rafmacalaba/data-use-mentions-extended huggingface dataset:
# **References**

- Abate, G. T., De Brauw, A., Hirvonen, K., & Wolle, A. (2023). Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia. _Journal of Development Economics_ , _161_ , 103026.

- Aiken, E. L., Bedoya, G., Blumenstock, J. E., & Coville, A. (2023). Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan. _Journal of Development Economics_ , _161_ , 103016.

- Altındağ, O., O'Connell, S. D., Şaşmaz, A., Balcıoğlu, Z., Cadoni, P., Jerneck, M., & Foong, A. K. (2021). Targeting humanitarian aid using administrative data: Model design and validation. _Journal of Development Economics_ , _148_ , 102564.

- Arthi, V., Beegle, K., De Weerdt, J., & Palacios-López, A. (2018). Not your average job: Measuring farm labor in Tanzania. _Journal of Development Economics_ , _130_ , 160-172.

- Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. _Annual Review of Economics_ , _11_ , 685-725.

- Beegle, K., De Weerdt, J., Friedman, J., & Gibson, J. (2012). Methods of household consumption measurement through surveys: Experimental results from Tanzania. _Journal of development Economics_ , _98_ (1), 3-18.

- Beegle, K., Christiaensen, L., Dabalen, A., & Gaddis, I. (2016). _Poverty in a rising Africa_ . World Bank Publications.

- Carpenter, J. R., Bartlett, J. W., Morris, T. P., Wood, A. M., Quartagno, M., & Kenward
NL-2/2-1-88

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With all the sluggishness of the price response we should not forget that inflation was coming down in Chile, albeit slowly. In this respect the policies were more effective than the experierces of Brazil and Mexico in the 1980s where inflation advanced to a new plateau following the stabilization shock (combined with an external crisis). This difference is probably attributable to the fact that the Chilean experiment implemented a monetary crunch which provided some nominal anchor for inflation while this was not the case in the other experiments.

b. Argentina 1976-8 In the corresponding Argentinian experiment, following the overthow of the Peronist government, the fiscal-monetary stage lasted two years, starting April 1976 [see Ramos (1986)]. During the last year of the latter government inflation was raging at about 450% in annual terms.

The policies of the new military regine were directed by Martinez de Hoz who initiated a stabilization policy based on a sharp budget cut (reducing the public sector deficit from 10% of GDP in 1975 to 3% in 1977, according to Ramos (1986)1.!/ This was accompanied by tightening of monetary policy in 1976 and in the first half of 1977 which is reflected by a drop in the M2/GNP ratio (see Table 7.b). Examination of quarterly data reveals that while inflation fell considerably with the initiation of Martinez de Hoz policies real M2 did not increase as expected, indicating a contractionary monetary policy.

- 1/ According to de Pablo and Martinez' (1937) data, the public sector deficit was reduced from 15.61 in 1975 to 5% in 1977. Excluding interest payments the figures are 14.9Z and 3.9% for these two years.
assistance to improve productivity, and organized marketing facilities. Family income is therefore highly variable and there is little opportunity for saving. They have very few assets, including education, and are very vulnerable.

The differing characteristics of the smallholders, sharecroppers, and wageworkers suggest that a poverty reduction strategy needs to provide multiple paths out of poverty tailored to the heterogeneous cross-section of poor rural households. This will involve at least a five-pronged approach aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural nonfarm sector, (iv) migration of the young, and (v) provision of safety nets for those “trapped” in poverty. The recommended measures include improving human capital endowments, reforming the land, labor, and financial markets, enhancing research and extension, improving the supply of public goods and services, pricing and trade policies, and transfer programs.

In order to increase land productivity and labor-intensive farming, it is necessary to facilitate the movement toward farming medium-sized land holdings, in part via facilitating land rentals and sharecropping arrangements. This can be done by providing more secure titles to land and by the revision of the land legislation so as to secure longer-term tenancy arrangements, resolution of disputes regarding interpretation, and enforcement of land rental arrangements. The impact of such a program would be greatly enhanced by simultaneous adjustments of the labor code and of the land tax system. Labor laws have had an antisharecropping bias. In this context, the experience with the Rural Leasing Exchange in the Triângulo Mineiro in Brazil contains useful lessons that could be worth considering for Mexico.

Extreme rural poverty is not distributed equally across population groups. The incidence of poverty is much larger among indigenous peoples that are at the bottom of the income distribution.<sup>6</sup> According to 2000 Census, 44 percent of indigenous groups are in the bottom 20 percent of the overall distribution of income, and 80 percent in the bottom 50 percent (Hall
nologies, changes in the production process, or changes in the organization of work are more complementary to skilled workers, so that shifts in the level of technological capabilities of the economy increases the demand for skilled labor relative to unskilled labor and the wages of skilled labor. While the skill bias technological change impact on wages has received some empirical backup, there is no full consensus on the importance of this effect (Card and DiNardo, 2002), and some studies looking at the use of particular technologies, for example, computers, also find that the premium can be exaggerated if not controlled for the sorting of higher-paid workers. For example, Entorf et al. (1999) study the effects of computer use on wages and find that workers who use computers are better paid than non-users; however, computer users were already better paid before, with only a small wage increase afterwards.

Looking more directly at the impact of innovation on the wage premium, Reenen (1996) uses a panel of British manufacturing firms to analyze the wage differential between innovative and non-innovative firms. Innovating firms are found to have higher wages due to the rent sharing associated with the R&D projects. Martinez-Ros (2001) also finds for a panel of manufacturing firms in Spain a positive wage premium associated with product and process innovation and that, in fact, innovation and wages are jointly determined. Cirillo (2014) estimates the wage premium for Chile using both factor and cluster analysis, as well as panel data techniques, and finds that the innovation premium is not significant when including firms fixed effects. However, as these papers lacked information on workers’ characteristics, it is not clear whether the observed premium is due to the sorting of high (low) paid workers into high (low) paying firms or due to firms paying higher (lower) wages to seemingly similar workers. Aghion et al. (2019) are the first to use matched employer-employee data for larger firms in the United Kingdom to investigate the innovation wage premium. The
The multivariate regression results with country fixed effects in Table 2, Columns 3-4, confirm this finding and show that unbanked adults are significantly less likely to be able to use an account if they belong to typically underserved groups, such as women, poor adults, less educated adults, and adults out of the workforce.

# **6. Explaining why account owners do not use their accounts**

Having an account is only the first step towards achieving the benefits of access to formal financial services. More important is how people use their account. There are about 430 million people in developing countries (including 280 million in India), representing about 13 percent of banked adults, who have an account but do not use it—these are the financially inactive consumers, sometimes referred to as the ‘ _underbanked’_ . Inactive account holders at banks and similar financial institutions are concentrated in one country—India—where 35 percent of accounts were inactive in 2021. India’s share of inactive accounts coupled with its large population has the statistical effect of pushing the overall developing economy average of inactive accounts to 13 percent of account holders. Yet, when we examine the inactive account data for India separately from the rest of the world’s developing countries, the average developing economy inactive account rate decreases sharply to 5 percent, making India’s inactive account share about seven times larger than the developing economy average. For this reason, additional data was collected _only_ in India on the reason for account inactivity.

We also examine why adults in Sub-Saharan Africa do not use a mobile money account on their own, without help from an agent, family member, or friend. Previous research has shown that adults that transact through their account without the help of a mobile money agent can sidestep illicit extra charges. For example, in Ghana, consumers are overcharged in 1 out of 4 mobile money transactions, with female customers more likely to face misconduct (Annan 2022b).

21
status.<sup>13</sup> However, only women who were also interested to join RMGs entered the lucky draw and are captured in our sample as RMG-eligible. For simplicity and clarity of exposition, we may refer to this sample as village head selection, though formally speaking this implies village head selection conditional on self-selection.

Overall, Table 1 suggests that the RMG program effectively targeted poorer households within villages. We next compare actual selection to a hypothetical selection based on PMT used by the Government of Lao PDR to identify the poor for participation in social programs. This PMT index was developed by the World Bank and Government of Lao PDR, following administration of LECS V in 2012-2013 (Pimhidzai et al. 2014). It relied on regression analysis of household consumption on surveyed household characteristics. The model aimed to accurately distinguish between poor and non-poor households. Nonsignificant characteristics were removed from the model; characteristics that minimized misidentification of poor and non-poor households were kept. Index variables and weights were determined based on the regression outcomes. Households scoring below 64 were classified as poor. Table A2 in the Appendix provides the complete list of variables used in the PMT index (World Bank forthcoming).

When we compare village head selection with those selected through PMT, we find that, though both methodologies selected approximately the same number of beneficiary households, they disagree on which households should benefit in 44% of cases. The magnitude of this disagreement holds true whether one considers the full sample of 1,888 study households (Table 2) or only 1,245 households ranked in the village head targeting survey (Table A3 in the Appendix). Although we expect differences in how PMT and village heads identify poor households, the consensus on population poverty rates and need for welfare support is striking.

**Table 2: Village heads vs PMT – Full Sample of 1,888 Households**

|RMG-eligible|PMT b<br>No|elow 64<br>Yes|Total|
|---|-
Policy Research Working Paper 7579

# **Abstract**

The relative return to input-augmentation versus inefficiency-reduction strategies for improving education system performance is a key open question for education policy in low-income countries. Using a new nationally-representative panel dataset of schools across 1297 villages in India, this paper shows that the large investments over the past decade have led to substantial improvements in input-based measures of school quality, but only a modest reduction in inefficiency as measured by teacher absence. In the data, 23.6 percent of teachers were absent during unannounced visits with an associated fiscal cost of $1.5 billion/year. There are two robust correlations in the nationally-representative panel data that corroborate findings from smaller-scale experiments. First, reductions in student-teacher ratios are correlated with increased teacher absence. Second, increases in the frequency of school monitoring are strongly correlated with lower teacher absence. Simulations using these results suggest that investing in better governance by increasing the frequency of monitoring could be over ten times more cost effective at increasing teacher-student contact time (net of teacher absence) than hiring more teachers. Thus, at current margins, policies that decrease the inefficiency of public spending in India are likely to yield substantially higher returns than those that augment inputs.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at jdas1@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
, even if adequate food supplies can be ensured globally, access to food remains a concern, especially in low-income countries where food insecurity is associated with conflict and extreme weather events (FAO 2021). The number of people subjected to acute food insecurity worldwide has more than doubled during the past five years, exceeding an estimated 280 million in 2023 (Food Security Information Network 2024). Achieving equitable access to food across income groups will entail financial assistance, food delivery through aid, as well as investments and policies aimed at increasing productivity but also reducing food waste at the production level (Nature 2020).

The calorific-based indices along with the estimation procedure presented in this paper not only deepen our comprehension at both aggregate and regional levels, but also pave the way for multifaceted avenues of future research. For instance, by incorporating additional factors, like water usage or chemical input use, the aggregation of production _— 17 —_
therefore that many countries fail at making appropriate transitions or even fail to recognize that such a transition is needed....Ironically, old strategies become new weaknesses.” This suggests that the challenge which Latvia set for itself – becoming an innovative economy -- is especially daunting. In effect, Latvia must navigate not one, but two, simultaneous transitions. First, to succeed in the transition for the Factor-Driven to the Investment-Driven stage, Latvia it must upgrade the technological sophistication of its existing industries by absorbing knowledge produced outside Latvia. Unfortunately, current Latvian policies are not particularly geared to this challenge. And at the same time, to succeed in the Innovation-Driven stage Latvia will need to reform its NIS so that it makes better use its strong human capital endowment and historic tradition of scientific excellence. But as this report (and many others) suggest, Latvia is not particularly strong in this area either.

# <u>B. World Bank Knowledge Economy Index (KEI)</u>

The World Bank’s KEI presents a mixed picture of Latvia’s progress toward becoming an innovative economy. For example, relative to its per capita GDP, Latvia’s KEI score is higher than expected. Nevertheless, in absolute terms, Latvia is behind all NMS’s except Bulgaria and Romania. In other words, Latvia is doing better than the statistical average. But Latvia is not competing against the average. It is competing against other NMS’s and, from that perspective, it is not doing well.

**Figure 17 KE Index in Latvia and Selected European Countries**

<!-- Start of picture text --> KE Index and GDP in Latvia and Selected European Countries, 2000<br>35000<br>30000 Ireland<br>Denmark<br>25000 Finland<br>Sweden<br>20000<br>Slovenia<br>15000<br>Czech Republic<br>Hungary<br>Slovak Republic Estonia<br>10000<br
generate a balance of interests by adding more countries with export interests, many of which are developing countries, to the potential coalition of gainers; and dealing with the fact that the traditional “fuel” for multilateral negotiations—the scope for tariff cutting of non-agricultural products in the industrial countries—is much depleted following eight multilateral rounds focused on this issue. Another important reason was that the remaining barriers to trade in agriculture were much more important than the sector’s diminutive share of world trade would suggest. This reflects not only their level and the fact that many are prohibitive, but also the costly variations across sectors and over time (Francois and Martin 2004) in rates of protection.

As shown in Figure 1, trade in the products classified as agricultural by the WTO (WTO 1995) made up only 12 percent of world trade in 1988 (when the Harmonized System data needed for compatibility with the WTO’s relatively broad definition of agriculture begin), and declined to just over 6 percent of trade in 2006, before beginning to increase—largely in response to higher agricultural prices—to almost 9 percent in 2009. While not shown in Figure 1, the share of exports from developing countries follows a similar, but sharper, profile to that for the world as a whole, but declining from 17 percent in 1988 to 6 percent in 2006, before increasing to 8 percent in 2009.

7
which are uniform only within stipulated categories.

While Latin America has attracted the bulk of the past research on PMT, we study the method using survey data for the world’s poorest region, Sub-Saharan Africa (SSA). This is also the region where existing social spending has been least effective in reaching the poorest.<sup>9</sup> The specific countries studied are Burkina Faso, Ethiopia, Ghana, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda, being all those countries in SSA with recent and reasonably comparable surveys in the World Bank’s Living Standards Measurement Study (LSMS).<sup>10</sup> For a subset of these countries we also have panel data.

> 8 If the regression model is _y_   _x_   with _Cov_ ( _x_ ,  )  0 then _Cov_ ( _y_ ,  )ˆ  _Var_ (  )ˆ  0 (in obvious notation). 9 For evidence on the point see Ravallion (2016, Chapter 10).

> 10 Existing government safety net programs in Ghana, Malawi, Nigeria, and Tanzania, and World Bank projects in Burkina Faso, Ethiopia, Ghana, Malawi and Niger use PMT, often in combination with geographic and community-based targeting. At the time of writing, PMT is also being considered for Mali.

4
Subte stations were closed: initially, only 39 of 106 stations were kept open, extended to 50 stations in April 2020 and remained at this number during the October 2020 analysis period. By October 2021, all the stations of the network were operating, while the available supply on the rail riles measured in train-kilometers was still 15 to 20 percent below October 2019 levels.

The pandemic led to an increased urgency to implement policies that benefit and protect active travelers (World Bank 2021). Also in the case of the Bogota and Buenos Aires metropolitan areas, albeit in the latter case concentrated in CABA, aggressive measures were taken to facilitate active mobility. Bogota implemented 84 km of temporary bike lanes. Buenos Aires built 17 km of new bicycle lanes on two of the city's main avenues, Córdoba and Corrientes.

# **5. The impacts of the pandemic on mobility in Buenos Aires and Bogota: Comparative results**

- _5.1. Initial changes (2020): Decline in public transport use, non-home based, and peak-hour travel_

The initial reduction in total trips was much more significant in AMBA (25 percent) than in Bogotá (15 percent) (Figure 3). The fall was stronger in the central cities of both metropolitan areas, at 18 percent in Bogota D.C. and 33 percent in CABA on weekdays. The decline in travel was somewhat lower on Saturdays compared to weekdays although the trends on Sundays differed between the two metropolitan areas. Total trips in Bogota fell by 7.4 percent on Saturdays and by 2.7 percent on Sundays, while the corresponding reductions in AMBA were 14.0 percent and 24.8 percent.

**_Figure 3:_** _Change in the number of daily trips on weekdays in October 2020 vs. 2019_

<!-- Start of picture text --> a. Bogota b. AMBA<br>45,000,000 45,000,000<br>40,000,000 40,000
>auditors?|42|41|43|
|Do regulations explicitly prohibit auditing firms from providing non-<br>audit services to the banks whose financial accounts they audit?|55|31|65|
|Are external auditors subject to independent oversight by:<br>Ministry of Finance or other government department?|24|22|25|
|Specialized public entity (e.g. independent audit regulator)?|42|51|39|
|Bankingsupervisoryagency?|23|11|28|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

> 10 If we focus on the sample of countries that responded to both the 2007 and 2011–12 survey (i.e., the “matched” sample) the increase is smaller from 82 to 88 percent.

> 11 Note that in the “matched” sample, the increase is smaller from 68 to 73 percent.

> 12 Once again, in the “matched” sample, the increase is smaller from 11 to 27 percent.

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# **5. Model calibration**

# _5.1 Setting up the baseline and scenarios_

To make empirical comparisons _between_ the Standard LTGM and the public capital extension (LTGM‐PC) as well as _within_ the public capital extension (public versus private investment, increasing quantity vs quality of public investment), we run simulations using both models for a sample of 147 countries. To solve each model, we need to first input baseline parameters, initial conditions and paths of variables for individual countries. Data sources and calculations are detailed in Table 3.

**<u>Table 3: Baseline Parameters, Initial Conditions and Paths of Variables</u>**

|<br>**Constant Parameters**|<br>**Model(s)**|<br>**Source/Calculation**|
|---|---|---|
|Labor shareβ|Std LTGM<br>LTGM‐PC|Latest individual countryβ, based on Penn World Tables (PWT) version 8.1,<br>or the Global Trade Analysis Project (GTAP) database. Labor shares below<br>0.45 or above 0.7 were trimmed to those values to remove outliers.|
|Elasticity of output to public<br>capitalφ|LTGM‐PC|0.170 (default) and 0.100 for all countries. See Section 5.2 for discussion of<br>this calibration.|
|Congestionζ|LTGM‐PC|1(congestion)or 0(purepublicgood)is assumed for all countries.|
|Efficient public capital stockθ|LTGM‐PC|Infrastructure Efficiency Index (IEI) – see Section 4. Unchanged efficiency is<br>assumed for all countries.|
|Aggregate depreciation rateδ|Std LTGM|Latest value from PWT 8.1.|
|Public capital and private capital<br>depreciation ratesδ<sup>�</sup>, δ<sup