rafmacalaba/datause-encoder-data huggingface dataset:
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**Fig.** 2.15. Search methods in Moscow (by quintiles based on per capita income; % of mover households in 1993 - 1995)

<!-- Start of picture text --> 25<br>20<br>0 waiting fit<br>15 0 exchange<br>10 3 0 purchase<br>5 -:^<br>-0 27-l i2.i<br>1 2 3 4 5<br><!-- End of picture text --> Note: 1 - lowest quintile, 5 - highest quintile Source: The Moscow Longitudinal Household Survey, January 1996 On the whole, in Moscow the ratio of non-market to market search methods (including exchange) was 36.2 to 63.8%, that is, about two-thirds of the volume of residential movement were determined by the advancement of the housing market. A comparison of this ratio with households' non-market vs. market preferences (47.6% to 52.4%) shows that the hopes of many for free housing are unrealistic; the market is a more effective provider.

# 2.3.3. **Changes in the housing conditions produced by residential movement**

As was shown above, at present the orientation of households' potential is towards improving their housing conditions; very few families said they would agree to move to worse housing (see Fig. 2.2). The evaluation of households who changed their place of residence in 1993-1995 shows that in most cases (82.7%) their housing conditions improved, and in 8.5% of the cases they remained unchanged. However, 8.9% of the households said that their housing situation became worse.

The actual situation depended on the year of the move (Fig. 2.16). In 1993, when the secondary market still did not have a large place in the overall housing market, only 3% of the households noted a change for the worse, while in 1994 this figure rose to 8.5%, and in 1995, to 12.7%. That is, instances of exchange of apartments for inferior ones became more common. As noted above, this is indication of a reallocation of the housing stock from its intially inefficient state.

Fig. 2.16. Change in mover households' living conditions in Moscow by year of the households in each year).

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70% - '<br>60% 0 improved<br>50%^ 50% _ o~~~~~~~~~~~ becamre _q ,.,:no changeworse<br>40% ~<br>30%-<br>20% 199.1<br>10%<br>1993 1994 1995<br><!-- End of picture text --> Source: The Moscow Longitudinal Household Survey, January 1996
issuers in most countries. Conversely, many exchanges retain an important in role in setting corporate governance standards for listed issuers.

# **<mark>Country Examples</mark>**

Table 4-2 summarizes the division of regulatory responsibilities between regulators and SROs in the same six developed markets shown in chapter 3. The table covers six key responsibilities that SROs perform in many countries.

ASX clearly stated the nature of its self-regulatory role on its website: it is not a regulator.

It is more accurate to describe ASX as a regulated commercial organisation that monitors specific aspects of the businesses of other organisations (for example, the governance of listed companies and the on-exchange or on-market trade execution by brokers). ASX does so as a condition of the licences it has been granted by government to conduct its own commercial undertakings.<sup>29</sup> ASX does not regulate compliance with laws or regulations, or participants’ overall business conduct or off-exchange trading. ASIC regulates dealings with customers.

> 29 ASX website, <u>http://www.asx.com.au/supervision/approach/index.htm.  The ASX website contains charts</u> showing what ASX does, and does not do, as an SRO.  The charts illustrate the scope of many SROs’ responsibilities.

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20 'the best way of raising a given revenue ... is by a system of taxes, under which the rates become progressively higher as we pass from uses of very elastic demand or supply to uses where demand or supply are progressively less elastic." 10/ Next, it is clear that raising revenue by taxes which do not discriminate by country of origin is more efficient. This is clearly seen by comparing the proposed import tariff on oil and gas with an excise tax on domestic sales of oil and gas. Not only does the excise tax raise far more revenue because it applies to all domestic sales, but it also generates revenue at a much lower welfare cost than an import tariff (see column 3).

# 3.3 Comparison with Earlier Studies

In their partial equilibrium study for the US Federal Trade Commission, Anderson and Metzger (1987) estimated that a $5 per barrel import tariff on both crude oil and petroleum products will generate $6.7 billion per year for the government, but at a cost of $3.8 billion in deadweight losses. Since the bulk of the government revenues generated by tariffs in our model derive from the crude oil tariff, our estimate of the government revenue generated is within their range (see table 4). Their implied estimate of the welfare costs per dollar of revenue generated (55 cents) is on the high side of those we present in table 4, but within our range. 11/ Only Boyd and Uri have conducted general equilibrium experiments similar to those discussed in this section. They estimate (1988a) the effects of a $5 per barrel import fee on crude oil and the effects (in
Table 3: The effects of 10 FDI projects on wages by skills and gender (% change)

||Me|n|W|omen|
|---|---|---|---|---|
||Skilled|Unskilled|Skilled|Unskilled|
|Brazil|0.00|1.01|0.00|1.01|
|Colombia|1.01|4.08|1.01|3.05|
|Ethiopia|-11.31|8.33|-1.98|55|
|Mexico|0.00|3.05|1.01|5.13|
|Philippines|4.08|10.52|5.13|13.88|
|South Africa|-1.00|2.02|0.00|6.18|
|Vietnam|4.08|4.08|2.02|4.08|
|From Eq. 2|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_η_+</sup><sup>_ω_+</sup><sup>_ν_)</sup>|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_ω_)</sup>|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_η_)</sup>|_e_<sup>10</sup><sup>_λ_</sup>|

Note: The numbers are percentage increases based on estimates from equation 2.

tariffs reduce the skill premium.

# **4 Conclusion**

In this paper we examined the effect of FDI on wage skill premia in seven emerging economies. We matched information on FDI penetration from a project-level FDI data set with data on wages from household surveys by sector, location and year. This allowed us to estimate the effect of FDI on the skill premium across sectors within regions within countries. We found that FDI is associated with a higher probability of employment and higher wages for unskilled workers, relative to skilled workers, in 6 of the 7 countries. While our results contrast with the previous literature suggesting that FDI has effects similar to that of skill-biased technical change on increassing the skill premium, we noted that our results are in line with those of trade liberalization. Because our estimates are done at the country-sector-region level, we may also capture potential spillover effects (e.g. backward and forward linkages) of FDI within sector-regions. Our results suggest that globalization may thus have inequality-reducing qualities via FDI that should not be overlooked.

13
share, with an acceleration until the mid-1980s followed by a fall. Unlike in other nations, however, in the Brazilian case, this was accompanied by a U-shaped path of the share of services. The reason for this fall in the services share till the mid-1980s and later recovery was the unusual strength of the agriculture sector, whose share remained more of less stable around 11.6% until 1985, and then declined steadily reaching 5.5% in 2007.

Another important component of the model, the size of firms, has also changed substantially during our sample period in Brazil. The number of occupied people per establishment at the municipal level can be constructed from data on occupied individuals and number of establishments in each sector collected by IBGE and published by IPEA for the years 1970, 1975, 1980, 1985 and 1995. We added average numbers at the national level for 2006 in agriculture and for 2007 in the other two sectors, reflecting information obtained from the agricultural census and different surveys in manufacturing ( _Pesquisa Anual de Indústria_ ) and services ( _Pesquisa Anual de Serviços_ and the _Pesquisa Anual de Comércio_ ). These data sources include formal as well as informal employment.

Figure 2 plots the evolution of the mean number of employees per establishment in each sector. Starting from the late 1970s the agriculture sector becomes the one with the smallest average firm, followed by the services sector; the largest average establishment is located in the manufacturing sector. More specifically, the average for the period is 3.9 employees per plant in agriculture, 21.8 in industry, and 5.0 in services. That is, the average size of a firm in the services and the manufacturing sectors is 1.3 and 5.6 times larger than the one in agriculture, respectively. A closer examination of the chart also tells that over the period 1970 - 2007 the average size seems to evolve cyclically in the manufacturing sector, decline in agriculture and increase in services.

# **3.2 Econometric Analysis**

We start now with the econometric analysis to establish the causal evidence of the effect of access to electricity on structural transformation. In the econometric analysis we use 27
The instrumental variable approach shows that the children of mothers with some education (relative to uneducated mothers) spend more time on educational activities outside school hours. The effect is large— an extra 75 minutes per day—and closely aligned with the results from Behrman and others (1999). Mothers with some education also spend more direct time with children on their school work. In households with no older children (>12 years) where the mother is likely to be the primary care-giver, time spent by mothers with some education directly with children on school work is large (an extra 40 minutes per day). In addition, mothers with some education facilitate learning by employing other members of the household in helping and reading to children by an extra 4.64 hours per week. Finally, and as a likely consequence of the increased time spent studying at home, there is a large impact of maternal education on child test-scores. Children of mothers with some education report test-scores that are between 0.24 and 0.35 standard-deviations higher than others; this represents to our knowledge the first causal estimate of maternal education on child learning outcomes in low-income countries. Given the poor learning environment in developing countries (see the TIMSS Report 2007, Andrabi and others, 2009, for Pakistan and Das and Zajonc, 2009, or the ASER Report, 2008 for India) even the “educated” mothers in our sample can barely read, write or perform simple mathematical operations. The results therefore shed light on how maternal education has an effect on child outcomes even at very low levels of cognitive achievement.

We also present supporting evidence that these effects are unlikely to arise from additional leverage “educated” mothers might have in the household’s decision making process. This rationale asserts that educated mothers have more say in the bargaining process that takes place between the adult members of the household, particularly on decisions that directly affect the child such as enrollment. However, we do not find any evidence for increased bargaining power due to maternal education. Moreover, we do not see evidence for an impact on child outcomes that are more likely to require household-level (joint) decision making. Maternal education does not increase spending on child-specific goods and we are unable to detect an effect of maternal education on child enrollment although the latter could also be due to a lack of precision in our enrollment estimates. These results are therefore consistent with a direct increase in maternal productivity or a change in maternal preferences in contexts where bargaining power is low (or non-existent).

3
# **4.2 The Impact of the Liberalization of Migration Policies on the Labor Force and Migration**

Table 4 presents an overview of the changes in the labor force, by skill type and by country. The total change in the labor forces are decomposed into two main components: forecasts and changes due to liberal migration policies. The forecasts can be further divided into changes due to the natural rates of changes in permanent residents and our assumption that migration increases with population forecasts. The liberal migration policies component can be further divided into changes in changes in the number of migrants located in the country/region and changes in permanent residents through outward or return migration. The labor forces of all countries, except for Japan, increase over time. The decrease in Japan is due to the fact that the demographic changes occurring in this country are not offset by migration. China’s and Indonesia’ labor forces increase the most, due to positive natural population growth over the period and high initial populations. These changes are the result of the forecasts discussed above.

In this model, it is the changes in the real wages occurring as a result of the financial crisis, economic growth and demographic changes that drive the endogenous migration. Migration between two countries depends on the changes in the real wages in both the host and the home country and any changes in unemployment (Equation 1). Moreover, this model depends on a database that only captures migrants recorded in the census – illegal migration is therefore likely to be under-reported.

Hong Kong SAR, China’s labor force increases the most as a result of more liberal migration policies, with 1.72 million more workers, of which 1.71 million are new migrant workers and 0.02 returning migrants. China and Malaysia have the largest decreases in their labor forces as a result of liberal migration, with 0.95 and 1.57 million fewer workers respectively. The implication is that migrants that would have otherwise moved to China or Malaysia (as in the baseline), would rather migrate to Hong Kong SAR, China when given the opportunity (as under endogenous migration as a function of changes in expected real wages).

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as they may have exhibited greater dependence on welfare over years, and their negative labor supply response may have grown stronger with respect to newer NE beneficiaries. If so, time limits on NE eligibility may be considered.

On the other hand, older program participants may not even need the program as much as their newer counterparts. For instance, even if a household may have been eligible for NE and started receiving transfers at the inception of the program in 1995, it is not for certain that the household is still poor or in need of social assistance by 2002 or 2005. If so, this group of households is likely to contribute to the leakage rate of the program, measured in terms of the percentage of non-poor that are NE participants. If the pool of NE beneficiaries is further restricted in order to extend benefits to the households in greatest need, the average benefit level will subsequently increase, and the desired impact on household welfare may be achieved.

Furthermore, it is possible that the NE transfers may need to be larger if there are nonlinearities in amounts. A small transfer may not yield the desired impact on household objective or subjective measures of welfare, but doubling the transfer may more than double the impact. The lack of impact may in fact stem from the inadequacy of the funding. While the available data sources do not allow us to explore this possibility, panel data on household welfare would be useful to address the question.

In any case, the revisions in the program administration in order to extend benefits to households in greatest need, achieve a positive impact on household welfare, and reduce work disincentives should be matched with efforts to expand employment, particularly in urban areas. This is especially true since NE benefit in itself is often not as high as potential wage or selfemployment earnings.

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**Table A.1:** Regressions to determine correlates of bad geocodes

||(1)<br>Coefcient (diference)|(2)<br>Constant|
|---|---|---|
|Rural|0.231<sup>_∗∗∗_</sup>|0.0314|
||(0.00577)|(0.00349)|
|Interviewed with paper survey|0.830<sup>_∗∗∗_</sup>|0.0476|
||(0.00925)|(0.00221)|
||0.38|0.49|
|Taliban areas|0.344<sup>_∗∗∗_</sup>|0.176<sup>_∗∗∗_</sup>|
||(0.0182)|(0.00356)|
|Contested areas|0.206<sup>_∗∗∗_</sup>|0.125<sup>_∗∗∗_</sup>|
||(0.00771)|(0.00397)|
|Household size|0.00245<sup>_∗∗_</sup>|0.187<sup>_∗∗∗_</sup>|
||(0.00104)|(0.00839)|
|Proportion of households with female head|-0.116<sup>_∗∗∗_</sup>|0.206<sup>_∗∗_</sup>|
||(0.0223)|(0.00373)|
|Proportion of households with literate head|-0.0317<sup>_∗∗∗_</sup>|0.213<sup>_∗∗∗_</sup>|
||(0.00746)|(0.00485)|
|Average age of household head|-0.00292<sup>_∗∗∗_</sup>|0.327<sup>_∗∗∗_</sup>|
||(0.000239)|(0.0113)|
|Distance to PHC (hrs)|-0.0144<sup>_∗∗∗_</sup>|0.229<sup>_∗∗∗_</sup>|
||(0.00325)|(0.00689)|
|Proportion of households with school in community|-0.116<sup>_∗∗∗_</sup>|0.245<sup>_∗∗∗_</sup>|
||(0.00713)|(0.00485)|
|Poverty rate|-0.0213<sup>_∗∗∗_</sup>|0.218<sup>_∗∗∗_</sup>|
||(0.00806)|(0.00551)|

_Note_ : Each row is a regression of a dummy for whether the household has a badly geo-coded location on the covariate in the row. We include survey weights and report robust standard errors.

41
well (that is, trade is of an inter-industry nature), should be positively related to imports. On the other hand, if international trade mostly takes place in differentiated products and is hence of an intra-industry nature, similarity in country size should bear a positive relationship to imports. Since only the latter variable is significant and correctly signed, it appears that our model mostly captures trade flows of intra-industry variety—or perhaps that absolute difference in per capita GDP is not a good predictor of inter-industry trade, at least in our sample. The geographic distance between countries serves as a main proxy for trading costs, and has a negative effect on imports. The signs on the dummy variables of the model are largely self-explanatory—countries are likely to trade more if they share a common official language, a land border, or have colonial ties.

The coefficient on the PTA dummy variable is positive and significant, indicating that, controlling for all other factors, entering a preferential agreement tends to increase trade flows between member countries by 114 percent.<sup>36</sup> Although this variable is specified so that it is equal to zero in the years before a particular PTA enters into force, the short time dimension of the model does not allow us to clearly distinguish whether the PTA variable is capturing the threshold effects of signing a PTA or the “natural trading partner” effect for a given pair of countries. In any case, our model supports the notion that PTAs are associated with increased imports from partner countries, although the value of the PTA coefficient is considerably lower than individual agreement dummy estimates from the earlier literature (which, if significant, tend to be greater than one and commonly reach double digits). This discrepancy is likely due to the fact that the number of PTAs in our model (163) is much greater than in the previous gravity studies of preferential trade, which generally confine their analysis to a sample of up to twenty of the most well-known agreements. Therefore, while the estimates reported in the previous literature focused on the PTAs that are the largest and most likely to influence trade flows, our estimates reflect the contribution of _all_ PTAs to bilateral trade. Consequently, our estimates are lower since the larger and more significant effects of bigger agreements are diluted by the lower and, possibly, insignificant effects of the smaller PTAs.<sup>37</sup>

> 36Because the dummy implies a discrete change in the value of the dependent variable, its coefficient cannot be interpreted as a semi-elasticity unless it is very small. In other words, _d_ ln( _y_ ) becomes a poor approximation to<sup>∆</sup> _y_<sup>_<u>y</u>_</sup> as its absolute magnitude rises. However, it is always the case that<sup>∆</sup> _y_<sup>_<u>y</u>_=</sup><sup>_eβ−_1,where</sup><sup>_β_isthedummycoefficient.</sup> 37 We use the term “smaller” here to refer not only to the PTAs that create a smaller common market, but also to the agreements that are likely to have small (if any) effects on trade due to poor design and/or implementation.

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Page 40

**Table 11. Minimum Unfined Offer**

|**Minimum Un-fined Offer**|**Percent**|**Cum.**|
|---|---|---|
|0|16.44|16.44|
|0.1|14.22|30.67|
|0.2|10.67|41.33|
|0.3|13.00|54.33|
|0.4|22.33|76.67|
|0.5|21.44|98.11|
|0.6|0.78|98.89|
|0.7|0.78|99.67|
|0.8|0.33|100|

Source: Serra, Barr and Packard (2011) 120. To investigate the correlations between parents‟ behavior in game and their behavior and attributes in the school context two series of multivariate analyses are conducted. The first of these is a series of Probit analyses focusing on a dependent variable that equals one if a parent in the third-party role participates in fining and zero if the parent never fines (columns 1 to 3 in Table 12). The second is a series of linear regressions taking the minimum un-fined offer chosen by each parent in the third-party role as the dependent variable (columns 4 to 6 in Table 12). The sample upon which this analysis is based is restricted to the parents who fine at least once. Positive (negative) coefficients in the Probits identify variables that are associated with parents deciding to participate in (abstain from) fining. Positive (negative) coefficients in the linear regressions identify variables that are associated with a lower (higher) degree of tolerance of teachers‟ selfish behavior.

121. We start our empirical analysis by testing whether parent class representatives are more likely to participate in holding teachers to account in the game, while controlling for parents‟ individual characteristics, such as economic status, education, and perceptions of corruption in the appointment of teachers. We then look at the parent representatives who, contrary to the _de jure_ institutional provisions, have been appointed by teachers. We then expand the empirical specification to control for the decision to vote in the most recent elections of parent class representatives. Finally, we include a measure of parents‟ lack of information with respect to the existing participatory accountability structures.<sup>41</sup>

> 41 In order to reduce the bias generated by multicollinearity between the various proxies of parental awareness and involvement, we conducted factor analysis on the measures of parents‟ lack of information with respect to the existence of class parent representatives and School Board; we include the first factor in the empirical specifications appearing in columns 3 and 6 of Table 12.