rafmacalaba/datause-encoder-data huggingface dataset:
- 14 -

and URPR are derived from Bhattacharya et al (Tables 3b,4). CPIlWs are the Consumer Price Indices for Industrial Workers (Labour Bureau, Ministry of Labour); CPIIWs are constructed as state-specific simple averages over centers in each state and over months during the relevant period.<sup>10</sup> The above two price indices are used to transform 1983 local-price values into 1983 all-India rural prices.

Mean Consumption Levels and Regional Inequality Table 1 summarizes the relevant data. The following observations can be made: i) The national mean consumption expenditure for 1983 is about Rs.116 per capita per month at 1983 all-India rural prices. At the state level, average consumption ranges from the lowest, Rs.90, in Bihar to the highest, Rs.163, in Punjab. Intra-regional inequality, as measured by the Gini coefficient, is low to moderate for most regions, ranging from about 0.18 in urban Manipur to about 0.36 in urban Tamil Nadu.

ii) Mean per capita consumption levels in urban areas are generally higher than those in rural areas, even after allowing for sectoral differences in the cost of living. At the all-India level, the difference is about 11 percent of the rural mean consumption. However, inter-regional variation in mean consumption levels is greater amongst the rural areas (of the 20 states) than amongst the urban areas.

iii) The Gini coefficient of per capita household consumption is generally higher in urban areas than rural areas, and it is significantly correlated with the mean (r-0.37; t=2.5) across the 40 regions.
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Table 3: Estimations of GQ improvements for productivity of plants in manufacturing organized sector

||All area|s of|districts|Urban are|as of districts|R|ural areas of districts|
|---|---|---|---|---|---|---|---|
||Labor prod.||TFP|Labor prod.|TFP|Labor prod|.<br>TFP|
||(1)||(2)|(3)|(4)|(5)|(6)|
||A. B|ase|spatial hori|zon measuring e|ffects relative|to districts 50+|km from the GQ network|
|Post GQ upgrades *|0.084||-0.022|0.037|0.010|0.126|0.011|
|Nodal district|(0.135)||(0.033)|(0.145)|(0.043)|(0.264)|(0.167)|
|Post GQ upgrades *|0.177||0.086|0.165|0.098|0.198|0.099|
|District 0-10 km from GQ|(0.093)||(0.042)|(0.139)|(0.066)|(0.099)|(0.056)|
|Post GQ upgrades *|0.043||-0.005|-0.000|-0.021|0.115|0.068|
|District 10-50 km from GQ|(0.132)||(0.074)|(0.186)|(0.085)|(0.175)|(0.099)|
|District and year fixed effects|Yes||Yes|Yes|Yes|Yes|Yes|
|Observations|1248<br>B. Exte|nded|1244<br>spatial ho|1108<br>rizon measuring|1100<br>effects relative|1160<br>to districts 20|1160<br>0+ km from the GQ network|
|Post GQ upgrades *|0.158||-0.003|0.095|0.038|0.236|0.012|
|Nodal district|(0.144)||(0.041)|(0.167)|(0.055)|(0.272)|(0.171)|
|Post GQ upgrades *|0.253||0.104|0.223|0.125|0.309|0.100|
|District 0-10 km from GQ|(0.103)||(0.048)|(0.161)|(0.075)|(0.116)|(0.066)|
|Post GQ upgrades *|0.118||0.013|0.058|0.006|0.225|0.070|
|District 10-50 km from GQ|(0.141)||(0.078)|(0.204)|(0.092)|(0.186)|(0.105)|
|Post GQ upgrades *|0.076||0.043|0.034|0.060|0.138|0.005|
|District 50-125 km from GQ|(0.149)||(0.076)|(0.170)|(0.084)|(0.148)|(0.092)|
|Post GQ upgrades *|0.214||0.029|0.203|0.050|0.278|0.002|
|District 125-200 km from GQ|(0.129)||(0.066)|(0.189)|(0.084)|(0.159)|(0.092)|
|District and year fixed effects|Yes||Yes|Yes|Yes|Yes|Yes|
|Observations|1248||1244|1108|1100|1160|1160|

Notes: See Table 2. Labor productivity is total output per employee, and TFP is the average residual in each district from a weighted regression of log of value added on logs of employment and capital for each industry and year.

29
Exports and Productivity: The Role of Imported Inputs and Investment in R&D Ruchita Manghnani<sup>_∗_</sup>

**JEL Classification:** D22, D24, F10, F14, L25, O30, O33

**Key Words:** Gains from Trade, Productivity, Pricing heterogeneity, Exports, Imports, R & D

> _∗_ World Bank (email: rmanghnani@worldbank.org). I thank Patrick Conway, Tiago Pires, Helen Tauchen, Ivan Kandilov, Jonathan Williams and Simon Alder for their helpful comments and suggestions. All remaining errors are mine. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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.
_Table 8: Reduced-form growth equations for BMI, income, medical spending and non-medical consumption—full sample_

||(1)<br>Change in earned<br>income|(2)<br>Change in<br>unearned income|(3)<br>Change in medical<br>spending|(4)<br>Change in food<br>consumption|(5)<br>Change in<br>nonfood<br>consumption|(6)<br>Change in BMI|
|---|---|---|---|---|---|---|
|Average age of all|-0.001|0.032|-0.006|-0.023|-0.024|0.002|
|household members<br>1992|(0.05)|(0.89)|(0.22)|(3.33)|(2.25)|(2.11)|
|Average age|0.000|-0.001|0.000|0.000|0.000|0.000|
|squared|(0.34)|(0.85)|(0.18)|(1.87)|(0.25)|(2.51)|
|Average age cubed|0.000|0.000|0.000|0.000|0.000|0.000|
||(0.38)|(0.86)|(0.07)|(0.81)|(0.73)|(2.8)|
|Change in|-0.029|-0.246|-0.068|-0.093|-0.082|0.000|
|household size|(2.58)|(11.41)|(4.17)|(21.55)|(12.57)|(0.1)|
|Change in|-0.003|0.007|-0.002|-0.001|-0.003|0.000|
|household size|(1.28)|(1.46)|(0.41)|(0.94)|(1.61)|(0.99)|
|squared|||||||
|Change in|0.000|0.001|0.000|0.000|0.000|0.000|
|household size<br>cubed|(0.21)|(3.01)|(0.83)|(3.94)|(2.22)|(0.39)|
|Intercept|0.497|0.151|0.153|0.405|0.762|-0.008|
||(2.48)|(0.39)|(0.53)|(5.33)|(6.62)|(0.60)|
|R-squared|0.1682|0.1132|0.1203|0.3065|0.2607|0.1528|

Note: _t_ -statistics in parentheses. Regressions include commune fixed effects, which were jointly significant.

13
and school administrative registers. Based on our discussions with these committees, it appears that conditionality was enforced. Cash transfer take-up rates (the fraction of eligible households receiving transfers for at least some children) in the CCT villages declined as the school year progressed, which is also consistent with conditionality being enforced.<sup>20</sup> In households randomly assigned to UCTs, the mother or father received a quarterly stipend for each child. There were no requirements or conditions linked to receiving the stipend.

CCT and UCT households were told they could use the funds at their convenience and no instructions were given as to how to spend the money. CCT and UCT cash distribution was done separately in each village to minimize any risks of cross-village information contamination of the randomization, since we did not want households in UCT villages to believe that health clinic or school attendance was going to be checked in their villages as well. In addition, each village had only one primary school, and no children attended a primary school that is not in their village.

Furthermore, our program design explicitly assumed each treatment group would receive equal amounts of resources per capita over the two-year pilot, if households randomly allocated to the CCTs fully satisfied conditionality. In practice, because there was not full compliance with conditionality, households receiving UCTs, on average, received more money per capita.

In the CCT and UCT programs, for each child under age seven, the mother or father would receive 4,000 FCFA per year distributed in quarterly payments (approximately $8.8 USD or 4.0 percent of household per capita expenditures). For each child ages 7 to 10 (or in grades 1 to 4 in the CCT villages), the mother or father would receive 8,000 FCFA per year in quarterly payments (approximately $17.6 USD or 8.0 percent of household per capita expenditures), while

> 20 The CCT take-up rates by quarter for school year 2008-2009 are 99.0, 91.0, 90.7, and 85.3 percent, respectively. In school year 2009-2010, the rates are 94.7, 91.6, 89.9, and 89.7 for each quarter, respectively. The take-up rates in the UCT villages are considerably higher. In school year 2008-2009, they are 99.4, 98.8, 98.6, and 94.5 percent for each quarter, respectively. In 2009-2010, they are 99.1, 98.8, 98.5, and 97.1 percent for each quarter, respectively.

12
# S. Concluding Remarks

What do these educational elasticities mean? In Table 8 the estimated mean percentage change in the real GDP of the region in response to an increase of one year in the average educational attainmnent of the working age population in 1985 are presented for each category of education. The estimated effects range from negative to over 5 percent per annum. The estimated effects are plotted against the average educational attainments in Figures I through 3. The results are suggestive of a positive relationship **between** the size of the effect of primary education and the level of its attainment, indicating the possible existence of a threshold of 4 years for primary education to have a significant impact. Similar thresholds for the effectiveness of primary education have been found in microeconomic level studies. The effect of secondary education appears to be independent of the level of secondary educational attainment, although here local factors may predominate, as witnessed by the large negative estimated effect for South Asia.

We conclude that education is an important determinant of aggregate real output and productivity but that its effect varies considerably across countries and regions. Further research is necessary in order to explain the variability of its effectiveness, especially where the effect appears to be negative. It is speculated that the institutions, the organizations for production and distribution, the industrial compositioii and its skill requirements, the structure of education', and war and pestilence may all have played a role in determining the effect of primary and secondary education on aggregate real output.

'This includes the composition of the educational capital stock by primary, secondary and tertiary as well as the rate of progression through the educational process. For example, students who continue to advance educationally are effectively removed from the labor force for the duration. The educational system may also select out the best students to continue. All of these considerations tend to lower the effective educational capital stock in the short to intermediate term.

22
as tra and basa. Subsequently, the 10-digit Harmonized System line corresponding to frozen catfish fillets, 0304.20.60.30, was split into three different lines: 0304.20.60.32 for catfish of the _Ictaluridae_ family; 0304.20.60.33 for catfish of the _Pangasiidae_ family (the Vietnamese catfish); and 0304.20.60.34 for all other _siluriformes_ . The passing of the bill, however, did not lead to a significant recovery in prices. While public awareness increased, most Vietnamese catfish was being sold to American wholesale distributors, not final consumers, and a change in names was not enough to break the commercial networks that had already been established.<sup>6</sup> In June 2002, the CFA filed a dumping lawsuit against Vietnam. A few months later, in January 2003, the U.S. DoC ruled in favor of U.S. farmers, arguing that Vietnamese exporters were dumping frozen fish fillets on U.S. markets by margins that varied by exporter and ranged from 37 to 64 percent of the “normal value.”<sup>7</sup> When the exporting country is a “market economy,” the DoC determines the normal value of an imported product using either the domestic price or an estimate of the cost of production in the country of origin. Vietnam, however, is considered as a “non-market economy” by the U.S. government, which implies the presumption that domestic prices are distorted. As a consequence, prices and costs in a surrogate country are used instead. In the case of Vietnamese catfish, the surrogate countries used by the DoC were Bangladesh and India. As the last step of the lawsuit, in July 2003, the USITC found that American catfish processors were materially injured by imports from Vietnam, confirming the application of anti-dumping import tax rates equivalent to the dumping margins of 37 to 64 percent.<sup>8</sup> Figure 1 plots the time series of U.S. imports of tra and basa from Vietnam (in tons) between January 2002 and July 2004. Data are from the disaggregated monthly import series

> 6For more details on labeling issues and a general description of the evolution of imports of Vietnamese catfish see USITC (2003) and Seafood Business Magazine (2001).

> 7The DoC established margins of 36.84 percent for Vinh Hoan, 45.55 percent for Afiex, CAFATEX, Da Nang, Mekonimex, QVD, Viet Hai and Vinh Long, 45.81 percent for CATACO, 47.05 percent for Agifish, 53.68 percent for Nam Viet, and 63.88 percent of all other exporters.

> 8The USITC decided to exclude American catfish farmers from the investigation on material injury, and focused only on catfish processors. The argument was that the percentage of unprocessed domestic farm-raised catfish that was used as input for frozen fillets, which was about 50 percent, was not high enough.

7
The results presented here suggest a number of implications for the current IDP crisis in Mali. It appears overall that the UN, NGOs, and the Malian government working with IDPs in Mali have succeeded in providing resources to IDPs and IDP hosting villages in a way that modestly enhances average village level consumption as well as not exacerbating inequality or poverty within the hosting communes. The results generally do not suggest that IDPs would have a detrimental effect on social cohesion due to the economic effects from their presence. But one should be cautious since we are observing the effects of IDP presence when there is a large humanitarian operation to help them, and acknowledge that results might differ where humanitarian operations are of a smaller scale or unable to reach IDP populations due to the conflict.

There are some limitations that are worth highlighting before we turn to policy and program implications. First, our measure of IDP presence comes from the UN and NGOs that work with IDPs and may miss IDPs who are not known of or registered with the UN or NGOs in the area. Our EMOP data do suggest that there are small numbers of people who might fit this category, but not of a scale likely to significantly bias our results. Nonetheless, the effects reported here should be considered effects of officially registered IDPs. Second, this work only measures the effects of IDP presence on non-IDP households, such that our measures of poverty inequality are only within the host community estimate and do not measure inequality between host community and IDP wealth. It is along this dimension that some of the potential effects of inequality on social cohesion might be most felt. Third, the results presented here are specific to the Malian cultural context, a country that has a long-standing cultural history of accepting and housing outsiders. Called _“Diatiguiya”_ in the local language, Bambara, which translates as the keeper of hospitality, this feature of Malian society means that all members of a community are expected to provide hospitality to strangers. Other places without a tradition of _Diatiguiya_ may not have the same results. Fourth, due to the inability to measure prices at a fine enough scale, the current analysis does not account for potential localized inflation in communes with IDPs that might bias 32
# **Table C.14: Effects on time use**

## **Panel A: Women’s time use (Hours)**

|||Last wo|rkday||Last|non-work|day|
|---|---|---|---|---|---|---|---|
||(1)<br>Domestic<br>work|(2)<br>Leisure|(3)<br>Sleep|(4)<br>Work|(5)<br>Domestic<br>work|(6)<br>Leisure|(7)<br>Sleep|
|selected|0_._09|_−_0_._09|_−_0_._20 *|0_._19|_−_0_._10|_−_0_._01|_−_0_._07|
||(0_._13)|(0_._16)|(0_._11)|(0_._27)|(0_._13)|(0_._23)|(0_._18)|
|N|336|336|336|336|363|363|363|
|Nominated mean|2_._86|1_._83|7_._02|10_._26|4_._56|4_._00|8_._96|
|Nominated SD|1_._02|1_._26|1_._03|1_._86|1_._48|2_._29|1_._22|

## **Panel B: Husband’s time use (Hours)**

|||Last wo|rkday||Last|non-workd|ay|
|---|---|---|---|---|---|---|---|
||(1)<br>Domestic<br>work|(2)<br>Leisure|(3)<br>Sleep|(4)<br>Work|(5)<br>Domestic<br>work|(6)<br>Leisure|(7)<br>Sleep|
|selected|0_._03|_−_0_._12|_−_0_._13|0_._05|0_._11|0_._05|0_._01|
||(0_._11)|(0_._15)|(0_._13)|(0_._25)|(0_._18)|(0_._19)|(0_._17)|
|N|330|330|330|330|350|350|350|
|Nominated mean|1_._06|2_._92|7_._34|10_._60|2_._33|4_._89|9_._05|
|Nominated SD|0_._82|1_._71|0_._84|1_._47|1_._25|2_._47|1_._18|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies.

**Table C.15: Effects on other outcomes**

||Economi|cally active|Slapped o|r beaten by husband|Hiding money|Controlling money|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Wife|Husband|Ever|In last 5 months|||
|selected|_−_0_._02|_−_0_._03|0_._03|_−_0_._03|0_._03|_−_5_._32|
||(0_._03)|(0_._04)|(0_._06)|(0_._04)|(0_._08)|(17_._90)|
|N|363|350|363|363|181|182|
|Nominated mean|0_._96|0_._97|0_._30|0_._09|0_._28|146_._77|
|Nominated SD|0_._21|0_._17|0_._46|0_._29|0_._45|147_._72|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Column (2) uses data from the husband’s surveys.

81
**Table 3: AMR impact on livestock production relative to a world without AMR**

|**Region by income level**|**Difference in**|**trillions of 20**|**07 USD**|**Perce**|**nt deviation**||
|---|---|---|---|---|---|---|
||2020|2030|2050|2020|2030|2050|
|**Low Income**|||||||
|Low case|0.000|-0.002|-0.006|-0.2%|-2.9%|-3.1%|
|Middle case|0.000|-0.004|-0.015|-0.2%|-5.5%|-7.4%|
|High case|0.000|-0.006|-0.022|-0.2%|-8.2%|-11.1%|
|**Lower Middle Income**|||||||
|Low case|0.000|-0.018|-0.043|-0.1%|-2.9%|-3.1%|
|Middle case|0.000|-0.033|-0.080|-0.1%|-5.2%|-5.8%|
|High case|0.000|-0.048|-0.123|-0.1%|-7.7%|-8.9%|
|**Upper Middle Income**|||||||
|Low case|-0.001|-0.027|-0.048|-0.1%|-2.2%|-2.5%|
|Middle case|-0.001|-0.048|-0.088|-0.2%|-3.8%|-4.6%|
|High case|-0.001|-0.072|-0.136|-0.2%|-5.8%|-7.1%|
|**Middle Income Total**|||||||
|Low case|-0.001|-0.045|-0.091|-0.1%|-2.4%|-2.8%|
|Middle case|-0.001|-0.080|-0.168|-0.1%|-4.3%|-5.1%|
|High case|-0.001|-0.121|-0.259|-0.1%|-6.5%|-7.8%|
|**High Income**|||||||
|Low case|0.000|-0.013|-0.016|0.0%|-1.9%|-1.8%|
|Middle case|0.000|-0.025|-0.033|-0.1%|-3.7%|-3.6%|
|High case|0.000|-0.040|-0.053|-0.1%|-5.8%|-5.9%|
|**World**|||||||
|Low case|-0.001|-0.061|-0.114|-0.1%|-2.3%|-2.6%|
|Middle case|-0.002|-0.110|-0.216|-0.1%|-4.2%|-4.9%|
|High case|-0.002|-0.167|-0.335|-0.1%|-6.3%|-7.6%|

Source: Simulation results and authors' calculations

**Table 4: AMR impact on health expenditure relative to a world without AMR**

|**Region by income level**|**Difference in**|**trillions of 20**|**07 USD**|**Perce**|**nt deviation**||
|---|---|---|---|---|---|---|
||2020|2030|2050|2020|2030|2050|
|**Low Income**|||||||
|Low case|0.001|0.005|0.014|1.5%|4.3%|5.5%|
|Middle case|0.001|0.009|0.038|1.5%|7.9%|15.5%|
|High case|0.001|0.014|0.062|1.5%|11.9%|25.3%|
|**Lower Middle Income**|||||||
|Low case|0.002|0.013|0.036|0.6%|3.1%|4.4%|
|Middle case|0.002|0.023|0.075|0.7%|5.7%|9.3%|
|High case|0.002|0.038|0.128|0.7%|9.2%|16.0%|
|**Upper Middle Income**|||||||
|Low case|0.007|0.038|0.075|1.0%|3.9%|4.5%|
|Middle case|0.008|0.066|0.148|1.1%|6.6%|8.9%|
|High case|0.008|0.104|0.245|1.1%|10.5%|14.7%|
|**Middle Income Total**|||||||
|Low case|0.009|0.051|0.110|0.9%|3.6%|4.5%|
|Middle case|0.010|0.089|0.223|1.0%|6.4%|9.0%|
|High case|0.010|0.141|0.374|1.0%|10.1%|15.1%|
|**High Income**|||||||
|Low case|0.008|0.162|0.201|0.1%|1.9%|1.7%|
|Middle case|0.011|0.323|0.429|0.2%|3.9%|3.6%|
|High case|0.011|0.531|0.735|0.2%|6.3%|6.2%|
|**World**|||||||
|Low case|0.018|0.218|0.325|0.2%|2.2%|2.2%|
|Middle case|0.022|0.422|0.690|0.3%|4.3%|4.7%|
|High case|0.022|0.687|1.170|0.3%|6.9%|8.0%|

Source: Simulation results and authors' calculations 24
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 3. Hidden costs of power utilities**

<!-- Start of picture text --> Congo, Dem. Rep. of<br>Nigeria<br>Ghana<br>Chad<br>Congo Rep<br>Ethiopia<br>Cameroon<br>Benin<br>0 100 200 300 400 500 600<br>% of revenues<br>unaccounted losses underpricing<br>collection inefficiencies overmanning<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and others 2008.

**Figure 4. Congo’s electricity tariffs are relatively high**

Effective residential tariff at 100 kWh <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US$ cents<br>Chad Cape Verde Madagascar Uganda Burkina Faso Senegal Congo, Rep. of Kenya Rwanda Cameroon Niger Benin Côte d'Ivoire Namibia Tanzania Ghana Lesotho South Africa Mozambique Ethiopia Congo, Dem. Rep. of Malawi Nigeria Zambia<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and others 2008.

12
**Table 2: Share of Selected Partners in Imports, 1994, 1998 (Contd.)** _(Percent)_

||_Armenia_|_Azerbaijan_|_Georgia_|
|---|---|---|---|
|**1994**||||
|Armenia|X|--|0.3|
|Azerbaijan|--|X|7.0|
|Georgia|4.5|1.0|X|
|Russia|28.5|15.1|7.9|
|Other CIS|19.2|46.4|*<br>65.5<br>*|
|Turkey|0.1|9.8|11.4|
|Iran|10.8|8.6|0.2|
|UAE|0.3|1.2|0.0|
|EU|9.4|9.0|3.6|
|USA and Canada|24.5|0.0|_1.5_|
|Rest of world|2.7|8.9|2.6|
|**1998**||||
|Armenia|X|--|1.1|
|Azerbaijan|--|X|8.3|
|Georgia|3.0|2.3|X|
|Russia|21.2|18.0|14.8|
|Other CIS|1.3|17.3|6.0|
|Turkey|6.3|20.4|11.1|
|<br>Iran|7.1|4.0|0.6|
|UAE|6.0|4.2|2.2|
|EU|27.8|20.8|31.0|
|USA and Canada|11.3|4.0|8.7|
|Rest of world|27.3|13.0|24.9|
|**Change between 199**|**4 and 1998**|||
|Armenia|X|--|0.8|
|Azerbaijan|--|X|1.3|
|Georgia|-1.5|1.3|X|
|<br>Russia|-7.3|2.9|6.9|
|Other CIS|-17.9|-29.1|-59.5|
|Turkey|6.2|10.6|-0.3|
|<br>Iran|-3.7|-4.6|0.4|
|UAE|5.7|3.0|2.2|
|EU|18.4|11.8|27.3|
|USA and Canada|-13.2|4.0|7.2|
|Rest of world|13.3|0.1|13.6|

_Source: Statistical Yearbook of South Caucasus, 2000._

* The largest partner was Turkmenistan (energy).

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