rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. The effect of victimization on food insecurity by event and perpetrator type**

|||Event<br>|type<br>||||Perpetrato<br>|r type<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|VARIABLES|||||||||||
|Property|0.104**<br>(0.039)|0.091***<br>(0.028)|||||||||
|Violence|||0.084*<br>(0.047)|0.061*<br>(0.036)|<br>||||||
|Insurgents|||||0.074***<br>(0.020)|0.062***<br>(0.022)|||||
|Bandits/criminals|||||||0.253***<br>(0.079)|0.249***<br>(0.074)|||
|Pastoralists/nomads|||||||||-0.002<br>(0.010)|0.010<br>(0.011)|
|Observations|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|
|R-squared|0.012|0.039|0.010|0.038|0.013|0.039|0.013|0.041|0.009|0.038|
|Number of households|717|717|717|717|717|717|717|717|717|717|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note_ : Dependent variable is the standardized CSI. Data used are the six visits of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 35
poorest region, the Central Visayas. A further 4.1 million people were reportedly displaced.<sup>43</sup> As of 2014, the GoP reported that more than 1,000 people were still “missing”.<sup>44</sup> Yolanda affected some of the country’s poorest regions and was projected to increase national poverty incidence by 1.9 percentage points, with projections of up to an additional one million people falling into poverty.<sup>45</sup> Pre-typhoon poverty incidence in the six hardest hit regions<sup>46</sup> ranged from 29.1 percent in Western Visayas to 45.2 percent in Eastern Visayas.<sup>47</sup> Estimates in the GoP’s Reconstruction Assistance on Yolanda Plan (RAY), 2014, indicated that the poverty gap in these regions had already increased as a result of the disaster. The Eastern Visayas region is likely to experience the largest increase, with the poverty rate estimated to grow from 41.2 to 55.7 percent.<sup>48</sup> The results of the 2014 Annual Poverty Indicators Survey (APIS) released by the Philippine Statistics Authority showed poverty incidence among Filipinos rising to 25.8% in the first half of 2014, from 24.6% in 2013.

The impact on production and livelihoods was significant, and remains potentially debilitating, particularly for the hardest hit regions. The large-scale destruction of crops and other physical assets has disrupted industry and has increased unemployment and underemployment. Vast quantities of crops were destroyed, including 161,400 hectares of coconut farms. Farmers also reported losses of livestock, agricultural equipment, post-production facilities, fishing vessels, and irrigation systems. These losses have had a substantial long-term negative impact on the domestic economy and household welfare. There also are strong multiplier effects to the informal sector, which constitutes a large share of the local economy in the affected regions. The government estimates that around six million workers were affected by the typhoon, of which around 2.6 million are vulnerable workers without adequate social insurance or unpaid family workers.<sup>49</sup> The occurrence of other intense weather events before and after Yolanda has further increased the fiscal burden and hardship experienced by significant segments of the population. Weeks before Yolanda struck, Bohol Province and other parts of central Philippines experienced a 7.2 magnitude earthquake, with damages estimated at P2.2 billion (US$50 million). Following Typhoon Yolanda, Typhoon Glenda struck the north and central regions in the Philippines in July, 2014 resulting in an estimated total cost of P10.8 billion (US$ 247 million). Indeed, Yolanda or any other typhoon / disaster in the Philippines should not be thought of in isolation, but as one in a succession of frequent events, the impacts of which are accumulative for the most exposed and vulnerable.

10
turbulent times. Similarly, the relatively beneficial effects of the crisis on contract terms for credit card consumers and small business customers is ambiguous.

Finally, we briefly review extant research on COVID-19 bank credit supply changes from international or non-U.S. studies, despite the fact that they do not provide direct evidence on whether bank credit decisions hindered the “economic surprise” in the U.S. Non-U.S. studies of bank credit supply changes during COVID-19 is valuable evidence on its own of bank reactions to essentially the same crisis, and may give valuable information about bank behavior in the U.S. as well.

A number of cross-country studies find the COVID-19 crisis resulted in tougher credit conditions for borrowers. They faced higher loan spreads at the intensive margin (Hasan, Politsidis, and Sharma, 2021), and received lower credit quantities at the extensive margin (Colak and Oztekin, 2021; Dursun-de Neef and Schandlbauer, 2021; Park and Shin, 2021), with some key differences about which borrowers suffered the worst credit reductions.<sup>35</sup> Some of these differences in credit conditions are due to variations in how COVID-19 affected credit risks. Some research suggests that corporate CDS spread reactions to the pandemic, which reflect these credit risks, vary in systematic ways with firm, debt, and industry characteristics as well as national policies (e.g., Liu, Qiu, and Wang, 2021; Hasan, Marra, To, Wu, and Zhang, 2021).

One study of bank credit supply in Brazil illustrates the effects of different types of government policies. Norden, Mesquita, and Wang (2021) show that credit supply in the nation decreased during the COVID-19 crisis, but that the effects may be moderated or aggravated by government policy interventions. “Soft interventions,” such as social distancing and mass

> 35 In some cases, these additional borrowing difficulties may have been offset to some degree by asset value gains from holdings of “safe haven” currencies that appreciated during the COVID-19 crisis, especially the U.S. dollar (e.g., Hale and Juvenal, 2021).

61
Contract Labor (Abolition and Regulation) Act of 1970. The Industrial Employment Act provides for the definition of job content, employee status and area of work by state law or by collective agreement, after which changes would not be made without getting the consent of all workers.<sup>15</sup> Zagha (1999) points out that this has always made it difficult for businesses ‘to shift workers not only between plants and locations, but also between different jobs in the same plant.’ As a way out of such restrictions businesses may resort to contract workers, as per the provision of yet another law, namely, the Contract Labor Act. This law gives state governments the right to abolish contract labor in any industry in any part of the state. In states where recourse to contract labor has been more restricted as a result, keeping employment below the threshold level of 100 employees or contracting out jobs has been the only way of maintaining flexibility in the allocation of manpower.

Although labor law in India is primarily federal in origin, ample room has been provided for variation across states in effective restriction on labor market transactions by the fact that states have always been responsible for the enforcement of the same laws. They also have the power to amend some of them substantially. Based on the extent to which states have actually used this power for subsidiary legislation since the late 1950’s, Besley and Burgess (2004) have classified states into categories reflecting restrictiveness of labor regulation. Their analysis of shows that states that amended laws in a “prolabor” direction experienced slower growth on the average while those that changed laws in favor of employers grew significantly faster. In this paper we assess the link between industrial productivity and a plant-level indicator of the restrictiveness labor regulation which enables us to control for a potential gap between legislation and enforcement. The indicator we use is the plant manager’s response to the World Bank Investment Climate Survey question on whether or not managers considered labor regulation to be a major or severe obstacle to the operations or growth of their businesses. As can be seen from Table 2, there is sharp contrast in responses across states. While between one-third and 70 per cent of managers consider labor regulation as a major to severe obstacle in states such as

> 15 This too applies for establishments with more than 100 employees, but Zagha (1999) notes that some states have made the provisions mandatory to firms with 50 or more workers while other states have abolished the employment size limit altogether.

11
**_Poih Fscal Crisis 11_**

# D. **Zloty** appreciation

One of the reasons the CPI increased faster than the PPI in 1991 was because of the use of the exchange rate as a 'nominal anchor" in macro policy. From January 1990 to May 1991 the zloty/dollar rate was fixed in nominal terms; since the domestic price level continued to increase following price liberalization this amounted to a substantial appreciation of the zloty. The zloty appreciated in real terms in 1991 by 25% compared to 1990. Furthermore, import prices increased in 1991 more than export prices.

A guesstimate of the effect of the zloty appreciation on profitability can be had as follows. Write profitability as the difference between domestic and export sales and domestic and import costs, as a percentage of total sales: <!-- Start of picture text --> T pOyD+P'yZ . -. cD..P'c<br>S p y +p pD<br>po<br>YD+ tUyr<br><!-- End of picture text --> Take first differences holding output constant, ignore the effect of changes in relative prices on the value of total output, and we get <!-- Start of picture text -->
-<br>A(P)Yx  A(z<br><!-- End of picture text -->

To get the approximate effect of the zloty devaluation on profitability, we use the following numbers:<sup>9</sup> The export price index increased by 14% in 1991, and the import price index, 20%. For the domestic price level we take the price index for consumer goods (up by 60% in 1991). Plug all these numbers in and we find that, ceteris paribus, changes in relative export and import prices decreased profitability by maybe 1%-2% in 1991.

# E. The cost of credit

Published enterprise sector data unfortunately do not include interest costs as a separate item for 1990. However, I have tried to estimate the scale of interest charges using NBP aggregate credit and interest rate data. The results suggest that "unit credit costs" (interest payments divided by sales) were about the same magnitude in 1990 and 1991; perhaps these costs were slightly lower in 1991. I conclude,

> **9.** Thewe figures **are taken** from **a** 1988 input/output **table** (GUS 1990) with **a** 30% **downwards** adjustmcnt **for gross** enterprise **sector** output.
Some of these may seem self evident, but they also require empirical testing. For example, competition might not influence public health care providers unless their budgets or incomes are affected by the number of services they provide; and market wages might have limited influence on the ability to recruit qualified staff if non-financial motivation for joining the public health care service is strong.

As a first step to analyzing governance of public health care provision, it would be useful to control for:

- share of consultations by public and private health care providers

- ratios of public nurses‟ and doctors‟ average monthly income relative to high school and college graduates

- number of annual graduates from nursing and medical schools relative to public health care service vacancies

- World Bank measures of governance of public institutions

More detailed information might be necessary. For example:

- average number of private health care providers operating within 10 km of each public health care facility

- ratios of health workers incomes to specific alternative professions (e.g. teaching, law) or statistically controlled analyses of wages relative to the larger labor market

- country governance indicators that are directly relevant to the health sector (e.g. the CPIA health sector component).

Additional contextual factors that have been measured and used in other studies include sociodemographic data (e.g. female literacy rates, income distribution, ethnic and religious fragmentation), labor market indicators (e.g. unemployment rates among high school graduates), and quality of public institutions (e.g. the World Bank‟s CPIA).

# **5. Ways to Measure Governance in the Health Sector**

This section addresses practical issues for measuring governance in the health sector. It describes leading approaches to measuring the five governance performance measures identified above. It concludes with a discussion of strategies for measuring governance determinants. The appendix provides links to websites for a number of survey instruments and measurement initiatives.

# **_Governance performance_**

The strategy for measuring governance performance measures in ways that are comparable across countries is reasonably clear. This section describes how to measure governance performance with a focus on the five indicators identified by WHO‟s task force, namely:

- Health worker absenteeism in public health facilities

- Proportion of government funds reaching district-level facilities

- Stock-out rates for essential drugs in health facilities

- Proportion of informal payments within the public health care system

______________________________________________________________________________________

-28-
**Table 2.** Robustness Tests

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Panel A: Least Squares_|_Dependent v_|_ariable: Subjec_|_tive evaluation of l_|_iving conditions_||
|Treatment|-0.09***<br>(0.028)|-0.09**<br>(0.02)|-0.15***<br>(0.024)|-0.14**<br>(0.021)|-0.13**<br>(0.44)|
|Excluding Gaborone|Yes|Yes|No|No|No|
|Sample centered on<br>discontinuity|No|Yes|No|Yes|No|
|Excluding urban<br>respondents|No|No|Yes|Yes|No|
|Excluding rural<br>respondents|No|No|No|No|Yes|
|Observations|1,063|176|679|128|519|
|R-squared|0.008|0.027|0.016|0.045|0.051|
|_Panel B: Ordered logit_||||||
|Treatment|-0.63***|-0.64**|-1.06***|-0.89***|-0.61*|
||(0.173)|(0.091)|(0.129)|(0.114)|(0.317)|

_Source_ : Authors’ analysis based on data sources discussed in text. Treatment denotes the Pula devaluation. Days before devaluation are coded as 0; days after devaluations are coded as 1. All models contain a constant term (not reported to save space). Robust standard errors clustered at the region level in parentheses. *** _p_ < .01, ** _p_ < .05, * _p_ < .1.

In column (1), we report the basic unconditional association after omitting all respondents from Gaborone, which reduces the sample from 1,198 to 1,063 respondents. In absolute terms, the coefficient is reduced from 0.16 to 0.09, but it remains highly significant and shows that the relationship between the currency treatment and subjective well-being cannot be accounted for by the presence of respondents from the Gaborone area in the pretreatment group. In column (2), we continue to exclude respondents from Gaborone but also zoom in on the two days surrounding the devaluation (the first day before; the first day after). This does not change the results substantially either.

However, in further estimates (available upon request) we show that this is not the case in the present sample or the subsequent fourth round of the _Afrobarometer_ survey in Botswana.

22
stration activities on technology diusion. The prole of the seed adopters inuences whether farmers act on the information they receive. When focusing on farmers with similar cropping patterns as their CF, we observe modest (yet statistically signicant) technology diusion. Complementary interventions, such as assigning dierent types of seed adopters (Beaman et al., 2014; BenYishay and Mobarak, 2014; BenYishay et al., 2015) or encouraging experiential learning in the community (Jones et al., 2015), may increase the pace of technology diusion in the context of decentralized extension services.

# References

- Abdi, H., 2007. The bonferonni and 2idák corrections for multiple comparisons. Encyclopedia of measurement and statistics 3, 103107.

- Akroyd, S., Smith, L., 2007. Review of public spending to agriculture. A Joint Study by the Department for International Development and the World Bank 5.

- Anderson, J. R., Feder, G., 2004. Agricultural extension: Good intentions and hard realities. The World Bank Research Observer 19 (1), 4160.

- Anderson, J. R., Feder, G., 2007. Agricultural extension. Handbook of agricultural economics 3, 23432378.

- Anderson, M., 2008. Multiple inference and gender dierences in the eects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. Journal of the American Statistical Association 103 (484), 14811495.

- Bandiera, O., Rasul, I., 2006. Social networks and technology adoption in northern mozambique*. The Economic Journal 116 (514), 869902.

- Banerjee, A., 1992. A simple model of herd behavior. Quarterly Journal of Economics 107, 797817.

- Banerjee, A., Cole, S., Duo, E., Linden, L., 2007. Remedying education: Evidence from two randomized experiments in india. The Quarterly Journal of Economics 122 (3), 12351264.

- Banerjee, A. V., Banerji, R., Duo, E., Glennerster, R., Khemani, S., 2010. Pitfalls of participatory programs: Evidence from a randomized evaluation in education in india. American Economic Journal: Economic Policy, 130.

- Beaman, L., BenYishay, A., Magruder, J., Mobarak, A. M., 2014. Can network theory based targeting increase technology adoption.

- Benin, S., Nkonya, E., Okecho, G., Pender, J., Nahdy, S., Mugarura, S., Kayobyo, G., 2007. Assessing the impact of the national agricultural advisory services (naads) in the uganda rural livelihoods. IFPRI Discussion Paper.

24
Policy Research Working Paper 9519

# **Abstract**

In recent years, researchers have demonstrated that digital footprints from mobile phones can be exploited to generate data that are useful for transport planning, disaster response, and other development activities—thanks mainly to the high penetration rate of mobile phones even in low-income regions. Most recently, in the effort to mitigate the spread of COVID-19, these data can be used and explored to track mobility patterns and monitor the results of lockdown measures. However, as rightly noted by other scholars, most of the work has been limited to proofs of concept or academic work: it is hard to point to any real-world use cases. In contrast, this paper uses mobile data to obtain insight on urban mobility patterns, such as number of trips, average trip length, and relation between poverty, mobility, and areas of Freetown, the capital of Sierra Leone. These data were used in preparation of an urban mobility lending operation. Additionally, the paper describes good practices in the following areas: accessing mobile data from telecom operators, frameworks for generating origin and destination matrices, and validation of results.

This paper is a product of the Global Indicators Group, Development Economics and the Transport and Digital Development Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at dmatekenya@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 5: OLS and Heckman estimation results for final goods and parts and components separately.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|Heck|man|Heck|man|
||Final|Parts|Final|Final|Parts|Parts|
||||Outcome|Selection|Outcome|Selection|
|**LPI (exp.)**|**1.841*****<br>**(0.000)**|**1.387*****<br>**(0.000)**|**2.005*****<br>**(0.000)**|**1.067*****<br>**(0.000)**|**1.537*****<br>**(0.000)**|**0.962*****<br>**(0.000)**|
|**LPI (imp.)**|**0.365*****<br>**(0.000)**|**0.466*****<br>**(0.000)**|**0.452*****<br>**(0.000)**|**0.720*****<br>**(0.000)**|**0.568*****<br>**(0.000)**|**0.758*****<br>**(0.000)**|
|Log(distance)|-0.978***|-0.837***|-1.024***|-0.411***|-0.882***|-0.384***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Contiguous|1.063***|0.923***|1.027***|-0.133|0.872***|-0.206|
||(0.000)|(0.000)|(0.000)|(0.363)|(0.000)|(0.137)|
|Language|0.469***|0.359***|0.507***|0.294***|0.403***|0.236***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Colony|0.570***|0.590***|0.636***|0.876***|0.655***|0.776***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Colonizer|0.709***|0.600***|0.581***|-0.739***|0.377**|-0.809***|
||(0.000)|(0.000)|(0.001)|(0.000)|(0.019)|(0.000)|
|Log(GDP exp.)|0.962***|0.740***|1.011***|0.305***|0.783***|0.273***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(GDP imp.)|0.625***|0.560***|0.661***|0.247***|0.594***|0.235***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(Doing Business)||||-0.172***<br>(0.000)||-0.181***<br>(0.000)|
|Constant|-32.667***|-27.710***|-35.404***|-14.438***|-30.220***|-13.189***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|R2|0.654|0.600|0.026||0.027||
|Observations|9231|9952|20736|20736|20736|20736|

_Note: The dependent variable in each case is exports. All trade costs variables are transformed as per Baier and Bergstrand (2009), as discussed in the main text. Estimation methods are indicated at the top of each column. Prob. values based on robust standard errors adjusted for clustering by country pair are in parentheses beneath the parameter estimates. Statistical significance is indicated by * (10%), ** (5%), and *** (1%)._ 28