rafmacalaba/datause-encoder-data huggingface dataset:
19

# 4.2 The Effect on Agricultural Wazes.

A second testable implication of -he existence of an effective employment guarantee is that one would expect to see a relationship between the EGS wage rate and the agricultural wage rate. The strength of that relationship will depend on specific labor market conditions. Many EGS workers are also regular participants in the agricultural labor market, and vice versa. Furthermore, the work involved appears to be quite similar, as are the hours worked. In these circumstances, with EGS and agricultural wages roughly equal initially, higher EGS wages with guaranteed employment would have to result in higher agricultural wages.

A visual inspection of Figure 1 does not suggest that the agricultural wage rate responded much to changes in the EGS wage rate during this period. Granted the simple correlation coefficient between the two wage rates is high (r-.74). But this is la gely a spurious correlation, reflecting the serial correlation of both seri.;;22 the correlation coefficient is only .32 between the innovations in each series around its first order autoregression. The following dynamic regression performs fairly well in reproducing the agricultural wage series in Figure 1: Within the current month, the impact on the agricultural wage rate of a (say) RplO increase in the EGS wage rate is Rpl.3. This is substantially off-set by
likely be part of the reason why linkages of local manufacturers in Ghana are not as strong as those of their counterparts in other resource rich economies such as the Democratic Republic of Congo, South Africa, and Zambia. These additional factors include countries’ physical, cultural, political proximity to larger economies, as well as their trade policies and membership in regional trading blocs. An estimated gravity model of sources of the import content of manufactured exports and destinations of the indirect component of those exports highlights and quantifies the role of these factors.

Furthermore, using firm-level data from World Bank Enterprise Surveys, the paper shows that the domestic actors in these linkages are typically relatively large establishments of 100 or more employees that have been in operation in for five years or longer. These manufacturers are also more likely to have foreign equity holders or foreign technology licenses.

The next section of the paper describes the conceptual framework and analytic methods underlying these findings. This is followed by an account of the data sources in section 3 and how and why countries are classified in producing those findings. Sections 4, 5, and 6 provide details of the findings. Section 7 concludes.

# **2.  Conceptual Framework and Data**

## 2.1. Conceptual framework

The term “value chain” refers to the sequence of stages of productive or value-creating activities or tasks leading to the delivery of a product as a final good or service (that is, a good or service in end use) and starting from its conception and design via intermediate phases of a production plan and its execution. The concept relates to the analytic technique of value chain analysis, which is a method of identifying cost-saving or product differentiation opportunities across the various stages of production or delivery of a good or service. A value chain is said to be a global value chain (GVC) if it involves processes and tasks carried out in the framework of contractual relations between firms across international borders that are not necessarily in the same region.

In a GVC, firms from different countries are tied together in a vertically integrated system of production (or supply chain) of goods and services at each link of which a participating firm is 4
**-** **<u>Table 4. GMA and Participation impact on welfare of poor and non poor households</u>**

||GMA Effect<br>|Participation Effect<br>|
|---|---|---|
|Outcome: percapita consumption|Estimates|Estimates|
||(1)|(2)|
|Asset Poor: Bottom 2 quintile|0.205|0.137|
|Asset Non-Poor: Top 3 quintile|0.596<br>***|0.536<br>***|
|_Alternate Definition_|||
|Asset Poor: Bottom 3 quintile|0.323|0.182|
|Asset Non-Poor:Top2quintile|0.657<br>***|0.541<br>***|

Significance: *=Significance at 10%; **=Significance at 5%; ***=Significance at 1% Source: Authors’ calculation, Data from the IGMAW survey, 2006 Table 4 shows 54 to 60 percent of the welfare benefits to the non-poor households are associated with GMA and participation effect. However, asset poor households enjoy none of the welfare gains that their non-poor counterparts are enjoying. This suggests that the GMA institution does not benefit the poorest in the community. Moreover, the poor do not gain any welfare benefits even when they actively participate in CRB/VAG activities. The results do not change when we redefine asset non-poor as the top 2 quintiles. Thus, the bottom 60 percent of the household appear to gain nothing and the top 40 percent benefit both from being in the GMA and participating in CRB/VAG. It appears that elites in the GMA capture all the benefits.

# _3.5 Community gains_

One of the main results of this study is that the economic gains from living in GMA and participating in CRB/VAG activities do not go to the poorer sections of the community. The top 40 percent of the households appear to appropriate the benefits. One possible reason of this variation is that the gains from the GMA institutions may be invested in community infrastructure such as schools and health clinic buildings. The economic impact of education may not be evident in the short term and newer buildings do not always imply better health care. However, if GMAs are associated with improved community infrastructure, they may benefit all the sections of the community. To understand investments in community infrastructure in the GMAs we look at the average age of the newest infrastructure in the community in the GMAs around the four National Park Systems.

In the Park Systems with no household welfare impacts the infrastructure are relatively recent (Table 5). For example, in Kafue and Lower Zambezi the average age of the newest infrastructure is between 2 and 5 years, whereas in Bangweulu and Luangwa the average age of the newest infrastructure is between 8 and 10 years. Thus, it may appear that GMAs that show no measurable economic impact at the household level indicate some community infrastructure improvements.

<u>Table 5. Age of the newest infrastructure in GMA and control areas across park systems</u>

|||Lower|||
|---|---|---|---|---|
|(Ageinyears)|Kafue|Zambezi|Bangweulu|Luangwa|
|GMA areas|1.9|4.8|10.4|7.8|
|Control Areas|4.1|9.3|7.4|2.6|

Source: Authors’ calculation, Data from the IGMAW survey, 2006 11
migrant share from 1995 levels on consumption against prior year consumption (Figure 7). We calculate both the short-term effects (Panel A) and the long-term effects (Panel B), using the delta method to compute standard errors and 95 percent confidence intervals.<sup>32</sup> For values of consumption less than median per capita consumption in 1995, the point estimate for the effect of migration is positive and statistically significant at the 5 percent level. Therefore we can be confident that migration is positively associated with consumption growth among those households.

At the median level of per capita consumption in 1995, the coefficient estimates imply that if the migrant share of the village labor force increases by 10 percent, in the next period per capita consumption will increase by slightly more than 0.5 percent, ceteris paribus. The long term increase in consumption associated with the same one-time increase in migration is quite a bit higher, at 1.2 percent. Clearly, migration was a significant factor in increasing living standards in rural China. Finally, it is worth noting that although estimates are not statistically significant for the whole consumption distribution, point estimates are positive for nearly the entire distribution, so we can be reasonably confident that migration opportunity had a positive effect on consumption for most households.

# 5.3.1 Specification Issues

Although our main result suggests that increasing migration has a larger impact on poor households than richer ones, it is possible that the interaction term that which yields this finding is actually proxying for nonlinearities in the effect of past shocks on current consumption growth. Such nonlinearities might arise, for example, in the presence of credit constraints. If the interaction term is proxying for nonlinear effects of past shocks and we control for them, then the negative coefficient on the interaction term should disappear.

To examine whether our results are robust to such nonlinearities, we re-estimate equation (9) including ∆(c<sup>2</sup> it−1<sup>)asaregressor.Weinstrumentthistermwitht−3andt−4valuesoflog</sup> consumption squared and include it in a new estimate of equation (9) (Table 6). Whether we estimate the basic model (column 1) or the full model (column 2), we find virtually no change in the coefficients of interest, β2 and β3. We thus conclude that nonlinearities related to past shocks are not behind our finding that migration raises consumption of poorer households more than well off households. Therefore nonlinearities are not driving the findings in Table 5.

> 32 The long-term effect of a one-time 10 percent increase in the share of the village employed as migrants is calculated as γ = 1−β1−β2β3Mj<sup>95,whereM</sup> j<sup>95</sup> is the village migrant share in village j in 1995.

22
15 In general, a small ownership stake need not weaken the power of a relative controlling shareholder even in the presence of high majority requirements. In Germany, for example, the proxy process tends to support the creditor banks of the firm, which themselves are most often minority equity holders. German banks effectively hold the proxy votes of small shareholders and they can use these if a larger absolute majority is necessary. In the US, by contrast, access to shareholder lists tends to be controlled by managers and the proxy process thus tends to support incumbent management. Since the custodial and proxy voting systems are likely not yet well developed in the Czech and Slovak Republics, passive shareholders' votes are essentially lost to either minority shareholders or managers. Tie regression results here suggest that this is to the detriment of all shareholders as the value of the firm is lower.

Secondary market hypothesis I next verify the _secondary market hypothesis_ by rnning (for the PSE-prices only) regressions which include the relative amount of trading which occurs in each stock. I use the (log of the) ratio of volume (number) of stocks traded relative to _24_ original book value as our measure of _turnover._ I expect that this liquidity measure will have a positive influence on prices for two reasons: one, it will lower bid-ask spreads and the required rates of return, thus raising stock prices; and two, it will make it easier for a given investor to build up a controlling stake in a firm. The first effect would show up as a positive sign for _turnover_ in the regressions. The second effect would imply a lower importance of _reictrl_ (relative to not having _turnover_ included), as having a relative controlling stake currently becomes less relevant.

Table 9 reports the result when using the same right hand side variables as in the basic regression.25 The results of the right two panels confirm the first effect as the sign for _turnover_ is positive. Regarding the second effect, the importance of _relctrl_ is not affected, however, by the inclusion of _turnover_ and it still maintains a negative sign (middle column) which is not less than that for the basic regression (left column). This is true for all specifications. It thus appears that an inability to acquire more stocks due to a lack of liquidity does not explain the negative sign for _relctrl._ something it was not doing very strictly earlier.

23An anecdote can illustrate this. For its first General Assembly meeting, the Czech Savings Banks had rented a sports stadium to accormmodate its about 80,000, mostly individual shareholders. Supposedly, only 12 shareholders, mostly representatives of investment funds, attended the meeting.

24As I use the PSE-prices as of November 30, 1 use the average of the daily volume traded prior to that date.

2sFor all four specifications (with and without _nonwide_ and/or _H), tunover_ is always positive, albeit only significant in two of the four regression specifications. H isn't always significant. I use the specification with both _nonwide_ and H.
Figure 4: Alternative strategies for developing countries: impact on exports

# (A) Total exports (B) Exports to the United States

<!-- Start of picture text --> Percent Join the trade dispute Join the trade dispute<br>2.5 No action No action<br>2.0 RTAs with non-US regionsTurn the other cheek Percent RTAs with non-US regionsTurn the other cheek<br>1.5 0.0<br>1.0 -2.0<br>0.5<br>-4.0<br>0.0<br>-0.5 -6.0<br>-1.0<br>-8.0<br>-1.5<br>-10.0<br>-2.0<br>-2.5 -12.0<br>Regions Developing Global Regions Developing Global<br>involved in countries* involved in countries*<br>trade dispute trade dispute<br><!-- End of picture text --> Source: Authors’ simulations.

Note. Comparative static results. Percent deviation from the baseline in real exports. All scenarios assume an escalated trade dispute between the United States on the one hand, and China, Mexico and other high-income countries (regions involved in trade dispute), on the other hand. *Developing countries other than China and Mexico.

The results also show that developing countries could reap significant benefits from a cooperative response, i.e., trade liberalization and increased intra-regional integration. The negative effects of U.S. protectionist measures could be mitigated by pursuing regional trade agreements with non-US regions. This strategy could boost developing countries’ GDP by 0.4 percent and exports by 1.7 percent.

"Turning the other cheek", unilateral liberalization in response to higher U.S. tariffs, could benefit countries in Latin America and the Caribbean that are highly interconnected with the U.S. but does not alter results for other coun- economy significantly beneficial developing tries.

Looking at the results in detail, countries and regions closely linked to the United States through trade are expected to be hurt the most by a trade war. These include Canada, Mexico, Central America, and the Caribbean. For the rest of Latin America, although exports to the United States decline, aggregate effects on their GDP and total exports are ameliorated compared to the others. Among other developing countries, India gains or does comparatively better in all scenarios because it has a large supply of excess unskilled labor, and 10
percentage reductions in can be applied. In addition, we need figures on cost per illness and family size.

The estimates of avoided costs using this information are presented in Table 5. The figures indicate huge ranges for these avoided costs. This reflects the wide ranges of figures for avoided illness and cost of illness in Table 4. Also of considerable interest is that with the exception of the improved woodstove technology, the differences among the technologies in terms of avoided illness cost per family, per year, are not that large. This is a relevant consideration since gas and electric cooking methods tend to be costlier than ICS using biomass.<sup>11</sup>

**Table 4:  Parameters for Assessing Relative Impacts of Different Cooking Technologies on Social Cost of Acute Respiratory Disease**

||Low<br>Mid|High|
|---|---|---|
||_Percent reduction acute re_|_spiratory relative to_|
|_Technology_|_traditional wood stove_||
|Traditional wood|NA<br>NA|NA|
|Improved wood|10<br>40|70|
|Traditional charcoal|0<br>20|40|
|Improved charcoal|10<br>40|70|
|Kerosene|45<br>60|75|
|Propane|45<br>60|75|
|Electric|45<br>60|75|
|_Cost of illness ($/case)_|2<br>15|60|
|_Baseline_<br>_incidence_|||
|_(cases/person-year)_|0.1<br>0.5|1.0|
|_Household_<br>_size_|||
|_(number)_|4<br>5|6|

Source: Jeuland and Pattanayak (2012).

> 11 WHO (2014b) expresses significant reservations about kerosene use based on safety considerations, and <u>worrisome health risks from vapor inhalation as well as post-combustion air pollution.</u> **11 |** P a g e
Skoufias & Shapiro, 2006; Sawada & Ragatz, 2005; Gunnarsson et al., 2004; Eskeland & Filmer, 2002). However, rigorous evidence base for the effectiveness of SBM in boosting student performance is thin. A recent review of the empirical literature on SBM since 1995 indicates that only 14 studies utilized rigorous methods to assess the impact of SBM, and only six reported positive impacts on students’ test scores (Barrera-Osorio et al., 2009) . Eleven studies are country-specific from Latin America, one from Kenya, and two exploit data from multiple countries. No empirical evidence is available from East Asia.

This paper contributes to the small but growing empirical literature on the effectiveness of SBM by extending the research to East Asia. The paper provides an initial analysis of the potential of schoolbased management for improving educational outcomes in the Philippines, using aggregated school-level test scores and administrative data. The data are quite limited and do not allow for a thorough analysis of the processes and approaches through which SBM reforms affect outcomes. Nonetheless, the paper attempts to use existing data to answer an initial question: did the introduction of SBM lead, on average, to enhanced educational outcomes? It also demonstrates how administrative data can be mined for exploratory assessments of potentially larger programs.

The remainder of the paper is organized as follows: the next section provides a brief overview of the SBM program in the Philippines; section 3 outlines the methodological approaches used for the analysis; section 4 describes the data; section 5 discusses the empirical results; and section 6 provides a summary of our conclusions, discusses the limitations of the study, and highlights the implications for future, more rigorous SBM evaluations in the Philippines.

# **2.  SBM Program in the Philippines**

SBM was implemented in between 2003 and 2005 in 23 districts participating in the Third Elementary Education Project (TEEP) supported by the World Bank.<sup>9</sup> The project provided funding for school infrastructure, training, curriculum development, and textbooks. SBM was introduced as an integrating framework for obtaining school-level project inputs and building school capacity for education planning and program implementation beginning in school year 2003-04. Schools participating in SBM were required to design a five-year School Improvement Plan (SIP) in partnership with parents and the community using data such as student achievement and students’ learning needs assessments, with the school principal or head teacher leading the process. Based on the SIP, schools developed an Annual

> 9 Although some other school districts in the Philippines had ongoing SBM initiatives, they are not included in this analysis as insufficient information is available about the nature and timing of those initiatives.

3
send them for child labor. This adds an element of uncertainty (on top of ability dierences) for the children born into disadvantaged households, resulting in a higher conditional variance in completed schooling. The conditional variance of children's schooling attainment is likely to decline with the education of parents when such economic shocks (income or health shocks) are the primary sources behind the observed variance in the data. In this case, children born to higher educated parents not only have higher expected years of schooling (as found in numerous studies of intergenerational educational mobility), but also a lower variance in schooling attainment. Under the plausible assumption of risk aversion, this implies being born to higher educated parents brings double advantages for children part of which is ignored by the existing measures of intergenerational mobility.

We analyze the relationship between family background and conditional variance of children's outcome in the context of intergenerational educational mobility. We make two contributions to the literature. First, using data from three large developing countries (China, India, and Indonesia, with 42 percent of world population in 2000 (2.56 billion)), we provide the rst empirical evidence that the conditional variance of children's schooling is systematically related to his/her family background as captured by father's education.<sup>3</sup> Second, we develop a methodology that combines the eects of father's education on both the mean and conditional variance of children's schooling. We propose new measures of relative and absolute mobility that adjust the standard mean eects by the risk premium associated with the conditional variance in educational outcomes faced by children. With risk neutrality, our proposed measures reduce to the canonical measures of intergenerational educational mobility widely used in the current literature (see, for example, Hertz et al. (2008), Azam and Bhatt (2015), and Narayan et al. (2018)). But, under the more plausible assumption of risk aversion, the measures of mobility developed in this paper incorporate the eects of family background operating through conditional variance.

For our empirical analysis, we use household survey data from China Family Panel Studies (CFPS) 2010, India Human Development Survey (IHDS) 2012, and Indonesia Family Life

> 3We are not aware of any studies on intergenerational mobility that estimates the eects of parent's economic status on the conditional variance of children's economic outcomes.

2
Table 1. Effects of Economic Growth During 1970-2010 on the Share of Income Held by the 3<sup>rd</sup> and 4<sup>th</sup> Quintile

||Predicted Effect on Income Share Held by<br>3<sup>rd</sup>Quintile|Predicted Effect on Income Share Held by<br>4<sup>th</sup>Quintile|
|---|---|---|
||(1)|(2)|
|Brunei|3.12|3.88|
|Cambodia|4.66|5.80|
|Indonesia|6.74|8.39|
|Lao PDR|6.22|7.74|
|Malaysia|6.82|8.49|
|Philippines|4.24|5.28|
|Singapore|7.24|9.01|
|Thailand|6.08|7.57|
|Vietnam|6.36|7.92|
|ASEAN Average|5.72|7.12|
|ASEAN Median|6.22|7.74|

Note: The table reports effects (in percent) of changes in the natural logarithms of GDP per capita between 1970 and 2010 on the share of income held by the 3<sup>rd</sup> quintile (column (1)) and 4<sup>th</sup> quintile (column (2)). The predicted effects are generated by combining estimates reported in Brueckner et al. (2015), see there columns (4) and (5) of Panel A in Table 1, with the change of the natural logarithm of GDP per capita between 1970 and 2010 for each country.

15
where mi is the size of household i, M is the total population of the area in question, N is the number of households, and X is an N x k matrix of household characteristics. The advantage of using the Census data, of course, is that the large number of households allows estimation of poverty headcounts for geographic units much smaller than would be possible with the VLSS data.

Provided that a) the error term is homoskedastic, b) there is no spatial auto-correlation, and c) the full Census data are used, the variance of the estimated poverty headcount can be calculated as follows: where n is the sample size in the regression model. Thus, n, k, and a<sup>2</sup> are from the regression analysis, while **mi,** M, and N are obtained from the census data. The partial derivatives of P* with respect to the estimated parameters can be calculated as follows: The first two terms in equation 5 represent the "model error", which comes from the fact that there is some uncertainty regarding the true value of P and a in the regression analysis. This uncertainty is measured by the estimated covariance matrix of P and the estimated variance of a **2** , as well the effect of this variation on P*. The third term in equation _5_ measures the "idiosyncratic error" which is related to the fact that, even if P and a are measured exactly, 9