rafmacalaba/data-use-mentions-extended huggingface dataset:
protected, pirates are more likely to switch to less-protected smaller ships. The convexity of the iso-crime curves hints that it is hard to eliminate the piracy problem using a single policy measure, and that a cost-effective policy would consist of a combination of both public and private protection instruments.

To estimate the cost-effective combination of policy interventions to reduce the number of attacks, we first estimate the costs of private and public security provision. The cost estimates of private maritime security contractors are available in the literature. While studies such as Besley, Fetzer and Mueller (2015) or World Bank (2013) give aggregate costs associated with Somali piracy, they do not provide a breakdown of these costs. The methods used are based on increased private costs borne by shipping companies and are also likely to ignore the contributions of taxpayers of each country involved in the naval coalitions. The One Earth Future Foundation, a not-for-profit organization, first provided the estimates of private maritime security in their Oceans Beyond Piracy (OBP) 2012 report, and they set the cost of private maritime security contractors at US$50,000 per trip through the troubled waters (Oceans Beyond Piracy 2013). In the later years, they provided more detailed estimates: at around US$33,250 per trip in 2013 (Oceans Beyond Piracy 2014), and US$22,975 per trip in 2014 (Oceans Beyond Piracy 2015). For our exercise, we use the year-specific estimates of costs after 2011, and the rough estimate of US$50,000 per trip for earlier years. To map the per trip cost to an increase in _k_<sup>_t_</sup> , we also need information on the traffic volume through the troubled waters. We again turn to the traffic statistics from the Suez canal for a traffic estimate in each year, and compute the costs of a one percentage point increase of protected vessels per year. As the Suez canal traffic underestimates overall traffic through the troubled waters off Somalia, our estimates of private team
Financial crises generally beget research analyzing the impact of the financial system on growth and welfare. In this regard, the recent financial crisis was no different. A number of papers, theoretical and empirical, have built on previous research studying on the one hand, the contributions of finance to growth and on the other, to volatility. The benefits of deep financial systems have long been recognized for consumption smoothing purposes and to support long term growth. More recently, research has focused on whether finance in all forms supports growth or whether some types of finance should be constrained (regulated) in its development because it is not productivity/ growth enhancing. Previous research has also addressed whether financial systems can increase volatility in an economy. New research reexamines this issue in the context of the changing global markets where credit creation has been linked closely to large cross border financial flows and financial markets have been progressively liberalized.

This paper builds on recent research that examines the link between financial system growth and economic growth and volatility. Most research indicates that financial system development enhances growth and allows consumption smoothing. Enhancing access to credit for firms and households is an important goal of policy around the world. The paper examines whether greater financial depth can increase output, or consumption volatility (instead of smoothing consumption per capita). Specifically, it uses panel data estimation covering 153 countries over the period 1964-2013, extending the period over which other papers have investigated this issue, using both 2SLS and GMM estimations and it is found that private credit is associated with lower volatility in GDP and consumption per capita, though the effect is nonlinear. At very high levels of financial system development as measured by the level of private sector credit to GDP or the value of financial assets to GDP, finance has a positive effect on economic volatility. This effect holds true even after accounting for trade and financial openness, institutions, the level of development among other things, and even after accounting for regional differences, including income levels. The paper also examines whether the speed of
initial and last year and multiplied by 100 to convert to percentage terms. The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against the residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment (logs) and (log of) GDP per capita. Informal competition variable equals the proportion of firms in the country (excluding own firm) that report competing against informal firms. 3) Group averages (mean values) of the residuals are shown. Close to the median value of the informal competition residuals is used to categorize the two groups in Figure 1A. The cut-off value used for the two groups in Figure 1B is close to the 75<sup>th</sup> percentile value of informal competition residuals.

34
# **4.   Seasonality in financial intermediation of MFIs: Descriptive evidence**

Household borrowing and its seasonal pattern are very likely a reflection of both demand for and supply of loans. If household borrowing is lowest during _Monga_ season, for example, even though the demand for loans for smoothing income and consumption is high, this is perhaps due in part to the supply of loans being lowest during _Monga_ season, compared to other seasons. So we ought to verify whether there is a seasonality of rural financial intermediation. We use the data on MFIs supported by a microfinance wholesale agency to verify this as they are readily available on a monthly basis than that of formal financial institutions. This also makes sense as MFIs are largely engaged in rural financial intermediation in Bangladesh. The question is, do MFIs suffer from the seasonality of agriculture?

Palli Karma-Sahayak Foundation (PKSF), an apex organization for Bangladeshi microfinance institutions (MFIs), finances various programs of 262 MFIs—called Partner Organizations (POs) of PKSF (PKSF 2011). These POs are scattered all over the country and are part of the 750 registered MFIs in Bangladesh.<sup>7</sup> The analysis of the 262 PKSF POs captures well the microfinance sector of Bangladesh. PKSF lends to POs at a subsidized rate of interest, while POs lend to household borrowers at market rates and repay PKSF from revenues generated.<sup>8</sup> We use monthly data from all five major projects of the 262 POs from FY2004-05 to FY201011 (see the discussion of program level data in Appendix A). The monthly data over the seven years help establish a seasonal trend of MFI portfolio. If there are any distinct seasonal patterns (persistent upward or downward movements) in indicators in most years, we can say that seasonality is at play for financial intermediation. We present the monthly distribution of loan disbursement, loans recovered, and other indicators of program performance.

> 7 In 2010, Bangladesh Microfinance Statistics reported around 750 MFIs.
have an average score of 3.1 on the index of executive constraints and an average degree of heterogeneity close to 55 out of 100. Countries with ongoing slumps score 1.54 in terms of executive constraints and have an average level of heterogeneity of about 77. The values refer to the smaller sample, but are not much different for the data underlying panel (b). This strongly suggests that it is the composition of the two groups that matters and not the fact of censoring. The whole point of duration analysis is that we can use censored observations without introducing bias. Note that if the slumps are defined in terms of GDP rather than GDP per capita, there are considerably fewer censored spells and our results hold in both samples without the censored data points.

– **Table 4** Robustness: heterogeneity, outliers and recurrent spells

||||_Current _|_modifcation_|||
|---|---|---|---|---|---|---|
||Country<br>RE<br>(1)|Without<br>Ex-Soviet<br>(2)|Without<br>Africa<br>(3)|Without<br>Censored<br>(4)|Single<br>Spells<br>(5)|Endogenous<br>Institutions<br>(6)|
||_P_|_anel (a) Stru_|_ctural break_|_s estimated _|_with size = _|_0.10_|
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.292***<br>(0.100)|-0.274***<br>(0.078)|-0.093*<br>(0.054)|-0.035<br>(0.051)|-0
model would not be expected to perform well in a sample of only 24 districts, we only report average evaluation statistics for the two types of direct estimates and the household level unit level models.

Results for the household-level model are reported with and without benchmarking. As above, the benchmarked point estimates were rescaled to match the direct estimates for each of the three regions in the population, for each sample drawn in each simulation, and the mean squared error were multiplied by the square of the same scaling factor. The direct estimates are computed in each simulation by calculating the weighted share of sample households that are poor in each district, using the weights calculated in step 3 above. We again compare the direct estimates obtained using the Horvitz-Thompson approximation with results from standard enumeration area clustering, which give identical point estimates but differ in their estimated variances.

Table 17 displays the results, which confirm that the household model continues to perform well when using repeated samples from the three selected regions. The household model in these simulations demonstrate a moderate amount of bias, with a mean of 42.4 percent prior to benchmarking as compared with 46.5 percent in the population. This bias results from the distribution of the residuals deviating substantially from normality at the tails.<sup>42</sup> The estimates produced by the household level model, however, are far more precise than the HorvitzThompson direct estimates, with average mean squared errors that are about a quarter as large and average CVs about half the size. The household model estimates are only moderately more precise than the standard direct estimates, reflecting a high degree of correlation in household welfare across EAs within district. The standard direct estimates ignore this correlation and therefore greatly underestimate uncertainty, which are reflected in a coverage rate of 48.7 percent. In contrast, the coverage rate of the benchmarked estimates from the household model is 68 percent, which is moderately below the coverage rate for all of Tanzania using the HBS sample reported in Table 8 (74.8%
||**202**<br>**0**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Educatio**<br>**n**|0.00|0.00|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**Health**|0.00|0.00|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|**Other**<br>**Services**|0.00|0.01|0.03|0.04|0.04|0.04|0.04|0.04|0.04|0.04|0.04|

_Table B. 2. Annual spending associated with investments and households’ consumption under the ISUI1 Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**
Instituto Nacional de Estadística, Geografía e Informática (INEGI) (2009). _Estados Unidos Mexicanos, Censo Agropecuario 2007, VIII Censo Agrícola, Ganadero y Forestal_ , Aguascalientes. INEGI, México.

Kimanzu, N., Schulte-Herbrüggen, B., Clendenning, J., Chiwona-Karltun, L., Krogseng, K., & Petrokofsky, G. (2021). What Is the Evidence Base Linking Gender with Access to Forests and Use of Forest Resources for Food Security in Low-and Middle-Income Countries? A Systematic Evidence Map. _Forests_ , _12_ (8), 1096.

Larson, A. M., Solis, D., Duchelle, A. E., Atmadja, S., Resosudarmo, I. A. P., Dokken, T., & Komalasari, M. (2018). Gender lessons for climate initiatives: A comparative study of REDD+ impacts on subjective wellbeing. _World Development_ , 108, 86-102.

Madrid, L., Núñez, J. M., Quiroz, G. and Rodríguez Y. (2009). La propiedad social forestal en México. _Investigación Ambiental, INE_ , 1 (2): 179-196 Marcos Morezuelas, P. (2021) _Género, bosques y cambio climático_ . Interamerican Development Bank. Washington DC, US.

Méndez-López, M.E., E. García-Frapolli, I. Ruiz-Mallén, L. Porter-Bolland, and V. Reyes-Garcia. (2015). From Paper to Forest: Local Motives for Participation in Different Conservation Initiatives. Case Studies in Southeastern Mexico. _Environmental Management_ 56: 695.

Sanders, M., Snijders, V., & Hallsworth, M. (2018). Behavioural science and policy: Where are we now and where are we going? _Behavioural Public Policy_ , 2(2), Shafir, E., ed. (2013) _The behavioral foundations of public policy._ Princeton University Press.

World Bank Group (2014). World Development Report 2015: Mind, Society, and Behavior.
0 1 2 3<br><!-- End of picture text --> Sources: Bank for International Settlements; Haver Analytics; International Financial Statistics, International Monetary Fund; World Development Indicators, World Bank.

Notes. The red lines show sample medians while the blue lines show the corresponding upper and lower quartiles. A credit boom is defined as an episode during which the cyclical component of the nonfinancial private sector credit‐to‐GDP ratio (using a Hodrick‐Prescott filter) is larger than 1.65 times its standard deviation in at least one year. The episode starts when the cyclical component first exceeds one standard deviation and ends in a peak year (“0”) when the nonfinancial private sector credit‐to‐GDP ratio declines in the following year. A deleveraging episode is defined correspondingly. To address the end‐point problem of a Hodrick‐Prescott filter, the dataset is expanded by setting the data for 2016‐18 to be equal to the data in 2015. Sample is for available data over 1980‐2015 for 55 EMDEs. 2015 data are not available for Gabon, Nigeria, Senegal, and Venezuela, RB. Data are not available for Argentina until 1994, Brazil until 1993, China until 1984, Hungary until 1989, Poland until 1992, Russia until 1995, Saudi Arabia until 1993, and Turkey until 1986. Please see the main text of World Bank (2016b) for a detailed description of the sample.

A.B. The cyclical component of investment in percent of GDP (derived by Hodrick‐Prescott filter). The yellow dashed line is the median annual investment growth rate of the six EMDEs (China, Indonesia, Malaysia, Mongolia, the Philippines, and Thailand) that were affected by the 1997 Asian Financial Crisis (year 1997 is set to be t=0). The light blue dashed line for 2012‐15 shows the sample median for the corresponding period.

C.D. The cyclical component of private consumption in percent
# **Annex I. The OECD approach to TFP measurement presented in Brandt et al. (2017)**

Brandt et al. (2017) highlight two broad approaches to TFP measurement depending upon whether estimates exist of the user cost of capital u (also termed the rental value of capital). The OECD productivity manual (OECD 2001) employs the approach of Jorgensen (1963) to measure this user cost in discrete time, u�t��p�t��r�t��d�t����p�t��p�t�1�� Here p is the price of a capital asset, r is the return on capital and d is the rate of depreciation of the asset, and the final term measures capital gains.

While the OECD productivity database has detailed estimates of u for most OECD countries and selected non-members, the estimates do not exist for the vast majority of developing countries. In Brand et al. (2017) the authors fall back to an assumption of constant returns to scale in production in order to measure TFP growth for the Russian Federation, Mexico, Chile and South Africa – countries where estimates of the user cost of capital are not available.

In countries where user cost estimates exist for fixed capital, the OECD approach is to measure TFP growth by subtracting the weighted average of the growth rates of factors of production from the growth rate of GDP; in this instance the weights are the factor costs, including the user cost of capital, normalized to sum to 1. Where these estimates do not exist then the weights applied to the growth rates of factors of production are simply the factor shares in value added – wages and profits derived from fixed capital, with the latter measured as a residual. These sum to 1 by assumption.

28
ton)|5|2.0|3|4.5|6.5|1.48|n.a.|
|Average liquid bulk handling charge<br>(USD/ton)|n.a.|0.5|1|3.5|2.68|n.a.|1|

_Source:_ Mundy and Penfold 2009. Derived from AICD ports database, http://www.infrastructureafrica.org/aicd/tools/data. n.a. = Not available Dar es Salaam’s performance indicators compare well to those of other eastern and southern Africa ports. It has a low container dwell time of seven days, low truck processing time of 5 hours, and high crane productivity of 20 containers or tons per crane hour. This strong performance can be explained by Dar es Salaam’s sizable terminal operations, specialized container handling equipment, and adoption of a container terminal concession to incorporate private management of operations. As a result, the port leads Sub-Saharan Africa in container handling productivity and ranks among the top in general cargo handling.

# **Challenges**

Despite its achievements in productivity, Dar es Salaam suffers from significant capacity constraints and congestion following double digit growth in the container sector during the 2000s. Its demand to capacity ratio is 140 percent in the container sector (demand of 350,000 TEU/year and capacity of 250,000 TEU/year) and 93 percent in the general cargo sector (demand of 3.8 million tons/year and capacity of 4.1 million tons/year). These are the highest ratios in Africa after Mombasa. In fact, Dar es Salaam took responsibility for transshipments that Mombasa could not handle due to severe capacity constraints and operational inefficiencies. As a point of reference, once a port’s capacity ratio surpasses the 80 percent mark, congestion becomes a serious issue that reduces the effectiveness of the port, and Dar es Salaam is already well past
production and turn around less developed areas. The New Industrial Policy of Uttarakhand could be a similar case in point.

However, it is challenging to separately identify agglomeration economies from the general impact of place-based policies when long-term data with sufficient post-intervention observations are not available. Both channels can lead to concentration of firms in the same sector in the targeted area. We circumvent this challenge by assessing spillovers between industries. Following Ellison, Glaeser and Kerr (2010), we construct the coagglomeration index measuring the overall tendency that two industries locate in proximity, and indices measuring two Marshallian mechanisms for spillovers, namely, input-output linkages and labor pooling, that can give rise to agglomeration across industries. We show that the industrial tendency for colocation leads to employment gains in sectors that were not directly incentivized by the New Industrial Policy. We find that controlling for the direct effects, sectors with labor requirements similar to those receiving excise tax incentives experience an additional increase in employment as one crosses the border from Uttar Pradesh to Uttarakhand, while sectors sharing a labor pool with those not receiving incentives see a decline in employment.

A legitimate concern regarding the significant job growth estimated in Uttarakhand is the potential displacement effect on the Uttar Pradesh side. The Economic Census data are not well suited to directly study firm closure and relocation, the data are cross sectional in nature and do not report the age of firms. We partly address this concern by assessing the growth in the number of firms using all three rounds of Economic Censuses. We show that the growth in firm number stays relatively stable in the bordering places in Uttar Pradesh in the periods before (1990-1998) and (1998-2013) after to the New Industrial Policy. In contrast, we find that the growth in the number of firms increased significantly for the comparable places on the Uttarakhand side of the border. While we cannot fully rule out the displacement effect completely, we take these results as evidence that the significant employment growth due to the policy is not fully driven by the relocation of firms from Uttar
city level approach in Ahmedabad, because many parts of the envisaged program of investment and reform have already been successfully piloted here. For the purpose of data gathering and analysis, we have taken the Ahmedabad Municipal Corporation (AMC) as extended in 2006 to represent the city of Ahmedabad. This area captures virtually all of the built up area in the agglomeration.

Consultations as the work progresses have been an integral part of the study. Likewise, the study includes documentation of the methodology used in preparing the assessment to illustrate how other cities wishing to take a similar approach might proceed.(Annex 5).

# **II. Introduction and Background on Factors Driving Demand for Housing in Ahmedabad**

Ahmedabad is a fast growing city in one of India’s most dynamic and rapidly urbanizing states. Table II.1 shows that Gujarat, at 42.6 percent is well over the all-India urbanization average of 31.2 percent as measured in the 2011 Census. Likewise the speed at which urbanization in Gujarat is taking place is considerably more rapid than in all of India, which changed less than 2 percentage points over ten years. The average annual growth rate of urban population in Gujarat

> 2 Throughout this study, when we refer to slums, we are referring to any underserviced substandard informal housing area. This may include chawls/challis that have conditions similar to slums, even through ownership arrangements and administrative treatment of slums and challis may differ. Our emphasis is on providing basic neighborhood improvements to areas that do not have them. As such, an underserviced chawl is not that different from a slum. There is of course considerably variability among different slums and challi areas, which must be taken into account when detailed investment plans are drawn up.