rafmacalaba/data-use-mentions-extended huggingface dataset:
|Vietnam|2010|11.5|13.0|7.1|11.0|1.5|4.4|0.0|
|Zambia|2011|106.0|14.7|10.6|10.3|1.0|95.4|83.2|
|Zimbabwe|2007|89.8|17.2|14.7|16.2|1.1|75.1|77.6|
|Average||46.3|17.0|9.1|11.6|1.3|37.2|27.7|

Notes: Bound and applied tariffs are based on simple average in mid 1990s and the latest year data available. For applied tariff rates in mid 1990s, detailed data available in each countrty can be found in Michalopoulos 2001, Table 4-1. Coefficient of variation is calculated as standard deviation divided by applied rate. Tariff margin is computed as the difference between bound and the latest applied tariffs. % of unbound is the share of tariff lines unbound.

Sources: UNCTAD TRAINS (applied tariffs) and WTO IDB (bound tariffs) databases.

5
Where a survey does not contain this extra information to rely on, we suggest using a rule of thumb: _code as active people who report doing domestic work but live in agricultural households_ . Table 10 below uses the IHS 1992/93 to show that while not a perfect adjustment, this rule of thumb on aggregate considerably adjusts for underreporting for participation when compared to a finer adjustment based on the use of information from other sections as stated above. Nonetheless, the rule of thumb is not a panacea, as it overstates men’s participation when compared to a detailed adjustment, while restricting it to agriculture household still excludes those in urban areas who carry out household chores that should be counted as economic activities.

**Table10: Comparison of unadjusted and adjusted labor force participation rates by gender, 1992/93**

|**Labor forceparticipation rate**<sup>**a**</sup>|**Male**|**Gender**<br>**Female**|**Uganda**|
|---|---|---|---|
|Unadjusted LFPR|81.8%|75.2%|78.3%|
|<br>Rule of thumb adjustment|90.9%|82.1%|86.3%|
|<br>Detailed adjustment(from table3)|86.7%|90.4%|88.6%|

**_Source:_** _Authors’ calculations based on the IHS 1992/93._ **_Note:_** _The unadjusted LFPR is based only on the main employment status questions, rule of thumb simply codes as active people who reported domestic work but resides in agricultural households where similarly aged people of their gender report agriculture work, and detailed adjustment makes adjustments using information from other questions and modules of the questionnaire (secondary activities, and time use and household enterprise module).
only partially addressed by allowing phone interviews. Additionally, the arm was subject to the sources of measurement error affecting all recall surveys: informants were asked to make the cognitive effort of recalling the consumption over the last seven days, without any help in identifying the starting of the recall period or the tracking of consumption throughout the reference period.

In contrast, the bounding/salience arm had the advantage of following traditional practices of interviewing only one person, but at the cost of eliciting the information from a less well-informed individual. To minimize the measurement error associated with relying on the report of one informant, a second visit was introduced. This allowed: a) giving the informant the opportunity to know what information was going to be asked and to collect the necessary information from other household members, 28
36%<br>30% 30%<br>20% 20%<br>10% 16% 17% 14% 13% 12% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br><!-- End of picture text --> 24
433|290|143|
|N|13925|13925|13687|13522|9734|3788|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on “started log” of household wealth score. Household sample weights from ESS2 applied. The first four columns report results for all households while columns (5) and (6) report results for rural and urban households, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for household (HH) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. The overall and the within R-squared are reported in the first two and other columns, respectively. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

44
production is common across firms (within an industry) and (ii) the materials choice is a static, otherwise undistorted (besides the markup) decision, we can directly measure these firm-specific markups using the average revenue product of materials, _arpm_ (the inverse of the share of revenues paid to intermediates). This result implies that the cross-sectional dispersion in _arpm_ can be mapped one-for-one into dispersion in markups (for detailed derivations, see Appendix B).

Data on intermediate inputs are only available for four of the countries in our sample – China, Colombia, Mexico and the US.<sup>17</sup> In the first column of Table 4, we report the (withinindustry) dispersion in the _arpm_ for these four countries. In the first column of the second panel of the table, we report the dispersion in _arpk_ that arises from markups (this is simply equal 2 to the markup dispersion itself, given by _σ_ ( _arpm_ )) and the share of observed _arpk_ dispersion 17In the US, we construct the measure of intermediate expenditures as sales less operating income before depreciation less wage bill. Following, e.g., Keller and Yeaple (2009), the wage bill is imputed as the number of employees multiplied by the average industry wage calculated using data from the NBER-CES Manufacturing Industry Database (available at http://www.nber.org/nberces/).

13
Table 3.1: Accounts in the Yemen 2004 Social Accounting Matrix

|**Sectors (25)**|**Sectors (25) -- cont.**|**Institutions (3)**|
|---|---|---|
|_Private(17)_|_Government(8)_|Hoseholds|
|Agriculture|Water and sanitation|Government|
|Crude oil,gas and other mining|Other infrastructure|Rest of the world|
|Food and beverages|Healthgovernment||
|Textil|Basic educationg1-g6|**Interestpayments (3)**|
|Wood,paper andpress|Basic educationg7-g9|Domestic interestpayments|
|Liquidpetroleumproducts|Secondaryeducation|Foreign interestpayments|
|Chemicalproducts|Tertiaryeducation||
|Non-metal industry|Othergovernment|**Capital accounts (3)**|
|Metal and equipment||Hoseholds|
|Other manufactures|**Factors ofproduction (13)**|Government|
|Construction|Unskilled labor|Rest of the world|
|Other services|Semi-skilled labor||
|Health Private|Skilled labor|**Investment accounts (9)**|
|Basic educationg1-g6|Private capital|_Private(2)_|
|Basic educationg7-g9|Natural resource|Gross capital formation|
|Secondaryeducation|Government capital(8)|Stock changes|
|Tertiaryeducation||_Government(8)_|
||**Tax accounts (4)**|Water and sanitation|
||Commoditytaxes|Other infrastructure|
||Factor taxes|Healthgovernment|
||Direct taxes|Basic educationg1-g6|
||Import taxes|Basic educationg7-g9|
|||Secondaryeducation|
|||Tertiaryeducation|
|||Othergovernment|

Source: Yemen SAM 2004.

The government is disaggregated into eight sectors: four cycles (levels) of education (basic grades 1 to 6, basic grades 7 to 9, secondary, and tertiary cycles), health, water and sanitation, other public infrastructure, and other
frastructure. This evidence is perhaps suggestive of the effects of mobile phones for developing countries in leapfrogging the digital barriers associated with the costly expansion of fixed line telephone networks.

While this paper does not present evidence on the potential mechanisms, it is noteworthy to emphasize that the literature is replete with evidence on the socioeconomic impact of access to mobile phones and mobile broadband Internet that are generally consistent with the findings of this paper. For instance, Aker (2010) shows that expansion in mobile phone access reduces price dispersion in agricultural markets in developing countries by reducing search costs. Several pieces of evidence also suggest that mobile phone access has improved financial inclusion through mobile money with positive impacts on household welfare (Aker and Mbiti, 2010; Suri and Jack, 2016; Suri, 2017; Batista et al., 2018). For instance, Bahia et al. (2020), using data from Nigeria show that access to mobile broadband Internet is associated with high household welfare via increasing labor market participation, particularly for women.<sup>3</sup> Further, there is evidence that access to digital infrastructure like broadband Internet exerts positive impacts on labor market outcomes (Akerman et al., 2015; Hjort and Poulsen, 2019; Zuo, 2020), and firm performance (DeStefano et al., 2018). There is also evidence of mobile phones as technologies of change via political mobilization (Manacorda and Tesei, 2020), encouraging migration (Adema et al., 2021), reducing corruption (Andersen et al., 2011; Gonzalez, 2021) and confidence in governments Guriev et al. (2021). These prior evidence highlight the numerous pathways through which the impact of mobile connectivity on economic growth, as shown in this paper, arises. Thus, this paper offers an important contribution to the literature by documenting the extent to which the various microeconomic impacts of mobile connectivity translate into aggregate economic outcomes.

This paper is closest with Czernich et al. (2011) and Goldbeck and Lindlacher
the new poverty line and an improved consumption aggregate derived from a 7-day recall of food expenditure. As such, it is not comparable to any of the earlier official estimates and doing so would be technically incorrect.

Figure A1: Poverty Trends in Nepal since 1995 <!-- Start of picture text --> 70%<br>63.8%<br>60%<br>49.4%<br>50%<br>40%<br>41.8%<br>30.8%<br>30%<br>30.8%<br>25.2%<br>20%<br>10%<br>12.5%<br>0%<br>1995-96 2003-04 2010-11 2014/15<br>Official Poverty Estimate in 2010 Trend on 1995 Poverty Line<br>Trend on 2010 Poverty Line<br><!-- End of picture text -->

# Source: World Bank (2013)

In light of constructing comparable poverty estimates over time, what is of interest is the poverty rates in the earlier years had we used the “new” poverty line and considered total consumption comparable to the 30-day recall versions. The appropriate trend for this version would require a recalculation of poverty for all three rounds of the NLSS using the new poverty line and new consumption aggregate based on the 30-day recall. Under this approach, the poverty rate in 2010 would be 30.8 percent. In the earlier rounds, poverty rates were 63.8 percent in 1995 and 49.4 percent in 2003. Regardless of the choice of methodology and poverty lines, poverty declined significantly between 1995 and 2010.

Some may argue that even the new poverty line may be too parsimonious and the resulting poverty estimates too low. One in four Nepalis were unable to afford basic minimum needs for food and nonfood items that cost only Rs. 1,605 a month. At the same time, the
(in 2016 dollars). Focusing on household consumption alone, the estimates suggest a burden of 1.1 percent of expenditure, or $203 per citizen per year. Applying the same approach to investment spending generates an estimate that the JA increases the cost of investment in PR by 3 percent.

Our estimates of missing trade among upstream products are more difficult to attribute directly to the JA, but are potentially much more important since they suggest dynamic effects of the JA. We find that PR’s total imports of sea-shipped upstream products are approximately 77% lower than imports of otherwise equivalent air-shipped products. Similar calculations for DOM, JAM and BHS finds no bias against sea-shipped products in upstream import demand. Although the bias against sea-shipped upstream products in PR’s import demand is suspiciously large, attributing these effects to the JA is difficult because the evidence of additional bias against U.S. mainland sources is weak. We therefore refrain from including the effects we observe among upstream products in our welfare estimates. If even a fraction of the effects we estimate are in fact due to the JA, the policy has imposed a large burden on PR’s long-run development.

There is a relatively small academic literature on the JA. The paper closest to ours is Olney (2020), who shows evidence of substitution away from waterborne shipping among data on shipments arriving in U.S. coastal states. Our econometric exercise is similar, but with a few key differences. We focus on PR (an island) rather than coastal U.S. states, and use data on Puerto Rican imports rather than freight movements destined for U.S. mainland ports. Our focus on PR’s trade means that only two modes of transport are relevant (air and sea), while Olney’s data contain possibilities for easier substitution towards rail and road transport. We estimate a structural parameter, the tariff-equivalent cost of JA restrictions that can explain cross-product variation in estimated home bias that is attributable to the relevant product characteristics
good indicator of the stance of monetary policy. The overnight rate, however, was instead chosen to proxy for changes in the stance of monetary policy (a monetary policy indicator) because sufficient time series of the RP rate were not available and the recent movements in the call rate have been very much similar to those in the RP rate."<sup>2</sup> Of particular relevance for our analysis is to exploit the December 1997 policy episode which appears to adequately capture apparently severe contractionary shift in monetary policy. This exogenous episode of tighter policy would provide more insightful information about whether the bank lending channel operating mainly due to the presence of credit market imperfections in the form of borrowing restrictions has played an important role in propagating the real effect of the monetary policy shock not captured by the traditional interest rate channel. Both consumer price index _(CPI)_ and the industrial production index (y ) were selected to control for macro conditions. One class of primary assets (bank loans and security holdings) of each bank group was considered in the VAR analysis. Data used were logarithms of the nominal level data of all variables considered except the call rate.

For estimation purpose, the following standard finite vector autoregressive (VAR) representation is considered: where A,is a square coefficient matrix; utdenotes the vector of mean-zero, serially uncorrelated variables, and its contemporaneous variance matrix, _Q,_ is assumed to be positive definite symmetric; a is a 4xl column vector of parameters; the vector Xit include three observable random time series variables under consideration, i.e., the index of industrial production (yj,, consumer price index _(Cpi,,),_ lending volume _(L,,)_ (or securities holdings (S,,)) and one of the call rate and the December 1997 policy episode as a monetary policy indicator ( _M, )._ We estimated the vector autoregressive representation (7) in the first difference to reflect the preliminary unit
for younger girls. Allowing them unfettered mobility may signal a loose attitude about female honor in general, with repercussions for other womenfolk. Second, because the decision to enroll a child is driven in large part by the option value of future schooling, and because schooling is costly, anything that raises the likelihood of dropping out of primary school in the future will reduce the incentive to enroll at the outset.

With these considerations in mind, we state our two main hypotheses:

- Hypothesis 1: Holding constant travel time to school, a girl who would have to cross settlement boundaries to attend school will be less likely to enroll than a girl who can attend a school in her own settlement. The same crossingboundary effect should not be observed for boys.

- Hypothesis 2: Children will be more likely to be enrolled in school if their caste status coincides with that of the dominant caste of the settlement containing their most convenient school (child-school caste concordance).

# 4 Salience of Settlement Boundaries

## 4.1 Empirical strategy

We use household and school GPS coordinates, and information on the year of school establishment, to identify the location of each primary school that was available in or near the child’s village by the time the child turned 9. As new schools are established (and old ones closed down), the set of available schools can differ between two children of different ages living in the same settlement. Moreover, since singlesex schools are common in Pakistan, the set of available schools will also vary within settlements by the gender of the child. From this information, we can construct the two key variables for our test of hypothesis 1: where settlement  of village  is that in which child  resides, and 9
of the optional questions takes the same amount of time, variable time is reduced to the proportion of grouped questions out of the full questionnaire total.

The programme we simulate is the PBI-JKN subsidized health insurance programme intended to cover 40% of the population nationally, but which the DKI government has extended to 51% of households. Rounding to 50% to align with our simulations, and adjusting household figures accordingly, SUSENAS data indicate an incidence of 62.8% in 2019. A simplifying assumption at baseline is that all households which are included in the social registry receive the programme, as full coverage of all the surveyed households mirrors actual practice at national level, where the DTKS social registry contained the same 40% share of households that the programme aimed to cover [Pahlevi, 2019].

We simulate the impact of a shorter questionnaire by separating households into consumption deciles and splitting each decile into a surveyed and

> 14Based on 26 core items out of a total of 112 ungrouped variables, and rounding up to the full minute.

26
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- Hummels, D. L. and G. Schaur (2013): “Time as a Trade Barrier,” _American Economic Review_ , 103, 2935–2959.

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- affect export patterns,” _Journal of International Economics_ , 108, 338 350.

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-
# _6. 5 The efficiency of public investment_

# <u>6.5.1  Stylized facts</u>

One of the reasons that public capital might have less effect on growth in developing countries is that it is inefficient. However, as already noted in Section 2, the _level_ of efficiency θ does not affect the marginal product of measured public capital and the effect of additional public investment on growth. For low efficiency countries, only a fraction of new investment might become productive capital, but this is offset by the greater need for public capital given that past investment was also inefficient.<sup>39</sup> However this does not mean that _increasing_ public investment efficiency cannot have a large effect on growth.

The potential for higher growth through greater efficiency of public investment depends on current efficiency being low. In the first row of Table 6 we calculate the median of the Infrastructure Efficiency Index (IEI) for each income group (see Section 4 for details on the construction of the IEI). As expected, based on the full set of computed IEI, HI countries have the highest median efficiency, with around 87% of roads being paved or water/electricity reaching their final destination. For middle‐income countries, efficiency is about 74%, or 1/7<sup>th</sup> lower. Efficiency is the lowest for LI countries, where only 59% of roads are paved or water/electricity reach their final destination; which is about 1/3 lower than efficiency in HI countries.

**<u>Table 6:</u> Efficiency – Income Group Medians (with φ=0.17; ζ=1)**

|**Parameter/Variable**|**HI**|<br>**UMI**|**LMI**|**LI**|
|---|---|---|---|---
the VSIC two-digit level using the correspondence file between HS 96 and ISIC (Rev. 3) which in turn was downloaded from the U.N. Statistics Division website.

- (c) Impact of a change in exports on wage growth

# (i) Model specification

This section investigates the effects of exports on _absolute_ wages for skilled and unskilled workers running regressions separately by skill levels. I specify a model which relates wage growth rates to changes in the province-specific Exports Index.

_∆Ln Wip = β0 + β1 ∆Exportp + β2 ∆Xip + β3 Yip+ eip_ (2) where _∆Ln Wip_ is the monthly wage growth rate during 2002-2004 of an individual _i_ who lives in province _p_ , _∆Exportp_ is the change in Export Index for province _p_ between 2002 and 2004, _∆Xip_ is a vector of differenced control variables, and _Yip_ is a vector of other control variables which may affect wage growth rates. The monthly wage calculations are based on the annual wages earned and the hours worked by panel individuals during the twelve months prior to the survey.<sup>34</sup> Monthly wages rather than hourly wages are employed in the model as they reflect both the compensation per unit of time and hours worked by individuals. Taking account of hours of work is potentially important since the trade-oriented work may involve long hours and the working hours appear to vary substantially in Vietnam across economic sectors. For instance, the panel individuals in the VHLSS reveal some evidence that those who moved from an informal to the formal sector increased their hours of work.<sup>35</sup>

> 34 The wages include bonus/award, social allowances, and trip subsidy. The wages are converted to January 2002 constant prices using the deflators available in the VHLSS.

> 35 According to the panel individuals in the VHLSS, the workers who moved from the informal wage and selfemployed sectors to the formal sector increased their hours worked by 8.3 percent and by 24.3 percent