rafmacalaba/datause-encoder-data huggingface dataset:
Among survey respondents, textile workers have been working for an average of 8.3 years in the industry, with their current employer for an average of 5.5 years, and living and working away from home overall for an average of 10. One‐third has had other paid work away from home, more than half of which was domestic work. Eight percent has previously worked in South Africa, almost all of which was in the domestic sector.

When it comes to _where_ to move, textile workers are drawn to a specific location (e.g., Maseru or Maputsoe) because they have a family member or friend already living there with whom they can live while they search for a job and then their own place to rent. About 60 percent of textile workers learned about their job through family or friends. Personal connections do not help in getting a job at a particular factory, but can help in navigating a new city and affording the move by providing temporary housing, and perhaps addressing some of the potential migrant’s concerns prior to migration. Another factor is distance from home and ability to visit family easily. For those who can, many women choose a particular city based on distance from their family household: “Some people indicate that Maseru is too far so they prefer to go to Maputsoe as it is closer to home”, rural woman.

Most textile workers do not have a job prior to moving (12 percent did), but rather move to where they know someone, then wait outside factory gates until they get a job. Almost all textile workers surveyed – 93 percent ‐ applied for their job by waiting outside the factory gate, which can take time. Women waited an average of 36 days to apply for their current job, with one waiting an entire year. Getting hired at a factory for the first time is seen to involve a bit of luck. Many factories seek some experience so they do not have to train workers, and the supply of women looking for work is deep. Once in the garment industry, however, women feel that their employability increases with work experience and on‐the‐job training.

Textile workers generally sign a contract: 92 percent of textile survey respondents did, and most of those who did not reported being casual employees (still on probation). However, many women do not know the terms of the contract, such as employment duration or sick and maternity leave, and perceive the terms to be non‐negotiable anyways. For those who (think they) do know the terms, their understandings of these rules and regulations vary widely. They report that contracts last anywhere between one month and four years. Most telling is that contracts are in English. At best, someone in Human Resources goes 27
# Urban Infrastructure Investment and Rent-Capture Potentials

V. Viguié<sup>a,1,</sup><sup>_∗_</sup> , S. Hallegatte<sup>b</sup> _aCIRED, Ecole des Ponts ParisTech, Paris, France bThe World Bank, Washington D.C._ _Keywords:_ Public transport, urban economics, transport, modeling, public finance _JEL:_ R14, R4, R51, H54, H71

## **1. Introduction**

Efficient public transport systems in cities lie at the core of sustainable development (Wright and Fulton, 2005; Dodman, 2011; Hoornweg et al., 2011; UN-HABITAT, 2011). However, financing both their development and maintenance at a global scale is challenging, because fares do not generally cover full costs. Capturing part of land value increase due to transport infrastructure construction, i.e. land value capture, has often been proposed as a promising alternative method of revenue generation and as a way to address this challenge (see for instance Batt, 2001; Martinez and Viegas, 2007; Peterson, 2009). It might be achieved through different policies such as land value taxes or land betterment taxes (see for instance Plimmer and McGill, 2003; Doherty, 2004; Rybeck, 2004; Medda, 2012).

The concept of land value capture has a history which dates back to Henry George, in the late 19th century (George, 1884; Arnott and Stiglitz, 1979; Arnott, 2004). The basic idea is that successful public transport systems lead to higher land values, as a result of the increase in accessibility, which can be considered as an increase in rent for some landowners. Capturing some or all of this increment in land value (through a betterment tax) can enable the recovery of part of the capital cost of the transport investment. It has been used since then in different cities across the world. Recent examples include Bogotá (Borrero et al., 2011; Peterson, 2009), London (Medda, 2012), Singapore, Hong Kong (Hong Kong SAR, China) (Hui et al., 2004), and various cities in Brazil, Argentina and India (Walters, 2012).

If such a betterment tax can be seen as an equitable, efficient (Mattauch et al., 2013) and easily understood levy, its implementation is however not a trivial task. It can lead to numerous difficulties, among which are political acceptability (Booth, Philip A, 2012), revenue volatility (Peterson,

> _∗_ Corresponding author

> _Email address:_ viguie@centre-cired.fr (V. Viguié)

> 1Tel: (33) 1 43 94 73 03 Fax: (33) 1 43 94 73 70, CIRED, Site du Jardin Tropical, 45bis, Av de la Belle Gabrielle F-94736 Nogent-sur-Marne, France.

_October 16, 2014_
Table 9: Robustness Check- Possible Effects of other Macroeconomic Factors on Realized, Planned FDI and DDI

||FDI||DDI||Planned FD|I|
|---|---|---|---|---|---|---|
|Manufacturing Sector (1vs0)|0.928***||-0.0275***||0.00269***||
||(0.0598)||(0.0217)||(0.0439)||
|100% Liberalized (1vs0)||0.228***||0.252**||0.189***|
|||(0.0221)||(0.0252)||(0.0198)|
|Observations|168,329|122,670|141,937|104,328|168,329|151,926|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects, time effects, and covariates (Exchange Rate and GDP). The sample period is 2019q1 to 2022q3.

Source: Author estimations.

# **Model Robustness**

Finally, we further conduct other robustness to the consistency of the model in subsection A3, where an alternative event study design is used to assess the impact of the reform on FDI growth in the pre-and post-policy periods. The alternative DiD model follows the approach by Clarke and Tapia-Schythe (2021). We denote states as z and time periods as t. Denoting the outcome variable as FDI, the panel event is defined as follows: Where, _φs_ and _ωt_ represents sector and time fixed effects and _ε_ st is the error term. From equation 1, the lags and leads to the event/policy of interest are defined as follows: The lags and leads are binary variables that indicate the status of the given state based on the number of periods away from the event of interest in the respective time period. Lags J and leads K are included to show the accumulative lags or leads beyond J and K periods.

We find that there was more growth in realized FDI in the more liberalized sectors, in line 23
components model is that any observed correlation between two measures of corruption, for example, is due to their common, but unobserved, signal of corruption. From this assumption it follows that data sources that are more correlated with each other provide more reliable information about corruption, and so receive greater weight. In past work, we have discussed in detail the merits of this approach--see particularly Kaufmann, Kraay and Mastruzzi (2006a, Section 3). We have also documented that, since the underlying data sources on average are quite correlated with each other, the choice of weights used to construct the aggregate indicator does not substantially affect the estimates of governance that we report (Kaufmann, Kraay and Mastruzzi 2006a, 2007a).<sup>7</sup> Here we briefly report some summary information on the weights for the 2007 indicators. Table 3 reports the weights assigned to each data source in each of the six governance indicators in 2007.<sup>8</sup> This table reports the weights that would be used in the case of a hypothetical country appearing in all of the available underlying data sources for each indicator. Because of gaps in the country coverage of all of our data sources, no single country appears in all data sources. Nevertheless, the information reported in Table 3 is informative about the _relative_ weights of the underlying indicators. The weights used to construct the aggregate governance indicators for any particular country are approximately equal to the _relative_ weights reported in Table 3 for the subset of indicators in which that country appears.<sup>9</sup>

> 7 It is also worth noting that a far more consequential weighting decision is whether to include a data source or not.  In the WGI we have for the most part opted to include as many data sources as possible, then allowing the data and aggregation procedure to select the weights.  For example, household-survey based data sources receive zero weight in the Transparency International Corruption Perceptions Index (TI-CPI) because the constructors of that measure have chosen to exclude all such data sources. Incidentally, while the WGI does include the disaggregate sources used by TI-CPI (and a number of other sources as well, which TI-CPI excludes) for the control of corruption aggregate component, the WGI does not include the TI-CPI itself, because it is also an aggregate poll of polls (in a similar vein to the recently launched Ibrahim Index of African Governance, which compiles individual sources). 8 A full version of this table reporting the weights for all years in Excel format is available for downloading on the Resources tab of <u>www.govindicators.org</u>

9 The precise expression for the weights used for each country can be found in Kaufmann, Kraay and Mastruzzi (2004, Equation (2)) and in the technical Appendix D to this paper. Information on the estimated variance of the error term of each source required to construct these weights can also be downloaded together with the weights reported in Table 3 for all periods at <u>www.govindicators.org.</u> 14
Figures in this section present both bound estimates and bootstrap point estimates, as well as a two standard deviations band (both plus and minus) around the point estimate. In discussing results, we will focus on this band, rather than the point or bound estimates.

# 3.1. Poverty dynamics

In this section, we analyze poverty transitions over adjacent survey rounds in the period 2004 -2022. We have four transition outcomes: chronic poverty, poverty exit, poverty entry, and not being poor in two rounds in a row. Chronic poverty is defined as being poor in both rounds. We discuss the unconditional probabilities of being in each state, which are equal to estimated population shares (see also Table C in the Appendix). In addition, we discuss conditional probabilities, such as the probability of poverty exit, given that the household was poor in the first round.

The conclusions in this section are based on three scenarios for setting the poverty line. These are:

1. The 40<sup>th</sup> percentile of the income distribution in 2004, at RM 527 per capita per month. This is the same line as used in Rongen et al. (2022).<sup>16</sup> We refer to this as the main scenario.

2. households in the 2019 sample, at RM 559 per capita per month. Lines are household-specific in the sense that they vary by location, household size and composition.

3. per month.

In all scenarios, the value of the poverty line is held constant in real terms across the entire study period. This may be regarded as a drawback of our analysis, since aspirations and consumption patterns will have changed between 2004 and 2022: what is considered poverty in 2022 may not have been seen as such in 2004. However, we have opted for absolute poverty lines in order to better track developments over time. By extension, we should note that our estimates do not correspond to the official Malaysian poverty figures, which are based on poverty lines that change between surveys. Hence, our findings may appear surprising given that the DOSM Poverty Report (DOSM, 2023) shows that poverty has risen recently, from 5.6 percent in 2019 to 6.2 percent in 2022. However, this may be

> 16 Note that all lines are given in 2016 Malaysian ringgit, so they represent the purchasing power of the ringgit in that year. The poverty line used in scenario 1 was selected because discussions about poverty and mobility in Malaysia often refer to the living standards of the bottom 40 percent of the income distribution. When our previous study commenced, the official Malaysian poverty line was very low and poverty virtually non-existent by those standards.

10
Policy Research Working Paper 9477

# **Abstract**

Many governments in developing countries have pursued policies targeting specific geographic areas over the past several decades. However, only a few have rigorously evaluated the causal impact of these interventions. This paper examines the effectiveness of a prominent place-based policy in India: the centrally sponsored New Industrial Policy for the state of Uttarakhand. Using georeferenced economic census data, the analysis applies a boundary discontinuity research design and zones in on the unique border between Uttarakhand and Uttar Pradesh, two states that were officially one before the implementation of the New Industrial Policy. The findings show that there was a significant and abrupt increase in employment at the town and village level when crossing the state border from Uttar Pradesh to Uttarakhand after the full implementation of the New Industrial Policy. The conclusion even holds for firms within the same sector. The increase is mainly due to larger firm sizes and expansions into new industries. A main component of the New Industrial Policy was excise tax incentives for certain industries. The paper finds that the increase in cross-border employment is higher for sectors receiving excise tax incentives than others. Additionally, exploring spillovers between industries, the paper shows that, controlling for the direct effects, the sectors with labor requirements similar to those receiving excise tax incentives also experience an increase in employment. Finally, the growth in the number of firms in Uttar Pradesh close to the border remained stable before and after the New Industrial Policy, which suggests the results are not fully driven by firms relocating from Uttar Pradesh to Uttarakhand.

This paper is a product of the Poverty and Equity Global Practice and the Office of the Chief Economist, South Asia Region. 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 yli7@worldbank.org and ssinharoy@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
United Nations Conference on Trade and<sup>Development (UNCTAD) (1996b).Regional</sup> Integration. Bilateral and Non-governmental Instruments.<sup>New Yorkand Geneva:United</sup> Nations, 1996. Vol. III of International<sup>InvestmentInstruments:A Compendium.</sup> United Nations Conference on Trade<sup>and Development (UNCTAD)(1 996c).World</sup> Investment Report 1996: Investment. Trade and<sup>InternationalPolicy Arrangements. New</sup> York and Geneva: United Nations, 1996.

United Nations Conference on Trade and<sup>Development (UNCTAD).WorldInvestment</sup> Report 1998: Trends and Determinants. New York<sup>andGeneva:UnitedNations,1998.</sup> United Nations Conference on Trade<sup>and Development(UNCTAD)(1 999a).Foreign</sup> Direct Investment and Development. New<sup>Yorkand Geneva:United Nations,1999.</sup> United Nations Conference on Trade and<sup>Development(UNCTAD)(1 999b).Investment-</sup> Related Trade Measures." New York and<sup>Geneva:United Nations,1999.</sup> United Nations Conference on Trade<sup>andDevelopment(UNCTAD). The</sup> Competitiveness Challenge: Transnational<sup>CorporationsandIndustrial Restructuringin</sup> Developing Countries. New York and Geneva: United<sup>Nations,2000.</sup> United Nations Conference on Trade and Development<sup>(UNCTAD).WorldInvestment</sup> Report 2001: Promoting Linkages. New York<sup>andGeneva:UnitedNations,2001.</sup> United Nations Conference on Trade and Development<sup>(UNCTAD) (2002a).</sup> "Experiences with Bilateral and Regional<sup>Approachesto MultilateralCooperation in the</sup> Area of Long-term Cross-border Investment,<sup>Particularly Foreign Direct Investment."</sup> Advance paper for the Expert Meeting on<sup>Experienceswith Bilateraland Regional</sup> Approaches to Multilateral Cooperation in the Area of<sup>Long-termCross-border</sup> Investment, Particularly Foreign Direct Investment,<sup>12-14June2002,Palaisdes Nations,</sup> Geneva. Geneva: UJNCTAD secretariat, May<sup>2002.-</sup> United Nations Conference on Trade and Development<sup>(UNCTAD)(2002b).Tradeand</sup> Development Report, 2002. New York and Geneva:<sup>United Nations,2002.</sup> United Nations Conference on Trade and<sup>Development (UNCTAD)(2002c).World</sup> Investment Report 2002: Transnational Corporations<sup>and Export Competitiveness. New</sup> York and Geneva: United Nations, 2002.

Urata, Shujiro. "The Formation of Regional<sup>InstitutionsinEast Asia." Asia & Europe:</sup> Beyond Competing Rezionalism. Eds.<sup>K.Fukasaku,F.KimuraandS. Urata.Paris:</sup> OECD. 1998. 13-44.

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9 Table 1: Total and agricultural GDP in Nicaragua 1990-1996.

||1990|1991|1992|1993|1994|1995|1996|
|---|---|---|---|---|---|---|---|
|Total GDPa|18156.2|18127.3|18202.2|18135.9|18742.8|19580.0|20648.2|
|Agricultural GDPa|4495.3|4420.3|4452.3|4533.4|5021.2|5299.6|5817.4|
|Share of Agricultural GDP (%)|25.0|24.3|<br>24.4|<br>25.0|26.8|<br>27.0|28.2|
|Exchange rate|140.92b|4.27|5.00|<br>5.62|6.72|<br>7.55|8.44|
|aMillions of 1980 Cordobas.||||||||

bA monetary reform in 1988 affected the local currency exchange rate compared to the $US. Source: For GDP, Banco Central de Nicaragua (1997).

For Exchange Rate, IMF (December, 1997) Scrutiny of the INTA's 1997 budget of $US 9.09 million<sup>7</sup> budget (INTA, 1997a) reveals that the total budget consisted of the following: 70.0% public funds, including a loan from the World Bank which accounted for 30 % of this total; 26% in foreign contributions; and 3.3% proceeds of the sale of products and services. The total number of producers reached by all three INTA's programs remained 21,500, so the average expected cost of extension provision per producer is about $US 423.8 Of the entire $US 9.09 million budget, about 12% is allocated to activities that provide extension services to more than half of the producers that are approached by INTA. However, ATPb is not the subject of the investigation of this paper. Scrutiny of the INTA's 1998 budget of $US 11.01 million<sup>9</sup> the total budget consisted of of the following: 74.6% public funds; 23% in foreign contributions; and 2.4% proceeds of the sale of products and services. It should be emphasized that INTA's budget does not include payments from producers

- 7 In June 1997 9.20 Cordobas = I$US.

> 8 For comparison, figures in Tacken (1997) for the private extension service in the Netherlands, suggest a range of extension provision cost per farmer between $US1360 and $US1590 for the period 1993 and 1996 (exchange rate of 1 .7Dfl per $US I was used). During this period, proceeds from producers range from 26% to 63% of the operating cost of the service. Although the cost of $423 in Nicaragua is lower than that in the Netherlands, it still should be viewed in the right perspective. The $423 value is the total budgeted cost, including the overhead of the coordinating units of INTA in Managua. This value is obviously higher than the direct cost of extension provision by ATPI or ATP2, as is calculated later in the paper.

9 In November 1997 9.85 Cordobas = I$US.
**Table A7: Crisis probability: Panel logit model with fixed effects**

|**Explanatory**<br>**variables**|**Debt crisis**|**Banking**<br>**crisis**|**Currency**<br>**crisis**|**Any crisis**|
|---|---|---|---|---|
|Inflation shock|2.670|-1.094|1.054|-0.178|
||[3.595]|[0.785]|[1.424]|[1.060]|
|Reaction shock|0.642|1.180|4.877***|3.197***|
||[3.464]|[1.041]|[1.619]|[1.218]|
|Real shock|-2.879**|-0.104|1.296**|-0.233|
||[1.299]|[0.318]|[0.571]|[0.387]|
|GDP growth (t-1)|-0.334**|-0.0260|-0.277***|-0.114**|
||[0.167]|[0.033]|[0.082]|[0.055]|
|Short-term debt (t-1)|0.078|-0.007|0.055|-0.013|
||[0.086]|[0.020]|[0.037]|[0.028]|
|Debt service (t-1)|-0.044|0.015|-0.033|-0.002|
||[0.037]|[0.010]|[0.022]|[0.015]|
|Reserves cover (t-1)|-1.402*|-0.069|-0.213*|-0.123|
||[0.754]|[0.077]|[0.130]|[0.096]|
|Change in government debt (t-1)|0.031||0.051|0.007|
||[0.031]||[0.034]|[0.014]|
|Change in private debt (t-1)||0.059*|-0.003|0.111**|
|||[0.030]|[0.064]|[0.047]|
|Change in government debt (t-1)|||0.008|-0.003|
|x Change in private debt (t-1)|||[0.005]|[0.003]|
|Concessional debt (t-1)|0.054|||-0.021|
||[0.154]|||[0.025]|
|Funding ratio (t-1)||0.003||0.015**|
|||[0.003]||[0.007]|
|Currency overvaluation (t-1)|||0.133***|0.089***|
||||[0.031]|[0.022]|
|Currency mismatch (t-1)|||-0.002|-0.000|
||||[0.001]|[0.001]|
|FDI (t-1)|||-0.019|0.009|
||||[0.047]|[0.031]|
|No. of observations|123|1,229|490|555|
|No. of countries|8|49|30|37|
|Estimated on annual data 1985-2018 e<br>*** p<0.01, ** p<0.05, * p<0.1|xcluding 2009. St|andard errors in b|rackets.||

49