rafmacalaba/datause-encoder-data huggingface dataset:
# 5. MEAN DECOMPOSITION RESULTS

The first step in the mean decomposition is the estimation of equation (1). This is done separately for the pooled, male-managed and female-managed plot samples, and the results reported in Table 3, Columns 1, 2 and 3, respectively.

We find that the log of GPS-based plot area has a negative coefficient that is statistically significant at the 1 percent level in each plot sample. This finding is consistent with recent studies that have investigated and provided support for the inverse yield hypothesis (see Larson et al., 2012 and the references cited therein). A key variable that is positively associated with the log of gross value of output per hectare, irrespective of the plot sample, is the log of inorganic fertilizer use per hectare. However, the return to inorganic fertilizer use (i.e. the coefficient) is higher within the male-managed plot sample in comparison to the female-managed plot sample, and this difference is statistically significant.

The log of household adult male labor hours per hectare has a sizeable and positive coefficient that is statistically significant at the 1 percent level within the male-managed plot sample, while the comparable estimate within the female-managed plot sample is not statistically significant. In contrast, the log of household adult female labor hours per hectare has a positive and statistically significant coefficient across both plot samples, albeit a larger effect, in terms of both magnitude and statistical significance, among female-managed plots.

The coefficients for the shares of plot area under improved seeds and under export crops have sizably positive and statistically significant coefficients at the 1 percent level across all plot samples of interest. Conversely, the child dependency ratio, which is defined as the number of household members below the age of 10 divided by the number of household members aged 10 years and above, has a substantial negative coefficient that is statistically significant at the 1 percent level only within the female-managed plot sample. The comparable statistics for the pooled and male-managed plot samples are not statistically significant.

In addition, although household size has a positive coefficient that is statistically significant irrespective of the plot sample, the magnitude of the coefficient within the femalemanaged plot sample is three times larger than within the male-managed plot sample. The gender differences in returns to household size and child dependency ratio imply that the burden of childcare is more likely to reduce female agricultural productivity.

The decomposition of the mean gender gap, which is estimated at 25.4 percent, is presented in Table 4. Panel B presents the aggregate decomposition components, namely the endowment effect, the male structural advantage, and the female structural disadvantage. Panel C 16
1

# 1. Introduction

The effect of adjustment policies on different groups in the population is an important and complex issue. The distributional implications can have a large influence on the sustainability of programs. In particular, there is a need to identify how different groups in society are affected by an adjustment program and how an alternative mix of policies would affect this outcome. This assessment will be useful for developing compensation schemes for those groups that may be negatively affected in the transitional adjustment period, and in generating support for adjustment programs.

A related issue is the effect of adjustment programs on the poorest groups of the population.' In some countries these poor groups provide needed political support; in other countries the poor do not have political power, but they are still important in the government's social welfare function. Furthermore, poverty alleviation is a development objective and a mandate of the Bank, and Bank-supported reform programs should address what the effects on the poor will be. To better determine how specific actions can be taken to protect the poor during the adjustment process, particular attention should be paid to this segment of the population in the analysis of distributional implications.

'Concerns about the overall income distribution would look at the whole distribution of incomes, while concern for the poor implies a focus on the lower end of the distribution relati.ve to a poverty line. This review looks at studies that examine either inequality or poverty.
**_14_**

||**Airpo**|**rts**<br>**Road**<br>**I**|**'Rail**|**Ports**|**Busas**|**Other**|**All**|
|---|---|---|---|---|---|---|---|
|**America0**|**.73063**|||||||
|||||_9_|||~~~~~~~~~~~9|
|**All**|<br>**0.6118**|**~~0.4209**<br>i|<br>**0-3918**|**_0.4186_**|**0.7769**|**0.4508**|**0.4471**|
|||**S**|~~~~|~~32<br>_6_|**_4_**|**_864_**||

Once the gearing is taken into **accounit, the** average risk level of the sector decreases further and quite significantly for most sectors. In fact, it decreases for all sectors, except for buses as a result of various aspects of the comnpanies covered by the Asia sample, for example significant net cash balances and regulatory regimes that discouraged borrowing. The interesting change in comparison to the pure equity risk is that once the possibility of debt financing is taken into account, rail becomes the least risky sector.

The average figures summnarized here hide some issues identified during the calculation that required further study. They include:

- _the impact of significant net cash balances jor some companies._ Clear examples of companies that have a positive net cash balance, and **so** an asset beta that is greater than the equity beta, include Vienna Airport and some companies operating in Hong Kong (including buses); and

- _the impact of market structure and inter-modal competition._ For example, although US rail regulation has traditionally focused on a rate of return approach, the market structure is such that the exposure to inter-modal competition for freight traffic (passengers are handled by a separate company) leads to a beta value closer to 1 than would have been expected.

Further, in estimating the betas an issue that had to be addressed was _the existence of negative equity betas for some bus companies in the UK._ While a negative equity beta is in principle possible, it is important to ensure that this is a fair measure of the underlying business risk and not a product of infrequent trading or one of a hiost of other possible measurement problems. The solution has been to test the robusteness of the- estimates by redoing for various time intervals (daily, monthly and quarterly) and focus on the: more consistent estimates. In the case where a negative value persisted after the investigation explained above, the company was excluded from the sample.

# **4. IMPACT OF REGULATORY REGIME ON TIHE SECTOR SPECIFIC RISK**

This section links the assessment of the characteristics of the regulatory regimes to the asset beta values. Table 4.1 provides a summary of the assessment of regulatory regimnes and industry structure for those countries and sectors where suifficient information wasavibl.6 .a.. _4._ Suar....ultryrgie

> **16** In a desire to ensure that as many companies as possible are inicluded in the analysis some subjective decisions have been taken. The raw data is provided in the annexes to the full paper and so re-estimation based on alternative interpretations of the subjective information is possible.
private firms. This finding suggests that subsidies are masking some of the profitability issues in SOEs but not generating moral hazard or complacency.

Infrastructure SOEs tend to exhibit weaker operational performance than comparable private firms (Andrés et al. 2008; Gassner, Popov, and Pushak 2009). But little is known about how large the contingent liabilities that SOEs generate are or how costly it is to operate infrastructure assets under state ownership relative to operating them under PPPs or privately (Engel, Fischer, and Galetovic 2010, 2014).

Our results contribute to the debate about who should operate infrastructure assets by bringing systematic evidence of the fiscal cost and financial inefficiency of infrastructure SOEs. We document the fiscal dependence of these SOEs, showing systematic performance differences with private firms. Our evidence highlights that state ownership has significant costs and incentive schemes that perpetuate inefficiency and fiscal dependence.

# **References**

- Andrés, L. A., V. Foster, and J. L. Guasch. 2006. “The Impact of Privatization on the Performance of the Infrastructure Sector: the Case of Electricity Distribution in Latin American Countries.” Policy Research Working Paper 3936, World Bank, Washington, DC.

- Andrés, L. A., J. L. Guasch, T. Haven, and V. Foster. 2008. _The Impact of Private Sector Participation in Infrastructure: Lights, Shadows, and the Road Ahead._ Washington, DC: World Bank.

- Blackwell, M., S. Iacus, G. King, and G. Porro. 2009. “CEM: Coarsened Exact Matching in Stata.” _Stata Journal_ 9(4): 524–546.

- Christiansen, H. 2011. “The Size and Composition of the SOE Sector in OECD Countries.” OECD Corporate Governance Working Paper, Organisation for Economic Co-operation and Development, Paris.

- Dinlersoz, E., S. Kalemli-Ozcan, H. Hyatt, and V. Penciakova. 2018. “Leverage over the Life Cycle and Implications for Firm Growth and Shock Responsiveness.” NBER <mark>Working Paper 25226,</mark> National Bureau of Economic Research, Cambridge, MA.

- Engel, E. M., R. D. Fischer, and A. Galetovic. 2010. “The Economics of Infrastructure Finance: Public-Private Partnerships Versus Public Provision.” _EIB Papers_ 15(1): 40–69.

- ———. 2014. The _Economics of Public-Private Partnerships: A Basic Guide_ . Cambridge: Cambridge University Press.

- Foster, V., and A. Rana. 2019. _Rethinking Power Sector Reform in the Developing World_ . World Bank, Washington, DC.

- Gassner, K., A. Popov, and N. Pushak. 2009. “Does Private Sector Participation Improve Performance in Electricity and Water Distribution?” Trends and Policy Options 6, World Bank, Washington, DC.

Gupta, N. 2005. “Partial Privatization and Firm Performance.” _Journal of Finance_ 60: 987–1015.

21
more confidence to invest. Employers stressed in interviews the importance of being able to get fruit off the vines and packed quickly at the optimal harvest time, otherwise it would not be saleable. One employer in these interviews with the evaluation team said that their fruit was graded and packed according to quality standards, with these standards met only 70 percent of the time in 2007/08, increasing to 90 percent in 2008/09 as they benefited from the second season of RSE workers. In employer surveys, 95 percent of RSEs agree that participation in the program has resulted in better quality and more productive workers and employers rate Pacific workers as more dependable, productive, and enthusiastic than all other categories of seasonal workers (Department of Labour, 2012).

To measure the impact of the RSE on employers, one would ideally like to measure changes in employer production and earnings (to account for quality improvements) relative to these same changes for a comparable control group of employers that do not participate in the RSE. We set out to try and collect data from farmers in New Zealand, but were unable to collect detailed production data from many farmers. However, in a couple of cases we were able to collect data that at least provide some case study evidence on the relative productivity of new and return RSE workers for farmers, and of RSE workers versus other workers. We present this evidence for the first time here.

Table 6 uses data from a citrus orchard from 2011 to compare the productivity of first, second, third, and fourth year RSE workers. Since workers are paid piece rates, earnings are a reasonable approximation to productivity. Considering only workers who worked for the full season, we see that workers in their second year earned approximately 10 percent more than workers in their first year of the RSE, with this premium increasing to 13 percent for two years of prior experience, and 18 percent for three years of prior experience.

**<mark>Table 6: Gross earnings of full season workers at one citrus orchard by</mark>** **<u><mark>years of experience in the RSE</mark></u>**

|Year of RSE experience|Number of<br>Workers|Mean<br>Earnings<br>per worker<br>in 2010/11|Productivity<br>premium over<br>1styear workers|Median<br>Earnings<br>per worker<br>in 2010/11|Productivity<br>premium over<br>1styear workers|
|---|---|---|---|---|---|
|First|24|14408||14318||
|Second|17|15726|9.1|15916|11.2|
|Third|10|16262|12.9|16258|13.5|
|Four|10|17050|18.3|16880|17.9|

18
outcomes, it is inefficient to fully correct for baseline imbalances between treatment and control groups.

Section 2 begins with a theoretical discussion of what power calculations tell us about the gain to be had from using more rounds of data, and provides examples for common types of economic data. Section 3 then derives implications for the choice of how many pre-treatment and post-treatment waves to use. The bottom line is that collecting multiple measurements posttreatment will make most sense when the data have low autocorrelation; whereas baseline data is of most use when the autocorrelation is high (although there are of course other important uses of baseline data apart from improving the power to estimate treatment effects, such as for examining treatment heterogeneity, and so one may still wish to do a baseline for these other reasons – something I discuss at the end of the paper). It would appear that for many economic outcomes, experiments with a fixed budget to conduct a total of K surveys would have greater power when dividing that K over multiple post-treatment rounds than having a baseline and single follow-up. Section 4 then discusses additional practical issues which are likely to arise when considering multiple measurements in economic experiments, many of which are not common concerns in clinical trials. Section 5 concludes.

# **2. What is the gain from more rounds?**

Let _Yi,t_ be an outcome of interest for household or firm _i_ in survey round _t_ . Consider an intervention which assigns units to receive a binary treatment (such as getting business training or not, or getting a conditional cash transfer or not). Suppose there are _m_ pretreatment survey rounds (labeled –( _m_ -1) through 0) and _r_ post-treatment survey rounds (labeled 1 through _r_ ). A common method in economics for estimating the treatment effect<sup>3</sup> of the intervention on the outcome of interest is via the following difference-in-differences specification: where _EVERTREATi_ is a dummy variable which takes value one if unit _i_ is assigned to the treatment group and zero if it is assigned to the control group, the are time dummies, and

> 3 I focus on estimation of intention-to-treat effects here, but the same points made here also apply for measurement of the treatment effect on the treated and other treatment effects of interest.

4
<!-- Start of picture text --> Per Capita Expenditure Food share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br><!-- End of picture text --> 83
general government consumption to GDP. Openness to international trade is proxied by the volume of trade (exports and imports) over GDP.

The consumer price index (CPI) inflation rate is a proxy for macroeconomic stabilization, with high inflation being associated with bad macroeconomic policies. Financial depth, the government consumption ratio, trade openness, and the inflation rate<sup>11</sup> enter the growth regressions as the log of the average for the corresponding fiveyear period. All these control variables are assumed to be either predetermined (independent of current disturbances, but they may be influenced by past ones) or endogenous and thus correlated with current realizations of the error term, one of the main reasons for using the GMM procedure outlined above.<sup>12</sup> With regard to the second group of growth determinants, the regressions include two variables that are assumed to be strictly exogenous: shocks to the terms of trade and period-specific dummies. Terms of trade shocks are measured by the growth rate of terms of trade (export prices relative to import prices) over each five-year period. The idea is to capture shifts in the demand for a country’s exports, and since terms of trade depend mainly on world conditions, it is assumed to be exogenous to contemporaneous growth of per capita GDP of a particular country. We include period-specific dummies to capture the impact of other global shocks to growth across countries.

Finally, to maintain consistency with the literature, data for natural disasters were obtained from the Emergency Disasters Database (EM-DAT). EM-DAT is a worldwide database on disasters maintained by CRED with the sponsorship of the United States Agency for International Development’s Office of Foreign Disaster Assistance (OFDA). It contains data on the occurrence and effects of more than 17,000 disasters in the world from 1900 to the present. The database is compiled from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies.

> 11 Inflation rate enters the regressions as log[100+inflation rate]

> 12 Specifically, regarding the difference regression corresponding to the periods _t_ and _t-1_ , the following instruments are used:  for the variables measured as period averages--financial depth, government spending, inflation, and trade openness-- the instrument corresponds to the average of period _t-2;_ for the variables measured as initial values--per capita output and secondary school enrollment-- the instrument corresponds to the observation at the start of period _t-1_ .

15
# **Firm-level Model**

In this section, we discuss how we test our hypotheses using firm-level panel data. This method has the advantage of enabling us to measure geographic diversification continuously and to add control variables for firm-level characteristics that might confound the relation between FDI and political conflict. However, the disadvantage is that for many of the observations in the original data set, firmlevel variables were not available. Although as many as 67.1% of all parent companies in the fDi Markets data set could be matched to firms registered in ORBIS, only for 1,413 of those firms (10.2%) data on firm-level characteristics were available. Although a sample of this size is not uncommon in IB research, it considerably decreases the precision of our estimates and the ability to detect statistically significant effects. In addition, a missing value logistic regression (Long & Freese, 2006) shows that firm-level data are not missing at random; the probability that data are missing depends on the sector and the level of political conflict. As this could considerably bias our results, we prefer the countrylevel estimates and show the estimates of the firm-level regressions merely for robustness.

The firm-level data set comprises data on the 1,413 MNEs that invested in a developing country between the years 2003 and 2012, according to the fDi Markets data set. For each MNE ( _m_ ) we report greenfield FDI to a host country ( _i_ ) in a certain year ( _t)_ , resulting in the following regression model: ������ � ∝��∝� ������� �∝� ������� � ∝� ������� �∝� �������� ��� � �� � �� � ����, (2) Similarly to our previous models, the independent variable of interest is political conflict _P_ for country _i_ in the previous year, while we control for destination country variables _Xi(t-1),_ a set of country dummies _μi_ , and a vector of time dummies _μ_ t. In addition, we add fixed effects for the country of origin _μ_ o, controlling for time-invariant characteristics of the home country of the MNE. We also add a set of firm-level control variables ������� , including greenfield FDI flows by the MNE _m_ to country _i_ in the previous year, the _age_ of the firm (ln), the number of _employees_ (ln), the rate of return on equity ( _ROE_ ), and _geographic diversification_ , measured by the number of countries in which the multinational is present (ln). The diversification variable is standardized to simplify the interpretation of the main effect of political conflict and the moderator. Finally, we include a set of bilateral variables, �������� , 32
- 28 -

The transport service demand is found to be significantly high when people engage in retail and other local businesses, such as bakery and carpentry: The coefficient of _SME_ is significant. Farmers’ demand seems to be weak: the coefficient of _FARM_ is small and statistically insignificant. This is broadly consistent with the above. The estimation results suggest that there is an important indirect effect on local businesses. As the feeder road network improves, agricultural production and market sales increase. Together with such agricultural growth, local economies will grow, with more transport services provided. This is a much broader impact that improved roads can generate.

**<u>Table 11. Censored regression for transportation demand</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|D<sup>RSDP</sup>|-0.265|(0.223)|-0.161|(0.214)|0.066|(0.339)|
|FEED15-30min|0.192|(0.210)|||||
|FEED30-60min|-0.323|(0.292)|||||
|FEED1-2hr|-0.611|(0.282) **|||||
|FEED2-3hr|-1.131|(0.327) ***|||||
|FEED>3hr|-0.086|(0.364)|||||
|D<sup>Less than 1hr</sup>|||0.563|(0.209) ***|0.642|(0.231) ***|
|D<sup>RSDP</sup>•D<sup>Less than 1hr</sup>|||||-0.280|(0.390)|
|TIME|-0.0018|(0.0007) **|-0.0018|(0.0007) **|-0.0018|(0.0007) **|
|FARE|-0.0041|(0.0026)|<sup>-0.0041</sup>|<sup>(0.0025)</sup>|-0.0040|(0.0025)|
|WAIT|-0.0014|(0.0008) *|-0.0010|(0.0008)|-0.0010|(0.0008)|
|FARM|0.029|(0.250)|0.043|(0.244)|0.025|(0.246)|
|SME|0.941|(0.245) ***|0.926|(0.246) ***|0.922|(0.247) ***|
|GOVT|0.022|(0.196)|0.002|(0.197)|-0.010|(0.197)|
|STUD|0.422|(0.246) *|0.409|(0.251) *|0.410|(0.252) *|
|MMinibus|-0.187|(0.177)|<sup>-0.135</sup>|<sup>(0.172)</sup>|-0.140|(0.173)|
|MBajaj, Motor|0.812|(0.528)|0.924|(0.558) *|0.946|(0.556) *|
|AGE|-0.004|(0.007)|<sup>-0.003</sup>|<sup>(0.007)</sup>|-0.003|(0.007)|
|MALE|0.262|(0.161) *|0.207|(0.162)|0.211|(0.162)|
|2EDU|-0.033|(0.160)|<sup>-0.026</sup>|<sup>(0.159)</sup>|-0.024|(0.159)|
|URBAN|-0.046|(0.211)|0.056|(0.196)|0.049|(0.196)|
|T2013|-0.119|(0.250)|<sup>-0.028</sup>|<sup>(0.246)</sup>|-0.026|(0.246)|
|T2014|0.828|(0.271) ***|0.809|(0.274) ***|0.811|(0.275) ***|
|T2015|-0.036|(0.246)|<sup>-0.012</sup>|<sup>(0.246)</sup>|-0.027|(0.245)|
|T2016|0.360|(0.292)|0.346|(0.295)|0.355|(0.298)|
|Constant|3.037|(0.419)***|2.289|(0.402)***|2.231|(0.412)***|
|Obs.|498||498||498||
|Wald chi2|102.05||91.53||91.98||