rafmacalaba/datause-encoder-data huggingface dataset:
The oversupply of labor keeps agricultural productivity low in China. Rural to urban migration is one of the major solutions for reducing the income gap between the urban formal industrial sectors and the rural traditional agricultural sectors (Lewis, 1954). However, in the case of China, the urban sectors are not capable of absorbing the large quantity of redundant labor in rural areas. Although the shortage of food is no longer a threat, the government continues nevertheless to control rural-to-urban migration through some direct or indirect measures for two principal reasons. First, the urban infrastructure is not sufficient to offer enough public services to rural migrants without lowering those to the urban citizens. The urban residents are not willing to share their relatively higher living standards with the rural residents. Second, the urban areas also have the problems of unemployment, in particular due to the state-owned-enterprises (SOEs) reforms. The urban sectors cannot offer enough additional job opportunities for rural migrants. The development of the service sector is going to be important for developing the “pull forces” to the cities. In China, the “push forces” from the countryside are strong, but the “pull forces” in cities remain insufficient. Excessive rapid rural-to-urban migration may lead to serious socialeconomic problems. In this context, the rural non-farm sector, consisting mainly of township and village enterprises (TVEs) and the rural private economies, becomes irreplaceably important for economic development in China.<sup>10</sup> Since 1978, the rural total household income per capita increased rapidly, but the income earned from the primary sector decreased relative to that earned from secondary and tertiary sectors. In early 2002, non-farm income represented about half of total rural household income (National Statistics Bureau of China, 2003).

Some studies show that rural inequality in China has significantly increased since the beginning of the economic reforms, and suggest that such an increase in inequality can essentially be explained by the growing importance of non-farm income in total income. Knight and Song (1993) and Hussain et al _._ (1994) argue that the distribution of non-farm income is more unequal than that of farm income in China. Bhalla (1990), Burgess (1998), Ravaillon and Chen (1999), Wagstaff (2005), Yao (1999), Zhu (1991) and Zhou (1994) suggest that the sharp increase in inequality in rural household income is mainly attributed to the difference in skills, knowledge, and capital endowments.

> 10 See also Aubert (1995); Banister and Taylor (1990); Byrd and Lin (1994); Goldstein and Goldstein (1991); Zhou (1994).

4
Policy Research Working Paper 9467

# **Abstract**

Poor households often rely on transfers from their social networks for consumption smoothing, yet there is limited evidence on how antipoverty programs affect informal transfers. This paper exploits the randomized roll-out of BRAC’s ultra-poor graduation program in Bangladesh and panel data covering over 21,000 households over seven years to study the program’s effects on interhousehold transfers. The program crowds out informal transfers received by the program’s beneficiaries, but this is driven mainly by outside-village transfers. Treated ultra-poor households become more likely to both give and receive transfers to/from wealthier households within their communities; and less likely to receive transfers from their employers. As a result, the reciprocity of their within-village transfers increases. The findings imply that, within rural communities, there is positive assortative matching by socio-economic status. A reduction in poverty enables households to engage more in reciprocal transfer arrangements and lowers the interlinkage of their labor with informal insurance.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at gulescis@tcd.ie.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|||Full Sample||Trimming Tail 5%|Top 25%*|
|---|---|---|---|---|---|
||(1)|(3)|(4)|(5)|(6)|
|_Power Structure_||||||
|YRD|-7.611***|-7.925***|-7.807***|-4.228**|-0.391**|
||(2.576)|(2.853)|(2.883)|(1.860)|(0.195)|
|_Initial Conditions_||||||
|Ln_GVOPC_52||-2.095*|-1.488|-0.366|-0.042|
|||(1.199)|(1.036)|(0.736)|(0.101)|
|Ln_pop_52||-3.077***|-1.711*|-1.423|-0.131|
|||(0.765)|(0.873)|(0.887)|(0.085)|
|_Geography_||||||
|Share of Plain (%)|||-0.105**|-0.089**|0.002|
||||(0.045)|(0.038)|(0.004)|
|Distance to Xiamen|||0.009*|0.010**|0.002**|
|(Km)|||(0.005)|(0.005)|(0.001)|
|Obs.|58|57|57|52|57|
|Adjusted_R_<sup>2</sup>|0.238|0.376|0.408|0.394|0.202|

Table 11: The Effect of Factions on the Death Rates During the 1959-1961 Chinese Famine. Note: (1). White Standard Errors are in parenthesis. (2). Intercepts are not reported. (3). *, **, *** respectively indicate 10%, 5% and 1% statistical significance.

[Columns (6)-(10)]. When guerrilla presence is included in the regression separately, we find that its coefficients are not statistically significant, while the coefficient estimates on YRD remain negative and significant at around -7.8‰When we interact guerrilla presence with YRD, we find that both YRD counties with and without guerrilla<sup> ̇</sup> presence have statistically significant lower death rates than FA3-affiliated counties. There is also strong evidence that YRD-affiliation joint with guerrilla presence reduce the probability of the county experiencing extreme famine severity (in terms of the county’s death rates being among the highest quartile among all counties) by about 40%.

36
van der Klauuw, W. 2008. “Regression-Discontinuity Analysis: A Survey of Recent Developments in Economics.” _Labour: Review of Labour Economics and Industrial Relations_ 22 (2): 219–45.

Wolpin, K. I. 1977. “Education and Screening.” _American Economic Review_ 67 (5): 949–58.

- World Bank. _World Development Indicators_ . http://data.worldbank.org/data-catalog/world-

- development-indicators, accessed on August 2013.

- Yüret, T. 2009. “Human Capital and Sorting Models Reconsidered.” _İktisat, İşletme ve Finans_ 25 (295): 73–90.

42
Policy Research Working Paper 5711

# **Abstract**

In this paper, the authors use a linked employeremployee database from Brazil to examine the impact of trade reform on the wages of workers employed at heterogeneous firms. The analysis of the data at the firmlevel confirms earlier findings of a differential positive effect of trade liberalization on the average wages at exporting firms relative to non-exporting firms. However, this analysis of average firm-level wages is incomplete along several dimensions. First, it cannot fully account for the impact of a change in trade barriers on workforce composition especially in terms of unobservable (timeinvariant) characteristics of workers (innate ability) and any additional productivity that obtains in the context of employment in the specific firm (match specific ability). Furthermore, the firm-level analysis is undertaken under the assumption that the assignment of workers to firms is random. This ignores the sorting of worker into firms and leads to a bias in estimates of the differential impact of trade on workers at exporting firms relative to nonexporting firms. Using detailed information on worker and firm characteristics to control for compositional effects and using firm-worker match specific effects to account for the endogenous mobility of workers, the authors find the differential effect of trade openness on wages in exporting firms relative to domestic firms to be insignificant. Consistent with the models of Helpman, Itskhoki, and Redding (2010) and Davidson, Matusz and Schevchenko (2008), they also find that the workforce composition improves systematically in exporting firms in terms of innate (time invariant) worker ability and in terms the quality of the worker-firm matches.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Pravin_Krishna@jhu.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
27

**Table 8: Effect of rice intensification on labor allocation in rice production by activity.**

|**Variables**|**Total labor use**|**Household labor**|**Men labor**|**Women labor**|**Children labor**|**Hired labor**|
|---|---|---|---|---|---|---|
||||**Land preparation**||||
|**Semi-traditional**|-15.12**( 5.98)|-5.85(3.83)|-8.33**(3.51)|-2.29(2.31)|0.63(0.83)|-4.48(3.56)|
|**Semi-modern**|-44.14***(6.29)|-22.79***(4.17)|-20.06***(3.87)|-0.44(2.13)|1.67**(0.72)|-25.93***(3.16)|
|**Modernpackage **|-40.49***(6.35)|-20.46***(4.32)|-19.12***(3.83)|-4.22*(2.36)|-0.53(0.69)|-21.43***(3.66)|
||||**Sowing**||||
|**Semi-traditional**|-1.92(5.65)|-4.15(3.33)|-3.47(2.24)|-3.60(2.22)|1.92**(0.94)|5.21(3.36)|
|**Semi-modern**|-26.93***(5.03)|-16.71***(3.86)|-9.48***(2.07)|-6.80***(2.19)|1.41**(0.66)|-13.86***(2.76)|
|**Modernpackage **|-12.41**(5.96)|-11.23***(3.43)|-8.11***(2.02)|-5.40***(2.04)|0.76(0.65)|-0.19(3.50)|
||||**Fertilizer application**||||
|**Semi-traditional**|6.69***(0.62)|6.25***(0.13)|4.05***(0.64)|0.78***(0.18)|0.71***(0.23)|1.26***(0.37)|
|**Semi-modern**|5.03***(0.56)|4.25***(0.14)|1.70***(0.26)|1.42***(0.22)|0.45***(0.14)|1.01**(0.42)|
|**Modernpackage **|4.59***(0.76)|1.42***(0.28)|2.61***(0.37)|1.01***0.28)|0.77***(0.27)|1.20**(0.49)|
||||**Weeding**||||
|**Semi-traditional**|-5.94**(2.69)|-3.43**(1.33)|-1.36(0.97)|-2.54(1.57)|0.35*(0.21)|-1.67(1.52)|
|**Semi-modern**|-14.37***(3.56)|-10.34***(1.67)|-5.05***(0.91)|-5.11***( 1.92)|0.39**(0.21)|-4.83**(2.10)|
|**Modernpackage **|-10.09***(3.15)|-7.89***(1.56)|-4.32***(1.08)|-4.18**(1.96)|0.35(0.31)|-2.58(1.64)|
||||**Harvesting**||||
|**Semi-traditional**|-11.98**(4.94)|-14.32***(1.92)|-6.22***(2.34)|-5.92(4.38)|0.55(0.66)|3.354( 2.68)|
|**Semi-modern**|-24.28***(4.95)|-31.40***(2.26)|-14.72***(2.28)|-9.91***(2.78)|2.33***(0.77)|-3.90(2.91)|
|**Modern package**|-18.14**(7.61)|-31.21***(4.79)|-13.51***(2.48)|-11.25*(6.19)|1.50(0.99)|3.44(4.64)|

Note: Control variables include age of the household head, year of education, household size, dependency ratio, plot management type (1=Individual), experience in rice production, number of training participation, plot visited by extension agent (1=Yes), off-farm activity (1=Yes), access to credit (1=Yes), attitudes towards information, risk attitudes, ln(total plot size), wealth index, ln(population density), ln(distance to plot), and ln(rainfall average). All labor outcomes variables are expressed in person-days. Clustered robust standard errors at village level in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
**<u>Table 7: Gender Heterogeneity in Impacts on Business Performance</u>**

||<br>Z<sup>a</sup>|<br>Z score|<br>Z score|<br> <br>Sales|Sales (U$S)|Profits|<br>Profits (U$S)|
|---|---|---|---|---|---|---|---|
||score|sales<sup>b</sup>|profits<sup>c</sup>|(U$S)|winzorized|(U$S)|winzorized|
||||Datapo|oled for all follo|w-upsurveys|||
|**Panel B: Results by Gender**||||||||
|Treatment 1: BRC|-0.019|0.002|-0.041|-64.228|-34.123|-10.280|-6.876|
||(0.041)|(0.044)|(0.043)|(89.485)|(62.204)|(14.082)|(9.971)|
||0.640|0.972|0.332|0.473|0.583|0.465|0.491|
||0.830|0.972|0.730|0.690|0.802|0.640|0.674|
|Treat 2: BRC + TPIN|0.024|0.038|0.012|100.668|-9.074|0.680|0.330|
||(0.056)|(0.061)|(0.058)|(162.885)|(80.126)|(17.952)|(13.651)|
||0.666|0.532|0.834|0.537|0.910|0.970|0.981|
||0.830|0.790|0.983|0.690|0.910|0.970|0.996|
|Tr 3: BRC + IS + BBA|0.116***|0.136***|0.098**|249.589**|137.709**|27.240**|23.024**|
||(0.040)|(0.043)|(0.041)|(102.957)|(60.007)|(13.208)|(9.758)|
||0.004|0.002|0.016|0.015|0.022|0.039|0.018|
||0.020|0.009|0.060|0.042|0.048|0.086|0.034|
|Treat 1: BRC * Female|0.069|0.055|0.085|257.575**|136.729*|23.608|22.194|
||(0.056)|(0.058)|(0.059)|(123.428)|(82.646)|(18.355)|(14.154)|
||0.217|0.345|0.153|0.037|0.098|0.198|0.117|
||0.596|0.790|0.421|0.081|0.180|0.312|0.184|
|Treat 2: (BRC + TPIN) *<br>Female|0.019|0.031|0.003|109.099|136.831|4.749|10.344|
||(0.079)|(0.086)|(0.081)|(216.548)|(113.846)|(22.839)|(18.978)|
||0.808|0.718|0.974|0.614|0.229|0.835|0.586|
||0.870|0.790|0.983|0.690|0.361|0.970|0.716|
|Treat 3: (BRC + IS +<br>BBA) * Female|-0.025|-0.045|-0.011|-61.304|-17.196|-1.295|-0.059|
||(0.054)|(0.056)|(0.057)|(126.132)|(76.049)|(18.085)|(13.430)|
||0.639|0.420|0.843|0.627|0.821|0.943|0.996|
||0.830|0.790|0.983|0.690|0.903|0.970|0.996|
|Control group: Male|0.115|0.121|0.110|921.486|822.898|185.967|177.783|
|Control group: Female|-0.174|-0.183|-0.166|444.180|434.831|118.840|114.324|
|p-value: Treat 1 = Treat|0.434|0.550|0.349|0.309|0.750|0.556|0.589|
|2 for males||||||||
|p-value: Treat 1 = Treat<br>|0.001|0.002|0.000|0.001|0.003|0.007|0.001|
|3 for males||||||||
|p-value: Treat 2 = Treat<br>3 for males|0.092|0.106|0.124|0.366|0.057|0.137|0.087|

37
coefficient movements but also movements in R-squared values when including additional independent variables. The _“bias-adjusted estimate”_ is calculated as: ) ∗<sup>RRm</sup> (5)<sup>xx−RRcc</sup> where ββ<sup>∗</sup> = ββ<sup>cc</sup> −(ββand<sup>uucc</sup> − ββ are the “<sup>cc</sup> _controlled estimate_ ” and the of the regression from which the RR<sup>cc</sup> − RR<sup>uucc</sup> “ _controlled estimate_ ββ<sup>cc</sup> RR<sup>cc</sup> ” was obtained respectively; and and RR<sup>2</sup> are respectively the coefficient estimate and of the uncontrolled regression, the regression in which the variable of interest is ββ<sup>uucc</sup> RR<sup>uucc</sup> the only independent variable. RR<sup>2</sup> m is the of a hypothetical regression in which both xx observables and unobservables are controlled for, which is clearly unknown. Oster (2015) suggests RR RR<sup>2</sup> that m , 1}. The from our models are such that > 1, suggesting that we xx choose RR m= min{2.2RR= 1 based on Oster (2015). Meanwhile Gonzalez and Miguel (2015) argues that<sup>cc</sup> RR<sup>cc</sup> 2.2RR<sup>cc</sup> xx m of 1 or close to 1 is likely to be too high. Based on relatively high-quality US data, Gonzalez RR xx and Miguel (2015) suggested that RR m should not be greater than 0.9. An m of 0.89 was xx xx therefore chosen for our analyses. RR RR

# _Fertilizer Profitability_

We measure fertilizer profitability by calculating the Marginal Value Cost Ratio (MVCR) which represents the extent to which farm income will increase if the rate of nitrogen application increases. MVCR is expressed as follows: N M (6) NN ∗ PPYY MMRR= PPNN where N is the nitrogen use efficiency; and and are the prices of maize and nitrogen YY NN respectively. Maize prices change over time and space, so three prices of maize are considered in MM PP PP the profitability analyses – i) the farm gate price at harvest, ii) the lean season market price, and iii) the import parity price. All three of these prices are considered in order to account for potential 14
Figure 7: HCFC Event Study Around Signature of RTA <!-- Start of picture text --> −3 −2 −1 0 1 2 3<br>Event Time (RTA Signature)<br>1.0<br>0.5<br>0.0<br>% Deviation from MP<br>−0.5<br>−1.0<br><!-- End of picture text -->

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

34
when one does not control for the interaction with small dams (as shown in the first two columns). When the interaction term is included, the effect of small dams on their own is approximately 40 percent smaller, implying that some of the small dam impact in the previous results is due to the benefit of being near a large dam. In other words, some of the nonnegative impact for large dams is due to a compensating effect of small dams. Perhaps most important, we find that the positive coefficient on the interaction term suggests that large dams can augment the positive effect of small dams, perhaps by being part of a coordinated system of irrigation.

# _Robustness Checks_

Our interpretation of the negative impact of large dams on NPP is that this impact is likely due to water seepage, soil salinization, and, perhaps, other negative spillovers from the construction of these large infrastructure projects on cropland. If this were indeed the case, one would most likely expect this impact to be particularly prominent for cropland located near the dam. To investigate this possibility, we restrict our dependent variable to capture only cropland within 5 km of any large dam. As depicted in the fourth column of table 5, the negative effect is indeed larger (22 percent) for these areas, although a simple z-test suggests that this difference is insignificant.

A major drawback of our satellite-derived productivity data is that we can only identify cropland that existed in 2000. Thus, we need to assume that these cells were used for cropping throughout our sample period. Apart from general measurement error, this assumption could feasibly introduce systematic misclassification if the presence of dams 35
The other source of adverse real shocks was the global growth slowdown that started in 2000. Relative to that year, in 2001 real GDP growth declined by 3 percentage points both in the U.S. and the industrialized world as a whole. Like with the terms of trade decline, however, Argentina was much less affected than other countries in the region, again because of its lower degree of openness. As a result, the growth deceleration and the ensuing slowdown in export markets translated into a fairly modest aggregate demand decline for Argentina - the smallest among the countries shown in Table 2.2.6

**Table 2.2. Impact of the Global Slowdown 2001: The Inicome Effect via Trade Volume**

||Exports/GDP<br>(%)<br>**(a)**|Exportsof<br>goodsto<br>US/Total<br>Exoorts**1%)**<br>**(b)**<br>(a)<br>(b)|Exportsof<br>goodsto<br>OECD/Total<br>ExDorts**(%)**<br>(c)<br>(c)|Impactofthe<br>decline inU.S.<br>growth(%of<br>GDP)<br>**21(c)[(ar(b)***<br>**~~~~~~~2.2**|Impactofthe<br>declinein<br>OECDgrowth<br>(%ofGDP)<br>**(d)-[(a)*(c)***<br>**1*0.03**<br>**2.21*0.03**|
|---|---|---|---|---|---|
|Argentina|10.81|10.90|33.09|-0.08|-0.24|
|Bolivia|17.55|13.86|25.27|-0.16|-0.29|
|Brazil|10.81|24.70|57.11|-0.18|-0.41|
|Chile|31.85|18.48|62.44|-0.39|-1.31|
|Colombia|21.34|43.45|60.22|-0.61|-0.85|
|CostaRica|48.09|41.38|72.01|-1.31|-2.29|
|DominicanRep.|29.93|86.47|94.37|-1.71|-1.86|
|Ecuador|42.43|38.55|67.05|-1.08|-1.88|
|Guatemala|19.93|56.40|70.80|-0.74|-0.93|
|Jamaica|42.94|30.86|85.80|-0.87|-2.43|
|Mexico|31.06|88.55|94.31|-1.82|-1.93|
|Peru|15.98|25.39|63.55|-0.27|-0.67|
|Venezuela,RB|28.45|53.81|64.14|-1.01|-1.20|

Notes: (a)Exports of Goods and Services and GDP of 2000, source: WDI.

(b) Exports of goods in 2001 source:lM F Direction of Trade. (c) 2.2 **is** the U.S. expenditure elasticity (Clanda,1994), and 3% is the decline in the U.S. economic growth between 2000 and 2001(d) 3 % **Is** the decline in the OECD economic growth between 2000 and 2001.

Next, we turn to the disturbances stemming from world financial markets.<sup>7</sup> Following the Russian crisis, Latin American countries - like other emerging economies - had to face a generalized increase in the cost of market borrowing. Figure 2.3, which offers a comparative perspective on the sovereign spreads faced by different countries, shows that Argentina did not fare worse than the rest of the region in this regard. As a matter of fact, Brazil's spreads rose above Argentina's in 1997-99, with speculative attacks on the real taking place in October 1997 and October 1998. It was only in late 2000 that the Argentine spread began to drift above Brazil's. And the same happened with Venezuela and Ecuador, whose spreads (not shown to avoid cluttering the graph) increased more than Argentina's in 1998 and remained higher until 2001.

> 6 The impact on export demand shown in the table is calculated using an income elasticity of 2.2 for both U.S. and OECD imports.

> 7 The role of external financial shocks in the Argentine crisis has been underscored in particular by Calvo, Izquierdo and Talvi (2002).

9
<!-- Start of picture text --> Figure 10: % of firms indentifying Figure 11: % of firms indentifying<br>transport as major or severe telecoms as major or severe constraint<br>constraint<br>60<br>40<br>50<br>35<br>40<br>30<br>30<br>25<br>20 20<br>15 10<br>10<br>0<br><!-- End of picture text --> Source: BEEPs 2008.

**Both Russian and other international data suggest a major deterioration of transportation infrastructure** (World Bank 2011a, b). The state of Russia’s transport infrastructure is generally poor, especially for a high-income country and has been declining because of underinvestment in maintenance and rehabilitation.  According to the 2010 Enabling Trade Index (World Economic Forum), Russia ranks 48th on the availability and quality of transport infrastructure (Figure 12). The index measures the state of transport infrastructure across all modes of transport in each country, as demonstrated by the density of airports, the percentage of paved roads, and the extent to which they are congested, as well as the transshipment connections available to shippers from each country.

16