rafmacalaba/data-use-mentions-extended huggingface dataset:
decrease in the share of landless from 49% to 34%, suggesting some ‘upward’ movement. For dynasties that split since then (84% of the sample or 80,527 households), the number of landless, owner-cum tenants, and bargadars increased slightly (by 2,665, 1,013, and 777), pattadars more than doubled (2,584 to 6,817), and the number of pure owners dropped correspondingly. Compared to other post-reform settings such as Taiwan, China or Korea over a comparable period, movement in the 30 years post-land reform seems limited.

# **3.2 Plot-level data for the owner-cum tenant sample**

Detailed information on plot characteristics is available for the sample of owner-cum tenants as reported in table 3. Plots measure 0.4 ac. on average (0.36 _vs._ 0.46 ac. for owned and _barga_ , respectively) with _barga_ plots about 170 m more distant from the cultivator’s home. Permanent plot characteristics such as soil type, color, or condition are nearly indistinguishable between owned and _barga_ plots. Although magnitudes are small, _barga_ plots are more likely affected by salinity and drainage problems caused by inadequate management over a long time, possibly a result of investment disincentives: 46.6% (50.6%) of own compared to 45.2% (52%) for barga plots have no (moderate) salinity problems. Similarly, 35.4% (14.1%) of own plots are “easy (difficult) to drain” compared to 34% (15.2%) for _barga_ plots.

Productivity per acre on tenanted plots is significantly lower than on owned ones -by some 24% for gross and net revenue excluding family labor (Rs 16,693 _vs._ Rs. 22,062 and Rs 11,051 _vs._ Rs 14,565, respectively). Interestingly, and consistent with predictions, this difference in net revenues is
**Table 6. Individual-level regressions of norm items within the HH roles index predicting women’s work outcomes**

||**Full**|**Sample**|**Fema**|**les Only**|**Mal**|**es Only**|
|---|---|---|---|---|---|---|
||(1)<br>Paid work|(2)<br>Paid work|(3)<br>Paid work|(4)<br>Paid work|(5)<br>Paid work|(6)<br>Paid work|
|PNB – Female caregiver|-0.13*||-0.17**||-0.07||
||(0.06)||(0.06)||(0.10)||
|SNE – Female caregiver|-0.07||-0.09||-0.05||
||(0.10)||(0.12)||(0.12)||
|PNB –Male breadwinner||-0.18**||-0.15||-0.23**|
|||(0.08)||(0.09)||(0.09)|
|SNE – Male breadwinner||-0.07||-0.18*||0.08|
|||(0.10)||(0.10)||(0.12)|
|Constant|-0.06|-0.03|-0.04|-0.00|-0.11|-0.09|
||(0.26)|
looking at labor supply for public works and remains scant and inconclusive: For the manufacturing sector, Ghani _et al._ (2014) find that female reservation triggered an expansion of the number of informal womanowned establishments without leading to higher female employment. In Uttar Pradesh, Bose and Das (2018) find that having a female leader increased female interest in public works as measured by the number of job cards issued and demand for work under the National Rural Employment Guarantee Scheme (NREGS) but failed to affect actual employment. In Andhra Pradesh, Afridi _et al._ (2017) find that in village councils where leadership was reserved for women, NREGS implementation was less efficient and leakage higher than in those where this was not the case, a finding attributed to women leaders’ limited formal education and experience.

In this paper, we use individual data to assess the short- and medium-term impact of female reservation on female labor supply, identify mechanisms that might underpin such changes and, for a subsample for which such data were collected, explore impacts on female empowerment. Identification relies on the fact that, in each period, villages to be reserved were randomly chosen. We analyze individuals’ outcomes by matching data on some 66,362 individuals in 23,350 households over India’s 12 main states from the Rural Economics and Demographic Survey (REDS) to villages’ reservation history. As our data were collected when NREGS was active, we assess if exogenous exposure to female leadership improved women’s ability to take advantage of this program, potentially catalyzing broader changes. By providing estimates of the impact of female reservation in current and the previous election periods, we can assess longer-term effects on labor force participation, agency, demand for work, and involvement in household decision-making.

Three findings stand out: First, contemporaneous reservation affected local governance as measured by the quality of NREGS implementation but had no measurable impact on female labor supply. Second, beyond the reserved period, female leadership reservation had sustained effects on female labor
*<br>|
|||**H**|**J**|**W**|**No**|||**H**|**J**|**W**|**No**|
||**H**|**21.1**|2.0|1.2|||**H**|**21.3**|3.2|1.6||
|**Husband**|**J**||**0.9**|1.3|4.9|**Husband**|**J**||**1.2**|2.1|5.5|
||**W**|||**19.6**|||**W**|||**20.2**||
||**No**||2.3||**46.7**||**No**||2.1||**42.9**|
|**Share agr**|**ee: 8**|**8.3%, S**|**hare di**|**sagree:**|**11.7%**|**Share ag**|**ree: 8**|**5.6%, S**|**hare di**|**sagree:**|**14.4%**|

<u>Notes: The findings are based on 1,719 parcel observations and the responses that were provided by 931 couples, about</u> 55.5 percent of which had more than
sup> VV<sup>�</sup> and intermediate share matrix . Equation (7) allows quantifying the impact of over-time changes in pp and on BB sectoral allocation. However, the unavailability of these data for Central American economies hinders pp pp pp MM BB MM this investigation.

I source the required data from multiple sources. However, the raw data appear spurious for some country-years. Perhaps, for this reason, the literature has not undertaken a long-term sectoral analysis of the Central American economies, especially when it comes to labor allocation. I make several adjustments to the raw data before using them. The data appendix contains the details of these adjustments. Still, I note the nature of adjustments briefly as I provide a summary of each series. **Employment shares (** p **):** The employment data are taken from the International Labor Organization’s (ILO) Key Indicators of the Labor Market database (ILO, 2018) that reports LL employment by sector. The database classifies total employment into six sectors – agriculture, manufacturing, construction, mining and utilities, trade and transportation (includes personal &

> 3 Sinha (2019) contains the detailed derivations of the accounting equations.

8
attempt at a meta-analysis was made, even though with the availability of more evidence we hope to be able to do so.

# **C. The context of public procurement reforms**

# 1. <u>The promise and challenge of Big Data in public procurement</u>

Big Data in public procurement, if appropriately harnessed, holds the promise of fundamentally transforming how procurement performance is understood and it can provide a vastly superior guide to effective policy decisions and implementation compared to our current knowledge. Such benefits are not automatic however, they also harbor considerable risks and require some investment.

Due to extensive regulations, the presence of multiple actors, and a demand for public security, public procurement has long been a data rich area of public spending. However, with the increasing use of electronic and online procurement tools, this rich set of administrative records have become more readily and more extensively available – giving rise to a data revolution experienced across many domains of social life (UN Global Pulse, 2012). This enables real-time data analysis using data sets tracking individual actions such as bids submitted to a tender, evaluation scores assigned, or invoices paid.

What fundamentally reconfigures our capacity to understand and govern public procurement systems is the move from individual records to a structured database.<sup>6</sup> This means that on top of the ability to identify and analyze a small set of tendering documents; governments, businesses, and citizens are becoming increasingly capable of systematically analyzing large swaths of procurement activities. In spite of the obvious informational basis and wide ranging benefits, making this move to a data rich approach has proven to be surprisingly challenging with some governments and international organizations even decreasing their ‘Big Data readiness’ rather than improving it (Fazekas & Saussier, 2018). On the one hand, building integrated data systems even if they only encompass the already collected information requires considerable investment. Linking disparate procurement data systems, standardizing data formats, and delivering reliable data management infrastructures are typically far
also important to note that these reforms occurred in the midst of high political instability that eventually turned into civil war. If we consider the political climate, the government took bold and risky reforms during the period that could have further compromised the political environment.

# Jordan: November 13, 2012

The Government of Jordan introduced a major subsidy reform on November 13, 2012. This move occurred unexpectedly, despite a prolonged period of public discussion about subsidy reforms. The reform reintroduced the automatic pricing adjustment mechanism on petroleum products and thereby discontinued the practice of discretionary adjustments. The liberalization of prices caused immediate increases in the prices of 90 octane gasoline and kerosene (+14.3 percent, from 700 fils per liter to 800 fils per liter for both products), diesel (+33 percent from 515 fils per liter to 684 fils per liter), and LPG (+53.8 percent, from JD 6.5 per cylinder to JD 10 per cylinder of 12.5 kilograms). This rise followed an initial price increase of the other petroleum products, including 95 octane gasoline, introduced during the second quarter of 2012. The reform was accompanied by the precautionary measure of freezing the price of bread, but the Ministry of Transport was instructed to adjust public transport tariffs according to the new fuel prices.

The Jordanian reform also included a compensation of JD 70 per person per year with a maximum ceiling per household of JD 420 per year, an amount excluded from any form of taxation or deductions. The only eligibility criteria were Jordanian residency and an annual household income below JD 10,000. An estimated 70 percent of the population were expected to be eligible for the program, and the administrators used several public databases to exclude noneligible households, including the rosters of public sector employees and retirees, military personnel and retirees, and social security subscribers. Beneficiaries of the National Aid Fund (NAF) would receive the compensation without application, but all other eligible citizens (private sector employees, the unemployed, and the inactive
plot-level (as opposed to the farm- or household-level). This is because a single farm may consist of multiple plots – cultivated under different production systems – and farmers tend to refer to each plot as they describe agricultural production activities. In such a situation, plot-level data collection reflects the natural flow of conversation between the interviewer and the farmer. In addition, collecting data at the plot level yields more observations than collecting data at the household-level, enables the analyst to control for land quality, and facilitates the analysis of agricultural productivity (as the plot can be used to link crops to inputs) and intra-household differences (e.g. productivity gaps between maleand female-managed plots).

For data on farm labor, recommended practice is to collect such data either in terms of aggregate persondays per plot, or to ask, specifically, about each household member’s labor input on each plot (Reardon and Glewwe 2000). The former approach is used in the 2009 Ghana Socio-Economic Panel Survey, which asks, separately for men, women and children, about the number of days, average hours per day and average

> 2 This section draws on Arthi et al (2018).

> 3 Multi-topic household surveys typically also administer a general labor module, which gathers information on different types of employment and work (e.g. farm work, non-farm household enterprise work, wage work, etc.) for each household member. These person-level measures of farm labor typically refer to the past 7 days and cannot easily be extrapolated to the entire agricultural season.

4
quarter of respondents cited cost and distance; a similar share said that they did not have an account because a family member already had one. About 20 percent of respondents cited lack of documentation and trust in the financial system, and 6 percent cited religious concerns as the reason they did not have an account.

In ECA only 7 percent of unbanked adults reported insufficient funds as the sole reason for not having an account. Thirty percent cited lack of trust in financial institutions. These figures were particularly high in Azerbaijan (42 percent) and Ukraine (50 percent).

Other factors underlying the lack of account ownership include cost, lack of documentation, and distance. For the region as a whole, 27 percent of unbanked adults reported cost as a barrier. This figure was higher in Albania (40 percent), Azerbaijan (39 percent), Bulgaria (39 percent), Kosovo (42 percent), Moldova (35 percent), Russia (34 percent), and Ukraine (40 percent).

On average, only 14 percent of unbanked adults in ECA cited lack of documentation as a barrier. The figure was about 40 percent in Azerbaijan, Georgia, and Turkmenistan.

In the Republic of North Macedonia and Turkey, more than half of unbanked adults (54 and 65 percent, respectively) reported that they did not have an account because a member of the family already had one. This pattern may be associated with the fact that male household members are more likely to own an account than their spouses.

# Use of Accounts for Payments Is Increasing in the Region’s Developing Economies

Most people in ECA make or receive payments, pay bills, send money to relatives, or receive wages or government transfers. The 2017 Global Findex survey asked people what kinds of payments they made and received and whether they did so via an account or in cash.

## _Government Transfer Payments_

Except in the poorest economies in the world , most people who get government payments receive them into an account. In the developing
# **Table 5: Regressions Including the Interaction of Competition and Concentration with Credit Information**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI_ ) and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). _Credit information_ is the country average credit information index that measures the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau. Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|||Ac|cess to finance||
|---|---|---|---|---|
|Variables|||||
||(a)|(b)|(c)|(d)|
|Lerner index|-2.758***<br>[0.568]|-3.219***<br>[0.476]||
Integrated Public Use Microdata Series (IPUMS-I, 2019), comprising four waves of individual and household survey data from 1980-2010. We complement these data with the information from Brazil’s national electricity regulatory authority, ANEEL ( _Agência Nacional de Energia Elétrica_ ), and from the Ministry of Infrastructure and Transport of Brazil, DNIT ( _Departamento Nacional de Infraestrutura de Transportes_ ).

> 3There is a growing literature on the economic impact of road quality improvements. Gallen and Winston (2021) have a dynamic general equilibrium model where transportation is treated as capital funded by taxpayers. Morten and Oliveira (2023) use a general equilibrium trade model and rich spatial data to study how road infrastructure facilitates the movement of traded goods. Trew (2020) and Allen and Arkolakis (2022) study how transportation improvements arise in decentralized settings. Balboni (2019) develops a dynamic spatial model to analyze whether transportation improvements should concentrate in coastal areas under the risk of rising sea levels. Alder (2023) builds a spatial model to analyze whether connecting large economic centers or mediumsized cities is more optimal. Reduced-form studies include Gertler et al. (2022), Asher and Novosad (2020), Bird and Straub (2020), and Jedwab and Storeygard (2021).

4
pooling together income- and expenditure-based observations in applied work could yield misleading test results in our case. Second, gross income could be lognormally distributed while net income is not -- for example if taxes and transfers are lump-sum rather than proportional to income.

To be specific, in addition to (i) the full sample we also consider sub-samples of (ii) income-based observations only (labeled “Income” for short); (iii) expenditure-based observations only (“Expenditure”); (iv) gross income-based observations only (“Gross Income”); (v) a combination of expenditure and net income-based observations (“Net”); and finally (vi) net income-based observations only (“Net Income”).

For the full sample, Figure 1.(a) shows a scatter plot of the observed quintile shares (vertical axis) against their theoretical counterparts, as computed under the null hypothesis of lognormality (horizontal axis). The data points cluster along the 45-degree line, suggesting that the lognormal distribution provides a fairly close approximation to

> 15 For this reason, Forbes (2000) and Deininger and Squire (1996) raise expenditure-based Gini coefficients by 0.066 to make them comparable with income-based ones.

16 Under consumption smoothing, current consumption will depend on some weighted sum of current and future anticipated income. In the textbook permanent-income model, the consumption level of household _h_ in period _t_ would take the form _Cth_ = θ _Ath_ + _s_ ∞∑= _t_ 1( + _r_ ) _s_ − _t Et_ [ _xsh_ ] , where _x_ is income, _A_ denotes

> financial wealth, _r_ is the real interest rate, θ is a parameter, and _E_ t denotes the conditional expectation. Even if _xs_ is lognormally distributed, the infinite sum in the square brackets will not be in general, a simple consequence of the fact that the sum of lognormally-distributed variables is not itself lognormally distributed.

-8-
survey<br>wave|Share of<br>respondents who<br>are heads(%)|Included in<br>HH business<br>sample|Included in<br>household<br>head def|
|---|---|---|---|---|---|
|Burkina Faso|March|June|87.3|---|X|
|Chad|April|June|n/a|X|---|
|Ethiopia|April|April|84.4|X|X|
|Madagascar|March|June|67.6|X|X|
|Malawi|April|June|97.2|X|X|
|Nigeria|March|April|82.3|X|X|
|São Tomé and Príncipe|N/A|July|100|---|X|
|Senegal|March|May|100|---|X|
|Sierra Leone|April|July|60.6|X|X|
|Somalia|March|July|n/a|X|---|
|South Sudan|March|June|54.4|X|X|
|Sudan|March|June|59|X|X|
|Zimbabwe|March|June|100|X|X|

Source: HFPS data, Our World In Data. Month of first lockdown based on first appearance of stay-at-home restrictions. n/a indicates that the data are not available and --- indicates that the country is not included in the sample of countries generated by the corresponding definition.

> 14 The countries with multiple waves are Burkina Faso, the Central African Republic, the Democratic Republic of Congo, the Republic of Congo, Ethiopia, Gabon, Ghana, Guinea, The Gambia, Kenya, Madagascar, Mali, Mozambique, Mauritius, Malawi, Niger, Nigeria,
appears in the full Uganda sample, indicating that endogeneity issues significantly bias OLS regressions. Interestingly, the positive association between hosts’ interactions with refugees – — and their attitudes towards refugees remains statistically significant albeit small when the analysis is restricted to Kampala, the capital city. This reflects important differences in the nature of refugee-host interactions and perceptions in urban and rural contexts, which we illustrate using qualitative data. The analysis also suggests that a significant part of attitude formation takes place at the intra-group level, within households and immediate neighbourhoods, independently of inter-group interaction.

For comparison, we use OLS and IV regressions to explore whether refugees’ perception of host trustworthiness are influenced by refugees’ intensity of interactions with host. Results are broadly similar to the analysis of host perceptions of refugees. While OLS regressions identify a positive and significant association between interactions and refugees’ perceptions of hosts, IV regressions show that this association is not robust. Results also suggest that refugee perceptions of hosts are partly formed at the intra-group level, within households and immediate neighbourhoods, independently of inter-group interaction.

Finally, we combine descriptive statistics, regression analysis, and qualitative evidence in order to unravel the complexity of host community attitude formation and to generate new hypotheses. In this process, the qualitative data plays a particularly important role in elucidating contradictions, puzzles or nuances in our findings, which are often less noticeable in quantitative surveys. We further find that ethno-linguistic proximity seems to affect host perceptions; positive attitudes towards refugees are usually observed in contexts where both host and refugee communities are of Somali ethnic origin. Our qualitative methods highlight that host attitudes also appear to depend on households’ socio-economic standing and relation to refugees in the local context. Refugees may be perceived as a threat or as competitors by certain groups of host nationals, yet simultaneously be viewed by other host members within the same neighbourhood as contributors to local economies and development. Moreover, at the local community – or even individual – level, host nationals can hold seemingly contradicting beliefs. For example, while