rafmacalaba/datause-encoder-data huggingface dataset:
# Contents

|Acknowledgements ............................................................................................................................................. 1|
|---|
|1.<br>Introduction ................................................................................................................................................. 3|
|2.<br>Mumbai city: Profile, rainfall pattern and recurrent floods ........................................................................ 3|
|3.<br>Methodology and data sources ................................................................................................................... 7|
|4.<br>Results and discussion ............................................................................................................................... 10|
|4.1<br>Exposure to floods ............................................................................................................................. 10|
|4.2<br>Vulnerability and impacts of floods ................................................................................................... 12|
|4.2.1 Socioeconomic profile ...................................................................................................................... 12|
|4.2.2 Impacts of the July 2005 extreme event .......................................................................................... 15|
|4.2.3 Impacts of recurrent floods .............................................................................................................. 18|
|4.3<br>Ability to respond .............................................................................................................................. 19|
|4.3.1 Short‐term responses ....................................................................................................................... 21|
|4.3.2 Structural responses ......................................................................................................................... 22|
|4.3.3 Relocation as an option .................................................................................................................... 24|
|5.<br>Policy implications ..................................................................................................................................... 25|
|6.<br>Summary and Conclusion .......................................................................................................................... 28|
|References ......................................................................................................................................................... 30|
|Annexure ........................................................................................................................................................... 32|
|A.1<br>Flood exposure and demographics in the 9 selected wards ............................................................. 32|
|A.2<br>Ward land use maps .......................................................................................................................... 35|

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among other things. The consumption charge must at least finance the operating costs of the system, except for technical assistance, which is funded through the Program. The MOP outsources this to the water companies and oversees execution. By late 1998 there were 1,049 rural drinking water services catering to 1,318 localities, with a total of 202,734 installations benefiting 1,117,842 households. By the end of 2000 the aim is to achieve a coverage of 98% of the concentrated rural population, representing between 30% and 40% of the total rural population.

**Table 3: National rural drinking water program**

|Year|Budget<br>Ch$ million<br>(current<br>prices)|Expenditure<br>Ch$ million<br>(current<br>prices)|Ch$ million<br>(Dec.1998<br>prices)|Connections<br>installed|Localities<br>benefited|Coverage<br>%|
|---|---|---|---|---|---|---|
|1994|2,048.4|1,551.8|2,060.8|1,103|9|81|
|1995|8,124.5|7,113.5|8,728.2|5,434|38|82|
|1996|10,254.7|9,657.3|11,049.5|5,353|38|83|
|1997|14,079.2|13,454.6|14,467.3|9,215|79|90|
|1998|20,787.2|20,439.4|20,835.3|8,789|64|93|
|1999|20,760.0|1||||_|

**Source: Ministry of Public Works**

As table 3 shows, spending on the program grew tenfold between 1994 and 1998, which made it possible to increase the number of localities benefited by seven times. Part of the funds are spent on technical assistance, although the budget for 1999 stipulated that no more than Ch$ 915.5 million should be spent on this item, in other words less than 4.4% of the total budget. Ignoring the fact that part of the funds are spent on technical assistance and on refurbishing existing services, the average annual cost per mains connection varied in this period between Ch$ 1.6 and Ch$ 2.4 million, so the subsidy received per family is 15
The results raise one interesting question. As entry barriers increase, the spread between the informal and the formal sector rises, as expected, and as entry procedures fall, the spread between the formal and informal sector falls. The implication is that barriers to entry are greater for corporate entrepreneurship than for young businesses that have not incorporated or for nascent entrepreneurs where they are in the process of starting a business. However, in developed countries, the spread between the informal and formal sectors not only decreases, but is often positive; i.e. the number of limited-liability companies is greater than the sum of sole proprietors and informal firms. This implies that it is at least as easy to start a limited liability company as a sole proprietorship.

# **4. Conclusion**

The purpose of this paper is to compare two datasets designed to capture entrepreneurial dynamics: The GEM data for early-stage entrepreneurial activity and the World Bank Entrepreneurship Group dataset for formal business registration. We find a number of important differences in the data. First, the GEM data tends to report significantly lower levels of early-stage entrepreneurial activity in developed countries. In other word it is more common to start a formal business in a developed country than a sole proprietorship. Second, the GEM data tends to be higher for developing countries than for developed countries. One possible explanation if the distinction between intent and informality of entrepreneurial activity particularly in developing countries that is captured by GEM data. However, important exceptions to this are found for both the United States and Germany in particular. This suggests that firms in developed countries have greater ease and incentives to incorporate, both for the benefits of greater access to formal financing and labor contracts, as well as for tax and other purposes not related to business activities.

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**Table 7. Cost estimates for the delivery of textbooks to school**

|School response:<br>Estimated cost if schools collect the books from<br>DEBS|DEBS respo|nse|
|---|---|---|
|Distance of 20 km<br>K 142|Actual cost|K 347|
|21–44 km away<br>K 202|Eid if||
|45–80 km away<br>K 323|stmate cost<br>DEBS delivered books|K 506|
|More than 80 km away<br>K 573|<br>to schools||
|All schools(average)<br>K308|||

_Note:_ The exchange rate from Zambian kwacha (K) to U.S. dollars was approximately 0.08 in November 2019.

- 5.2. Do the financial incentives have an impact on textbook distribution and availability? What kind of financing scheme is more effective?

The impact of financial incentive schemes on textbook availability is shown in Figure 1. Each bar indicates the coefficient estimate of the interaction term described in the empirical analysis strategy, which captures the effect of each treatment. It can be interpreted as: the incentive scheme increases or decreases the set of outcome variables compared to the control group. The left-hand column pair shows the impact of both the school incentive and the DEBS incentive. The school incentive increases the probability of receiving textbooks by 0.126 compared to the control group. However, the DEBS incentive does not show any statistically significant impact. The middle column pair shows that neither incentive scheme significantly increased the textbook-pupil ratio. This may be because planning of the distribution by the MoGE is not done according to the needs of schools or districts based on the current number of students in the district. Instead, the MoGE distributes a predetermined number of books to each district, which results in the DEBS distributing books to schools on an ad hoc basis. This is supported by a large variation in textbook-pupil ratios across districts and across schools within districts. The district-level textbook-pupil ratio ranges from 0.02 to 1.13 for the 27 sample districts and the textbook-pupil ratio for the middle 50 percent of schools ranges from 0.54 (25<sup>th</sup> percentile) to 1.63 (75<sup>th</sup> percentile). Lastly, the right-hand column pair shows the impacts of the incentive schemes on the probability of schools receiving textbooks in one month. Interestingly, the figure shows that the DEBS incentive scheme actually lowered the probability of receiving textbooks within a month by 0.324.

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-16 -

Table 5: EFFECTS CF NONTARIFF BARRIERS ON EXPORT PRICES OF CPEss

||point dif<br>the EEC i|Percentage<br>ference between averag<br>mDorts from CPEs and in|e unit value in<br>imports from:|
|---|---|---|---|
||198|2|1984|
||N|F<br>N|F|
|Developed Countries||||
|Agriculture|-16|4<br>-25|<br>3|
|Raw materials|12|0<br>13|<br>-6|
|Manufactures|-35|-29<br>-33|<br>-29|
|Total|-33|-22<br>-32|<br>-23|
|Developing Countries||||
|Agriculture|-30|-3<br>26|<br>-3|
|Raw materials|3|45<br>-16|<br>-20|
|Manufactures|-22|-17<br>-23|<br>-20|
|Total|-22|-11<br>-20|<br>-17|

Note: N = products subject to discriminatory NTBs. F = products free of discriminatory NTBs.

We have chosen to analyze the impact of NTBs rather than that of tariffs because NTBs differ from tariffs in one important respect relevant for our analysis. Unlike tariffs, NTBs are not usually extended on a multilateral basis and the product categories subject to NTBs are very often loosely defined. Countries with exports subject to NTBs are, therefore, relatively better positioned to adjust their exports to the restrictive measures. Moreover, NTBs appear to play a very important part in the set of trade-restrictive measures in the EEC.12/ As the table shows, NTBs appear to have had little impact on the price competitiveness of the CPEs' exports of manufactures. Once again, export prices of manufactures are consistently below the prices of their competitors not only in the case of exports subject to NTBs in the EEC, but also in the case of those exports which were free of discriminatory NTBs. Nevertheless, one systematic difference disappears; the average CPEs' export prices are lower whenever such exports are subject to discriminatory NTBs in comparison to prices of exports to markets that were free of discriminatory NTBs. What interpretation can be attached to these findings is unclear. NTBs appear to play a particularly important role in explaining the price 12/ On the extent and profile of NTBs applied by industrial countries against imports from Eastern Europe, see Olechowski (1986).
Table 7: Productivity Decompositions and Policy Changes: MP Decomposition

||Aggregate|AvgSurv|Realloc Surv|Entrants|Exiters|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Final Goods Tariff|-.021|-.018|-.004|.001|-.0009|
||(.021)|(.027)|(.018)|(.006)|(.004)|
|Input Tariff|-.415|-.501|.070|-.006|.023|
||(.096)<sup>_∗∗∗_</sup>|(.114)<sup>_∗∗∗_</sup>|(.085)|(.022)|(.020)|
|FDI Reform|.041|.043|-.002|-.001|.0009|
||(.013)<sup>_∗∗∗_</sup>|(.014)<sup>_∗∗∗_</sup>|(.011)|(.002)|(.003)|
|Delicensed|-.008|.004|-.006|.0004|-.006|
||(.015)|(.018)|(.017)|(.004)|(.008)|
|Obs.|19461|19461|19461|19461|19461|
|_R_<sup>2</sup>|.06|.065|.032|.055|.006|

Decomposition performed using the method suggested by Melitz and Polanec (2010), with false exit addressed as discussed in the text. Each observation is a state-industry. Dependent variable names are given at the top of each column. “Aggregate” indicates market-share weighted mean productivity, “Avg Surv” and “Realloc Surv” indicate unweighted mean productivity and the covariance between market share and productivity for surviving firms, respectively, and “Entrants” and “Exiters” indicate the contributions of entering and exiting firms. All specifications are fixed-effects analyses at the state-industry level, and include year dummies. Each observation is weighted by the total number of firms in the state-industry across all years, and standard errors are clustered at the state-industry level.

Table 8: Productivity Increases Implied by Policy Changes

||Final Goods Tariffs|Input Tariffs|FDI Liberalization|Delicensing|
|---|---|---|---|---|
|Aggregate|**3.2%**|**21.8%**|**2.1%**|-0.4%|
|Average|2.6%|**21.7%**|**4.6%**|0.2%|
|Reallocation|0.6%|0.2%|**-2.4%**|-0.6%|

Implied increases in aggregate, average, and reallocation productivity. Results are based on regression coefficients and average policy changes. Bold font indicates that the underlying regression results are statistically significant at the 10% level.

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circumstantial evidence for spillovers, but it must also be mentioned that there are hardly any studies where the connection between linkages and spillovers is explicit.

# _Training of Local Employees in MNC Affiliates_

The transfer of technology from MNC parents to affiliates is not only embodied in machinery, equipment, patent rights, and expatriate managers and technicians, but is also realized through the training of the affiliates' local employees. This training affects most levels of employees, from simple manufacturing operatives through supervisors to technically advanced professionals and top-level managers. Types of training range from on-the-job training to seminars and more formal schooling to overseas education, perhaps at the parent company, depending on the skills needed. Although higher positions are often initially reserved for expatriates, the local share typically increases over time. The various skills gained while working for an affiliate may spill over as the employees move to other firns, or set up their own businesses.

The evidence on spillovers from the MNC affiliates' training of local employees is far from complete, and comes mainly from developing country studies. Considering that the public education systems in developing countries are relatively weaker, it is also possible that spillovers from training are relatively more important there. However, there is scattered evidence of effects in the industrialized countries, and then perhaps mainly regarding management skills. It is possible, for instance, that the inter-firm mobility of managers has contributed to spread specific management practices from Japan to the United States and Europe, and, in earlier times, from the US to Europe (Caves, 1996). Moreover, casual observation suggests that the mobility of employees from MINCs in the computer and software industries contributes to spillovers, both within the industry and elsewhere.

Studies in developing countries have recorded spillovers of both technical and management skills. For instance, Gerschenberg (1987) examines MNCs and the training and spread of managerial skills in Kenya. From detailed career data for 72 top and middle level managers in 41 manufacturing firms, he concludes that MNCs offer more training of various sorts to their managers than private local firms do, although not more than joint ventures or public firms.

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Figure 1 Attainment profiles for ages 15 to 19, by economic group: Western and Central Africa <!-- Start of picture text --> Benin 1993 Burkina Faso 1991-92 I Cameroon 1991<br>1l 1l- °11 ,<br>0.8 i 0.8 i 0.81<br>'0.6 00.6 a \0.6<br>'0.4 0.4 0.4<br>0.2 0.2- 0.2 _<br>0 00<br>1 2 3 4 5 6 7 8 123456789 1 2 3 4 5 6 7 8 9<br>Grade I Grade Grade<br>C.A.R. 1994-95 Cote d'lvoire 1994 Ghana 1993<br>0.8 0. 0.8<br>_ 0.6 e , EO.6 0- 0.6 -<br>0.4 ~~~~~~~04 04-<br>0.2 0 >>*>0.2 ~~~ +9 ~ ~~~~0 0.2 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 89 1 2 3 4 5 6 7 89<br>Grade Grade Grade<br>Mali 1995-96 Niger 1992 Senegal 1992-93<br>. ~~~~I ] _ _ _ _ ,<br>0.8 . 0.8 1 0.8 -<br>.~0.6 - - 0.6 o0.6 -<br>o04 E 0.4 04<br>0.2 - 0.2 0.2<br>0 0~ 01<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 89 1 2 3 4 5 6 7 89<br>Grade I Grade Grade<br>* Poorest . Middle A Richest<br><!-- End of picture text --> 11
<!-- Start of picture text --> 160<br>140 2 — aan —<br>120 [(———<br>10080 —_ - ——s4 - _ :<br>60iee = x<br>40<br>2004 2005 2006 2007 2008<br>—t World —H— CentralAmerica —=t— Caribbean<br>——— East Asia = > —Sub-Saharan Africa —@®— South Asia<br>=== Southeast Asia<br><!-- End of picture text -->
# **<u>Table 3. Income of net seller-buyer households</u>**

|||**N**<br>|**et buyer **<br>|**Househo**<br>|**lds**<br>|||**Ne**<br>|**t seller **<br>|**Househo**<br>|**lds**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Ru|ral|Ur|an|A|ll|Ru|ral|Ur|ban||ll|
||1993|1998|1993|1998|1993|1998|1993|1998|1993|1998|1993|1998|
|Proportion ofpopulation|49%|53%|95%|91%|58%|61%|51%|47%|5%|9%|42%|39%|
|Real Income(in 98prices)|7596|12408|15709|23621|10097|15930|7675|12108|8837|10918|7701|12052|
|Real Expenditure(in 98prices)|7479|10078|12374|15849|8990|11892|7461|9852|9829|10998|7514|9905|
|Lower Income households|50%|56%|13%|13%|39%|43%|44%|50%|22%|21%|43%|49%|
|Livingin North|54%|58%|42%|43%|50%|53%|61%|57%|29%|47%|60%|56%|

Notes: 1) The proportions are calculated using the sample size in the subgroup of households. For instance, 50% of the rural net buyer households in 1993 are in the lowest 4 deciles of the population.

2) All the categories are year specific, thus include the switching between the categories within the two years.

3) Real Income and Real Expenditure are in 1000 Dongs.

Source: Author’s own calculations using VNLSS 93 and 98.

Examining Table 3, we see that on average net-buyers are as wealthy as netseller households. This is an interesting observation since the general conjecture in the literature was that the net sellers of rice are richer than that of their net buyer counterparts. The proportion of lower income households is higher for net buyer households in rural areas, while the reverse is true for urban. However, not all low income households are net buyers. In addition, net buyer households in rural areas are less wealthy than their urban counterparts dispelling the claim that net buyers are urban poor.

In order to determine the degree of vulnerability of net buyers to changes in the price of the staple food<sup>14</sup> , we introduce a measure, namely the intensity of net buying. It is defined as the proportion of the value of net buy to total expenditure where net buy is the net amount of rice bought<sup>15</sup> . The intensity of net rice buying declined from 1993 to 1998 pointing to a decline in the vulnerability of net buyers (Table 4). The negative percentage values represent the share of net-sold rice in total expenditure, and are present since we include the net-buy intensity of all households in our calculation of the mean. If we look at the net buy intensity only among the net buyer households, we find that on average households are moderate net-buyers and their net buying proportion has decreased from 1993 to 1998. This decline in net buying intensity results may be due to increased expenditure, and/or increased own consumption. Table 5 shows that the

> 14 Net buyers of food are always cited as being the most vulnerable to food price increases. The degree of vulnerability is thus introduced for only net-buyers.

> 15 Net buy of rice = Value of purchased rice +Value of own consumption of rice-Value of production.  Put into words, it reflects how much more rice the household is consuming compared to what they produced.

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# Table 3. Goods Entry and Exit

|||<br>Number of exitin|g goods|||<br>Number of new|goods||
|---|---|---|---|---|---|---|---|---|
|year|commodities|manufacturedgoods|others|total|commodities|manufacturedgoods|others|total|
|2003|1|90|8|99|0|115|10|125|
|2004|1|81|5|87|0|97|0|97|
|2005|3|70|9|82|0|94|7|101|
|2006|0|57|6|63|0|113|2|115|
|2007|19|1510|225|1754|20|1320|216|1556|
|2008|0|37|5|42|1|73|6|80|
|2009|1|40|11|52|2|63|12|77|
|2010|3|55|5|63|3|33|2|38|
|2011|3|307|67|377|10|108|16|134|

Note: Data are from the Foreign Trade Division of the U.S. Census Bureau.

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# **II. Data Description**

This study uses data from Wave 1 of the General Household Survey-Panel (GHS-Panel) conducted in 2010/11 by the Nigeria National Bureau of Statistics (NBS) in collaboration with the World Bank Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project. The GHS-Panel survey is modeled after the Living Standard Measurement Study (LSMS) surveys and is representative at the national, zonal, and rural/urban levels. The total sample consists of about 5,000 households covering all 36 states in the country and the Federal Capital Territory, Abuja. One of the main objectives of the GHS-Panel is to improve agriculture data collection in Nigeria by collecting information at disaggregated levels (crop, plot, and household levels), and linking such data to nonagricultural aspects of livelihoods. All households were visited at two points in time: right after planting (post planting visit) and right after harvest (post-harvest visit) and were administered multi-topic household, agriculture and community questionnaires. Amongst a variety of topics, the household questionnaire gathered detailed information on food and nonfood consumption and expenditure of households. The survey covers over 100 food items commonly consumed in Nigeria and collected information on household consumption (quantity consumed) of the items in the past seven days before the survey. The use of handheld GPS devices to record coordinates of household plots allows the linkage of the data with geospatial variables such as rainfall and temperature data from other sources. Of the 5,000 households in the survey, about 3,000 were agricultural households in rural and urban areas producing a wide variety of crops. The study focuses on the rural agricultural households interviewed for the GHS-Panel 2010/11 survey and all statistics are weighted to ensure representativeness at national, regional and rural/urban levels.

7