rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Plant Counts, Employment and Output - by Plant Location**

<!-- Start of picture text --> Plants<br><!-- End of picture text -->

|||**1989**|**1994**|**2000**|**2005**|
|---|---|---|---|---|---|
|**Plants**|**Household**|10,283,963|8,692,467|10,221,653|10,274,968|
||**Non-household**|2,801,001|3,093,324|4,253,446|3,765,933|
||**Unknown**|0|31,036|1,113|9,951|
|**Employment (in 000s)**|**Household**|19,293|17,934|18,160|17,018|
||**Non-household**|5,677|9,277|12,464|12,192|
||**Unknown**<br>|0|60|2|19|
|**Output (in MM constant 1993**|**INR) Household**|201,312|231,277|318,568|279,341|
||**Non-household**|190,950|407,673|1,059,623|1,142,656|
||**Unknown**|0|1,292|70|803|

Source: Authors' calculations using Annual Survey of Industries and National Sample Survey data.

**Table 4: Shares of Plant Counts, Employment & Output - By Plant Location**

<!-- Start of picture text --> Plants<br><!-- End of picture text -->

|||**1989**|**1994**|**2000**|**2005**|
|---|---|---|---|---|---|
|**Plants**|**Household**|79%|74%|71%|73%|
||**Non-household**|21%|26%|29%|27%|
||**Unknown**|0%|0%|0%|0%|
|**Employment (in 000s)**|**Household**|77%|66%|59%|58%|
||**Non-household**|23%|34%|41%|42%|
||**Unknown**|0%|0%|0%|0%|
|**Output (in MM constant 1993**|**INR) Household**|51%|36%|23%|20%|
||**Non-household**|49%|64%|77%|80%|
||**Unknown**|0%|0%|0%|0%|

Source: Authors' calculations using Annual Survey of Industries and National Sample Survey data.

33
# **Figure 6. Kaplan-Meier (K-M) survivor and cumulative hazard functions, and bivariate analysis**

_The Kaplan-Meier survivor functions suggest that copper mines are likely to experience a longer lead time to production at any given point in time. Hazard rates (the probability that a mine gets developed at any point in time) are not constant. Countries characterized by fiscal surpluses, the absence of a credit boom, and democratic institutions experience shorter lead times to production as do high income countries._

# **A. K-M survivor function curve, by type of deposit**

# **B. K-M cumulative hazard function, by type of deposit**

<!-- Start of picture text --> Probability<br>1.0 Gold Copper<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>0 20 40 60<br>Analysis time<br>C. Effect of country characteristics<br>With Characteristics Without charcteristics<br>Democracy*<br>Credit boom*<br>Low Inflation<br>Fiscal Balance<br>Surplus *<br>Current Account<br>Surplus *<br>Low Government<br>Debt<br>0 2 4 6 8 10 12 14 16 18<br>Average duration of delay<br><!-- End of picture text --> <!-- Start of picture text --> Probability<br>8 Gold Copper<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 20 40 60<br>Analysis time<br><!-- End of picture text -->

# **D. Country income classification**

<!-- Start of picture text --> Average number of years of delay<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Low Income Middle Income High Income<br><!-- End of picture text --> Source: World Bank, MINEX Consulting, World Bank staff estimates A, B. Copper = 1 indicates copper mines, copper = 0 indicators gold mines. C. Low inflation is below 10% while low government debt is below 40% of GDP. Credit boom is defined as periods when percent of private credit out of GDP is at least 0.5 times standard deviation above mean. * Indicates statistical significant of the log-rank test of the difference at the 5 percent level.

- D. Country income classification are defined by the World Bank, based on GDP per capita

cumulative hazard rate, for instance, for gold discoveries increases at an accelerating rate and then begins to taper off.

Next, we examine the Kaplan-Meier survivor functions based on policy, institutional or governance variables. From (1), using the sample with or without the characteristic of interest, we calculate the expected duration of non-development, captured in the Kaplan-Meier survivor 13
Dillon, Brian, Jenny Aker and Joshua Blumenstock (2020) “How Important is the Yellow Pages? Experimental Evidence from Tanzania”, Mimeo.

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18
**Table 1: Plausible responses to vignette questions when there is a frame-of-reference bias**

|||Res|pondent’s wealth|
|---|---|---|---|
|||Poor|Middle<br>Rich|
||Poor|2|1<br>1|
|Vignette|Middle|3|2<br>1|
||Rich|3|3<br>2|

**Table 2: Pre-vignette and post-vignette subjective welfare rankings**

|Pre-vignette|Post-vi<br>1<br>poores|gnette<br>t<br>2|3|4|5|6<br>richest|Total|
|---|---|---|---|---|---|---|---|
|1 poorest|247|64|23|3|4|0|341|
|2|72|933|232|53|7|5|1,302|
|3|34|242|1,735|223|37|4|2,275|
|4|4|33|112|592|34|1|776|
|5|2|0|2|11|51|1|67|
|6 richest|0|0|1|1|4|4|10|
|Total|359|1,272|2,105|883|137|15|4,771|

**Table 3: Summary statistics pre and post-vignettes**

||Pre-vignette|Post-vignette|
|---|---|---|
|Overall mean|2.75|2.8|
||(0.87)|(0.92)|
|_Household characteristics_|||
|Poor|2.53|2.59|
|Non-poor|2.94|2.99|
|Rural|2.67|2.73|
|Urban|2.91|2.93|
|Unemployed head|2.61|2.66|
|Employed head|2.82|2.88|
|Female head|2.58|2.62|
|Male head|2.79|2.84|
|Pensioner head|2.78|2.63|
|Non-pensioner|2.59|2.83|

Note: Standard deviation in parentheses.

16
Policy Research Working Paper 9332

# **Abstract**

Ecuador became the third largest receiver of the 4.3 million Venezuelans who left their country in the last five years, hosting around 10 percent of them. Little is known about the characteristics of these migrants and their labor market outcomes. This paper fills this gap by analyzing a new large survey (EPEC). On average, Venezuelan workers are highly skilled and have high rates of employment, compared with Ecuadorans. However, their employment is of much lower quality, characterized by low wages and high rates of informality and temporality. Venezuelans have experienced significant occupational downgrading, relative to their employment prior to emigration. As a result, despite their high educational attainment, Venezuelans primarily compete for jobs with the least skilled and more economically vulnerable Ecuadoran workers. Our simulations suggest that measures that allow Venezuelans to obtain employment that matches their skills, such as facilitating the conversion of education credentials, would increase Ecuador’s GDP between 1.6 and 1.9 percent and alleviate the pressure on disadvantaged native workers. We also show that providing work permits to Venezuelan workers would substantially reduce their rates of informality and increase their average earnings.

This paper is a product of the Poverty and Equity Global Practice. 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 authors may be contacted at solivieri@worldbank.org.

_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
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15
<!-- Start of picture text -->
-2 -1 0 1 2<br>% Change in Employment<br>New exporters Always exported<br>.8<br>.6<br>.4<br>.2<br>Kernel Density for Exporters<br>0<br><!-- End of picture text -->

**Note: Vertical lines indicate the means. Source:** World Bank Enterprise Survey.

Table 5.4 estimates the probability of becoming an exporter (Column 1) and becoming a non-exporter (Column 2). The probability of a firm becoming an exporter is higher when the firm uses imported inputs and if the value of machinery assets is low. While this last result might be puzzling, it could reflect a change in the type of goods these new exporters are trading. New exporters may use less advanced technologies. In contrast, the probability that a firm stopped exporting is higher for firms with high value of machinery assets and low usage of foreign technologies. This might reflect a reduced ability to compete with international firms. An interesting fact is that the probability of stop exporting is higher among firms that were highly intensive in female labor. This might reflect the fact that female-intensive sectors, such as the apparel and textiles, were severely affected by international competition and were left behind by public support.

**Table 5.4. Probability of Becoming Exporter or Non-Exporter**

|**Dependent Variable:**|New exporter (yes =1)|Stopped exporting(yes =1)|
|---|---|---|
||1|2|
|VARIABLES|||
|New products (yes=1)|-0.586|0.508|
||(-1.57)|(1.19)|
|Value of machinery<sup>1/</sup>|-0.084***|0.065**|
||(-3.29)|(1.97)|
|Cost per worker<sup>1/</sup>|0.074|-0.124|
||(0.84)|(-1.25)|
|Firms' age<sup>1/</sup>|-0.140|0.025|
||(-0.69)|(0.12)|
|Foreign ownership (yes=1)|-0.089|0.084|
||(-0.30)|(0.22)|

32 | P a g e
||||Bureau of Standards in order to access<br>regionalandinternational markets|
|---|---|---|---|
|Adaptation Strategy<br>for Malawian Sugar<br>Industry in Response<br>to the Reform of the<br>European Union<br>Sugar Regime|Sugar|2006|<br>Malawi’s sugar is among the most<br>competitive in the region. Since there is an<br>increasing number of small producers<br>through out-grower schemes, there is need<br>to enhance extension services to ensure<br>compliance to EU standards, which is the<br>majorexportmarketfor Malawi’s sugar.|
|Tea Sector Finance<br>Study for Food<br>Security|Tea|2007|Not so competitive due to low yields from<br>old Indian plantations. There is need to<br>promote replanting with high yielding<br>clonal varieties at both the smallholder and<br>estate levels.|
|Coffee Sector Finance<br>Study|Coffee|2007|<br>This study used the coffee value chain to<br>gain a better understanding of how the<br>commercial<br>growers/processors<br>and<br>smallholder coffee growers are currently<br>financing their activities. Production levels<br>are currently low and being threatened by<br>lack of capital.|
|Horticultural<br>Marketing and Food<br>Processing in Malawi<br>Final Report|Horticultural<br>products|2008|<br>Lack of processing capacity, direct flights<br>and high cost of air freightare some of the<br>major<br>set-backs<br>to<br>promotion<br>of<br>horticulture in Malawi. This results in lack<br>oforganized andreliablemarkets.|
|Groundnut Value<br>Chain Study|Groundnuts|2008|Prospects for groundnuts as a tradable crop<br>have been affected by lack of high yielding<br>and rosette resistant varieties. Sector<br>prospects are likely to improve with the<br>release of a new variety that has right<br>kernelsizes andisresistant to diseases.|
|Final Evaluation of<br>the STABEX Funded<br>Programme in<br>Malawi|Paprika,<br>Coffee and<br>Tea|2007|Same as for coffee, tea and horticulture.|
|Review of the Poultry<br>Industry in Malawi|Broilers and<br>layers|2007|Many informal small sector players who<br>face high costs of production due to the<br>increase in feed costs. The ban on imports<br>due to avian flu has also exacerbated the<br>increases in the domestic price of chicken<br>meat<br>and<br>eggs<br>thereby<br>making<br>it<br>uncompetitive. There is need to promote<br>medium<br>and<br>large<br>scale<br>production<br>systems.|
|Status and prospects<br>of Malawi’s tobacco<br>industry: a value<br>chainanalysis|Tobacco|2004|Malawi enjoys competitiveness in tobacco<br>but there is need to improve the efficiency<br>of its marketing system so as to consolidate<br>its competitiveness|

48
# **2.2.1 Evaluation of GP presidents under PVP**

In order to incentivize GP presidents to work with the VPRC to improve responsiveness and transparency in local governance, the design of the PVP project also included a program-wide performance-based financial incentive in its first phase of implementation. To implement this performance-based scheme, each GP president was assessed on a range of indicators that related to their performance on the overarching function of the GP, i.e. the delivery of public services and targeted safety net programs, and their role in overseeing the activities of the VPRCs.

To assess performance, PVP developed evaluation criteria based on project monitoring data. A team of external evaluators used a combination of project monitoring data, and random field verification visits to implement the evaluations. To avoid any semblance of bias in the evaluation process, the evaluation teams were always from a different district from the GP that they were assigned to evaluate which makes it very unlikely that they were familiar to the GP prior to visiting it. Evaluators also consisted of people intimately familiar with the functioning of GPs— such as retired local bureaucrats and PVP members. A team of 5-6 such evaluators visited the GP for a period of 2-4 days to conduct this evaluation.

A predetermined scoring system was then used to convert these indicators or evaluation criteria into scores; with a maximum possible score of 100. An absolute score was used to determine the threshold for the performance incentive, with cutoffs of 70 and higher, 69-55, and less than 55 used to assign letter grades A, B, and C, respectively. In this first phase, GPs with a letter grade A qualified for a financial incentive from the project.

Project monitoring data used in these evaluations were collected by the VPRC, which delivered all interventions under the PVP project; and they were verified through a multi-stage process. These data were collected through standardized reporting formats and informed a range of project functions from the identification of target and beneficiary populations to the tracking of expenditures and process-related outcomes. All monitoring data was also validated through a 3-step process which involved local social audit committees, PVP project staff, and international thirdparty monitoring and audit firms. The third-party verification included visits to a rotating random sample of GPs so that every GP was reviewed at least once in a calendar year, and audits of financial statements on an annual basis. Any data that related to private transfers was also required to be publicly disclosed through regular Gram Sabha meetings and public notice boards. Appendix B provides more details on the types of monitoring data collected and the data verification procedures 8
# **3 Catfish Farming in the Mekong**

Fishing and aquaculture are prevalent all over Vietnam, a country with a dense river network and hundreds of kilometers of coastal areas. While marine fishing, both offshore and inshore, are important, our analysis focuses on small-scale aquaculture production by Vietnamese farmers. Within aquaculture, there are three major fishing activities in the country: freshwater aquaculture (river fishing), brackish water aquaculture (medium-salinity waters as in estuaries) and marine aquaculture (saltwater). Since catfish is a river fish, we will only study freshwater aquaculture.

To investigate the impact of the U.S. anti-dumping duties on Vietnamese farmers, we focus on households residing in provinces where catfish is the major source of aquaculture income. We will label these provinces, which are located in the Mekong region of South Vietnam, ‘catfish provinces’. Data on fish production by species in Vietnam is not easily available to the public. In order to identify the catfish provinces, we must therefore follow an indirect approach consisting of two strategies. First, we examine the geography of the country and the ecological conditions needed for catfish production across regions. Second, we present supporting information on catfish production by provinces that we obtained from several scattered sources.

Within Vietnam, the production of catfish is geographically concentrated in the Mekong Delta. This is because catfish only develops in relatively flat rivers with sandy soils, a prevalent feature of the Mekong area. The Red River Delta, in North Vietnam, is instead a mountainous region not suitable for catfish, but rather for other fish like carp. The other regions specialize mostly in brackish and saltwater products. Table 1 supports this claim. Based on the description of the sector in World Bank (2005), a comprehensive report on Vietnam Aquaculture, we assembled evidence on region-specific forms of aquaculture. Two observations stand out. First, freshwater production is relevant in all North Vietnam and, within the South, only in the Mekong where 50 percent of the aquatic output comes from freshwater fishing. In addition, while the Mekong produces tra and basa (along with other fish like tilapia and barb) the North, and in particular the Red River, specializes in carp (common, Indian and Chinese). The main brackish aquaculture product is shrimp, particularly in the 9