rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9752

# **Abstract**

This paper investigates whether enhanced access to mobile communications, including internet, primarily through smart phones, increases competition as price information is more widely available to customers—both households and firms. The exogenous shock to identify these impacts is the transition from 2G to the 3G broadband network standard in 2008, and the induced changes in the geographic variation across districts of data plan availability for households. The operational mechanism is that better household and firm telecommunications access can close information asymmetry gaps between buyers and sellers, with increased competition leading to improved firm performance. Lower markups and reduced price dispersion can result from better incentives for firms to preserve and grow market share. And as price competition squeezes profit margins, there are more incentives for firms to reduce costs—inducing higher total factor productivity growth. Improved firm performance can generate jobs and economic transformation. Indeed, faster productivity growth, due to enhanced access for buyers to mobile telecommunications, can translate into higher formal employment and wages. One open question is whether the potential competition, driven by the increased mobile telecommunications access of buyers, which help them have the best alternative prices at their fingertips, will also impact export-oriented - companies. The prior is that the firm performance improve ment effect would be more salient for firms mostly focused on local markets. The primary data sources are manufacturing firm census data and household expenditure survey data across woredas (districts or counties) in Ethiopia. First, the paper investigates the relation between expanded access with the 3G network to price information through mobile phones (measured at the woreda level as share of households with substantive expenditure to access data through smartphones) and firm performance measures (markups, total factor productivity, labor productivity, wage growth, wage gaps and employment growth.), across districts with different shares of mobile telecommunication and data plan penetration subscription. The paper estimates models with difference-in-differences and triple differences. The evidence is consistent with competition intensification after the improvement in access to mobile communication due to the
measurements are standardized according to the International Referenced Population defined by the U.S. National Center for Health Statistics. The three commonly used indicators of malnutrition are height-for age, weight-for-age, and weight-for height. Children whose measurements fall below two standards deviations from the reference median are generally considered malnourished. Each indicator captures different aspects of malnutrition.

Stunting, or low height for age, is considered an indicator of chronic malnutrition. Height for age measures linear growth and therefore a low score is indicative of a cumulative growth deficit. Stunting is often the result of inadequate feeding practices over a long period and/or repeated illness. It is likely to persist even after these conditions are eliminated. Wasting, or low weight-for-height, measures body mass in relation to body length and is generally considered to reflect acute malnutrition. As an indicator, wasting is likely to vary over short periods of time due to food availability and disease prevalence. Underweight, or low weight for age, is a composite of height for age and weight for height. It is generally considered a general indicator of malnutrition, since a child that is underweight could be stunted or wasted, or both stunted and wasted.

Table 1 compares the proportion of children malnourished, according to the definitions discussed above, in Ethiopia and other low-income Sub-Saharan countries. The data used to calculate the indicator averages for low income Sub-Saharan countries include all countries for which DHS data for children under five years of age was available over the past decade.<sup>4</sup> If more than one survey was available for a given country, only data from the most recent survey was included. Compared to other low-income Sub-Saharan countries, Ethiopia has the second highest rate of stunting and underweight. The proportion of children stunted is 50 percent higher than the average for those countries. In general, children in rural areas are more likely to be stunted than children in urban areas. The difference between the proportion of children underweight is also significantly higher. However, comparisons between the proportion of
two of the primate city coefficients in this table are significantly different from each other.

# **5.1 Road surface**

The results shown so far have not used the available information on road surface. However, road surface helps to explain under what circumstances transport costs to intermediate cities might matter more than transport costs to the primate. The roads dataset includes (static) information on road surface type, so each route can be characterized by the fraction of its length that is paved. For simplicity, this measure is converted to an indicator denoting whether a city’s route is more paved than the route of the median city in its country. If road surface were randomly assigned, in the context of the model above, in the short run we might expect a less negative _β_ for the more paved routes, because driving on paved roads is cheaper, in fuel, time, and maintenance costs, than driving on unpaved roads. In a study on South African roads, du Plessis, Visser and best considered as a proxy for diesel and gasoline prices together in this context

> 46About a third of the cities in the sample (94 out of 287) have a 1992 population of at least 100 thousand.

20
**Figure 5. Distributional incidence of subsidy beneficiaries (by decile) in three Latin American countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Subsidy beneficiaries are households. All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure (Jamaica) or income (El Salvador and Panama) refers to the countrywide distribution of expenditure/income per capita, i.e., households are ranked according to their expenditure/income per capita.

Figure 6 shows the results for two Asian countries, Bangladesh and Vietnam. In Bangladesh, water consumption subsidies are strongly regressive and increase inequality. While subsidies in Vietnam are still regressive, albeit less so, they are actually reducing inequality, suggesting slightly better targeting than in Bangladesh. Figure 7, which depicts the distribution of beneficiary households across deciles, shows strong regressivity in Bangladesh but a more even distribution across expenditure deciles in Vietnam, once again suggesting better targeting.

In the next subsection, we look at a more synthetic measure of targeting performance that complements this analysis and consider both access and subsidy design factors driving this performance.

15
that, in the model, the public sector always has a balanced budget. The direct tax on households is adjusted each period to ensure a balanced public budget.

International trade is modeled by a system of Armington demands that give rise to flows of goods and services between the regions. On the national/regional level, import demand is driven by CES functions of domestic and imported components of demand for Armington commodities. Export supply is depicted by a two tier constant elasticity of transformation (CET) function, where, on the first tier, the total output of a sector is designated either to total exports or to domestic supply, and, at the second tier, total exports are partitioned according to their destinations.

The capital stock is composed of old and new capital, where new corresponds to the capital investments at the beginning of the period and old corresponds to the capital installed in previous periods. The ratio of new to old capital is also a measure of the flexibility of the economy, as new capital is assumed to be perfectly mobile across sectors. Furthermore, each period, a fraction of the old capital depreciates.

Population and productivity growth are exogenous drivers of the model‘s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to growth of the population aged 15-64 years. Productivity growth is modeled as exogenous and factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model.<sup>13</sup>

> 13 This implies that potential improvement in efficiency of energy transformation and utilization has been already incorporated through the exogenously defined AEEI parameter. Note that the AEEI does not have significant influence on the results of the study as the results presented here are on difference between carbon tax policy

12
other than their husbands who accompanied them to the clinic at least once (“Bring-a-Friend (BAF)” voucher group). To separately identify the additional effect of enabling women to seek peer support from the effect of having a voucher for one’s own use, a randomly selected group of women were provided the voucher package only for their own use and not for their peers (“Own” voucher group). The remaining sample was assigned to the control group and did not receive the voucher package.<sup>9</sup>

> 4In our sample, 56 percent of women at baseline reported not being allowed to visit a health facility alone and 44 percent of women reported having no say in decisions about their health care. Moreover, 75 percent of women reported misalignment in their fertility preferences with respect to their mothers-in-law (Anukriti et al., 2022), who typically want them to have more children than they desire.

> 5Women may prefer not to visit health clinics alone due to low confidence in interacting with health care providers and concerns about safety.

> 6Discordant mothers-in-law may strategically curtail their daughter-in-law’s ability to form peer connections to prevent outside influences from affecting the daughter-in-law’s fertility outcomes in ways that potentially deviate from the mother-in-law’s preferences (Anukriti et al., 2020).

> 7Based on data from 52 developing countries, Klugman et al. (2014) document that physical mobility barriers are widespread, especially among younger adult women, and can have detrimental effects on women’s opportunities and choices.

> 8Reproductive autonomy is defined as having the power to decide and control contraceptive use, pregnancy, and childbearing (Purdy, 2006; Upadhyay et al., 2014).

> 9The voucher package that was received by treated women comprised one free FP consultation, a voucher worth INR 2,000 (USD 28) for FP services, and reimbursements for transportation to visit the clinic.

2
insecurity worsens closer to the lean season (Berhane et al., 2010). Given the continued obstruction of humanitarian assistance, these simmering impacts are likely substantial in subsequent lean season-months not covered in our study.

To a certain extent, these results are not surprising – human suffering has been widely reported in contexts of war. In fact, the share of affected people who report continuing work is perhaps surprisingly high, given the circumstances, and even after allowing for selection bias. For example, despite the war, some households continued participation in various farm and non-farm activities. This reflects the exigencies of feeding one's family, even under conditions where the pursuit of livelihoods is fraught with danger. By the same token, our results may be taken as indicators of the resilience of some livelihood sectors and of ordinary people to persevere their livelihoods despite the complications of an active conflict.

Our insights into these outcomes are limited by the ways in which information was elicited in the surveys. For example, while we observe the extensive margin of economic activity (i.e., whether or not labor was allocated to a particular activity in the reference period), we are unable to directly observe the intensive margin (e.g., as the number of hours worked). It may be useful for further efforts to probe for other measures of economic disruption, including qualitative assessments by wage employees and business owners.

A final caveat is in our measures of conflict exposure. We have shown that our regionaldummy approach generates results which are consistent with those obtained from point location estimates of battle events derived from media reports (via the ACLED database). However, future rounds of the HFPS may also ask respondents to indicate their exposure to conflict via the survey instrument.

These caveats notwithstanding, our analysis indicates enormous potential for using highfrequency phone surveys and related remote data collection methods -ideally associated with a baseline sample of respondents -to monitor the impacts of active conflicts, and to quickly devise post-conflict responses that are tailored to
The paper makes a number of contributions to the literature. First, although the literature on wage subsidies has been growing in recent years, there remain relatively few studies that rigorously measure the impact of wage subsidies, particularly of those that cover the costs of social security contributions by employers. One reason for the scarcity of evidence is that wage subsidies, in the form of targeted cuts to employers’ social security contributions, have not been implemented in practice in many countries. Despite the large agreement in the theoretical literature that these subsidies should be effective, there is relatively little empirical research that has examined the effectiveness of the (employer-side) wage subsidies. This is in marked contrast to a large literature that has examined the effects of the targeted tax cuts for employees. Such evidence is even more sparse in the context of developing economies that face a high level of informal employment compared to high-income countries.

Second, our paper uses granular firm-level data from social security administrative records to estimate the effectiveness of an employment subsidy scheme that reduced the costs of social security contributions borne by the employer. It is thus one of the few studies that use such detailed information to study this question as opposed to more aggregate data from nationally representative surveys. Prior studies on employment subsidies for Turkey, such as Betcherman et al. (2010) or Balkan et al. (2016) use data aggregated at a large geographical level, or worker-level information from surveys to evaluate employment effects. In contrast, we are able to take advantage of granular administrative data, where registered employment is reported with monthly frequency over the past 15 years at the firm level. This allows to closely look at firm-level employment dynamics over time before and after the subsidy implementation.

Another important way in which the paper differs from the previous literature is that it studies the effect of a social security contribution subsidy on small firms.<sup>6</sup> This distinction is important in several respects. First, small firms tend to be less productive and typically face a
<!-- Start of picture text --> 2.5 90 2.5 90%<br>80 80%<br>2.0 70 2 70%<br>60 60%<br>1.5<br>1.5 50%<br>50<br>40%<br>40 1<br>1.0<br>30%<br>30<br>0.5 20%<br>0.5 20<br>10%<br>10<br>0 0%<br>0.0 0<br> Global Supply Chain Stress Index (LHS)<br>Stress, MTEU (LHS) Reliability (RHS)<br>U.S. ISM Manufacturing Supplier Deliveries (RHS)<br>Source: S&P and World Bank Source: Sea Intelligence and the World Bank.<br>Note: The PMI suppliers’ delivery time survey question Note: Schedule reliability is a measure of the actual<br>takes 3 positions: faster/slower/same delivery time on-time performance of individual vessel arrivals in<br>compared to last month. The index is the percentage of ports around the world. 4<br>respondents reporting improvement plus 0.5 the<br>percentage responding a stable trend, hence an above 50<br>values report an increase in the Supplier Delivery time.<br>Millions<br>TEU Index<br>Jan-16 Jul-16 Jan-17 Jul-17 Jan-18 Jul-18 Jan-19 Jul-19 Jan-20 Jul-20 Jan-21 Jul-21 Jan-22 Jul-22 Jan-23 Jul-23 Jan-24 1/1/2016 8/1/2016 3/1/2017 5/1/2018 7/1/2019 2/1/2020 9/1/2020 4/1/2021 6/1/2022 1/1/2023 8/1/2023 3/1/2024<br>10/1/2017 12/1/2018 11
plementation, which dramatically complicates the specification of efficiency improvements as endogenous relationships between additional current investment (the intertemporal analogue of _μ_ ) and declines in the values of different technologies’ coefficients on energy inputs in subsequent periods (the intertemporal analogue of _η_ ).

For all of these reasons we opt for the simpler approach of specifying opportunity costs not through the investment channel, but through intra-period endogenous transformation of the economy-wide stock of jelly capital into quantities of technology-specific capital input, following eq. (19). In each period the economy is endowed with a fixed quantity of jelly capital which is capable of being re-allocated across the various non-energy sectors as the relative prices of their inputs and outputs change. By Shephard’s Lemma, the derivative of the cost function represented by Figure 2.B with respect to the price of technology-specific capital determines the unconditional demand for this input, as well as the extent of transformation of jelly capital into technology capital that must generate the supply necessary for market clearance. It is this transformation process which mandates to improve energy efficiency are assumed to distort. Additional technology deployment policies such as subsidies may be introduced here as well, but for the purposes of this study we narrow the focus to mandates because their ultimate economic and environmental impacts are transparent, and uncontaminated by tax interaction effects associated with pre-existing distortions that tend to be more common in developing countries.

The model is spun up from the GTAP database’s year-2008 benchmark to 2015 by scaling regions’ endowments of labor and capital to generate increases in GDP consistent with IMF (2014) growth forecasts. In the 2015 base year technology options are introduced as “backstop” activities by recalibrating the model to accommodate both their demands for capital and fuels given in Figure 1.C, and corresponding demands for their outputs by the remainder of their encompassing sectors, per Figure 2.A. The upshot of this modeling choice is that the levels of base-year demand and activity solved for by the model diverge from the bottom-up values in Figure 1.C,
# 3. **DATA SOURCES, VARIABLES AND SECTORS SELECTED**

# **(a) Data sources**

The plant level data for this study are drawn from the Annual Survey of Industries (ASI), which is published by the Central Statistical Office of the Government of India. The individual plant or "factory" is the unit of analysis used in the survey and the data are based on returns provided by factories<sup>2</sup> . The ASI covers factories registered under sections 2m(i) and 2m(ii) of the Factories Act 1948, employing 10 or more workers and using power, and those employing 20 or more workers but not using power on any day of the preceding 12 months.

Data on various firm level production properties such as output, sales, value added, labor cost, employees, capital, materials and energy use have been used in our study. We closely examined data quality issues by examining the data as well as reviewing the comments of other researchers on this issue. We dropped observations for which information was either incomplete or where the sum of various components were found to be inconsistent with reported aggregates, on the basis of usual accounting principles. The geographic attributes allow us to identify each firm at the district level<sup>3</sup> .

Goldar's (1997) insightful review indicates problems with depreciation figures for capital as well as the head count index for measuring labor input. In this paper, we use the gross (undepreciated) value of capital stock to circumvent the depreciation issue, which appears to be justified in a cross sectional analysis. In addition, to capture variations in hours of work, we use employee man-days instead of the number of employees as the variable to best represent labor input.

# **(b) Variables selected to explain heterogeneity**

We have used several variables to explain inter-firm variations in efficiency, even when they are part of the same industry sector. For example, let
# **5.2 Matching aggregates to national accounts**

After the previous step, we have a comprehensive distribution of income for adults in Honduras, for each year in the period 2003-2019, constructed from microdata. It has been extensively documented, nonetheless, that these income aggregates built from microdata often fail to capture the same levels of income registered in national accounts: De Rosa _et al._ (2022), for example, show that income in survey data complemented with administrative tax records is often only 40-60% of the total pre-tax net national income in selected Latin American countries. We show the same gap exists for Honduras: in Figure 1a, we plot the ratio between total Net National Income (NNI) in national accounts and in our microdata. The gap is relatively stable over the years: each income in national accounts is 30-50% than what we observe year, larger using microdata.<sup>16</sup> Furthermore, we also document that this gap is vastly different across components of total income. In Figure 1b, we plot for each year the same ratio between national accounts and microdata aggregates for four components of income: wages, mixed income, imputed rent and capital income. While aggregate wages are systematically larger by approximately 20-30% each year, mixed income is often very similar in both sources, particularly for recent years when deviations are at the order of 1 - 5%, and imputed rents are often smaller in the national accounts by 10-20%. The largest driver of differences are income from capital – we observe three to five times more income from capital in the national accounts compared to microdata, even when accounting for undistributed corporate profits assigned to individuals.<sup>17</sup> The last step to obtain the full income distribution is to multiply each of the four income components described above by these adjustment factors, such that total income in the microdata, as well as each of its components, matches aggregates in national accounts.<sup>18