rafmacalaba/datause-encoder-data huggingface dataset:
to get the subsidy in the future) to producers who obtain a low social return but invest only because they are able to access the subsidy. The net effect on total investment is ambiguous, but the efficiency impact is negative as a consequence of effect (ii).

The above example is not just a curiosity. That subsidies, at least in the form in which they are usually allocated, do not generally promote investment has been shown by several empirical studies in various countries. Empirical studies using detailed firm-level data by Bregman, Fuss, and Regev (1999) for Israel, Fakin (1995) for Poland, Lee (1996) for Korea, Bergstrom (1998) for Sweden, Estache and Gaspar (1995) for Brazil, Harris (1991) for Ireland, and several others have shown that subsidies and corporate tax concessions targeted to specific firms are at best ineffective in promoting investment and technological adoption and, in some instances, even counterproductive. Crowding-out of private investment as a consequence of the subsidies occurs.

(iii) Subsidies may _indirectly_ crowd out private investment in the intermediate run. The third type of crowding-out of private investment as a result of public expenditure biases toward subsidies is long-run in nature. The low stocks of public goods caused by continuous underinvestment in public goods causes low productivity of private investments over the long run. This, in turn, is translated into slower investment and slower productivity growth over the long run (World Bank. 2000).

_The structure of public expenditure and rural poverty_ . Public goods affect rural poverty through _direct_ effects and through _indirect_ effects: (i) Direct effects. Important components of rural public goods are human capital and investment in the protection of natural resources and the environment. Human capital is the main productive asset for most poor. However, as a consequence of credit and other market failures they are not in general able to fully finance investments in human capital, regardless of how high the rate of return to these investments might be (World Bank, 2000). They are, therefore, largely dependent on the public sector as a source of financing for these investments. On the other hand, the rural poor are also highly dependent on natural resources as a source of subsistence (Barbier, 2004). The price of resource degradation is often disproportionately paid by the rural poor. Increasing investment to protect natural resources and to mitigate environmental externalities is, therefore, vital to reducing rural poverty.

8
those receiving treatment 5 (no network). In the matched sample those receiving treatment 6 also on average use their stoves 0.53 times more per week than those receiving treatment 2 (50 Birr use incentive, plus network).

# _5.4 Pooled OLS Regression Results_<sup>_8_</sup>

We now present our pooled OLS results with round and district (i.e. _woreda_ ) fixed effects with errors clustered by household. The dependent variable is the number of times per week respondents use the Mirt stoves. In these models, though our randomization appears to have been successful, we adjust for key household level variables that could confound our interpretation of the treatment effects. The regression models also allow us to analyze the effects of non-treatment variables on stove use frequency. The results of 6 models are presented in Table 9.

Under the null hypothesis that _R_<sup>2</sup> = 0, the _F_ statistic follows the _F_ distribution with 60 and 984 degrees of freedom. We find for model 5, which is the focus of our interpretation, that we can reject the null hypothesis that the explanatory variables have no impact on the stove frequency at the 1.0% level (F=16.4).<sup>9</sup> In model 6 we include interactions between treatments and rounds to see whether treatments have different effects over time, however when we compare model 5 (without interaction) with model 6 (with interaction), we find that the F statistic is insignificant. This result suggests that the interaction terms do not improve the explanatory power of the regression.

Based on model 5, we find that those who received stoves under treatment 2 and 6 use their Mirt stoves more frequently than treatment 5 (the omitted treatment). The dominance of treatment 6 vis-à-vis treatment 5 is consistent with the other empirical methods used. As the only difference between the treatments is the network component, the result indicates that without payment incentives, networking spurs actual use of the Mirt stoves. The two previous methods indicated that treatment 2 spurred stove use no better (and compared with treatment 5 worse) than other treatments. The pooled OLS results suggest the opposite conclusion and is statistically significant at

> 8 The random effects estimation results with round and district (i.e. _woreda_ ) fixed effects are found in Appendix B.

> 9 F= R<sup>2</sup> <u>/(k−1)</u> where _k_ is the number of parameters and _n_ is the number of observations. (1−R<sup>2</sup> )⁄(n−k)

**23 |** P a g e
||**Table 5**|**Regional H**|**HI for Te**|**lecoms– M**|**obile**|||
|---|---|---|---|---|---|---|---|
|**Market indicator**<br>|**GLOBAL**<br>|**Africa**<br>**Numb**<br>|**Asia &**<br>**Oceania**<br>**er of work**<br>|**Latin**<br>**America**<br>**ing lines pe**<br>|**North**<br>**America**<br>**r country**<br>|**Eastern**<br>**Europe**<br>|**Western**<br>**Europe**<br>|
|**HH min**|**994**|1113|655|1507|464|1000|1222|
|**HH max**|**2778**|2748|1838|3576|2521|2584|3402|
|**Aggregate market share**|**37%**|31%|22%|48%|36%|38%|50%|
|**Number of firms**|**9**|4|6|7|5|6|7|
|**Number of countries**|**65**|10|13|16|2|9|15|
|**Cases**|**125**|12|14|36|6|19|38|
|**Main Actors**|**Bell South, Deutsche**<br>**Telekom, France**<br>**Telecom, SBC,**<br>**Telecom Italia,**<br>**Telefonica de**<br>**España, Telstra,**<br>**Verizon and**<br>**Vodafone**|France<br>Telecom,<br>SBC,<br>Telefonica<br>de<br>España,<br>Vodafone|Bell<br>South,<br>Deutsche<br>Telekom,<br>France<br>Telecom,<br>SBC,<br>Telstra,<br>Vodafone|Bell South,<br>France<br>Telecom,<br>SBC,<br>Telecom<br>Italia,<br>Telefonica<br>de España,<br>Verizon,<br>Vodafone|Bell South,<br>Deutsche<br>Telekom,<br>SBC,<br>Verizon<br>and<br>Vodafone|Deutsche<br>Telekom,<br>France<br>Telecom,<br>SBC,<br>Telecom<br>Italia,<br>Verizon,<br>Vodafone|Bell, South,<br>Deutsche<br>Telekom,<br>France<br>Telecom,<br>SBC,<br>Telecom<br>Italia,<br>Telefonica de<br>España,<br>Vodafone|

Although the test are inconclusive at the regional level, in the case of Africa, Latin America and the two European Regions, the fact that the HH min is above the 1000 threshold suggests that a presumption of concentration could be argued by an hypothetical supranational competition agency. For North America and Asia & Oceania, the presumption would go the other way around.

To get a fuller sense of the situation, it is useful to point out that like in the fixed line business, there is a regional specialization of the main actors. In 2003, Deutsche Telekom and Vodaphone were the major actors in Western Europe who together controlled 35% of that regional market for the 9 leading companies of the world. They were followed at a distance by France Telecoms and Telefonica de España who add up to about 10% of that market. Deutsche Telekom and Vodaphone were also very strong in North America, Eastern Europe, Asia & Oceania. The developing world market is largely dominated by France Telecoms, Telefonica de España, Telecom Italia, SBC, Verizon and Vodaphone. These companies do seem to have regional specialization. Telefonica de España is, as in the case of fixed lines, the main player in Latin America but with a strong presence of Telecom Italia, SBC and Verizon. France Telecom is a leader in Eastern Europe (with Deutsche Telekom) and in Africa (with Vodaphone).

15
about the influence function, robust qualitative comparisons can be derived by exploiting ideas from the theory of stochastic dominance. All the proposed tests use readily available data on the citation profile of publication records.

We have illustrated these ideas using data on the citation records in economics of six prominent universities and the World Bank. We find that the _h_ -index rankings of aggregate influence are not robust to allowing any stable influence function that rewards higher citations. However, more robust rankings emerge if one also assumes that the marginal influence of extra citations declines with the level of citations. This turns out to be a powerful assumption in assessing publication records. Rankings in terms of average influence per publication based on mean citations are similarly sensitive to the underlying weights, though robust rankings emerge if one imposes declining marginal influence.

While the _h_ -index has questionable theoretical properties (as the literature has noted) it is striking that it ranks institutions in our empirical example identically to our theoretically ideal measure. And, across institutions, the cardinal values of the _h_ -index turn out to be very highly correlated with our _qi_ -index. For this empirical application at least, the rankings based on the _h_ - index are reliable. Our theoretically ideal rankings of average influence also turn out to be very close to those based on average mean citations per paper. It remains to be seen whether these two standard bibliometric measures perform as well in other applications.

14
Figure 3: Halon Event Study Around Signature of RTA <!-- Start of picture text --> −3 −2 −1 0 1 2 3<br>Event Time (RTA Signature)<br>0.6<br>0.4<br>0.2<br>0.0<br>% Deviation from MP<br>−0.2<br>−0.4<br>−0.6<br><!-- End of picture text -->

**Notes:** This figure presents event study coefficients for Halon emissions (represented as percent deviations from target). Black lines correspond to coefficient estimates and 95% confidence intervals for RTAs without ODS provisions, red lines correspond to coefficient estimates and 95% confidence intervals for RTAs with ODS provisions.

30
- 22 -

# **REFERENCE**

- Calderón, César, Luis Servén. 2004. The effects of infrastructure development on growth and income distribution. Policy Research Working Paper No. 3400, The World Bank.

- Canning, David. 1998. A database of world stocks of infrastructure, 1950-1995. _The World Bank Economic Review_ , Vol. 12, pp. 529-546.

- Christensen, Laurtis, William Greene. 1976. Economies of scale in U.S. electric power generation. _The Journal of Political Economy_ , Vol. 84, pp. 655-676.

- Fay, Marianne, Tito Yepes. 2003. Investing in infrastructure: what is needed from 20002010? Policy Research Working Paper No. 3102, The World Bank.

- Fay, Marianne, Mary Morrison. 2005. _Infrastructure in Latin America & the Caribbean: Recent Developments and Key Challenges_ . The World Bank: Washington D.C.

- Nerlove, Marc. 1963. Returns to scale in electricity supply, in _Measurement in Economics: Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld_ , eds. by Carl Christ, Stanford University Press.

- World Bank. 1994. _World Development Report: Infrastructure for Development_ . The World Bank: Washington D.C.

- World Bank. 2006. _Infrastructure in Europe and Central Asia Region: Approaches to Sustainable Services._ Infrastructure Department, Europe and Central Asia Region, The World Bank.

- World Bank. 2007. Enterprise survey: understanding the sampling methodology. Available at <u>http://www.enterprisesurveys.org/</u>
13

# **_2.4. Consequences_**

What have been the consequences of these shifts in the related market and product composition of Thai cassava exports over the past decade? Here it is helpful to distinguish between dried cassava exports and starch exports.

# <u>Dried cassava exports</u>

The transition from pellets to chips has essentially knocked out a stage of processing. Chip production is a labor-intensive operation occurring in open-air drying yards – the raw cassava is cut, sorted, placed in the sun to dry and then aggregated and exported in bulk containers. Pellet production builds on cassava chip production, adding value by steaming and molding the semi-processed cassava chips in a factory. It is an inherently more technologically complex operation, involving greater operational and managerial skills, not as a substitute to chip production, but as a complement to chip production. This change in product therefore represents a move down the technological chain, and at the margin pushes Thai producers backwards into agricultural rather than manufacturing comparative advantage.

This has implications for factor utilization. Table 2 simulates the employment and capital costs which would result if all of Thailand’s cassava production in 2008/2009 (29 million tonnes of cassava roots) were to be exported as either chips, or pellets or starches (as reflected in the current mix of exports). It shows that the additional stage of pellet production over chip production would lead to an additional 9,568 jobs (an increment of 52 percent in employment), but at a higher capital cost of $6m (increasing capital costs by 30 percent). The capital cost per job created in pellet production is only $627. It would also add an additional 168 production units, contributing to the diversification of entrepreneurship and to the potential geographical diversification of production.

**Table 2: Factor Utilization and Number of Establishments If All Cassava Were** **<u>to Be Produced as Chips, Pellets or Starches*</u>**

||Chips|Pellets(incl. chips)<br>|All starches|
|---|---|---|---|
|Employment|18,281|27,849<sup>~~5~~</sup><br>(of which 9,568 in<br>pelletization)|29,734|
|Capital cost$m)|19.97|25.96|44.73|
|Number of firms|1,373|168 pelletization plants<br>1,373 drying yards|114|

_Source:_ *Capital cost calculated from Roonnaphai (2006); all other data from Tijaja’s fieldwork interviews.

A further consequence of the transition from the EU-pellet market to the Chinese cassava market is a removal in the role of standards in production processes and in products. As we saw in Box 2 above, cassava chips exported to China are only required to have a minimum starch content. By comparison, pellet exports to the EU are required to satisfy demanding EU farm-to-fork GM and HACCP standards. Arguably, the achievement of these standards requires more sophisticated managerial

> 5 Assuming all pellets made out of chips. In reality many pellet factories also use starch residue as an input.
for women. The survival probabilities can thus be estimated as <sup></sup> = 1 − 0014 = 0986 for men and <sup></sup> = 1 − 0013 = 0987 for women. This yields, given that κ = 0501, The family savings rate for Brazil,  , is set equal to the private saving rate, which reached an average of 17 percent during the period 2003-10. Solving (30) backward for the preference parameter  yields, using the intergenerational discount factor 097<sup>25</sup> , which, using (44), yields According to 2010 IBGE data, average expenditure of a Brazilian family, in proportion of average income, is R$2626.3/R$2763.4 = 0942 percent, whereas the number of children per family, in proportion of the number of people per family is equal to 1931 = 0613. Assuming that family income is spent evenly between adults and children would suggest that the share of spending on children, in proportion of total income, is 0942 · 0613 = 058 percent. However, to account for scale economies in raising children, we reduce this share to 045 percent. In terms of the model, this can be taken as an approximation of the share of total family income devoted to child rearing, which corresponds to <sup></sup> . The gross fertility rate (number of births per woman) for Brazil, , is equal to 21 over the period 2000-09; given that value, the share of family spending on each child can therefore be estimated at <sup></sup> = 04521 = 0214. We assume that male spouses prefer to spend a smaller fraction, <sup></sup> = 018, on children; given (8), and that κ = 0501, this gives women’s preference parameter for child spending <sup></sup> = 025.

The parameter  is set at 35, slightly above the calibrated preference parameter for current consumption, to capture a relatively high family preference for children. Given that value,  is calibrated residually, by solving (36) backward, using the definition of Λ3 provided earlier, noting the above results for , , , and  , and using  = 045; this gives  = 2748.

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# 6. Estimation

The study design and all analysis methods were pre-registered before data analysis began.<sup>10</sup> We rely on randomized rollout at _baranagay_ level and calculate the intent to treat effect using the following estimating equation: estimating equation: 0 1T T T T (Eq.1) nn ii,bb bb ii jj jj iijj bb ii,bb where _yib_ is the outcome variable measured for individual yy = ββ + ββ TT + XX αα+ ∑θθ XX _i_ who was residing in barangay TT + εε _b_ during adolescence. T T is a dummy variable taking value 1 if the barangay where the respondent resided as an adolescent was randomly assigned to treatment during initial roll-out of the 4P program. bb _Xi_ is the row TT vector of _n_ demeaned control variables: age, gender, dummies for respondent’s religion and ethnicity, dummy variable for treatment LR assignment among women, years of schooling in 2008, and a dummy variable for being enrolled in school in 2008. Following Lin (2013) we also include a full set of control variables interacted with treatment indicator. Table 2 provides summary statistics on the control variables. is the error term. 1 is the main coefficient of interest. It will measure the intent-to-treat (ITT) effect of residing in a barangay that was randomly assigned treatment, compared to the control iibb εε ββ group. We cluster standard errors at barangay of residence level. We carry out estimation of genderspecific impacts by splitting the sample into women and men and running regression (1) separately on these two subsamples. As we are estimating a large-scale governmental program, ITT is our primary estimand of interest. The discussion of results is focused on ITT effects; however, we also estimate the treatment on the treated (ToT) effect by instrumenting participation in the 4P program with the random assignment to the treatment group at barangay level. We present the results in the Appendix.

# 7. Results

## 7.1. Balance

We take advantage of the data collected in 2008 for an impact evaluation of the 4P program to test whether randomization was carried out successfully. We can identify households in our sample in these data, which includes a rich set of characteristics of households where our respondents resided in 2008, including characteristics of the household head, spouse of the household head, access to social services, housing conditions and asset ownership. Of 48 available indicators, we find statistically significant differences in only three. 4P beneficiary households are less likely to be headed by a person with no

> 10 https://ridie.3ieimpact.org/index.php?r=search/detailView&id=923.

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Table 3.3 Gini Decomposition of Inequality in the Distribution of Household Expenditure<sup>in</sup> Guinea by Area of Residence

||Population<br>Share|Expenditure<br>Share|Mean<br>Expenditure|Mean<br>Rank|Gini|Global<br>Overlap|Cross-<br>Overlap|
|---|---|---|---|---|---|---|---|
|Conakry|0.17|0.31|849843.00|0.13|0.38|0.58|0.41|
|<br>Other|0.16|0.20|598799.30|0.10|0.38|0.85|0.69|
|Urban||||||||
|Rural|0.67|0.49|342785.10|0.27|0.33|0.96|0.29|
|Guinea|1.00|1.00|469461.30|0.5|0.40|1.00|0.00|

Data Source: 1994 Integrated Household Survey.

The overall Gini index is estimated at 0.40. The decomposition presented<sup>intable</sup> 3.3 above implies that the within-group inequality is equal to 0.14, the between-group component is equal to 0.11 while the overlapping component is 0.15.<sup>Thusabout28</sup> percent of the observed inequality in Guinea in 1994 is accounted for by<sup>the between</sup> group component (when the population is classified by area of residence).<sup>Lookingatthe</sup> overlapping components, it can be seen that no area stands out as a<sup>perfectstratum.</sup> However, in relative terms, the rural area has the least amount of overlapping<sup>withthe</sup> rest of the distribution compared to the urban areas. This fact is also supported by<sup>the</sup> pattern of mean expenditures.

# **_The_** _Use of_ **_Poverty Gaps_**

At the individual level, poverty measurement involves two basic steps:<sup>(1)the</sup> selection of a poverty line indicating a threshold below which<sup>the personis declared poor,</sup> and (2) the computation of poverty gaps measuring the relative<sup>distancebetweenan</sup> individual level of welfare and the chosen poverty line. Kakwani(1999:605)<sup>definesa</sup> class of additively separable poverty measures starting from the notion<sup>of</sup><sup>_deprivation._</sup> Let z be the poverty line, and **xh** the level of welfare enjoyed by individual<sup>h ina society</sup> comprising n individuals. Let xV(z,xb) stand for an indicator of deprivation at the individual level. The following restrictions are imposed on the indicator:<sup>(i) it isequal to</sup> zero when the welfare level of the individual is greater or equal to<sup>thatspecifiedby the</sup> poverty line; (ii) the indicator is a decreasing convex function of welfare,<sup>giventhe</sup> poverty line.

The class of poverty measured defined by (3.10) is called _additively separable_ because the deprivation felt by an individual depends only on a fixed poverty<sup>lineand</sup> her/his level of welfare and not on the welfare of other individuals in society.

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_CHAPTER 3. BASIC INDIRECT ESTIMATORS_ 11 post-strata within that area, as Assuming that the mean _Y_<sup> ̄</sup><sup>_j_</sup> of the study variable in post-stratum _j_ is constant across the _D_ areas and only varies between post-strata, that is, assuming that _Y_<sup> ̄</sup> _d_<sup>_j_=</sup><sup>_Y_ ̄</sup><sup>_j_,forall</sup><sup>_d_=1</sup><sup>_, . . . , D_,andforeach</sup><sup>_j_=1</sup><sup>_, . . . , J_,</sup> we can replace _Y_<sup> ̄</sup> _d_<sup>_j_by</sup><sup>_Y_ ̄</sup><sup>_j_in(3.1),resultingin</sup> Then, the post-stratified synthetic (PS-SYN) estimator of _Y_<sup> ̄</sup> _d_ is obtained by replacing the mean in each post-stratum _Y_<sup> ̄</sup><sup>_j_</sup> in (3.2) with the direct estimator _Y_<sup>ˆ ̄</sup> _j,R_ , as follows Since post-strata sample sizes are assumed to be sufficiently large, estima- _j,R_ tors _Y_<sup>ˆ ̄</sup> , _j_ = 1 _, . . . , J_ , have small design variances, resulting in a small design variance for the PS-SYN estimator of _Y_<sup> ̄</sup> _d_ . However, the assumption of a constant mean across the areas within the same post-stratum is hardly true, leading to typically large design bias in PS-SYN estimators. It is important to note that a reduction of design variance at the expense of a large design bias is not an acceptable estimation strategy.

Hansen, Hurwitz and Madow (1953), p. 483, were the first to use a model for synthetic estimation, in an application based on the 1945 Radio Listening Survey. The objective was to estimate the median number of radio stations heard during the day in family houses from _D_ = 500 US counties. They had estimates _xd_ , _d_ = 1 _, . . . , D_ , obtained from a mail survey conducted in the 500 counties, which were biased due to only 20% response rates and incomplete coverage. Additionally, they had estimates