollar terms, that means a relative fall terms).

<!-- Start of picture text --> Figure 5 presents the<br>impact of efficiency gains in Sensitivity Analysis<br>agriculture on the income Income relation and efficiency gains in agriculture<br>relation. In the base run, no<br>such gains was assumed. In 1.40 noome relation ffamnerseaver a)<br>the sensitivity analysis we have<br>tested the impact of 1-2% 1.30 -.<br>yearly efficiency gains (the . yearly effidency gains 2%<br>gains of this order were 1.20 - y e n g<br>recorded in Western Europe). no effidency gains (s<br>The higher the efficiency 1.10 - ,no effldency gains os)<br>gains, the smaller the risk of .<br>the fall in the income relation. 1.00 -<br>0.90 -i ioi , sobs 2010<br><!-- End of picture text --> Figure 5 The conclusions that can be drawn from the sensitivity analysis are that a fast rate of growth of non-agricultural sectors, accompanied by the real appreciation of the domestic currency (caused either by a good current account performance, or by a significant capital inflow), may seriously jeopardize the relative income position of farmers. Such developments are quite probable, if the positive scenario of the economic development and integration with the EU realizes. The agricultural sector may defend successfully the income relation by increasing its efficiency (reducing input requirements).

# **2.3 Integration with the European** Union

An active agricultural policy may be required by the process of Poland's accession to the EU. Extending the CAP to the new members from Central and Eastern Europe is seen as one of the main challenges for the enlargement. The basic question is whether CEE countries should introduce a price supporting system as a necessary condition for the accession (a price _supporting_ system, not only a price _stabilizing_ system), and what would be costs of such a system.

It is generally known that the most important part of the CAP is a price supporting system, that causes EU market prices to be far higher than the world prices level for the lion's share of the