rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Agriculture and food security**

- Econometric analysis of agricultural price and production variation based on accessibility to markets

- Econometric analysis of food insecurity based on accessibility to markets

- Identifying areas ecologically suited for high‐value cash crops but poorly connected to markets

- Researching what degree of development project success is due to accessibility challenges vs. program design flaws

# **Transportation and infrastructure investment**

- Optimizing facility and road placement using accessibility criteria

- Calculating improvements in accessibility (cumulative person hours) and health/economic impacts from proposed new facilities or roads

- Integration with global accessibility modeling tools like OpenRouteService for more automated, interactive modeling of accessibility in Nepal

- Extending the model to reflect the quality or reliability of services

- Detecting road condition deterioration by comparing recorded GPS speeds over roadways to modeled speeds

# **Governance**

- Modeling facility catchment areas to assess overuse/ underuse of facilities based on populations within them

- Detecting possible corruption or misreporting using the above

- Breaking down accessibility to various services by ethnicity, caste and/or religion

# **Health care**

- Assessing the impact of accessibility on health care utilization and outcomes, by health care service type

# **Economic development**

- Overlapping access to financial institutions with population disbursement and poverty rates to highlight areas where mobile banking may be more financially sustainable than brick‐and‐mortar formal institutions

# **Disaster response**

- Highlighting areas where cash‐based assistance for disaster relief may underperform due to low access to formal financial institutions

We intend to address some of these topics in follow‐up research. As a first step, we are in the process of regressing HRVS data sets related to recovery from shocks on the remoteness data.

We recognize that the data resulting from this research are just as important as the analysis. Providing others useful tools to address accessibility quantitatively was an explicit aim of this research given its foundational importance in Nepal. To this end, we are publishing all output spatial data on the Nepal GeoNode
515|0.191|
|...accross treatments, year 2 (c1=c2=c3)|0.285|0.085|0.000|0.003|0.178|0.398|0.606|0.923|
|Coef. are jointly 0 (b1=b2=b3=c1=c2=c3=0)|0.561|0.000|0.000|0.000|0.003|0.037|0.388|0.538|
|**(IV) impact of Formalization:**|||||||||
|Formalization instrumented by 1st stage|0.053|0.031|0.669***|0.152**|0.009|‐0.255***|0.068|‐0.059|
|treatment variables|(0.074)|(0.061)|(0.048)|(0.066)|(0.034)|(0.091)|(0.050)|(0.067)|
|P‐values|0.469|0.613|0.000|0.022|0.782|0.005|0.172|0.377|
|Sharpened two‐stageq‐values<sup>μ</sup><br>Note : Panel data from midline and endline<br>each triplet). Standard errors (in parenthes<br>baseline value. μ: Sharpened two‐stage q‐va<br>the following questions: "Was asked to pay<br>inappropriate request from a tax inspector<br>6 months"; " Received more than 1 visit by a<br>inspector "; "Feel that he/she paid more tax<br>their duty and ask firms to pay too much
|_0.875_|_0.493_|_0.560_|
|Has cultivable land larger than 1 Ha (0/1)|0.389|0.392|0.385|0.445|0.427|0.479|_0.932_|_0.526_|_0.632_|_0.284_|
|Maize yield (Kgs/acre) in 2009/10|676.1|733.1|619.0|745.8|750.2|737.1|_0.254_|_0.932_|_0.899_|_0.345_|
|Used agricultural inputs in 2009/10 (0/1)|0.658|0.629|0.688|0.664|0.640|0.708|_0.353_|_0.494_|_0.903_|_0.834_|
|Number of Observations|193|97|96|137|89|48|193|137|186|144|
|P-value ofjoint F-test that all coefficients are 0|||||||_0.425_|_0.824_|_0.605_|_0.679_|

Note: This table shows descriptive statistics using individual level data from the baseline household survey. The sample is restricted to households in non-lottery who are similar in observable characteristics based on a nearest neighbor propensity score matching algorithm. In lottery villages we also restrict the sample to households who share the propensity score distribution support of households in no lottery villages. Column 1 reports averages in lottery villages. Columns 2 and 3 report averages for beneficiaries and non-beneficiaries of the NAIVS voucher program in 2010/11 in lottery villages. We report analogous figures for non-lottery villages in Columns 4-6. In Columns 7-10 we report the p-values of the mean comparison between different groups. P-value of joint F-test corresponds to the p-value of a joint F-test that all coefficients of a regression of beneficiary status (Columns 7 and 8) or lottery dummy (
used by the ONDH for its surveys in the rural areas was specifically designed to provide a valid counterfactual. This was done by sampling communities (households and children) that had only slightly different poverty rates. In this manner, they can be expected to be otherwise similar _except_ for the fact that those with poverty rates above 30 percent were treated with the INDH program. Exploiting this counterfactual as well as the panel nature of the data, we use a variety of models to estimate a robust range of potential INDH impacts on economic outcomes and ECD.

# _4.2 Regression Models_

The first method we use is regression on the impact evaluation panel data to compare outcomes in INDH and non-INDH communities just above and below the 30 percent poverty cutoff for participation while controlling for the poverty rate. We also estimate quantile (median) regression models in case the impact on the median is different from that on the mean and to control for outliers. Several early childhood outcome variables are captured by looking at whether there is access to certain services. These are binary variables, such as whether or not a child attends pre-primary education. To study the impact of the INDH on such variables, discrete choice models (logistic regressions) are used.

# _4.3 Regression Discontinuity Design_

The impact evaluation leverages the fact that the sample was designed to undertake RDD. The basic idea of RDD is that treatment status, _Wi_ , is determined, either entirely or in part, by some ‘forcing variable,’ _Xi_ (in our case, the poverty rate), being on one side or the other of a treatment threshold (in our case, a commune level poverty rate of 30 percent from the 2004 poverty map) (Imbens & Lemieux, 2008). For lower poverty rates than the critical threshold of 30 percent, communes are not treated, while for higher poverty rates they are. The difference or jump in an outcome, _Yi_ , at the treatment threshold can be attributed to the program, assuming a ‘smooth’ (
3, 4, 5, 8|
|||Natural resources rents (%GDP) (natural|||||||
||Natural Resources Rents (ln)|logarithm)|136727|2.21|0.71|0.89|3.92|World Development Indicators|
||Natural Resources Rents|Natural resources rents (%GDP)|136727|11.62|8.81|2.43|50.25|World Development Indicators|

34
The proportion of the interview month which lies within the Ramadan period which is defined by the Islamic lunar calendar is also controlled for. Ramadan advances through the calendar underlying the LSMS‐ISA data with the consequence that lunar seasonality may confound the solar seasonality reflected in the crop year and food prices. Many urban households return to their villages at Christmas and it is plausible that this results in higher caloric intake at that time. This departure is controlled for by including a dummy variable which takes the value one if the household was interviewed in December. The seasonality observed in food consumption is reported net of these confounding effects. Caloric intake is obtained by converting reported food expenditures or food quantities from the expenditure module into calories. Food expenditures and intake are reported at the household level for the past 7 days.

Columns 1 and 2 in Table 3 report the pooled OLS estimates of equations (8) and (9) respectively (for comparison). The pooled OLS estimates indicate significant seasonality. The trigonometric specification is preferred over the dummy variable approach (lower BIC). Figure 3 charts the dummy variable (solid line) and trigonometric (broken line) seasonal factors from pooled OLS. The dummy estimates show caloric intake to be low from November to January and high from March to June with an estimated seasonal gap of 10.6 percent. The estimated trigonometric factors are (inevitably) much smoother. Relative to the dummy estimates, they give a lower estimate the depth of the November and January troughs but also the extent of the March to May recovery. The vertical bars in Figure 3 show the 95% confidence intervals for the dummy variable seasonal factors. November is the only month in which this confidence interval excludes zero. Although the trigonometric factors are generally much lower than the dummy variable estimates, there is only one month (again November) in which the trigonometric estimate falls outside the 95% dummy variable confidence intervals.

The FE seasonality estimates are reported in columns 3 and 4 of Table 3. The dummy variable specification
|83|5.1|82.6|80.9|-1.7|
|Croatia|1993|32.3|17.1|613|53|-5.7|67.6|64.6|-3.0|
|Romania|1999|37.1|29.5|5,276|80|0.7|64.0|66.2|2.2|
|**Turkey**|**1992 **|**46.9**|**27.2**|**12,057 **|**58**|**-4.2**|**98.3**|**95.4**|**-2.9**|

Note: Manufactured goods excluding chemicals Source: Computations based on world import data from UN COMTRADE Statistics.

A feature setting Turkey aside from EU-8 firms’ involvement in ‘buyer-driven’ value chains is a relatively much smaller presence of furniture chains in exports of ‘buyer-driven’ chains. The value of their exports of US$ 180 million was about half of exports of furniture network and one-tenth of T&C exports (Table 17). In contrast to most EU-8, the T&C chain towers over other buyer-driven’ value chain with the share in total ‘buyer-driven’ chains’ exports at 96 percent in 2003. Except for Macedonia, the dominance of T&C chain is much less pointed in exports from other countries.

Yet, Turkey appears to be moving along the same path that the EU-8 economies took earlier, with its furniture sector rapidly becoming integrated into international supply chains. Although the share of furniture network’s products in exports of manufactured goods (excluding chemicals) remains well below the levels in other transition pan-European countries,<sup>15</sup> there are some signs of change in EU-25-destined exports. The contraction
**_<mark>(4) Access to finance.</mark>_** <mark>Ayyagari, Demirguc-Kunt and Maskimovic (2008) explore whether financial development promotes growth by fostering innovation and thus increasing efficiency, with such an effect occurring if the financial system has an important role in supplying capital to firms that are innovating. Ayyagari, Demirguc-Kunt and Maksimovic (2010) explore the role of corruption and tax evasion on innovating firms and firms that use external finance. In a similar vein, Aterido, Hallward-Driemeier and Pages (20119) examine the impact of access to finance, business regulation, corruption and infrastructure on employment growth. With this literature as motivation, we test whether the following measures are associated with firm performance:</mark>

- **<mark>Sources of financing for new investments</mark>** <mark>. Fernandes and Pakes (2008) find that firms with higher TFP are more likely to receive loans, suggesting that loan-granting institutions are able to select out the more productive firms; and that states in which a disproportionate number of firms received loans are the states with less underutilization of both labor and capital.</mark>

**_<mark>(5) Access to quality physical infrastructure.</mark>_** <mark>Calderon and Serven (2004), using an infrastructure-augmented growth regression (including as non-infrastructure growth determinants indicators of trade openness, government burden, inflation and real exchange rate overvaluation, plus indicators of human capital and financial depth), show that growth is positively affected by the stock of infrastructure assets, where their aggregate index of infrastructure stocks includes data from telecoms (the number of main telephone lines per 1,000 workers, also augmented by mobile phones), power (the electricity generating capacity of the economy in MW per 1,000 workers), and transport (the length of the road network in km per sq km of land area, also augmented by the railroad length)
refugee intensity on individual hosts’ salaried permanent (the first six columns) and temporary (the last six columns) employment. The first and third three columns report results for a binary measure of permanent and temporary salaried employment, respectively. The second three columns report results for number of hours worked in permanent salaried employment while the fourth three columns report results for number of days worked in temporary salaried employment. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban individuals, respectively. Individual sample weights from ESS2 applied. All columns control for individual (ID) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

47
683|0.683|0.682|
|Adjusted R-squared|0.676|0.682|0.682|0.681|0.681|

_Notes_ **:** a **.** Base category for regions is central; b. Base category for household livelihood is crop farming; c. Base category for quintile of household CSI is low. In each specification, we use a binary indicator for the incidence of shocks (=1 if household reported at least one shock, and zero otherwise). This variable is correlated to region (a categorical variable indicating the six regions of Sudan) in column 1; household livelihood in column 2; four categories indicating the intensity in the use of coping strategy (constructed from the household CSI) in column 3; and binary variables indicating households’ receipt of credit and help in columns 4 and 5, respectively. We focus on household consumption per capita as our main indicator of household welfare. Each specification is estimated using ordinary least squares. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1.

Households in the Central<sup>16</sup> region affected by shocks are least affected when compared to the impact on households in Khartoum, Northern, and Darfur regions. Consumption per capita for households affected by at least one shock in Khartoum is on average 9.6 percent lower than consumption per capita for affected households in the Central region.<sup>17</sup> In the Northern and Darfur regions, consumption per capita of households exposed to at least one shock is 4.9 percent and 3.7 percent lower, respectively, than in the Central region. Although these results illustrate the spatial heterogeneity in the impact of shocks across Sudan, differences in prevalence of shocks across states, region-specific characteristics such as the types of shocks most prevalent, and the common household livelihoods are likely to drive
the effect of Ethiopia’s RSDP on firm-level outcomes. For example, Fiorini et al. (2021) focus on the role of road infrastructure on the effect of trade. Using the Ethiopia manufacturing census from 1998 to 2009 to construct measures of firm productivity, they find that better local road infrastructure increases the benefits from lowering tariffs on imports and exports. In a related paper, Fiorini and Sanfilippo (2019) find that road access via RSDP increases overall employment, reduces agricultural employment, and increases service sector (although not manufacturing) employment. Using the same manufacturing census data, Shiferaw et al. (2015) estimate the relationship between RSDP road investments and firm performance, finding that road quality is linked to firm entry. Moneke (2019) studies the effects of roads and electricity and finds that roads alone had smaller welfare effects compared to roads combined with electrification. He uses an “inconsequential units” identification strategy based on colonial-era straight-line road plans, using units that were not directly of interest for the colonizer to identify the impact of the program. Relying on land use and economic activity as outcome variables, we similarly find notable spatial heterogeneity in outcomes. Adamopoulos (2018) studies the impact of RSDP on agricultural productivity in Ethiopia, using a panel from 1996-2014 at the medium administrative level (Woreda) and estimated travel times to agricultural markets.

4
of longer unemployment spells is still low compared to Eastern European countries and other developed countries; during the 1990s the incidence of unemployment spells longer than one year was 11 percent among Latin American countries but 42 percent among

> 1 This section draws on background work by Saavedra

> 2 based on information from household surveys compiled by the International Labor Organization (ILO).

> 3 CEPAL’s early evaluations found if anything a positive impact on wages (see Weller 2000) Stalling and Peres (2000).

2
than producers of loan and deposit account services. Banks produce intermediation services through the collection of deposits and other liabilities and the transfer of these funds to interest-earning assets. Deposits are included as a third input along with capital and labour. As a result, operating costs, as well as interest cost, are taken into account in the production process. In this study we will use the intermediation approach. However, as Mester (1996) points out, both approaches have the disadvantage that they do not take into account risk factors. Also, both concepts assume that all banks produce the same output quality. It is likely, however, that there are unmeasured differences in quality, because banking data do not fully capture the heterogeneity in bank output. Berger and Mester (1997) try to alleviate this problem 3
14 distribution, there is no apparent relationship between both legal entitlement to and actual severance pay received.

If a job separation is due to an economic cause beyond the control of the employee, the compensation package (both severance pay and long-service leave) is significantly larger and the cost to the firm is greater. Hence, it is important to identify those employees who were fired for an economic cause. In the IFLS (2007) survey 16.3 percent of wage and salary employees with a job separation were fired in the past five years because the firm merged or restructured. While the proportion of these job separators who were legally eligible to receive severance pay on termination was 0.8807, only 0.4041 actually received a payment.<sup>11</sup> The average severance entitlement amounted to 11,827,577 IDRs and the average amount of severance paid was 4,864,027 IDRs; about 40 percent of the amount to which an employee dismissed for an economic reason was entitled. This proportion of severance pay actually received (0.4112) was similar to the overall average of 0.3853 for all employees legally entitled to severance payments. Given that a job separation due to an economic cause beyond the control of the employee would automatically confer eligibility, the similarity in both proportions suggests a high degree of non-compliance.

In the Sakernas (2008) survey, 16.0 percent of employees were separated from their job in the last two years because they were laid-off by their firm; approximately one-third were laid-off because the company failed.<sup>12</sup> This proportion represents 4.2 percent of all job separations in the previous two years. While the proportion of these job separators who were legally eligible to receive severance pay on termination was 0.7859, only 0.1607 actually received it. The average severance entitlement amounted to 45,256,847 IDRs and the average amount of severance paid was 15,646,787 IDRs; about 34.
**Table A6: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (1980 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.696|0.014|6402|Benin|0.840|0.028|2439|
|Indonesia|0.791|0.012|10148|Burkina Faso|0.688|0.029|1636|
|Philippines|0.830|0.019|4746|Burundi|0.740|0.041|1396|
|Vietnam|0.867|0.036|1232|Cameroon|0.841|0.028|1755|
|||||Chad|0.753|0.033|887|
|Europe & Central Asia||||Congo, Rep.|0.863|0.048|1212|
|Albania|0.858|0.065|1106|Cote d'Ivoire|0.845|0.055|620|
|Armenia|0.746|0.035|1735|Ethiopia|0.781|0.021|3006|
|Kyrgyz Republic|0.786|0.086|575|Gabon|0.672|0.057|485|
|Tajikistan|0.700|0.055|1469|Ghana|0.781|0.032|1588|
|Turkey|0.767|0.024|3265
up land markets. Then section 7 turns to the relationship between the geographic pattern of landlessness and poverty measures.

# _5.1 Incidence of rising landlessness and land transactions_

Figure 3 gives the relationship between the mean landlessness rate and log consumption per person (in 1998 prices) for 1993 and 2004. (These are non-parametric regressions, using locally-smoothed scatter plots.) Rural landlessness in 2004 tends to have a positive consumption gradient; the poorest tend to be the least likely to be landless. In terms of our theoretical analysis in section 3, this pattern is consistent with sufficiently high labor-market returns to schooling or other assets.<sup>34</sup> Note also that the landlessness rate for 2004 is nearly linear in log consumption, implying that it is concave in the level of consumption; thus higher (lower) consumption inequality at a given mean will tend to lower (raise) the aggregate landlessness rate.

How will the fact that we are using 2004 consumption (which reflects gains from the reform as well as other changes since 1993) affect the estimated relationship with landlessness in 2004? This will depend on how the consumption gains since 1993 vary with landlessness. If the (proportionate) gains tend to be higher (lower) for those who become landless and landlessness rises with consumption then we will over-estimate (underestimate) the true economic gradient in landlessness. The fact that the proportionate gain in consumption over 1993-2004 was slightly lower for those with land suggests that we will be underestimating the economic gradient in landlessness measured against pre-reform consumption.

Over the period 1993-2004, there was a reduction in landlessness among the poorest (Figure 3), though less than 3% of households in 2004 had consumption per person below the

> 34 It is not clear that returns to schooling need to be high for a positive gradient in landlessness. In common with China (Fleisher and Wang, 2004), wage compression in the more organized labor markets has kept returns to schooling relatively low in Vietnam; see Gallup (2004) and