rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_3.2. Gender Attitudes Composite Indexes_**

The module on gender attitudes consisted of 35 questions on employment, gender-based violence, decision-making, and general attitudes, answered by the main respondent of the household survey (Table 1). These 35 questions, asked to the respondents in Malayalam and English, were structured to elicit pointed answers that were recorded as: “Yes,” “No,” “Depends on the situation,” and “Don’t know.”<sup>7</sup> This analysis took the affirmative responses into account, whereas “No,” “Depends on the context,” and “Don’t know” are classified as 0. Thus, all the answers were coded as a binary variable with 1 for “yes” and 0 for “no.” The employment section had three questions: whether it is shameful for a wife to earn more than her husband, if a woman threatens a man’s job opportunities and livelihood, and if a woman should not work outside her home if economic conditions improved. General attitudes, the second section, included 13 questions related to whether a woman’s capacity to carry out her household chores was important to earn community respect and if it is shameful for a man to carry out household work. It also included paired questions to assist in the comparison of attitudes of male and female respondents on specific issues, such as whether girls and boys had to be brought up to be submissive and modest, and if outspoken and assertive boys and girls should be disciplined. Such a structure is crucial as it allows establishing if girls and women are at a comparative disadvantage than boys and men. The third section, decision-making, consisted of nine questions asked whether women should leave decision-making to men on a range of issues, such as property, family planning, and female participation in political and religious organizations, and self-help groups.

Thirteen questions on GBV, included in the last section, are the following
submission period does not adversely impact companies’ capacity to prepare bids adequately (for more details see section 6.2 below). Instead, e-procurement makes very short advertisement periods (less than 15 days) virtually disappear which should improve advertisement quality leaving more time for bidders to prepare bids. Moreover, it also significantly reduces very long periods (more than 30 days) which is the primary reason behind the declining average advertisement length.

74. In Models 2a and 2b, we observe that, contrary to our expectations, e-procurement increases decision period length compared to manual tender evaluation. While overall small, our models predict that e-procurement increases the decision period by 2.0-2.2 days (from 20.5 to 22.7 days in Model 2a and from 22.8 to 24.9 days in Model 2b). The surprising increase in decision period length can be attributed to two potential reasons. First, as shown, the number of bidders per tender increased and more bidders naturally mean more work for bid evaluators. When normalizing the decision period length by the number of bidders, we find a decrease in decision making time as expected (see normalized regressions in Appendix 9.5). Second, the increasing participation of non-local bidders implies more validating work for procuring entities because they have to obtain documents to assess the validity of the experience quoted by bidders. This takes comparatively longer compared to a procuring entity contacting a bidder it already contracted with.

75. Regarding the signing period, that is the number of days needed for signing the contract once the contract is awarded, we find a substantial decrease in processing time. E-procurement reduces the average signing period by 4.1-6.9 days compared to manual tenders (21.9 versus 14.9 days in Model 3a; and 18.2 versus 14.1 days in Model 3b).

76. The observations from the administrative data are further corroborated by the perceptions of the times as reported by surveyed procuring entity officials. PE officials reported similar
also report that public officials have considerable discretion in implementing government regulations, and that corruption is widespread in courts too.

There are strong elements of discretion in contract labor law as well, for example in the definition of ‘core’ activities and other activities for which use of contract labor may be prohibited in certain states, and the precise responsibilities of the labor contractor and the employer (the firm) in ensuring the contract is enforced. A qualitative survey suggests that contract labor law is poorly enforced, with workers complaining about collusive arrangements between contractors, firms and inspectors (Rajeev, 2009).

Governance seems to matter equally crucially for land-related issues (Chakravorty, 2012). Despite the DILRMP reforms, for example, a report by the World Bank (2015) on six Indian States<sup>39</sup> highlights that rural land records are generally not updated regularly (in Bihar, for instance, most maps seem not to have been updated since 1922) and lack spatial reference, while urban land records are lacking for many properties. Even when the legal and institutional framework is relatively robust, the World Bank assessment points to significant challenges in terms of implementation and enforcement: the frequent sharing of responsibility for land management by a range of different departments (up to four in certain states) and agencies causes horizontal overlap and lack of coordination in the first place, and registration of land transactions seems to be often plagued by errors and fraud. Duranton et al. (2015a), for example, note how the process of evaluation for land that is publicly purchased offers wide opportunities for corruption, as valuation is done by the public agent acquiring the property without specific guidelines. Morris and Pandey (2007) report widespread poor protection of land property rights, as well as a high degree of discretion in granting the non-agricultural clearance for the conversion of use of farmland.

> 37 Detailed information and comments on NIMZ guidelines and benefits can be respectively found in DIPP (2011) and ASA (2015). 38
_Table A2.3 Weighted Intention‐to‐treat effects of the original intervention on main socio‐economic outcomes._

|(1)<br>ITT<br>(no controls)|(2)<br>ITT<br>(controls)|
|---|---|
|_Main outcomes – Socio‐economic_||
|Employment index<br>0.065|0.075|
|(0.285)|(0.211)|
|Consumption index<br>0.095|0.094|
|(0.146)|(0.146)|
|Savings, investment and debt<br>0.266***|0.265***|
|index<br>(0.000)|(0.000)|
|Business skills index<br>0.012|0.018|
|(0.814)|(0.744)|
|Observations<br>1523|1507|

_Note: Observations are weighted by their inverse likelihood to be in the final sample, based on who was easy to reach during the phone survey. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐ percent (five‐percent, one‐percent)
transfers to recipient countries—effectively providing more gross transfers to those countries with higher debt-service payments—debt sustainability is a particularly blurred concept in these countries. Debt can be serviced for long periods, or suddenly become unsustainable, depending on the willingness of official creditors and donors to provide positive net transfers through concessional loans and grants." We recognize an important qualification. Discounting remains of crucial importance for two purposes:

1. Discounting concessional loans from donors to calculate their grant element.

2. Assessing fair burden sharing across creditors when a country defaults and the net present value of debt is being reduced, in line with the ‘comparability of treatment’ principle.

But in the assessment of debt sustainability which, by its nature, attempts to provide early warning and is aimed at _crisis prevention_ rather than _debt restructuring_ , discounting only obscures the picture, as we shall demonstrate. In the case of debt restructuring, the choice of an appropriate discount rate remains a complex topic (see, for example, Kozack (2005) and Lazard (2022)).

_Nominal Public Debt and Its Dynamics, Plus Market Signals_ Table 2 presents data on Ghana’s nominal public (government) debt levels and the variables determining its dynamics during 2013-15 using the data available in IMF (2016).

**Table 2: Ghana’s Public Debt Dynamics 2013-15**

||2013|2014|2015(estimate)|
|---|---|---|---|
|1. Public Debt(% of GDP) **d**|56.7|70.7|77.9|
|_2. Foreign currency share(%)_|_54.1_|_60.0_|_67.0_|
|3. Primarydeficit(% of GDP) **pd**|5.6|3.3|-0.1|
|4. Real interest rate**r**(%)|6.0|19
Other data used in our analysis include commodity export price shocks, conflict, population and income per capita.

The commodity export price shocks data is a comprehensive database of country-specific commodity price shocks created by Gruss and Kebhaji (2019). A commodity export price shock for each country is the combination of changes in international prices of up to 45 commodities and the country’s exposure to these commodities’ exports (as share of GDP). The weights of each commodity can be fixed weights (based on average exports over GDP over several decades) or time-varying weights (which can account for time variation in the mix of commodities traded and the overall importance of commodities in economic activity). We used fixed weights in our regressions, although using time-varying weights yields consistent results.

Conflict data are from the UCDP/PRIO Armed Conflict Version 21.1 Dataset. This is a dataset that tracks all country-year pairs when conflict occurred, having at least 25 deaths in a given year, from 1946 to 2020. The dataset covers 4 types of conflict: extra-systemic (any conflict between a state a non-state entity outside the territory of the state in question), interstate (any conflict between two states), intrastate (any conflict between a government and non-government entity on the same territory of the government) and finally internationalized intrastate (any intrastate conflict that also includes foreign government intervention). For our paper, we have focused on internal conflict, that is captured through intrastate and internationalized intrastate conflict. A dummy variable is created to identify the country-year pairs when either of these types of conflict occur.

Population and GDP per capita data are from the World Bank’s World Development Indicators (WDI) Dataset. They are a proxy for country size and the level of economic development in a country, respectively. GDP growth rates used to measure growth volatility and indicate years of economic booms are also obtained from the WDI Dataset.

Table 2 presents summary statistics for all the variables of interest. In addition, summary statistics of longer-horizon forecasts are
**Table 1:** Household sample descriptive statistics

||(1)<br>Mean|(2)<br>sd|
|---|---|---|
|Average age of household head (yrs)|42.11|14.16|
|Proportion of households with literate head|0.38|0.49|
|Proportion of households residing in rural areas|0.75|0.43|
|Household size|7.27|3.37|
|Proportion of households with female head|0.02|0.12|
|Distance to PHC (hrs)|1.69|0.95|
|Distance to hospital (hrs)|2.53|1.59|
|Proportion of households with school in community|0.35|0.48|
|Proportion of households using clean fuel|0.32|0.47|
|Proportion of households with electric connection|0.90|0.30|
|Proportion of interviews after COVID restrictions|0.51|0.50|
|Proportion of households with private water connection|0.19|0.39|
|Proportion of households owning land|0.44|0.50|
|Proportion of households owning livestock|0.54|0.50|
|Proportion of households in Taliban district|0.09|0.28|
|Proportion of households in Contested district|0.39|0.49|
|Proportion of households in govt controlled district|0.53|0.50|

_Note_ : This table reports summary statistics for household demographic characteristics from IE-LFS 2019-20.

25
_Lee (2009); columns (2) to (3) and (5) to (6) replace outcome values for the attritters with +/- 0.x standard deviations of their respective treatment group-wave means as in Kling and Liebmann (2004), lower bounds subtract this value from the treatment group and add it to the control group and vice versa for the upper bounds._

**Appendix Table 12: Treatment effect on components of the Livelihoods Coping Strategies Index.**

||**Control**<br>||**Lower bounds**<br>|<br>|**Unadjusted**<br>||**Upper bounds**<br>||**IV**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**mean**<br>**(standard**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||<br>**deviation)**|Lee(-)|(+/-).25 SD|(+/-).1 SD||(-/+).1 SD|(-/+).25 SD|Lee(+)||
|Panel A: Live<br>|lihoods Copi<br>|ngstrategyI<br>|ndex(inverted a<br>|nd standardize<br>|d to baseline c<br>|ontrol)<br>||||
|6 months|0.18<br>|0.12***<br>|0.08***<br>|0
**Table D.23:** Main outcome families when controlling for baseline imbalances

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|General utilization|0.000|0.097|0.106|0.087|4451|
||(1.000)|(0.033)<sup>_∗∗∗_</sup>|(0.035)<sup>_∗∗∗_</sup>|(0.039)<sup>_∗∗_</sup>||
|||[0.036]<sup>_∗∗_</sup>|[0.014]<sup>_∗∗_</sup>|[0.080]<sup>_∗_</sup>||
|Maternal utilization index|0.000|0.060|0.178|_−_0.059|878|
||(1.000)|(0.070)|(0.081)<sup>_∗∗_</sup>|(0.082)||
|||[0.277]|[0.083]<sup>_∗_</sup>|[0.473]||
|Health outcomes index|0.000|0.082|0.170|_−_0.010|5003|
||(1.000)|(0.050)|(0.056)<sup>_∗∗∗_</sup>|(0.058)|
the North, and the Centre and South regions of the country besides including all rural households, we limit the sample to only those households in the North and to only those households in the Centre and South. The first set of specifications uses the (ln) per capita expenditures on all non-health items and the second set uses the (ln) per capita expenditures on

> 22 Given the way in which the expenditure survey was administered, we are unable to separate the value of consumption from own production from the value of goods received as gifts.  For about 7% of the rural households more than 50% of their food comes from non-purchased sources.  On average for a rural household about 7% of all food comes from non-purchased sources.

> 23 The asset index is based on the principal factor analysis of how many parcels of land the household owns, whether or not the household owns their residence, another house, bicycle, motor vehicle, an electric device, a washing machine or a stove, a domestic appliance, machinery or a tractor, bulls or cows, horses or mules, pigs or goats, or poultry.

18
decreases in projections ranging from 5.5 percentage points in Guatemala to 17.6 percentage points in Peru.

> 4The OxCGRT data for the three indices were averaged for the period January 1 to May 31, 2020. The OxCGRT measures aim to analyze the government responses to the pandemic. These indicators are compiled to measure the various policies, such as school closures and restrictions, across more than 180 indicators, resulting in several indices and sub-indices. The economic support index records measures of income support and debt relief from 0 to 100. The stringency index records the strictness of ‘lockdown style’ policies that primarily restrict people’s behavior from 0 to 100. The workplace closing index, a subindex, records the closing of workplaces from 0 to 3. In each of these indices, a higher number implies more restrictions.

11
0.12 and that from energy to food commodities at 0.25. Hanson and others (1993), based on a general equilibrium model, found a significant effect of oil price changes on agricultural producer prices in the United States. Borensztein and Reinhart (1994), using quarterly data from 1970 to 1992, estimated a transmission elasticity to non‐energy com‐ modities of 0.11, and Chaudhuri (2001), too, found a strong relationship between energy and non‐energy prices. Baffes (2007), using annual data from 1960 to 2005, estimated elasticities for non‐energy and food commodities of 0.16 and 0.18, respectively. Moss and others (2010) found that the demand of U.S. agriculture for energy responds strongly to changes in the energy prices.<sup>**12**</sup>

# **_3.3 What matters most?_**

During the commodity price boom, most of the macroeconomic and sectoral fundamentals

> **12** Yet not all studies concur with a strong oil‐non‐oil price relationship. Saghaian (2010) established a strong correlation among oil and other commodity prices (including food prices) but the evidence for a causal link was mixed. Gilbert (2010) found a correlation between oil and food prices, but noted that this could reflect common causation rather than a causal link. Zhang and others (2010) found no direct long‐run relationship between fuel and agricultural commodity prices, and only a limited short‐run relationship. Reboredo (2012) concluded that grain prices are not driven by oil price fluctuations. The mixed evidence on the energy‐non‐ energy price link could reflect the frequency of the data series used in the analysis or the presence of biofuels (Baffes 2013). Zilberman and others (2013) noted that higher frequency (and hence “noisier”) data are typically associated with weaker correlations. On the other hand, an exogenous
against the FAO data on tractors.<sup>21</sup> The prevalence of vertical clusters suggests that the data on tractors were more stagnant than the data on the value of agricultural fixed capital.

Looking at Table 1, we see from the decade growth rates that the data series for tractors display the same pattern of slowing growth as fixed capital. However there are remarkable differences between the growth rates of tractors and fixed capital by income groups. In higherincome countries, there was very little growth in tractors, while fixed capital stocks grew at over 6%. It is clear from these comparisons that tractors are not a convincing proxy for agricultural fixed capital, and, by inference, of total agricultural capital.

We compare the data on agricultural capital to other economic variables to obtain a sense of its relevancy for economic growth. Figure 6 shows the relationship between average labor productivity (output-labor ratio) and the fixed capital-labor ratio for agriculture.<sup>22</sup> This scatter diagram traces the production function in terms of capital intensity, without allowing for the effects of other pertinent variables. In Figure 7, we plot capital intensity in the 1980s (the ratio of fixed capital stock to GDP in agriculture) against structural transformation in the 1990s (as measured by the percentage decline in the ratio of agricultural labor to total labor). Both figures suggest positive relationships between our data set on agricultural fixed capital stocks and measures of economic growth. However, plotting tractors per worker against agricultural GDP per worker (Figure 8) shows a weaker and much more volatile relationship.

# **Agricultural production function**

Mundlak, Larson, and Butzer (1999) first utilize the panel data set on agricultural capital in an analysis of agricultural productivity for 37 countries for the time period 1970-1990. They present a model of production under heterogeneous technology, where the implemented technology is chosen jointly with the level of inputs. The empirical formulation allows for the dependence of parameters of the function, as well as the inputs
We complement the WBES with other data sources. These include World Development Indicators (WDI), World Bank; Global Financial Development Database (GFDD), World Bank; Inter Parliamentary Union (IPU); Doing Business, World Bank; and Women Business and Law (WBL), World Bank.

# 3.2 _Estimation methodology_

Our baseline results are based on the following logistic equation: where the subscript _i_ denotes the firm, _j_ the industry (at the 2-digit ISIC Rev. 3.1 level), _k_ the country where the firm operates, and _t_ the year the WBES was administered in the country. p(.) is the probability of success. _Y_ is a dummy variable equal to 1 if the top manager of the firm is a woman and 0 otherwise. _X_ is bank credit to GDP ratio, our main explanatory variable that varies across countries and time. CFE, YFE, and IFE denote country fixed effects, year fixed effects, and industry fixed effects, respectively. _Firm controls_ and _Country controls_ include various controls for firm and country characteristics, and _u_ is the error term. The parameters in equation (1) are estimated by applying maximum likelihood estimation to the following transformed log odds equation: 9
remains timid with the exclusion of some important industries and relatively long transition periods. The economic growth performance in recent years has proved to be too modest to convince foreign investors, which is reflected in our indicator but not clearly in the ICRG or II index.

It might be useful to examine the variations in the business climate, as a source of attraction for FDI, for the group of 29 African countries over the past decade (table 3). At the end of the 1 980s, the most attractive countries were Zambia, Mauritius, Chad and Benin. Then, in the early 1 990s, Benin, Namibia, Chad, Zambia and Mozambique were ranked as the most performing countries. In the last few years, Namibia, Mali and 8
At the upstream service provider level, the hypotheses to be tested are: _H3: The presence of competition-related pressure is the most significant factor associated with the development of innovative road freight service offerings._ The analysis will attempt to distinguish the relative importance of different sets of factors in affecting the provision of innovative business services, in particular the most appropriate measure of intensity of product-level competition relative to other factors such as access to finance.

_H4: Larger, internationally-connected service providers are more able to offer or respond to innovative business user requests than smaller, locally-based enterprises._ The analysis will try to disentangle the relative importance of foreign ownership, enterprise size (only large providers, mix of large and small), the legacy of state ownership, provision of exclusively local, nation-wide and/or international haulage services, and factors such as technology and timing (older firms benefiting from first mover advantages versus recent entrants) in stimulating user-related innovation.

# **3. The enterprise-based surveys and data description**

The survey instruments were implemented in summer 2000 and during the subsequent months to complete all sectoral quotas. The surveys are cross section, though they ask detailed questions not only about the latest full calendar year (end 1999) but also often comparable values for 1997 to allow calculation of changes over three years. By sampling design, the objective was to administer the provider questionnaire to 50 road freight logistics providers in each of the Czech Republic and Hungary and to 60 providers in Poland as the largest economy of the three, with quotas to randomly select an appropriate number of enterprises in each size category to roughly match corresponding population ratios.<sup>6</sup> The only other deviation from full randomization was a requirement to try to include a minimum number of foreign-owned enterprises (company’s or mother company’s headquarters located in another country) for each of the industrial sectors, in order that a sufficient number of enterprises with linkages to international best practices be represented.<sup>7</sup