rafmacalaba/datause-encoder-data huggingface dataset:
and 54 percent, respectively, also face significant challenges in this regard (Roberts, Shyam, and Rastogi, 2006).

Evidence on the development impact of rural roads in Asia is quite mixed. While the improvements in road access appear to positively contribute to socio-economic outcomes in several developing Asian nations (Emran and Hou, 2013; Khandker et al, 2009; Muralidharan and Prakash, 2017), the effects are not always found to be substantial (Asher and Novosad, 2020; Jacoby, 1999; Li et al, 2019). The available literature for Latin America is much more limited, but highlights the significant positive contribution of rural road investments to creating job opportunities in remote areas (e.g., Blankespoor et al, 2017) as well as stimulating the growth of firms through lower-cost access to markets (e.g., Martincus et al, 2017).

Emran and Hou (2013) present evidence on the effects of access to domestic and international markets on the per capita consumption of rural households in China. The results, based on a household survey in 1995, show that better access to both domestic and international markets has positive effects on per capita income, with the domestic market effect being the larger one. A 10-kilometer reduction in the distance to a domestic (international) market increases per capita consumption by 42.10 (29.68) yuan when the household is initially located 150 kilometers away from both the domestic and international markets, which is equivalent to 8 (6) percent of the rural poverty line income. The authors also find evidence of complementarity between access to domestic and international markets, with a reduction in the distance to one market yielding a higher benefit for a household when it is located closer to the other market.

Khandker et al (2009) find that the overall effect of road improvement on household per capita annual consumption was about 8 –10 percent across affected project areas in Bangladesh. In addition, road improvements have led to a poverty reduction of 3 – 6 percent on average over the period of about 5 years. This implies that extreme poverty fell by about 1 percent each year, solely due to rural road improvements. Road improvement also has a significant positive impact on aggregate crop output and price indices. In addition, school enrollment of boys and girls increased significantly in both project samples.

Jacoby (1999) investigates the benefits from road projects at the household level using the relationship between the value of farmland and its distance to agricultural markets in Nepal based on the 1995 – 96 Living Standards Survey. Providing extensive road access to markets would confer substantial benefits on average, many of these going to poor households. However, the benefits would not be large enough or targeted efficiently enough to appreciably reduce income inequality in the population. The results highlight that rural road construction is not always the magic bullet for poverty alleviation.

30
sub-sample (F=6.16, p< .001). This implies that we should carry out separate analyses on rural and urban samples.

The next level of disaggregation is the stratum used in the VLSS sample. The VLSS was designed to be representative for each of ten strata, comprising three urban strata and seven rural strata. For this analysis, it was necessary to collapse the three urban strata (Hanoi/Ho Chi Minh City, other cities, and towns) into two (Hanoi/Mo Chi Minh City and other urban areas) because the Census data do not allow us to distinguish between "other cities" and towns. Within urban areas, a Chow test suggests that Hanoi and Ho Chi Minh City differ significantly from other urban areas (F=2.20, p<.001). In addition, the seven rural regions differ significantly from each other (F=12.61, p<.001). In other ways, however, the stratum-level regressions are not very satisfactory. Because of the small sample size in each stratum (ranging from 368 to 1111 households), many of the coefficients are not statistically significant at conventional levels or have counter-intuitive signs. Furthermore, the goodness-of-fit of most of the stratum regressions is below 0.5, compared to 0.54 and 0.55 for the rural and urban regressions. One result of this is that the standard errors of the poverty estimates from the stratum-level regressions are higher than those obtained from the urban-rural regressions (see Section 4.1).

In this paper, we will present the results of both the urban-rural regressions (see Tables 2 and 3) and the stratum-level regressions (see Annexes 2 and 3), as well as the poverty estimates derived from each (Tables 4-6 and Annex 4). However, we will give greater prominence to the results from the urban-rural regression analysis. As will be shown later, the two methods yield similar poverty headcounts and rankings, particularly for the poorest provinces. In the six sub-sections that follow, we summarize the results of the regression analysis to "predict" per capita expenditures.

12
page 18 Table 4. Estimarion of Life-Days Saved Per Year in a Population of 50,000 in Rural Java When 200 Village Health Workers are Added to an Exiscing Health Cencer.

||W|ithout V|HWs|||With V|HWs||
|---|---|---|---|---|---|---|---|---|
|Age Greup<br>(1)|P op.<br>in<br>Thous<br>(2)|Life<br>Expect.<br>(3)|Death<br>Rate<br>(4)|Disa-<br>bility.<br>Rate<br>(5)|Death<br>Rate<br>(6)|Disa-<br>bility<br>Rate<br>(7)|Life-<br>Y ears<br>Saved<br>(8)|Thousands<br>Days<br>Saved<br>(9)|
|0-1 Years Old|1.5|48|104.0|*|67.2|*|2647|*|
|1-4 Years|7|52|28.3|21.0|13.4|17.2|5424|81.4|
|5-14 Years|13|50|2.7|*|1.6|*|696|*|
|15-44 Years|21.5|35|5.6|3.6|4.6|3.2|639|9.2|
|45 Years Old<br>and Older|7|15|*|5.5|*|4.7|*|5.3|
|TOTALS|50||11.0|11.4|6.9|9.4|9405|96.0|

- SOURCE: R.N. Grosse, J.L.deVries, R.L.Tilden, A.Dievler,S.R.Day, "A Health Development Model Appladon to Rural Java," Final Report of Grant No. ATD/otr-G-1651, Department of Health Planning and Administration, School of Public Health, University of Michigan, October, 1979.

- NOTES:- Village Health Worker Program as defined by Grosse et al (ibid., pp. 5-7 of Appendix D) consists of one VHW per 250 people (or per 50 households) handling 4.7 encounters per person per year, for a total of 235,000 encounters in the population of 50,000. Of the 31 disease categories included in their analysis, Grosse et al assume that treatment at a rural health center can have a beneficial effect on either morbidity or mortality in 20 of these, but that a VHW has some impact in every disease where the health center has an impact.

- Columns (2) through (5): Repeated from Table 2, this paper.

- Column (6): From Alternative 6, PV 1 the results of which are given on the eighth line from the bottom of page 4 of Appendix F of Grosse et al (1979). Interpolated as for column (4).

Columns (7) through (9): Same notes as for Table 2 this paper.
The downward trend in current migration flows is shared by all regions in Albania except the poorer Mountain region, which instead exhibits increasingly positive trends over the entire period analyzed and which, until recently, appeared to have been cut off from this type of long-term, and presumably more lucrative, migration. Compared with the 1990s, proportionately more permanent migrants are now coming from the Mountain region. However, in terms of destinations, a lower proportion of the migrants from this poorest region make it beyond Greece and, to an even greater extent, beyond Italy. The flow to farther, more preferred destinations is still predominantly from Tirana and other urban centers. Surprisingly, though, split-off migrants from the poorer Mountain regions have a higher propensity to remit and, on average, they remit more. The poverty reduction potential of these relatively new flows of migrants, and the resulting remittances back home, is enormous; indeed, some of these effects may already be at work, as reflected in the progress in poverty reduction achieved by the Mountain region over the past few years.

As pointed out, the socio-demographic characteristics of migrants have been changing over time, suggesting an overall deterioration of the human-capital quality of more recent flows: less educated and older migrants appear more likely to migrate in recent years, in addition to an ever-increasing number of women (however the latter have higher educational levels, on average, than men). Excluded from these patterns is migration to destinations other than Greece and Italy. These other destinations continue to attract better educated and potentially more productive migrants, mostly from Tirana and other large cities. Thus, two clear flows emerge, with increasingly less educated migrants from rural areas seeking a better life in Greece and Italy, and more educated urban dwellers, often female, migrating to farther destinations in Northern Europe and North America.

Also, not surprisingly, the unstable economic situation and sudden shocks have created stronger push factors at different points in time, resulting in larger numbers of less-suitable individuals taking the migration path. However, as reflected in the trends and composition of the flows of returnees, these less endowed migrants are also more likely to return, and to return sooner. As confirmed by the ALSMS data for 2005, returnees are on average worse-off than permanent migrants and the likely stayers. Although a large share of these returnees has been successful in securing a job – often not 24
|Tibet|-|
|---|---|
|Xinjiang|<br>Urumqi|
|Yunnan|<br>Kunming|

Source: China Knowledge Online (2009).

# **Annex B:  China’s 15 Free Trade Zones**

|Province|FTZ(15)|
|---|---|
|Fujian|Fuzhou<br>Xiangyu(in Xiamen)|
|Guangdong|Futian (in Shenzhen)<br>Guangzhou<br>Shantou<br>Shatoujiao (in Shenzhen)<br>Yantian (in Shenzhen)<br>Zhuhai|
|Hainan|Haikou|
|Liaoning|Dalian|
|Jiangsu|Zhangjiagang|
|Shandong|Qingdao|
|Shanghai|Waigaoqiao|
|Tianjin|Tianjin|
|Zhejiang|Ningbo|

Source: China Knowledge Online (2009) and Annual Report on the Development of China’s Special Economic Zones (2009).

45
marginal benefits of higher (kharif) rainfall are lower in more intensively irrigated districts. Unlike the cross-sectional results, however, there is a significant and negative interaction between temperature and irrigation, at least in rabi season. This has a straightforward interpretation: Since irrigation is essential for most rabi cultivation, any impact of rabi season temperatures must be concentrated in heavily irrigated (i.e., rabi-cultivated) districts. Based on this specification, a 1<sup>_◦_</sup> C increase in both kharif and rabi temperature would reduce output per hectare by around 29 percent for the average district. Damages would vary, however, from -21 percent in nonirrigated districts to -35 percent in fully irrigated districts. Lastly, we reconsider temperature-precipitation interactions in column 6. Though not as overwhelming as in the cross-section, the panel evidence suggests that the negative impact of higher temperatures in kharif season is, once again, attenuated in wetter districts.

Guiteras (2009) also estimates the impact of weather shocks on gross agricultural productivity in India, using district-level panel data for 1960-99. Besides the less recent production data, Guiteras’ productivity index consists of just six major crops, compared to our twenty, and his sample drops most of eastern India as well. On the other hand, Guiteras uses daily gridded temperature data (not available to us) from which he constructs growing season degree-days, an arguably more relevant measure of weather than average annual or seasonal temperature. In any event, Guiteras’ sophisticated econometric analysis uncovers a much smaller response to warmer temperatures than we do, even for the latter half of his sample period.<sup>25</sup> If our cross-sectional estimates based on land values are to be believed and the long-term decline of agricultural productivity due to a 1<sup>_◦_</sup> C increase in temperature is indeed near 13 percent, then Guiteras’ damage estimate of less than 11 percent is implausibly small, implying virtually no scope for adaptation. By contrast, our weather shock-based damage estimate is two and a half times that of Guiteras and is, therefore, consistent with considerable farmer adaptation to warmer temperatures in the long-run.

# **4.4 Rural wages and climate**

The methodology for estimating the climate sensitivity of rural wages by employment sector follows closely the approach used for land values. Drawing upon the NSS employmentunemployment survey of 2004-05, the best source of labor data in India (Himanshu, 2005), we estimate the analog of equation (6) on a sample of over 36 thousand rural workers from 556 districts. We take the individual’s log daily wage (adjusted for regional cost-of-living

> 25As Guiteras points out, this was a period of increasing adoption of high-yielding varieties, which are more vulnerable to bad weather. Production data from later years would presumably exhibit even greater weather sensitivity.

15
14 futures bring to hedging, and hence in attracting more liquidity into the market. Finally, inflows of portfolio equity are always significant in reducing transaction costs. This might not only reflect the added liquidity in the market, but also the added heterogeneity of the investor base which facilitates trading, and hence reduces the costs of trading.

# Table 2. Determinants of Stock Return Synchronicity

This table shows least square regressions estimated using pooled OLS for a panel of 50 countries between 2000 and 2004. A constant is estimated but not reported. t-statistics using robust standard errors are shown in brackets. An * means <u>significance at 5 percent. See Appendix table 1 for the definition of the variables.</u>

||Dependent variabl|e: stock retu|rnsynchron|icity|||
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Log GDP per capita|-0.02*|-0.02*|-0.02|-0.02*|-0.02|-0.02*|
||[-3.77]|[-3.34]|[-3.15]|[-2.75]|[-2.73]|[-3.15]|
|Disclosure|<br>0.00|||<br>0.00||<br>0.00|
||[0.59]|||[1.10]||[0.97]|
|Director liability||-0.01*||<br>-0.01|0.00||
|||[-1.88]||[-1.56]|[-1.46]||
|Shareholder suits|||-0.01*|<br>-0.01*|<br>-0.01*|-0.01*|
||||[-3.37]|[-3.34]|[-3.21]|[-3.48]|

|Num. Observations|223|223|223|223|223|223|
|---|---|---|---|---|---|---|
|R-Squared|0.05|0.07|0.10|0.12|0.10|0.10|

# Table 3. Determinants of Transaction Costs

This table shows least square regressions estimated using pooled OLS for a panel of 50 countries between 2000 and 2004. A constant is estimated but not reported. t-statistics using robust standard errors are shown in brackets. An * means <u>significance at 5 percent. See Appendix table 1 for the definition of the variables.</u>

||Depen<br>|dent variab<br>|le: transacti<br>|oncosts<br>||||
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|
|Log GDP per capita|-1.93|-2.16*|-2.44*|-2.17*|-2.32*|-2.97*|-2.78*|
||[-1.76]|[-2.03]|[-2.10]|[-5.28]|[-2.00]|[-2.19]|[-2.07]|
|Short Selling|-2.77*||||||-5.02|
||[-2.10]||||||[-1.34|
|Stock Lending||-3.47||||-4.09*||
|||[-1.58]||||[-1.93]||
|Settlement period|||-0.17|||0.12|0.18|
||||[-0.49]|||[0.42]|[0.63]|
|Equity inflows||||-0.08*||-0.08*|0.09*|
|||||[-5.51]||[-3.01]|[-2.65]|
|Options and futures tr.|||||-1.16|0.17|-0.02|
||||||[-0.94]|[0.16]|[-0.02]|
|Num. Observations|50|50|50|229|50|49|49|
|R-Squared|0.23|0.24|0.18|0.11|0.18|0.35|0.34|
admittedly of varying size, but includes several heavyweights. Whether these ambitious targets can be realized, despite the growing opposition to privatization and the limited popular support and cohesion of the Government, remains to be seen.

Table 4. Central Government 1990 Budget (in trillions of Turkish Lira)

|Item|Amount|
|---|---|
|Revenue<br>|53.9<br>|
|Tax revenue<br>|43.7<br>|
|Other revenue<br>|10.2<br>|
|of which: land sales|1.5|
|Expendituxe<br>|64.4<br>|
|Personnel<br>|20.0|
|Interest payments<br>|14.6<br>|
|Other current<br>|20.0|
|Investment|9.8|
|Deficit|10.5|
|Memorandum items||
|Share sales of TL 1.1 trillion (70 per-<br>cent of total) in percent of deficit|10.5|
|Land sales of TL 1.5 trillion<br>in percent of deficit|14.3|
|Share and land sales of TL 2.6<br>|21.7|
|trillion in percent of deficit<br>||
|(considering land sales a financing<br>item instead of revenue)||

Source : Treasury 71. The state-owned petrochemical conglomerate Petkim has been on the privatization list for over a year, during which international consultants have helped with the preparation for privatization. Tangible progress has however been slow in coming. The company is a major SEE employing close to 8,000 people and recording gross sales of TL 1.6 trillion ($1.1 billion) in 1988. Petkim has been revamped into a holding company under MHPPA, but this has not ended its previous position in the government machinery in terms of 39
have more products, hence are less likely to exit. Without the efficiency-type heterogeneity, all producers would have the same number of products at a given size and thus would face the same hazard rate of exit no matter how old they are.

I can derive an analytical formula that shows these relations. To construct this formula, at any age , I need to know the hazard rate of exit, the number of products owned by the producer, and the efficiency-type distribution of producers. Using the solution of the system of equations for size evolution given in equation 12, having entered at size one, probability of exiting within one unit of time is given as From equation 13, the probability of having  ≥ 1 products at age  is (1 −  (|))  (|)<sup>−1</sup>  A −type producer at size  has  () =k <u></u> 1−<sup>kproducts.Age-conditionalefficiency-type</sup> ( <sup><u></u>)</sup> distribution with density <sup></sup> () can be derived using the type distribution at entry  (·) and the probability of surviving more than  years 1 − 0 (; 1|)  which is given as

Using equations 17 and 18, the hazard rate of exit conditional on age  and size  () can be derived as follows I plotted the graph of  () for different size  and age  values in Figure 2<sup>11</sup> . For comparison, I also plotted the lines implied by the Klette and Kortum (2004) model which are labeled as "KK". Each plotted line shows the hazard rate for a different size level. For the Klette and Kortum (2004) model, I just showed two size levels 200 and 800. In their model, at any particular size, the hazard rate is independent of age but the hazard rate decreases as size increases. On the other hand, in my model both relations hold independently. Conditional on age, the hazard rate of exit decreases in size and respectively conditional on size, it decreases

> 11The parameter values used for the graph are from the simulation results of Food industry which will be explained in the empirical analysis part.

14
# **<u>Graph 1: Tariffs and Real Exchange Rate, 1973-2003</u>**

<!-- Start of picture text --> 120.00<br>110.00<br>200.0%<br>100.00<br>First Institutionality<br>90.00<br>150.0%<br>Second<br>Institutionality 80.00<br>Definitive 70.00<br>100.0% Institutionality<br>60.00<br>50.00<br>50.0%<br>40.00<br>30.00<br>0.0% 20.00<br>YEARS<br>Maximum Tariff Average Tariff RER*<br>TARIFF 94.0% Index 1986=100<br>67.0%<br>44.0%<br>27.0% 24.5% 26.3%<br>15.7% 20.0% 20.0% 20.0% 11.0% 11.0% 11.0% 11.0% 11.0% 11.0% 10.0% 9.0% 8.0%<br>13.9% 10.1% 10.0% 10.0% 10.0% 15.0% 15.0% 15.0% 11.0% 11.0% 7.0% 6.0%<br>1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->

*RER: real exchange rate

<u>Sources: Tariffs: French-Davis (1980), Meller (1996) and the author’s analysis. Real exchange</u> rate provided by R. French-Davis, following calculus methodologies described in French-Davis (2003).

Although in 1973 protection was initially very high, at the end of the unilateral liberalization process, when the tariff level reached 10%, it could have been justified to introduce contingent protection instruments to support the adopted policy. Nevertheless, this only happened during the great economic crisis of 1982.

In the framework of the trade negotiations of the Tokyo Round, Chile signed the GATT Subsidy Code, because the existence of subsididized imports was considered to be the only situation that justified a temporary protection in favor of the domestic industry. Conversely, the economic rationale of safeguard measures was less sustainable. Besides, in their original form these measures contemplated compensations for the countries affected by them, which discouraged their application. Finally, it was considered that in an open economy, anti-dumping duties could lead to protectionist measures that would favor sectors that might feel affected by the opening and could have political influence to get protection. Besides, they did not have a solid economic basis.

5
# **_Gender differences in the returns to noncognitive skills_**

With the exception of Serbia for which noncognitive skills are not jointly significant in any specification, and Ghana and Kenya for which noncognitive skills are not jointly significant for all specifications related to women, we find that noncognitive skills together do affect the logearnings of men and women (Table 8). Correcting for selection bias switches the joint significance of these skills from not-significant to significant for women in Armenia, Colombia and Georgia.<sup>63</sup>

**Table 8. Joint significance of noncognitive skills in log earnings** **<u>regression estimates, adults aged 25-54</u>**

|||**Men**<br>**Sltiit**||**Women**<br>**Sltiit**|
|---|---|---|---|---|
||**OLS**|**eecvy-**<br>**corrected**|**OLS**|**eecvy-**<br>**corrected**|
|**All countries**|S***|S***|S***|S***|
|**Armenia**|S**|S***|NS|S***|
|**Bolivia**|S**|S*|NS|S*|
|**Colombia**|S**|S***|NS|S**|
|**Georgia**|S**|S***|NS|S***|
|**Ghana**|S*|S**|NS|NS|
|**Kenya**|S***|S***|NS|NS|
|**Serbia**|NS|NS|NS|NS|
|**Ukraine**|NS|NS|S**|S***|
|**Vietnam**|S*|NS|S***|S***|

Note: These are based on the full model results using schooling splines as a measure of education. The complete results are contained in Appendix Table 4. Results for all countries pool the samples of Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine and Vietnam and are estimated with country fixed effects. No data on time preference for Colombia; no data on hostile attribution bias, risk-taking and time preference for Serbia. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01. Data sources: STEP data for 9 countries, 2012-13 63 In an effort to understand the interrelationships among individual noncognitive skills and to find a more parsimonious measure of this aspect of human capital, we estimated a principal components model for noncognitive skills using the pooled sample of countries and repeated the analysis for individual countries. We find no individual noncognitive skill that explains a large enough share of the variation in the log-earnings of either men or women to justify a principal components approach, so we continue to include the full list of noncognitive skills in addition to schooling and cognitive skills as a reduced-form specification of human capital. Our results indicated the variables of nine distinct noncognitive skills were characterized more by “uniqueness” rather than being correlated. We also implemented the approach developed by Sarzosa and Urzua (2015); see footnote 16.

47