rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: FPM Transfers and the quality of infrastructure in preschool centers**

|||R|educed fo<br>|rm<br>|||||IV<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Playgr|ound|Ade<br>sanit|quate<br>ation|Fri|dge|Playg|round|Ade<br>sanit|uate<br>ation|Fri|dge|Obs.|
|Panel A: Municipal supply||||||||||||||
|Thresholds 1-7|0.002<br>[0.001]|0.002<br>[0.001]|-0.000<br>[0.001]|0.001<br>[0.001]|0.001<br>[0.002]|-0.000<br>[0.001]|0.003<br>[0.002]|0.002*<br>[0.001]|-0.000<br>[0.002]|0.001<br>[0.001]|0.001<br>[0.002]|-0.000<br>[0.001]|20990|
|Thresholds 1-4|0.003<br>[0.002]|0.003*<br>[0.001]|-0.001<br>[0.002]|0.000<br>[0.001]|0.001<br>[0.002]|-0.000<br>[0.001]|0.003<br>[0.002]|0.004**<br>[0.002]|-0.001<br>[0.002]|0.000<br>[0.002]|0.002<br>[0.002]|-0.000<br>[0.002]|18207|
|Panel B: Private supply||||||||||||||
|Thresholds 1-7|-0.001<br>[0.002]|-0.001<br>[0.002]|0.002<br>[0.002]|0.001<br>[0.002]|0.000<br>[0.002]|0.000<br>[0.002]|-0.002<br>[0.003]|-0.001<br>[0.002]|0.003<br>[0.003]|0.001<br>[0.002]|0.000<br>[0.003]|0.000<br>[0.002]|13471|
|Thresholds 1-4|-0.003<br>[0.003]|-0.001<br>[0.002]|0.004<br>[0.003]|0.001<br>[0.003]|0.001<br>[0.003]|0.001<br>[0.002]|-0.005<br>[0.004]|-0.002<br>[0.003]|0.006<br>[0.005]|0.002<br>[0.004]|0.001<br>[0.004]|0.001<br>[0.003]|10840|
|State fixed-effects|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No||
|Municipality fixed-effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes||

_Notes_ : Reduced form regressions relate the relevant outcome to theoretical transfers. In the IV regressions, theoretical transfers serve as instrument for actual transfers. All regressions include a three order population polynomial and year fixed effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
Table 8 The reasons for migration into towns in three provinces in China:2000

||1|2|3|4|5|6|7|8|9|
|---|---|---|---|---|---|---|---|---|---|
|||||(%)||||||
|Zhejiang|54.92|3.92|3.03|6.55|5.55|5.96|14.29|2.99|2.79|
|Henan|17.79|12.31|7.17|7.33|7.68|13.70|21.96|4.53|7.54|
|Sichuan|18.71|9.90|5.06|21.30|6.20|9.47|14.44|8.47|6.43|

Sources: calculated from Tabulation on 2000 Population Census of the P. R. China. L705B. Note: 1, engage in production and business; 2. job transfer; 3, job assignment; 4,study and training; 5, resettlement; 6, marriage; 7, move with family member; 8, seeking help from relatives; 9, others.

The age composition of in-migrants also seems to support for the view that migration is economically rewarding. When we tabulated in-migrants by age (figure 1), we found that most town in-migrants were in the categories of economic active groups of age between ages of 20 to 34. This inverted U-shaped relation of town in-migrants confirms the findings of other studies (Hare 1999; Zhao 1999). People in these age groups are most likely to migrate, and probability of migration decreases with age. In rural area, a piece of land that could be hold as a social security against old ages, whereas in towns, old people have to rely on some kind of social security, which has not been well established so far, preventing elderly moving into towns (Cai 2003).

Figure 1 Age differentials in migrants into town: 2000 <!-- Start of picture text --> The age distribution of in-migrants in town by<br>provinces<br>25<br>20<br>15<br>10<br>5<br>0<br>0-- 5-- 10- 15- 20- 25- 30- 35- 40- 45- 50- 55- 60- 65+<br>4 9 -14 -19 -24 -29 -34 -39 -44 -49 -54 -59 -64<br>age group<br>Zhejiang Henan Sichuan National<br>%<br><!-- End of picture text --> Source: calculated from Tabulation on the 2000 population census of the P. R. China. Table 7-5, page.1818-1827, and L705B.

9
11 Furthermore, the negative and significant coefficient of the dummy for NHICs (NONHINCM DUMMY) suggests that rating changes are generally smaller in size in these countries. Nevertheless, since these countries' rating levels are lower than for highincome countries, smaller-sized rating changes may still be larger in relative terms for non-high-income countries (NHICs).

The second specification adds the new variable NONHINCM*ASOVEREIGN, in order to check whether sensitivity to sovereign rating changes is larger for firms in NHICs. Results strongly support our priors: ASOVEREIGN is no longer significant while NONHINCM*ASOVEREIGN is significant and its positive coefficient is larger in size than the coefficient for ASOVEREIGN in the first specification (0.50 against 0.45). We interpret this as evidence that non-bank rating changes are sensitive to sovereign rating changes in NHICs but not in high-income countries.

The third specification includes the additional variable NONHINCM*ASOVEREIGN*DOWNSOV, i.e. the variable singling out only sovereign downgradings for NHICs.<sup>15</sup> This is meant to check whether firm ratings in NHICs are more sensitive to sovereign downgradings than to sovereign upgradings. Results are quite telling. On the one hand, NONHINCM*ASOVEREIGN becomes almost nonsignificant, its sign changes, and its coefficient shrinks (-0.14 in lieu of 0.50). On the other hand, NONHINCM*ASOVEREIGN*DOWNSOV is significant and its coefficient is even larger than the one for NONHINCM*ASOVEREIGN in the second specification (0.83 against 0.50). The fact that the R2 sizably increases indicates that this final specification improves on the previous one. This suggests that the excess sensitivity to sovereign rating changes is ample but it is by and large limited to sovereign downgradings in NHICs. In practice, a 10-point--or two notches-sovereign downgrading translates into a 6.9 point downgrading for NHICs' firms whereas it does not systematically affect non-bank ratings in high-income countries.

Finally, the specification of equations (2) and (3) are replicated for HICs. Their estimation shows the absence of short and long term relationships between sovereign and private sector ratings both in the upgrading and in the downgrading episodes.

in the non-bank panel than in the bank panel. This possibly precludes identification of the long-term relation in the non-bank panel.

> _15_ To be sure, in our sample, most downgradings refer to NHICs. However, there are also downgradings for HICs: e.g. Canada was downgraded in both 1994 and 1995, Denmark and Finland were downgraded in 1992, Japan was downgraded in 1998, New Zealand was downgraded in 1998, Sweden was downgraded in 1991, 1993 and 1995.

_Ferri, Liu, and Majnoni, "The Role of Rating Agency Assessments in Less Developed Countries: Impact ofthe Proposed Basel Guidelines"_
wages or the wages set by the government<sup>in the formal sector have added substantial</sup> importance to wages and employment matters<sup>in the formal sector. On the other hand</sup> even though there has been a decline<sup>in the aggregate employment, the real GDP has</sup> increased during this period (Figure 5),<sup>which means that increase in labor productivity</sup> was one of the factors that made this possible.<sup>It is not clear whether formal sector leads</sup> the wage determination process, an issue<sup>which needs further investigation.</sup>

**Fig 3: Trend of real monthly earnings per**<sup>**employee in**</sup> **public and private sector in Ghana.**

<!-- Start of picture text --> 120<br>100 _ -<br>X 60 _X - X<br>O 6. . ::=. .o - -. r ,:..= .r..<br>208<br>1984 1985 1986 1987 1988 1989 1990<br>Year<br>- -C * -Public - -X - Private Ail<br><!-- End of picture text -->

**Source: Ghana Statistical Service.**

**Fig 4: The Trend in Employment by**<sup>**sectors in Ghana, 1981-**</sup>

<!-- Start of picture text --> 500 U .<br>450<br>3 150<br>0...- 100<br>50 X _ X- - - X- - - X- - - Xv - X- X-<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990<br>ALL .o--PUBLIC - - -X - PRIVATE<br><!-- End of picture text -->

**Source: Ghana Statistical Service.**

27
redistribution by five percentage points. When we include past and future downward mobility in column 6, the estimates remain similar.

D. Analysis of Downward Mobility Experienced During the Recent Economic Crisis We now look at the effects of downward mobility caused by other sources of shocks, i.e., the recent economic crisis in 2007-08, using LITS II. Table 11 presents the results. We use three measures of _supports distribution_ (i.e., income should be more equal, poverty is caused by injustice in society, and market economy is not preferable) and three measures of _downward mobility_ (i.e., affected much by the crisis, responded to shocks by taking extreme measures, and responding to crisis by taking some measures). We find all estimates are positive and significant statistically. The estimates are economically large, in particular when we use whether poverty is caused by injustice in society as the measure of support for redistribution in columns 4-6: Affected much by the economic crisis in column 4, for example, increases support for redistribution by eight percentage points; responded to crisis by taking some extreme measures, which is similar to the measure we use in the basic results, in column 5 increases support for redistribution by ten percentage points.

E. Analyses of Individuals Whose Actual and Perceived Incomes Are Matched Cruces, Perez-Trugliain and Tetaz (2013) show that people have biased perception of their relative position in income distribution: Some people say they are poor when in fact their incomes are higher than average; some say they are richer than average when their incomes are actually low.<sup>17</sup> In Table 12 we check whether these biased perceptions of incomes may compromise our results. In column 1, we include only individuals whose perceived position in a ten-step economic ladder and actual decile-group of household spending are matched. In

> 17 They also find that people who are told that they overestimate their relative position are more likely to support redistribution.

19
Case studies of developing countries in Roberts and Tybout (1996) also show that industry exit and entry (one indicator of intersectoral reallocation of labor) generally do not increase with import competition after controlling for demand shocks. This suggests that the sectoral structure does not depend much on trade policy. Roberts and Tybout (1996) find that more plants were exiting manufacturing than were entering in Chile during 1979–1982, despite the growth in productivity. The size of entrants tended to be larger than those exiting, however, so the overall impact on employment is unclear (Goldberg and Pavcnik, 2004). Overall, the research summarized above suggests that trade reforms induce limited reallocation of factors across manufacturing industries, and that much of this may be associated more with export sectors attracting investment (including FDI entry) than with substantial downsizing of importcompeting sectors of the economy.

Wacziarg and Wallack (2004) is a recent cross-country study of the effects on labor of trade reform episodes across a number of developing countries. They conclude that the presumption that reforms will result in labor reallocation is not supported by the available data. Liberalization episodes are followed by a _reduction_ in the extent of intersectoral labor shifts at the economywide 1-digit level of disaggregation. Liberalization has a weak positive effect at the 3-digit level, but it is small in magnitude and not robust. There is no evidence of trade-induced structural change at the more disaggregated 4-digit industry level. Wacziarg and Wallack note that other (complementary) policies will matter. Other reforms such as domestic deregulation and privatization are found to have greater effects on intersectoral labor movements than trade reform in isolation. But their bottom line is that claims that trade liberalization generally leads to the absolute decline of entire sectors (broadly defined) are not supported by the data.

These findings are consistent with earlier case studies of liberalization episodes. For example, the 19 studies collected in Papageorgiou, Michaely and Choksi (1991) did not reveal large employment or reallocation effects following trade reforms. An exception was Chile, where liberalization had a significant effect on employment in manufacturing, with export sectors expanding and import-competing contracting (and net employment increasing).

16
22 While no similar analysis exists for ENTEL, a broad conclusion based on the partial indicators noted above is that ENTEL's expansion and lower tariffs must have benefitted consumers. Yet, ENTEL's excessive rates of return indicate that consumers could have done much better. Alternatively, government could have spent the windfall gains from incoming calls to expand rural telephony or other similar projects.

# _Anticompetive behavior and transaction costs_

Notwithstanding these positive effects, the recent regulatory reforms and privatization have also resulted in a large number of lengthy and costly lawsuits alleging anticompetitive behavior.'°/ Several factors lie behind these claims, including an excessive reliance on market forces to define market boundaries, the drive for profit maximization following privatization, the industry's large sunk costs, and strong incentives for vertical integration, cross subsidization, and cream skimming. Whatever the motives, these lawsuits have increased the transaction costs of conducting business and burdened the courts. A few cases are briefly reviewed below, to illustrate the nature of these disputes and how conflicts are resolved.

_Local fixed telephony._ To promote competition and private sector participation, SUBTEL granted concessions to a number of small companies in 1981 to provide local fixed telephony services in certain regions of the country, some of them overlapping with CTC's concession areas. These firms filed several antitrust complaints, accusing CTC of infringing on fair competition.' _II_ The Commissions ruled against CTC for anticompetitive behavior in all instances, according to Coloma and Herrera (1990).12/ But the costs incurred in the process '°/ For an analysis of these disputes, see Coloma and Herrera (1990) and Parades (1987). For a review of disputes under Chile's first antitrust law of 1959, see Furnish, (1971).

_"/_ These firms also suffered from inefficient plant size. The extent of economies of scale -in Chile has been studied by the Economics Department of the University of Chile (1987), and descrilbed in Ale (1989). These studies show that the elasticity of total cost with respect to output varies between _0.5_ and 0.95, depending on plant size.

**12/** According to Decree Law 211 of 1973, conflicts pertaining to anticompetitive behavior are resolved first by the thirteen Regional Preventive Commissions at the local level, and then by the Resolutive Commission. Decisions of the Preventive Commissions can be appealed to the Resolutive Commission and finally to the Supreme Court. To ensure neutrality, the Resolutive Commission is made of five members: the Chief of the Supreme Court as chair (appointed by his tribunal), two government representatives (appointed by the Ministers of Economy and Finance), and a dean of a law school and of an economics department (selected at random). The members of this commission remain in office for two years.
and older.<sup>26</sup> The PPVT has been widely used throughout the world for assessing the effects of various interventions on child language, including Mozambique (Spanish version of PPVT; (Martinez et al., 2012)) and Madagascar (Fernald et al., 2009). In administering the PPVT, the child was shown a page with four pictures, and then asked to point to or touch the picture (stimulus word) named by the enumerator. Specific items (both words and pictures) were modified for use in Malawi. For example, we replaced “apple” with “papaya,” a fruit that is well known throughout the country, and was estimated to be of similar difficulty as the word “apple” would be in the United States. Another example was changing a stimulus word from “tornado” to “whirlwind,” which was more familiar to children for describing the accompanying picture. All items administered were translated and back translated (English-Chichewa-English). The test publishers approved changes made to the items. Items were scored as pass or fail, and a summed score was calculated. This score was then standardized at each round (Baseline and 36-Month Follow-Up) using the control group’s mean and standard deviation.

3. _The Leiter-R Sustained Attention (LSA) task (Roid & Miller, 1997),_ a language-free measure that assesses how well children can continue to maintain attention and accuracy during a timed visual search task.<sup>27</sup> To administer this test, the child was taught to recognize a target picture (e.g., butterfly), and then asked to mark all of the exact same targets (butterflies) in an array of many pictures, in a fixed amount of time. There are three sets of age-specific tasks (2-3 years old; 4-5 years old; 6+ years old), and each set included four different tasks of increasing difficulty. The measure has successfully detected group differences in performance in Madagascar (Fernald et al., 2011). Total adjusted scores were determined by subtracting the numbers of errors from the number of correct responses. The scores were then standardized at each round (Baseline and 36-

> 26 The PPVT can be purchased through

> <u>http://www.pearsonclinical.com/language/products/100000501/peabody-picture-vocabulary-test-fourthedition-ppvt-4.html.</u>

> 27 The Leiter-R test materials can be purchased through

> <u>http://www.stoeltingco.com/psychologicaltesting/intellectual-cognititve/nonverbal/leiter-3-kit-in-rollingbackpack.html.</u>

57
## **FIGURE 2: ACTIVITIES CONDITIONING THE MOVEMENT OF FOREIGN TRADE FREIGHT**

<!-- Start of picture text --> ACTIVITIES FUNCTIONS COMPONENTS<br>Internal Freight movements Roads, vehicle transport,<br>Flows within national territory railroads, river navigation<br>Nodes of Transfer nodes for Ports, airports, border<br>transference foreign trade crossing<br>External Freight movements Sea transport, intl. road<br>Flows outside national transport<br>Interfaces and Commer. & operational Reception and delivery<br>Coordination coord. between modes coordination<br>Inspections Fiscal, custom and Custom, phytosanitary,<br>para-custom control and migration control<br>Security Security control in Control in port, scanners<br>supply & control through chain<br>C. organization Design & operation in Inventory and material<br>of supply chain of supply management, distribution<br>Logistic operators Provision of integrated Logistic & multimodal<br>& Intermediaries logistic services operators, 3PL, ZAL<br>*Full circle indicates high relevance, empty circle indicates minimum relevance<br>Source: Banco Mundial, 2006: Argentina: El Desafío de Reducir los Costos Logísticos ante el Crecimiento del Comercio<br>Exterior.<br>Infrastruc. Regulation Private Perf.<br>Transport<br>Trade<br>Facilitation<br>Logistics<br><!-- End of picture text -->

# **3.1 Relevance of the physical movement of goods and available measures of logistics costs**

In recent decades there has been a profound change in the way companies organize their physical flow of goods, with the development of modern business logistics that integrate movements over distance (transport) and time (storage), from supply to distribution. The process of change in the organization of physical flows of goods began in the more developed economies, and has been extending gradually into the rest of the world. Until the 1980s, companies managed the transport of their inputs, distribution of final products, and storage systems in a relatively independent manner. In recent years, companies began to integrate processes, considering logistics to be the complete cycle of materials, documentation and information, from purchase to final delivery to 7
convergence, so both the integration of domestic markets and the rice export quota are likely responsible for the price changes that are exploited in this study.

# **3.2 Household and Sibling Composition**

The aim of this empirical work is to relate rice prices movements to changes in child labor supply allowing the effects to vary based on household and sibling composition. In the analysis, this study focuses on households with children 6-15 in the 115 rural communes that are visited in both rounds of the VLSS. Age 6 is chosen as a lower bound, because the VLSS does not collect data on the activities of children below 6. 15 is the upper bound following convention on many international treaties on child labor. This restriction leads to 2,255 households in 1993 and 2,375 households in 1998. The increase in sample size in part reflects a decision on the part of the General Statistical Office of Vietnam to increase the sample size between rounds of the VLSS as well as a rise in the number of children ages 6-15. Table 1 summarizes household and sibling composition in 1993 and 1998 for the nationally representative VLSS samples and the restricted sample of households with children 6-15 in rural panel communes used in the analysis.

Unsurprisingly, households with children tend to be larger than average. Likewise rural households have more children than do urban households. Thus, household size is approximately half a person larger in the study sample than in the nationally representative dataset.

Overall household size is smaller in 1998 than in 1993. This is true in the nationally representative sample and in the selected sample of rural households with children in panel communes. The decline in household size between years is about half a person. Most of the decline is in children under 5. The onset of economic reform in Vietnam in 1986 is associated with a large decline in fertility that is associated with a significant increase in the average age of 13
Table A8. Peru. Farm Earnings

||2004|2010|
|---|---|---|
|Head or Spouse with at least High School|0.133***|0.128***|
||(0.0296)|(0.0273)|
|26 to 35 yrs old|0.267***<br>|0.231***<br>|
||(0.0610)|(0.0736)|
|36 to 45 yrs old|0.412***|0.333***|
||(0.0598)|(0.0722)|
|46 to 55 yrs old|0.473***|0.272***|
||(0.0600)|(0.0724)|
|56 to 65 yrs old|0.373***|0.214***|
||(0.0607)|(0.0736)|
|Female|-0.420***|-0.385***|
||(0.0349)|(0.0318)|
|Head Farmer|0.421***|0.304***|
||(0.0409)|(0.0351)|
|More than 1 farmer in HH|0.688***|0.513***|
||(0.0571)|(0.0453)|
|Land size 0.5 to 2 ha|-0.0127|0.437***|
||(0.0377)|(0.0281)|
|Land size 2 to 5 ha|0.288***|0.799***|
||(0.0443)|(0.0348)|
|Land size >5 ha|0.604***|1.055***|
||(0.0468)|(0.0371)|
|Share of Adults >50%|0.0476*|0.0206|
||(0.0254)|(0.0251)|
|Improved Irrigation System|0.130***|0.177***|
||(0.0340)|(0.0249)|
|Owns land|-0.218***|0.0647**|
||(0.0336)|(0.0252)|
|Sierra|-0.151***|0.00957|
||(0.0336)|(0.0326)|
|Selva|0.0414|-0.0303|
||(0.0404)|(0.0422)|
|Rural|0.0757**|0.0935***|
||(0.0302)|(0.0303)|
|Constant|4.249***|4.182***|
||(0.0784)|(0.0845)|
|Observations|6,870|7,117|
|R-squared|0.123|0.238|
|Sigma|0.922|0.894|
|Standard errors in parentheses. *** p<0.01,|** p<0.05, * p|<0.1|
|Source: Own estimates based on Peru's EN|AHO 2004 -20|10.|

58