rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9590

# **Abstract**

Mobile technologies show great potential to accelerate internet access and usage, especially in developing countries. A better understanding of key drivers and main constraints for mobile internet access is the first prerequisite for governments to design targeted policy solutions. This study exploits a household survey that collects information on information and communications technology access and usage at the household and individual levels in 22 countries in the Global South. The study finds that in addition to infrastructure investment, which has been the main focus of many developing countries, other demand-side factors are of critical importance. Across the developing world, females, the elderly, those who live in rural areas, and those who have a relatively low level of income or education are less likely to adopt mobile internet. Social network effects are found to have a significant positive impact on the usage of mobile internet. Those who have more close friends using an online social network are more likely to adopt mobile internet. Individuals whose five closest friends are using an online social network (such as Facebook or Twitter) are 63.1 percent more likely to adopt it than those without any close friends using such online social network sites/apps. Across regions, although the factors affecting the adoption of mobile internet remain largely the same, the magnitudes of their impacts vary. In Asia, gender differences are negatively associated with mobile internet. In Africa, the impact of education level is more salient than in the other two regions, implying an urgent need to improve digital literacy.

This paper is a product of the World Bank’s _World Development Report 2021_ Team, Development Economics. 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 author may be contacted at rchen5@worldbank.org.

_The Policy Research Working Paper Series
continue. Secondly, in the absence of pollution intensity data from Vietnam, we have used pollution measures from the U.S. as proxies (as suggested by previous IPPS studies). Should pollution intensity data from Vietnam become available, it would be useful to re-examine the issue using Indian measures of pollution intensities.

Despite these caveats, this study has highlighted important gaps in the environmental policy implementation in Vietnam and enables us to make useful recommendations. In order to make specific policy recommendations on future steps, we need to identify the manufacturing sectors that have resulted in greatest increase in water pollution intensive output and toxic pollution intensive exports. Annex 3 shows the important water pollution intensive sectors as: iron and steel, non-ferrous metals, industrial chemicals, rubber and leather products. These sectors are also considered significant contributors for toxic pollution (in addition to ceramics) and consistently rank high based on the Linear Acute Human Toxic Intensity (LAHTI)<sup>13</sup> . Analyses of Vietnamese exports shows that the sectors have shown highest increase in exports are: textile (91 percent), leather (49 percent), and rubber products (26 percent)<sup>14</sup> . Of these the textile industry is also large consumer for industrial chemicals. Therefore, increased textiles exports seems to have also fueled a simultaneous increase in industrial chemicals. Similarly, export revenues from craft villages have increased significantly in recent years to the tune of almost half a billion US dollars a year. These are again a major sources of toxic and water pollution. It is thus not surprising to note that Vietnam’s exports are getting dirtier over time since its export specialization has moved away from more tradition oil and other primary commodities towards manufacturing especially leather and textile industries. The results also suggest that foreign direct investment in Vietnam reflects a similar trend with toxic intensive heavy and light industries attracting most FDI.

These findings suggest that while trade liberalization measures have been pursued to promote economic growth in Vietnam, they have led
Textiles and Clothing. We draw on global results for an assessment of the macro-level impacts of further reform under a WTO–Doha round and feed these into the village-level model. Specifically, we simulate the impacts of the standard _Doha_ scenario, as well as the _Full-Lib_ scenario. In both cases we disentangle the effects of changing prices for inputs and outputs from the effects of increases in off-farm employment and wages.

The full liberalization scenario provides a useful benchmark against which the less ambitious Doha scenario can be compared. Analyzing consumption we find that poorer households face a stronger rise in expenditures. This is due to a larger share of agricultural goods in their consumption and a shift from being net-sellers to self-sufficiency in the case of some households.

Analyzing the impact of changing agricultural input and output prices (with unchanging employment and wages), we find an increased village supply of rice and other livestock, which corresponds with the findings of national level studies and with the results of the farm household analysis in Huang _et al._ (2003). This increased supply of rice, however, is the net result of three household groups increasing rice production, and one group reducing rice production.

The net impact of more off-farm employment (at constant prices) following global trade reform is a decrease in rice supply, caused by an increasing scarcity of labor. Again we find divergent household responses, with some households increasing rice production due to lower costs of animal traction rented within the village. Since this is opposite in sign to the impact of higher prices, it is interesting to ask which dominates. We find that the impact of price changes dominates the impact of increased employment.

A more modest liberalization within the context of the Doha round has a similar, but less pronounced, impact as observed under full trade liberalization. The notable exception is 5
(as argued earlier) but would also make the existing stock of private capital more productive. An important issue in this context, however, is the existence and magnitude of congestion costs, which imply that the productivity gains associated with a greater stock of public capital may diminish over time because the services produced by this stock are over-used. As discussed later, this may be a particularly relevant consideration for public infrastructure (such as roads, for instance) which tends to get overcrowded due to the lack of alternatives.

The evidence linking public investment to private capital formation and growth has grown significantly in the past few years. For instance, Milbourne, Otto, and Voss (2003), using an extended version of the Solow-Swan neoclassical model, find evidence of a positive conditional correlation between public investment and economic growth in a sample of 74 industrial and developing countries. Aschauer and Lachler (1998), using cross-country growth regressions for a group of 46 developing countries, find that public capital contributes significantly to productivity — growth, as long as it is financed by lower current government spending as opposed to a higher level of public debt (which may signal higher current and future taxation, or a future increase in the cost of borrowing). Ahmed and Miller (2000), using a sample of 39 industrial and developing countries for the period 1975-84, find that expenditure on social security and welfare reduces private investment (through the crowding-out effects alluded to earlier) in both groups of countries, whereas expenditure on transport and communication raises aggregate investment in developing countries. Bose, Haque, and Osborn (2003), using panel data for 30 developing countries and an econometric methodology that explicitly accounts for the government budget constraint and possible biases arising from omitted variables, find that the share of government capital expenditure in GDP is positively and significantly related to income growth per capita, whereas current expenditure is insignificant.

Belloc and Vertova (2004), using a vector error-correction approach, find a complementarity relationship between public and private investment, and positive
7 _Table 1: Annual Average Number of inpatient and Outpatient Visits in Provinces by Quintiles Per Capita Income_

|_Quintiles of Per_<br>_Capita Income_|_1980_||_199_|_3_|
|---|---|---|---|---|
|_in Provinces_|_Inpatient_|_Outpati_|_ent Inpatie_|_nt Outpatient_|
|Poorest<br>I|2.9|3.7|1.8|2.8|
|II|2.6|3.5|1.7|2.7|
|III|2.7|3.4|1.9|2.4|
|IV|2.2|2.8|2.1|2.5|
|Richest<br>V|1.8|2.5|1.6|2.2|

# _Source:_ Staff Estimates from MOPH data.

Indeed, according to the 1993 National Health Survey (NHS), utilization varies positively with household income per capita. Province-wide data indicate greater under-use of service in poorer provinces is associated with economic reasons, and vice versa. Of total households, 58.8 percent in rural areas and 39.8 percent in urban areas do not use public health facilities due to economic difficulties. Another factor is the deterioration in quality of public health services due to the reduced public spending since the mid-1980s. The emergence of private sector and liberalization of pharnaceutical sales permitted users, particularly the rich, to move away from public sector to seek costly and perhaps better quality private health care providers. This is partly substantiated by the 1993 NHS. Of total households 11 percent in urban areas and 13 percent in rural areas did not seek public health provider due to poor quality, and about 7 percent used private health clinics but mostly in big cities and urban areas. A third factor may be the declining agespecific morbidity rates, requiring fewer visits to health facilities. But, age
# **Table 5.  Sensitivity to Credit Risk**

(Regulatory Capital to Risk-weighted Assets, percent)

|||**Provisions i**<br>|**ncrease by**<br>|**Migration**<br>|**to NPLs of**<br>|
|---|---|---|---|---|---|
||**Before**<br>**the shock**|**50%**|**100%**|**25% of**<br>**Performing Loans**|**50% of Performing**<br>**Loans**|
|**Peer Group – Large**|18.3|17.1|15.3|18.0|17.8|
|**Peer Group -**|18.7|15.0|11.1|18.1|17.6|
|**Medium**||||||
|**Peer Group - Small**|35.2|32.5|29.6|34.7|34.2|
|**# Banks with CAR**<br>**<12%**|0|11|17|2|3|
|**Share of Assets**|0|20|24.0|5.1|12.3|
|**# Banks with CAR**<br>**<0%**|0|1|4|0|0|
|**Share of Assets**|0|0.99|2.2|0|0|

Source: Central Bank of Kenya data

# **4. Banking Sector Efficiency**

In this section we
||
|Total Income from All Sources|788.26<br>|||
||(778.89)|||
|Number of Clients Seen|1.52|||
||(1.12)|||
|Number of Regular Clients Seen|0.54<br>|||
||(0.66)|||
|Had Unprotected Sex|0.18|||
|# of Times Unprotected Sex|0.42|||
||(1.10)|||
|Observations<br>|12526<br>|||
|Number of Women|192|||
||||_Occurred at Least_|
|||_Prob.of Occurring_|_Once over 3 Month_|
||_Daily Average_|_in a Week_|_Sample Period_|
|**Panel C. Shocks**|<br>(1)|<br>(2)|<br>(3)|
|Someone in Household Sick (other than respondent)|0.37|0.60|0.93|
|Respondent Sick|0.34|0.60|0.98|
|A Friend or Relative of Respondent Died|0.04|0.14|0.46|
|Respondent was Asked for Money|0.07|0.23|0.70|
|Respondent had STI|0.03|0.08|0.34|
|Observations|12481|2384|209|
|Number of Women|192|192|192|

Notes: Figures in Panel A are averages across the 192 women in the sample, from a background survey. Figures in Panels B and C are daily averages from self-reported diaries. There are more observations than IDs in Panel C, Column 3 because 17 women took part in 2 rounds of diaries. Sickness is an indicator variable equal to 1 if the household or respondent reported having
Figure 8: Digital services trade, market size, and data models (2015) <!-- Start of picture text --> Market size and data model Market size and data model Market size and data model<br>US exports of digital services EU exports of digital services China's exports of digital services<br>2 2 2<br>1.75 1.75 1.75<br>CAN<br>1.5 1.5 1.5<br>MEX<br>VNM RUS<br>BRA IDN<br>AUS<br>SAU<br>1.25 TWN 1.25 CHE 1.25<br>HKG<br>PHLTHA JPN<br>TUR SGPNOR KORIND<br>KAZ<br>NZL<br>1 1 MARZAFMYSISRARG 1<br>CHL<br>PERCOL<br>TUN<br>ISL<br>.75 .75 CRI .75<br>KHM<br>BRN<br>.5 .5 .5<br>.9 .95 1 1.05 1.1 1.15 .9 1 1.1 1.2 .9 1 1.1 1.2<br>Size GDP (GRC=1) Size GDP (GRC=1) Size GDP (GRC=1)<br>Open Safeguards Control<br>Digital services exports (GRC=1) Digital services exports (GRC=1) Digital services exports (GRC=1)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI. GRC stands for Greece, a mid-level country in economic size, which is normalized to 1 (GRC=1) following Head and Mayer (2014). Countries are categorized on the basis of the data model regarding the cross-border data transfers.

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Table 4: Operations management and intermediate outcomes, public schools in Latin America

||(1)<br>z-teacher<br>shortage|(2)<br>z-teacher<br>motivation|(3)<br>z-teacher<br>efort|(4)<br>z-household<br>efort|
|---|---|---|---|---|
|Operations Management Index|-0.080|0.238|0.076|0.160|
||(0.043)<br>[0.061]|(0.041)<br>[0.000]|(0.038)<br>[0.044]|(0.054)<br>[0.003]|
|_R-squared_|_0.0787_|_0.171_|_0.154_|_0.242_|
|Observations|2407|2414|2414|2414|
|School controls|Y|Y|Y|Y|
|Country FE|Y|Y|Y|Y|

Notes: All regressions use data from public schools only. The table reports coefficients from regressions of the standardized operations management index on each of the intermediate school outcome. The operations management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include PISA _school final weights_ and country fixed effects. School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources, and average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added to the specifications.

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2021) points to more deep-rooted regulatory or institutional issues.

> 14 To fend off potential cyber-security threats, all land-related digital registries were taken offline immediately after the Russian invasion with only leases allowed to be registered locally on paper using simplified procedures.

> 15 26% of lease transactions affecting 18.6% of leased area with an average size of 1.6 ha per lease involved land for personal farming and 81% of leased area was land for use by commercial agriculture (below referred to as ‘commercial land’ for simplicity). Similarly, 78% of land transacted in the sales market was commercial, though with an average parcel size of 3.2 ha as compared to 1.2 ha for personal land, commercial and personal land account for 42% and 58% of transactions in the sale market, respectively.

> 16 The difference in area points to a shorter maturity of individuals as compared to legal entities’ lease portfolio (often referred to as ‘land bank’).

9
cross validation, averaged over several fold draws. Offset indicators are derived from only half of the data (the first half for early transitions; the last half for late transitions); the out of time model is estimated on the early half of transitions and tested on the late half. AUC represents the area under the receiver operating characteristic curve, and BIC the Bayesian Information Criterion. For middle two columns, model is trained on all individuals except the omitted fold, and performance is reported for the given subsample within the omitted fold. Source: authors’ analysis from telecom data.

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specific to many communities in India – a prime example of group-specific public goods. These goods are often provided through voluntary contributions from community members for the benefit of their particular religious, linguistic, or ethnic group. We argue that in the perceptions of their economic standing, individuals assess the benefits they derive from these groupspecific public goods. Hence, household nominal income alone is an inaccurate barometer for an individual when forming their beliefs about economic inequality and their demands for redistribution.

As the local (within community) supply of religious and social goods increases, do households feel richer on average? Using data on expenditures on social and religious obligations as a proxy for the district-level supply of social and religious goods, we find that as district-level spending on social and religious obligations increases, the perceived household decile rises significantly on average. This result remains valid even after controlling for household income and household spending on religious and social obligations, along with other covariates. The magnitude of the relationship is substantial. Moving from a district below the median in terms of spending on religious and social goods to a district above the median is comparable to the change in perceived decile that would result if the household’s income-based position rose by one decile.

In sum, the benefits from these group-specific public goods are included in the perceptions of relative economic standing, and therefore these perceptions – rather than the position in the distribution of nominal incomes – are what matters for demands for redistribution. In a community with widespread community-specific public goods, inequality gauged solely on the distribution of nominal incomes is not expected to motivate demands for redistribution and providing information about the correct position in the distribution of _nominal_ incomes does not necessarily change the subjective perceptions of economic standing. Thus, such informational intervention is not expected to change demands for redistribution.

4
method to gauge support for more restrictive immigration policies in the U.S., and finds more support for cutting off immigration when asked via list randomization than when asked directly, particularly among more educated individuals and self-identified political liberals.

# **3. Implementing List Randomization for Measuring Illegal Migration**

# **3.1 The List Randomization Question**

We randomly allocate individuals into two groups. Group A is asked “can you tell me how many of the following four statements you regard as true”:

1. At least one member of my household plans on opening a new business in the next five years

2. The economic situation of my household has improved considerably over the past five years 3. Corruption in my country is a less serious problem than ten years ago 4. This household has at least a member currently residing abroad without a legal residence permit Group B is asked how many of the following three statements they regard as true:

1. At least one member of my household plans on opening a new business in the next five years

2. The economic situation of my household has improved considerably over the past five years 3. Corruption in my country is a less serious problem than ten years ago Subtracting the mean number of true statements reported by group B from the mean number of true statements reported by group A then gives the proportion of the sample which has a member residing abroad without the proper documentation.

Several factors enter into the decision of which three questions to make common to the two groups. We chose questions that we thought i) would be a natural fit in either a typical economic survey or would not seem too unusual in the context of a specialty migration survey; ii) would be considered relatively innocuous by most respondents, but not so bland that the illegal migration 5
0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|0.002|0.024|0.031|0.018|4993|
||(0.995)|(0.102)|(0.115)|(0.115)||
|||[1.000]|[0.513]|[1.000]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.Maternal death is defined as death relating to either pregnancy complications or childbirth. The Pentavalent Vaccination targets diphtheria, tetanus, whooping cough, hepatitis B, and haemophilus influenza type B. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A17
continues to obtain if an adjustment is made for the higher initial level of productivity observed in firms that attract foreign participation.

We find a statistically significant negative spillover effect of foreign participation in an industry-through joint ventures and FDI-on firms without such links. This finding is consistent with the results found by Aitken and Harrison (1996) for Colombia and Haddad and Harrison (1993) for Morocco. It suggests that although foreign ownership and/or collaboration has a beneficial impact on the performance of the domestic partner entities, this has not spilled over to the rest of the industry in the time period studied. In part this result may simply reflect the fact that in a transition economy like the Czech Republic, the amount of time required to independently adapt and learn to apply more efficient techniques takes more time than is spanned by the four-year period for which we have data. The result should also be interpreted in light of the fact that on average firms with foreign partnerships account for almost 50 percent of total assets and more than 40 percent of total employment in our sample. If the analysis of spillovers is restricted to the impact of FDI (foreign majority ownership) on the rest of the industry, the spillover effect remains negative, but becomes much smaller and is no longer significant. This is likely to reflect not only the fact that joint venture firms have higher TFP growth than firms without any foreign partnerships, but may also indicate that know-how spillovers from FDI require a minimum level of technological capacity and effort to be absorbed. Survey data on training and investment in technologies suggests many domestic firms may have relatively weak capacities in this regard compared to firms with joint ventures. This illustrates the importance of taking into account differences in the characteristics of firms in each industry, in particular their 3