rafmacalaba/datause-encoder-data huggingface dataset:
The rural road network follows the assumptions described in Table 1. With the project intervention, travel time will be reduced (increase speed) and the road will become more resilient as drainage capacity (hydraulic design) will be larger (fourth and fifth columns in Table 1).

**Table 1: Road Network Characteristics and Assumptions**

|**Road type**|**Bef**|**ore Project**|**Aft**|**er Project**|
|---|---|---|---|---|
||**Speed**<br>**(km/hour)**|**Hydraulic Design**<br>**(years)**|**Speed**<br>**(km/hour)**|**Hydraulic Design**<br>**(years)**|
|Primary|50|20|80|50|
|Secondary|40|10|60|20|
|Tertiary|30|10|60|20|
|Rural roads (cars)|10|5|40|10|
|Rural roads (bicycle)|10|5|20|10|
|Walking|4|—|4|—|

The accessibility model has three main steps:

1. The travel time to the closest point of interest (schools, hospitals, or markets) is calculated for each cell in the project area.<sup>13</sup> The cell resolution is 500 m.

2. The population and value of agricultural production are aggregated at the 500 m resolution.

3. The population and value of agricultural production are combined with travel time and aggregated for three thresholds (30, 60, and over 60 minutes). In some instances, some population or agriculture cells may not have road access to a certain point of interest (for example, some villages in the river islands); those populations are considered ‘isolated’ and would be counted as such. Isolation would increase in cases of flood impacts as some feeder roads, lacking alternative routes, would be disrupted.

# 2.2. Climate Resilience Analysis

To quantify the climate vulnerability, the accessibility model was used imposing the disruption of certain roads based on the flood levels and their hydraulic designs. The climate analysis is done as follows:

1. Intersect floods map with road network. Flood event model is a 50-year return event.

2. Disrupt road network based on hydraulic designs. For example, a main road will be disrupted if the difference between the 50-year flood and the 20-year flood is more than 0.5 m. This is calculated for each segment based on the design assumption.

> 13 In rural context, density of critical facilities is very low, and the population normally has only one choice based on distance, as it is the case in the pilot provinces. In urban context, this assumption may not be valid as people’s choices for critical facilities may depend on a wide range of factors (e.g. facility reputation, insurance, social network...).

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# Inventory of Public Land in Ahmedabad, Gujarat, India

Shirley Ballaney, Marie-Agnes Bertaud, Patricia Clarke Annez, C.K. Koshy , Bindu Nair, Bimal Patel, Vidyadhar Phatak (Consultants) Vasudha Thawakar (World Bank)

**Keywords:** Urban land, Land Policy in India, Urban Infrastructure, Infrastructure finance

**JEL Classification:** R53 Public Investment and Capital Stock, R14 - Land Use Patterns, R38 - Government Policy; Regulatory Policy

**Sector Board:** URBAN
Ponomareva 2000, Sinai and Meyer 2004, Abraham et al. 2010, and Du, Harrison, and Jefferson 2011). In contrast, some studies find that the gains from FDI are larger for exporting firms (e.g. Barrios and Strobl 2002, Schoors and van der Tol 2002, Lin at al. 2009, Jordaan 2011b).

# **2.3 Host Country Characteristics and Institutional Framework**

_Labor market regulations_ can influence the effect of FDI on domestic firms via various channels, including the amount and type of FDI being attracted in the first place, the domestic firm’s absorptive capabilities, and the frequency of labor turnover as a transmission channel of spillovers. Using firm-level data for 19 Western and Eastern European countries between 1998 and 2001 Javorcik and Spatareanu (2005), for instance, find that higher absolute and relative labor market flexibility compared with the foreign investor’s home country has a positive impact on the likelihood of foreign investment. Besides the amount of FDI, labor market regulations also affect domestic firm’s absorptive capacities. In a recent study, Hale and Long (2011) conclude that labor market regulations in general, and wage constraints in particular, affect the level of skills in a firm and, thus, their absorptive capacity. Labor market regulations directly affect the frequency of labor turnover and, thus, the transmission channel. Overly rigid labor markets can reduce the possibility for labor turnover and FDI spillovers. However, overly flexible labor markets may result in frequent labor turnover, lowering the possibility of domestic workers to acquire skills and knowledge in foreign firms.

A few studies stress the role of _financial markets_ in developing countries as a mediating factor for the absorption of spillovers (Alfaro, Chanda, Kalemli-Ozcan, and Sayek 2010; Havranek and Irsova 2011). Multinationals can have an ambivalent impact on access to finance for local firms. They might ease access to finance by bringing in scarce capital to developing countries, but if multinationals borrow from local financial institutions, they might increase local firms’ financing constraints (Harrison, Love and McMillan, 2004). This, in turn, can influence a local firm’s absorptive capacity and, thus, actual FDI spillovers. Harrison et al. (2004) find evidence for the second effect using firm-level data for a cross-section of 38 high- and low-income countries.

Other studies look explicitly at the role of financial development – representing access to finance – for FDI spillovers. Studies find that well-developed financial markets may facilitate a domestic firm’s absorptive capacity linkages. Agarwal, Milner, and Riaño (2011), for instance, find that FDI spillovers are lower or even negative for Chinese manufacturing firms that are creditconstrained. Javorcik and Spatareanu (2009) find that less liquidity-constrained firms are more 9
**Table 5. Share of estimates documenting exposure and impact by climate shock and channels**

A. Share of estimates that report the poor are more exposed by specific climate shocks

||All Stu<br>Number of<br>estimates|dies<br>Share that finds<br>the poor are more<br>exposed<br>(%)|Excludin<br>Number of<br>estimates|gCCDR<br>Share that finds<br>the poor are more<br>exposed<br>(%)|
|---|---|---|---|---|
|Heat|13|84.62|8|75|
|Flood|43|76.74|35|71.43|
|Drought|28|78.57|24|75|

B. Share of estimates that report the poor are more affected by specific climate shocks

||A|ll Studies|Exclu|dingCCDR|
|---|---|---|---|---|
||Number of<br>estimates|Share that finds<br>worse impact on the<br>poor<br>(%)|Number of<br>estimates|Share that finds<br>worse impact on<br>the poor<br>(%)|
|Heat|3|100|0|.|
|Flood|37|91.89|27|88.89|
|Drought|25|88|12|75|

<u>C. Share of estimates that find worse outcome for the poor through specific channels</u>

||Number of estimates|Share that reports the poor are<br>more adversely affected<br>(%)|
|---|---|---|
|Income loss|123|80.48|
|Human capital loss|74|81.08|
|Expenditure reduction|17|52.94|
|Lower education|7|100|
|Poor health|25|68|
|Food security|26|100|
|Asset loss|12|83.33|
|Growth reduction|61|65.57|
|Poverty|66|89.39|

_Note_ : CCDR = _Country Climate and Development Reports_ . A. Sample covers 33 studies, of which 22 are CCDRs. B. Only CCDRs estimate the impact of extreme heat. Sample covers 61 studies, of which 34 are CCDRs. C. Sample covers 61 studies, of which 34 are CCDRs. Income includes earnings. Human capital includes education, health, crime, and food security. Sample includes 123 estimates on income from 11 studies, 74 estimates on human capital from 16 studies, 17 estimates on household expenditure cuts from 5 studies, 12 estimates on asset losses from eight studies, and 28 estimates on mortality from five studies.

19
functions from which productivity measures are defined. In the context of country panels, these studies most often entail decomposing changes in total factor productivity, via a Malmquist index fitted to measured inputs and outputs.<sup>6</sup> In some instances, a second-stage regression is used to explain variations in calculated productivity in terms of a set of determining state variables – for example, past research expenditures or the strength of political institutions.

Still, while the approaches applied to the problem of estimated agricultural productivity have become more varied, the underlying measures have not, and the alternative methods often build on the same data and the same proxies for capital as earlier studies. See, for example, Lusigi and Thirtle (1997), Arnade (1998), Coelli and Rao (2005), Fleming (2007), Fuglie (2008), Alene (2010), Headey, Alauddin, and Rao (2010), Nin-Pratt and Yu (2010), and Fulginiti (2010).<sup>7</sup>

# **Data set on agricultural capital stock**

The series of agricultural capital stock presented here consists of three components: fixed capital, livestock, and treestock. In this sense, we distinguish between stores of capital that arise through investments and privately held productive natural endowments including land and water resources. National accounts report fixed capital investment, which does not wholly include livestock and treestock; therefore, we compute each component separately. Data sources along with the computer program used to calculate the capital series are documented in Crego et al. (1998).<sup>8</sup> We construct the fixed-capital series based on national account investment data, using a modification of the perpetual inventory method. The method requires integration of the investment data to obtain capital stocks. For livestock the initial data are the number of animals. We need only calculate the values of the individual herds and then aggregate these values to obtain the total for the full stock of animals. For treestock we use the estimated present value of future income derived from the area planted in orchards.

> 6 See Färe et al. (1994) for an early application.

> 7 Exceptions include the studies by Ball et al. (2008, 2004, 2001) in which the authors construct capital stocks from investment data for various OECD countries.

> 8 The paper can be downloaded from the World Bank at <u>http://go.worldbank.org/IJ0CWCLVR0.  More recent</u> agricultural investment series were obtained from updates of the original sources, as well as from United Nations National Accounts data.

4
<!-- Start of picture text --> —— January<br>—— june<br>0.200 oN == February<br>— july<br>0.175 \<br>0.150 \<br>0.125<br>>uu<br>c<br>ou<br>]<br>ios<br>nw2 0.1000.075 \\\‘<br>0.050 \<br>0.025 N“N<br>~~<br>0.000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15)<br>Radius of Gyration (Km)<br><!-- End of picture text -->
this. Competition‐adjusted preference margins are calculated as the percentage‐point difference between the weighted average tariff rate applied to the rest of the world and the preferential rate applied to the beneficiary country, where weights are represented by trade shares in the preference‐granting market.<sup>13</sup> Unlike a traditional preference margin, the competition‐adjusted preference margin can assume positive as well as negative values. A negative value indicates that, in a specific market, a certain country faces worse market conditions than its trade competitors.

In terms of competition‐adjusted preference margins, relatively small shares of world trade receive a significant preferential advantage or suffer a significant preferential disadvantage. Specifically, only 5.2 percent of global trade benefited from a preferential advantage over 5 percent and only 3.3 percent of global trade suffered from a preferential disadvantage higher than 5 percent (see Figure 14).

Figure 14: Most countries benefited from a competition‐adjusted margin between ‐2 and 2 percent <!-- Start of picture text --> 70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Competition Adjusted Margin Competition Adjusted Margin<br>Share of Tariff lines<br>Share of global imports<br><‐15 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2 0 2 4 6 8 10 12 14 >15 <‐15 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2 0 2 4 6 8 10 12 14 >15<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database Lower income countries, tend to benefit the most from preferential access, with competition‐ adjusted margins over 3 percent. About 84 percent of competition‐adjusted preference margins are concentrated within the range of ‐2 percent and +2 percent (see Figure 15), 15 percent of countries benefit from competition‐adjusted margins of over 2 percent. Some countries such as

> 13 � � **Competition‐adjusted preference margin for product i granted to partner j by country k** = ������,� ���,� ���,� . Where ��,�� � ∑��∑����,���,����,� is the export‐weighted (X in the formula denotes exports of v into k) average tariff imposed by country k on all other exporting countries v (excluding country j) in respect of product i. The preferential rate � applied to country j is ��,� .

15
We supplement the previous descriptive figures by plotting the logarithm of the initial GDP per capita with the average growth rate of the corresponding period (Figure 6). The first scatter plot shows that states with a low GDP per capita in 1970 grew faster in the following 15 years than the richer states. This convergence process is supported by the significant negative value of the regression (Table 3). The trend of the poor states catching up with the rest of the country and thereby reducing inequalities among regions was broken down after 1985. Between the mid 1980s and the mid 1990s there was a reverse trend of divergence, which was significant at the 5 percent level. On first inspection, richer states did not grow faster following 1995, but after excluding from the sample the only rich southern state, Quintana Roo, which had negative growth during this period, we observe that initial richer states grew faster than the one which were relatively poor in 1995. The positive coefficient of the regression is significant at the 10 percent level.

**Figure 6:  Initial State GDP per capita and average annual growth rate of GDP per capita for three different time periods between 1970 and 2006.**

<!-- Start of picture text --> 1970-1985 1985-1995 1995-2006<br>0.06 0.06 0.04<br>0.05 0.04<br>0.03<br>0.04 0.02<br>0.02<br>0.03 0.00<br>0.01<br>0.02 -0.02<br>0.00<br>0.01 -0.04 Quintana Roo<br>0.00 -0.06 -0.01<br>8.2 8.6 9 9.4 9.8 8.8 9.2 9.6 10 8.6 9 9.4 9.8 10.2<br>Log GDP per capita 1970 Log GDP per capita 1985 Log GDP per capita 1995<br>Annual per capita growth 1970-1985 Annual per capita growth 1985-1995 Annual per capita growth 1995-2006<br><!-- End of picture text --> Source: Calculations based on information from INEGI, excluding Campeche and Tabasco.

# **Table 3: Regression results of initial State GDP per capita and average annual growth rate of GDP per capita for three different time periods between 1970 and 2006.**

||1970-1985|1985-1995|1995-2006|1995-2006 w/o QR|
|---|---|---|---|---|
|Coefficient|-0.0196118|0.0200788|0.0018592|0.0065042|
|_t_-statistic|-5.58|2.07|0.45|1.93|
|_p_-value|0.000|0.047|0.653|0.064|
|R<sup>2</sup>|0.5263|0.1331|0.0073|0.1215|

12
important data, especially how we estimate the crucial ad valorem equivalents of the barriers against foreign direct investment in services. In section 3.3, we present and interpret the large estimated welfare gains, and why WTO accession is counterproductive to the government‘s objective of diversifying the economy. In section 3.4 we examine the impact of different modeling assumptions (or model closures) on the results. In section 3.5, we present the results of our piecemeal and systematic sensitivity analysis.

# **3.2 Overview of the Model and Key Data**

# **Overview of the Model Formulation**

The model is a small open economy model, with Russia modeled endogenously and an aggregate rest of the world modeled as vectors of perfectly elastic supply and demand prices for the sectors in the model. An algebraic formulation of the full model is available in Jensen, Rutherford and Tarr (2004). Here we provide a general description. Primary factors include skilled and unskilled labor; mobile capital; sector-specific capital in the energy sectors reflecting the exhaustible resource; sector specific capital in imperfectly competitive sectors; and primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. The existence of sector specific capital in several sectors implies that there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up. There are 35 sectors as shown in table 3.1. Regardless of sector, all firms minimize the cost of production.

In all sectors, firms optimize their sales between exports and the domestic market to maximize revenue for any composite output level, based on a constant elasticity of production transformation schedule. Prices on the export market are perfectly elastic. Total capital and labor 30
7

# B. ExDorts of pervices: tourism

The World Tourism Organization comp.les national statistics reported by some of its membcrs, including Hungary. Although the data available **are** incomplnte, they show (Table 5) that a third of the hotel nights spent<sup>by fnreign</sup> tourists originate in five out of the twelve EC countries for which there **are** data. The EC-12, therefore, probably has a much larger weight in Hungary's exports of tourism than of goods, comparable to the total CMEA and double as much as the USSR. Moreover, the EC tourists brought convertible currency with theml thus their relative effect on Hungary's balance of payments was likely much larger than the hotel nights demonstrate.

Table 5: Foreign Tourists Nigihts Spent in Hotels (number of nights)

|Countries<br>1985|1986|1987||
|---|---|---|---|
|France<br>German Federal Republic|81,000<br>1,406,000|84,000<br>1,223,000|92,000<br>1,481,000|
|Italy|157,000|217,ono|307,000|
|Netherlands|94,000|89,000|12F,000|
|United Kingdom|77,000|85,000|87,000|
|EC-5|1,815,000|1,698,000|2,095,000|
|Bulgaria|90,000|97,000|123,000|
|Czechoslovakia|220,000|255,000|260,000|
|German Democratic Rep.|237,000|24C,000|220,000|
|Poland|529,000|537.000|450,000|
|Romania|50,000|27,000|23,000|
|Soviet Union|881,000|928,000|1,216,000|
|CMEA|2,007,000|2,084,000|2,292,000|
|world|5,343,000|5,384,000|6,392,000|

Source: Yearbook of Tourism Statistics, Table 5, 1987 and 1988.

The higher share of the EC in Hungary's tourism may be explained by the facts that: 1) trade in tourism services is much less distorted than trade in goods; 2) the CMEA arrangements have little bearing on tourism; 3) tourism demand is directly related to income in the originating countries and to relative transport costs. Since the latter is roughly similar, the different income levels in the two blocs probably explains the present flows more than any other factor. It seems likely that in the future the ECs share in foreign tourist nights spent in Hungary will increase because of the economic crisis in Eastern Europe and the increasing appeal of Eastern Europe to potenti - Western tourists at the end of the Cold War.

# C. Imports

Turning to imports, Hungary's trade with the EC has been increasing since - the relatively low level and share of 1984 see Table 6. According to Hungarian
Figure 10: Specification Charts for Mean Daily Temperature <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.4 .1<br>.2 0<br>0 -.1<br>* Weather Product * -.2 * Weather Product * -.2<br>MERRA-2 MERRA-2 -.3<br>ERA5 -.4 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.5 .6<br>.4<br>0<br>.2<br>* Weather Product * -.5 * Weather Product * 0<br>MERRA-2ERA5 -1 MERRA-2ERA5 -.2<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.2 .2<br>.1<br>0<br>0<br>-.1 -.2<br>* Weather Product * -.2 * Weather Product *<br>MERRA-2 -.3 MERRA-2 -.4<br>ERA5 ERA5<br>CPC CPC<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 18 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

41
pursuing the activities of wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods; public administration and defence; private households with and extra-territorial and bodies. As in the employed persons organizations case of the nsso surveys for manufacturing establishments, the sample stratification in services is also includes both the district level and the two-digit nic sectors.<sup>9</sup> The survey covered the whole of the Indian Union except (i) Leh (Ladakh), Kargil, Punch and Rajauri districts of Jammu & Kashmir, (ii) interior villages situated beyond 5 km of a bus route in Nagaland, (iii) villages of Andaman and Nicobar Islands, which remain inaccessible throughout the year. Thus the corresponding State/UT level estimates and the all-India results presented in this report related to the areas covered under survey.

As in the case of manufacturing surveys, the services counterpart of the nsso survey collects analogous establishment-level information on the output, inputs (raw materials and other inputs), fixed assets, value of land and buildings, employment, energy consumption and so on. The sample stratification is also similar to that of the nsso.

Altogether a total of 190,142 service sector enterprises considering both list frame and area frame were surveyed in the 63rd round while the 57th round generated 359,702 establishment level observations. Thus, we have a total of 549,844 observations across the two years. We note that industries with two-digit National classification code 61 (Inland, sea and coastal water transport), 73 (research on natural sciences, engineering, social sciences and humanities) and 90 (sanitation services) have fewer than 400 observations each year and hence not suitable for any industry level computations, such as total factor productivity estimations. Thus, we drop these observations, and are left with 359,050 observations in 2000 and 189,584 in 2005, giving us a total of 548,634. For our calculation of productivity and misallocation metrics, we need to further clean up the data by dropping observations belonging to unknown district names, and small and conflict states. These include Andaman and Nicobar Islands, Dadra & Nagar Haveli, Daman & Diu, Jammu & Kashmir, Tripura, Manipur, Meghalaya, Mizoram, Nagaland and Assam. The final

> 9Furthermore, as the nic definitions have changed over time, we make them consistent using concordances that come with the data.

19