rafmacalaba/datause-encoder-data huggingface dataset:
-2-

- Freight cars are old, badly maintained and their braking systems need to be modernized;

- Communications are very primitive and prevent management from acting promptly throughout the system;

- Signalling, in arnd oetween stations on the network, is also unreliable and very often constrains capacity and safety.

- b. In the Marketing and Sales area:

- Marketing railway services is an activity which is often not considered a priority and is, in general, confused with sales;

- Sales is an activity which consists in waiting for the -

- client to come to the railway rather than the opposite.

- Railway traffic costing is very aggregate and, therefore, of limited use, resulting in ir.adequate knowledge about the costs incurred by commodity and by type of service;

- 3 Freight traffic is often "less than carload" (LCL) and container traffic is not encouraged.

- c. In the Financial and Accounting area:

- the general accounting system is always late in producing financial statements. Moreover, the cost accounting system doesn't function and does not provide management with t::mely cost data for decision making or the costing department with basic inputs;

- The working capital (cash on hand) is very low and makes the use of revolving funds a constant necessity. This increases short-term debt to unacceptable levels;

- The long term debt is high, due to the successive devaluation of the national currency since most of the loans were incurred; debt service consumes most of the net income.

- d. In the Operations and Maintenance area:

- Operations management is weak due to poor motive power and rolling stock management;

- Lack of real time communications and management information systems prevent immediate corrective actions in operations;

- Stores management is inefficient and most of the times produces bottlenecks in the maintenance schedules, increasing down times and affecting availability;
Table D9: Ethnicity randomization checks, before 2011, 2

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kisii|Judge plur. Luhya|Judge plur. Luo|
|Pla. plur. Kisii|-0.0345<sup>_⇤⇤_</sup><br>(0.0164)|||
|Def. plur. Kisii|-0.0140<br>(0.0159)|||
|Pla. plur. Luhya||0.0141<br>(0.0285)||
|Def. plur. Luhya||-0.00399<br>(0.0188)||
|Pla. plur. Luo|||0.0197<br>(0.0268)|
|Def. plur. Luo|||-0.00110<br>(0.0267)|
|DV mean|0.0802|0.216|0.221|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

45
The local allocation tax aims to equalize the fiscal capacities of local governments by supplementing the shortage of their tax revenues. This tax enables local governments to provide public services aL **t** he standard level prescribed by the central government. When a local government does not maintain the level prescribed for public services, or has paid an excessive amount for the services, the central government may reduce the local allocation tax for that local government.

Compared to other transfer schemes, the local allocation tax is the only equalization scheme in Japan. klt is allocated both to prefectures and municipalities in the same way. Table 2.6 presents the distribution of the local allocation tax to prefectures and municipalities. The amount allocated to prefectures is slightly larger than the amount allocated to municipalities.

Table 2.6. Distribution of Local Allocation Tax among Prefectures (Per Capita base, in Yen), 1989

||Index of<br>Fiscal<br>Capacity|Per Capita<br>Tax Revenue<br>(A)|Per Capita<br>Allocation Tax<br>(B)|(A)+(B)|
|---|---|---|---|---|
|High-capacity group|||||
|Tokyo|1.527|324,898|...|324,898|
|Osaka|1.102|145,185|...|145,185|
|Aichi|1.075|143,109|...|143,109|
|Kanagawa|1.051|115,614|...|115,614|
|Low-capacity group|||||
|Kochi|0.224|58,973|192,572|253,545|
|Shimane|0.227|66,898|205,484|272,379|
|Aomori|0.244|54,837|152,677|207,514|
|Akita|0.250|59,201|160,437|219,638|

Source: Yonehara (1993).

The local allocation tax is distributed mainly (94 percent) as an ordinary allocation tax and partly (6 percent) as a special allocation tax. The ordinary allocation tax is paid to local governments whose basic fiscal needs exceed their basic fiscal revenue. Generally, local governments located within large metropolilAn areas have strong fiscal capacity compared with those in rural areas. Among the 47 prefectures, in fiscal year 1989, Tokyo had the highest index of fiscal capacity followed by Osaka, Aichi, and Kangawa prefectures. The low-capacity groups are Kochi, Shimane, Aomori, and Akita prefectures (in ascenc[ing order of index fiscal capacity). The strong prefectures receive no allocation tax, while the low-capacity prefectures receive a large per-capita allocation tax. In Table 2.6, the top four prefectures and the bottom four prefectures in the ranking of fiscal capacity index are listed for comparison.

# Mathematically, the formula to calculate the local allocation tax transfer to a locality is:

## Transfer = Basic fiscal needs (N) - Basic fiscal revenues (R).

23
<!-- Start of picture text --> 30°000<br>25°000<br>20°000<br>€<br>&<br>FSS5 15000<br>£3<br>wax 10000<br>$000<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>@ Mangoes @ Beans, green O Tomatoes O Others fruit & vegetables<br><!-- End of picture text -->
<mark>But abstracting from these more narrow debates, the general concept of transaction costs is helpful in describing how new technologies impact economic development. A simple thought experiment will help. If we could measure the costs of all transactions in an economy, we could arrange them from highest to lowest as in Figure 1. New technology, especially the internet, facilitates information exchange and other forms of communication and thereby lowers the curve. In reality we do not know the shape of the curve and the drop in transaction costs will not be uniform as implied in the chart, leading to a reordering of transactions by cost. But most generally, we can expect three effects.</mark>

**Figure 1: The effects of falling transaction costs**

<!-- Start of picture text --> Pre‐internet<br>Post‐<br>internet<br>Inclusion Efficiency Innovation<br>Transactions, arranged from high to low cost<br>Transaction costs<br><!-- End of picture text --> Source: World Bank (2016).

<mark>On the far left of the curve there were some transactions whose cost was so high that they simply did not take place. In the simplest case, two parties to a potential transaction simply did not know about each other—for example, a producer of a specialized product for which there might be high demand in a distant place. Or someone faced insurmountable barriers to participating in a market. Examples are persons with disabilities who are mobility-constrained or prevented from easily communicating with others; or women who cannot enter the labor market because child rearing or cultural norms prevent them from working outside the home. In other cases, the two</mark> 5
<!-- Start of picture text --> 100<br>Biomass<br>80<br>60<br>40<br>20<br>Electricity<br>Kerosene<br>0<br>0 50 100<br>Household monthly income (Tk. x 100)<br><!-- End of picture text --> <!-- Start of picture text --> Source: BIDS Survey (2004).<br><!-- End of picture text -->

**Figure 3: Household energy consumption share by income**

The results actually confirm much of the household energy research that was reviewed earlier in this paper. When income is low, biomass dominates the energy use, with some kerosene and very little electricity (Figure 3). As household’s income goes up, share of biomass expenditure goes down and eventually becomes steady. The share of kerosene goes down too with increasing income. Finally, share of electricity rises from almost zero percent at lowest income to above 20 percent for the highest income households. This confirms the positive relationship between electricity and income. However, it is obvious that biomass remains the most dominant energy source for all income levels in rural Bangladesh.

# **6. Estimating energy poverty**

The results shown in the earlier sections clearly underscore the role of household energy consumption in a household’s overall welfare. They also establish the fact that energy, besides being a determinant of various household welfare indicators, can itself be a measure of household welfare, just like consumption expenditure or income. Thus, a good understanding of a household’s basic 18
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the Country level. All models are estimated via three methods: ordinary least squares (OLS) model, robust regression model and instrumental variable model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), status dummies (commercial bank, credit union / coop, NGO, non-bank financial institution, omitting rural bank), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. The details of instrumental variable model are shown as below.

*1 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Independence of Supervisory Authority - Political respectively

*2 1st excluded IV is Supervisory Forbearance Discretion, and 2nd is Percent of 10 Biggest Banks Rated by Domestic Credit Rating Agencies respectively

# Panel E: Formation/operation of non-regulated microcredit institutions <u>(0-4)</u>

||||<br>Dep var:|<br>FinancialSelf_Su|<br>fficiency|||
|---|---|---|---|---|---|---|---|
|_Microscope variable_|Robust Reg<br>w/o Lending<br>methodology<br>dummy|OLS<br>Dropping top<br>5% of dep var<br>w/o Lending<br>methodology<br>dummy|Robust Reg|OLS<br>Dropping top<br>5% of dep<br>var|OLS w/ the<br>same<br>observations<br>to IV|Instrumental<br>Variable 1*<sup>1</sup>|<br>Instrumental<br>Variable 2*<sup>2</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Formation/operation of non-regulated microcredit institutions (0-4)|-0.003|-0.0018|0.0023|0.0002|0.024|0.1580**|0.2112*|
||[0.817]|[0.937]|[0.860]|[0.993]|[0.364]|[0.023]|[0.098]|
|Observations|751|713|636|611|554|554|532|
|Number of countries|41|40|39|39|29|29|28|
|R-squared|0.207|0.252|0.229|0.263|0.255|0.176|0.046|
|Adjusted R-squared|0.189|0.234|0.205|0.240|0.229|0.147|0.010|
|F statistic for weak identification||||||1.893|1.556|
|Shea partial R-squared||||||0.070|0.076|
|Hansen J statistics||||||0.152|0.894|

40
# Introduction

The United Nations High Commissioner for Refugees (UNHCR) estimated that the global number of forcibly displaced persons (FDPs) in the world surpassed 84 million in 2021, up from around 40 million in 2010 and accounting for over 1 percent of the global population.<sup>3</sup> This sharp growth in displaced people during the past decade can be largely attributed to the Syrian conflict that started in 2011, the displacement of the Rohingya people since 2017, and the intensification of several conflicts in Sub-Saharan Africa, particularly along the Sahel region. These numbers are unprecedented in the history of displacement when recording started with the establishment of the UNHCR in 1950 and the signature of the Geneva Refugee Convention in 1951.

FDPs are not a homogenous group. They include internally displaced persons (IDPs – defined by the UNHCR as citizens of a country that have been displaced within the boundaries of their own country due to conflict or security reasons), asylum seekers (displaced people outside their own countries who formally ask for asylum), refugees (people who have obtained asylum in the host country), and other displaced groups that defy simple categorizations. These categories of people fall under the mandate of the UNHCR because they have been displaced “forcibly” due to conflict or violence and because they are in need of international protection. They exclude other categories of displaced people who were not forced to move because of conflict or violence, such as economic migrants and victims of natural or environmental disasters.<sup>4</sup> Of course, many people cannot be simply categorized in these groups and this makes statistics on FDPs gross estimates, but the growth and relevance of these numbers are undisputed.

The growth in the number of FDPs poses a challenge to the measurement of global and national poverty. Those who are forcibly displaced and in need of international protection tend to be persons who have lost their assets, financial resources, and social networks. They are typically very poor with no obvious path out of poverty. For refugees, their number vanishes from poverty statistics of their own country because they are no longer counted in the place of origin. Both IDPs and refugees are also not properly accounted for in the country in which they reside. Their numbers – even though high in absolute terms – are often low relative to the non-displaced population (with some exceptions like Lebanon and South Sudan). Hence, they do not explicitly show up in official statistics. Even if – as in some but not all countries – their locations are appropriately included in the sampling frame, they are unlikely to be sampled due to their small proportion relative to the population and high clustering in specific locations.

> 3 https://www.unhcr.org/refugee-statistics/ 4 Note that the IASC definition of IDPs explicitly includes those fleeing from disasters.

2
households falling into poverty. This procedure makes light demands on data and can be straightforwardly applied to synthetic panel data.

We then turn in Section V to a discussion of mobility between the three population segments that derive from this analysis: the poor, the vulnerable, and the middle class, and we produce some basic profiles of the population in different transition categories. We end in Section VI with concluding remarks.

# **II. Poverty Trends and Data**

Steady GDP per capita growth has helped drive down poverty rates in India in the late 2000s.<sup>6</sup> In particular, GDP per capita increased by almost half (47 percent) during the period 2004-2009 (World Bank, 2015), and poverty decreased by 21 percent over the same period. The country’s continued economic growth resulted in a further increase of GDP per capita over the subsequent two years, by almost one-fifth (19 percent) in 2011/12. While this robust growth rate should be expected to bring more poverty reduction, the contemporaneous fall in poverty rates turned out to be much larger than expected. To quite a few observers, the fall in poverty has been startling.<sup>7</sup> Figure 1 plots the annual growth rate of GDP per capita (left axis) and the headcount poverty rate (right axis) between 2004 and 2012. Since a large share of the labor force is employed in agriculture, the figure also displays the annual growth rate of the value added per worker of the agricultural sector. The disconnect between GDP per capita growth rates and poverty reduction is

> 6 See, e.g., Datt and Ravallion (2011) and Ravallion (2011) for comprehensive discussions on economic growth and poverty in India for earlier periods.

> 7 For example, Dutta and Panda (2014) observe that there is much controversy around the (arbitrariness) of the specification of the poverty line. Saxena (2013) points out a couple of inconsistencies such as the share of the population that need food subsidies or the slum population in major cities are much larger than the reported poverty rate, and that the specified poverty lines may be too low and may potentially be distorted due to political motives. In addition to these last two issues, Himanshu (cited in Rao, 2013) voices the concern that imputed spending values for certain social transfer programs may not be calculated correctly. See also the BBC (Limaye, 2013), New York Times (Gupta, 2013), and Washington Post (Lakshmi, 2012) for related discussion on the debates on poverty in this period.

6
- 38 -

# **VI. CONCLUSION**

Infrastructure is recognized as the engine for economic growth. Public resources are not sufficient for infrastructure development in developing countries. It is still arguable whether the public resources are used efficiently, particularly where the public procurement systems are fragile.

The paper, using auction data on official development assistance for infrastructure projects, focuses on efficiency in public procurement. In theory, competition is a key to increasing auction efficiency and thus achieving the lowest possible procurement costs. Intensified competition is also instrumental in preventing collusion and corruption and promoting local business development.

A main question is how much competition could contribute to reducing infrastructure investment costs. This is an attempt to quantify the impact of a particular form of public governance improvement on the economy. The paper examines how many bidders are required toward procurement efficiency. Three infrastructure sectors are analyzed: roads, water and sewage, and electricity.

The current level of in the infrastructure procurement process is not so high, except for road procurements. The estimated equilibrium bid functions generally support the view that by encouraging more competition a lot of public resources and aid money could be saved. It is shown that in road and water projects, at least seven firms are required for an auction to be competitive enough. The required level of competition for electricity projects may be much lower, perhaps at three, but this is less conclusive.

Unlike the existing literature, the current paper highlights the importance of procurement efficiency to contain infrastructure procurement prices, which have rarely been questioned in the past. By increasing competition to the optimal levels, the predicted financial needs could
labor. The endowment of arable land comes from the World Bank’s World Tables and it is as hectares of arable land.

# **IPRs**

Data on intellectual property rights protection come from Ginarte and Park [28]. The measure is an index of patent rights at the country level, which is based on the following categories: extent of patent coverage, membership in international patent agreements, provisions for loss of protection, enforcement mechanisms, and duration of protection. Each of these categories is scored from 0 to 1. The un-weighted sum of these five values constitutes the overall value of the IPRs index.

# **Net exports of computers**

Bilateral data on imports and exports of computers come from Feenstra et al.[12]. The data are available at the 4-digit level of the Standard International Trade Classification, Revision 2. To measure net exports of computers for the global industry, we consider the following categories, 7521, 7522, 7523, and 7528, which are the same as the ones employed by Caselli and Coleman[6] to study the determinants of cross-country technology diffusion. Code 7521 refers to _Analogue and hybrid data processing machines_ ; code 7522 refers to _Complete digital data processing machines, comprising in the same housing the central processing unit and one output unit_ ; code 7523 refers to Complete _digital central processing units, digital processors consisting of arithmetical, logical, and control elements_ ; codes 7528 refers to _Off-line data processing equipment, n.e.s._ To measure net exports of the computers in the final good industry we restrict our analysis to the 7521 and 7522 codes.

# **Wages**

Data on manufacturing wages come from Nicita and Olarreaga[26]. The wage variable includes all payments in cash or in kind paid to employees during the reference year in relation to work done for the establishment. Payments include direct wages and salaries, remuneration for time not worked, bonuses and gratuities, housing allowances and family allowances paid directly by the employer, and payments in kind. Excluded are employer social-security contributions on behalf of their employees, pension and insurance schemes, as 18
# **Table 11 - The Gradient of Political Power**

The table examines whether potentially stronger measures of political power result in higher political lending to connected firms. A unit of observation is a loan at the firm-bank-quarter level. The dependent variables in Panels A and B are loan terms at origination and ex post performance respectively. _MoreThanOne_ is a dummy variable that equals one if the state where the firm is located has more than one senator chairing committees. The state-level GDP growth and unemployment rate are included as controls in all specifications. Please refer to Appendix A for variable definitions. All regressions include firm*bank and bank*quarter fixed-effects. Standard errors are reported in parentheses and are clustered at the state level. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

||LoanV|olume|LoanS|pread|Mat|urity|D(Col|lateral)|D(D|efault)|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A More than One Ch**|**airman**||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman|0.742**|0.701**|-0.018|-0.019|0.105***|0.089**|-0.022***|-0.019***|0.006***|0.007***|
||(0.294)|(0.296)|(0.011)|(0.012)|(0.037)|(0.034)|(0.007)|(0.006)|(0.002)|(0.002)|
|Chairman*MoreThanOne|1.328*|1.237*|-0.043|-0.041|0.090|0.154|0.007|0.009|0.022**|0.020**|
||(0.697)|(0.724)|(0.055)|(0.056)|(0.234)|(0.202)|(0.028)|(0.025)|(0.008)|(0.009)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|0.021|0.022|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|480,354|480,354|
|**Panel B Alternative Defini**|**tion of Core**|**Committees**|||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman|0.845***|0.803**|-0.022*|-0.022*|0.108***|0.093***|-0.021***|-0.018***|0.007***|0.007***|
||(0.291)|(0.293)|(0.011)|(0.011)|(0.037)|(0.033)|(0.007)|(0.006)|(0.002)|(0.002)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|0.021|0.022|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|480,354|480,354|

45