rafmacalaba/data-use-mentions-extended huggingface dataset:
applied them to a broad sample of EMDEs. Our paper helps fill this gap in the literature. Third, we introduce a new approach to measuring public investment shocks based on the identification of the episodes of large changes in cyclically adjusted real public investment specific to each country. In contrast to many public investment shock identification frameworks, our approach relies on annual-frequency data on public investment that is publicly available and is easy to replicate.<sup>6</sup> Finally, in addition to the estimation of public investment multipliers, we examine the effects of public investment on private investment, productivity, and potential output, which helps to cast light on the long-run supply-side transmission channels of public investment.

Our local projections estimates, using the identified public investment shocks, suggest that public investment can stimulate economic growth. In particular, the scaling up of public investment equivalent to 1 percent of GDP leads to an increase in output of 1.1 percent after five years in EMDEs, on average. We also find that the size of the public investment multiplier depends on government efficiency and fiscal space. In countries with higher public investment efficiency and ample fiscal space, an increase in public investment equivalent to 1 percent of GDP can increase output by up to 1.6 percent over the horizon of five years.<sup>7</sup> By contrast, in countries with low public investment efficiency and high public debt, the output effects of public investment are smaller and not statistically significant. In line with the literature, we also find that public investment multipliers tend to be greater during recessions and in capital-scarce economies.

Our analysis also suggests that public investment can have significant crowding-in effects on private investment. In EMDEs, the scaling up of public investment by 1 percent of GDP leads to an increase in private investment by up to 2.2 percent over the horizon of five years, on average. Finally, the paper finds that public investment can boost productivity and potential output. In the EMDE sample with available data
determination of actual tariff preferences (i.e., those actually granted at customs and not ‘on paper’).<sup>35</sup> The data on MFN and preferential tariffs has been aggregated at the ISIC 3-digit

> 35The data on MFN and preferential tariffs used in this paper is the same one used in Kee, Olarreaga and Silva (2003). The interested reader can obtain information about the products and countries which were granted the highest and lowest preferential tariffs in that paper.

22
**Figure A.5:** Relationship between child mortality and health expenditure (as a percent of GDP)

<!-- Start of picture text --> SGP KOR JPNSWE<br>KAZ CHNRUSGEO USA<br>VNM<br>TUV<br>ZAF<br>NAM<br>TLS<br>ZWE<br>SWZ KIR<br>ETH<br>LBR<br>LSO<br>NGA<br>TCD<br>0 5 10 15<br>Domestic government spending on health (% of GDP)<br>High income Upper middle income Lower middle income Low income<br>: Relationship between adult survival rate and health expenditure (as a percent of GDP)<br>SGP KOR SWEJPN<br>CHN<br>USA<br>VNM<br>TLS GEO<br>KIR<br>KAZ<br>ETH RUS<br>LBR<br>NAM<br>ZAF<br>NGA ZWE<br>TCD<br>SWZ<br>LSO<br>0 5 10 15<br>Domestic government spending on health (% of GDP)<br>High income Upper middle income Lower middle income Low income<br>1<br>.95<br>.9<br>Probability of survival to age 5<br>.85<br>1<br>.9<br>.8<br>.7<br>Adult survival rate<br>.6<br>.5<br><!-- End of picture text -->

**Figure A.6** : Relationship between adult survival rate and health expenditure (as a percent of GDP)

30
proxies for the parameter _φ_ in our theoretical model, and we observe that a higher _φ_ lowers a recipient country’s incentive to improve policies in equilibrium. Also, a higher _φ_ makes it less likely for a recipient country to participate in the contest. Our data support that prediction, but results are somewhat different for different specifications and measures. Across all regressions we find a positive and significant GPS coefficient for democracies. With the outlier adjusted GPS measure we also find a positive and significant but smaller coefficient for non-democracies. The difference between democracies and non-democracies is significant only in Column (3) for both our GPS measures.

Columns (5) and (6) test for an interaction with natural resource wealth. We classify a recipient as rich in natural resources if its initial value of total natural resources rents, as reported in the World Development Indicators, is above the 75th percentile. In our data, that threshold is reached when a country has natural resource rents of above 6% of GDP. The evidence related to natural resource wealth is mixed. In Table 6 we find a statistically significant positive relationship between GPS and CPIA for non-resource rich countries and a non-significant coefficient for countries that are rich in natural resources. This finding is consistent with the model’s predictions, which suggest that countries with large amounts of domestic resources have weaker incentives to participate in the contest for aid. However, the difference in the GPS coefficient between the two types of countries is statistically significant only in Column (5) that does not control for region-year fixed effects. In Table 7 the relationship between GPS and CPIA is statistically significant for both, resource and non-resource rich countries. The GPS coefficient is larger for non-resource rich countries than for resource rich countries as predicted by our theory, however, that difference is not statistically significant.

Finally, Table 8 investigates interaction effects related to financial flows from China, using data obtained from AidData (Dreher, Fuchs, Parks, Strange, and Tierney, 2017).<sup>5
the groupings for the analytical classification are set on July 1 of each year and are not revised until the following July, even if there are revisions to GNI or population estimates. Third, countries are reclassified into a higher or lower group once their GNI per capita crosses any of the three thresholds; there is no “settling” period, unlike the operational guidelines. Fourth, the current income classifications are applied to historical series in the _World Development Indicators_ database. That means that historical data aggregates reflect the country classification groupings in force at the time of the latest database update; while this occasionally confuses some users, since it means that aggregates for past years are subject to revision, it is not clear whether there is any other workable alternative.

> 5 These are countries with an income level below which civil works can be awarded to eligible domestic contractors for bids procured under a competitive, international bidding process.

9
**

Table 6 reports the results of estimating the impact of the gender gap on comparative advantage in trade, i.e. eq. (21). The top panel presents the OLS results, the bottom panel the two-stage least squares results in which the interaction of _FLi_ with _GENDERc_ is instrumented with the interactions of _FLi_ with shares of Muslims and Christians in the population. All columns control include country and sector dummies. Columns 2, 4, and 6 in addition include the interaction terms means to capture Heckscher-Ohlin specialization forces, following the empirical model of Romalis (2004): capital intensity of sector _i_ is interacted with capital abundance of country _c_ , and skill intensity of sector _i_ is interacted with skill abundance of country _c_ .<sup>6</sup> We can see that the gender gap has a clear impact on trade flows, especially when the gender gap measures are instrumented with religious composition. The bottom of Panel B reports the first-stage results. The first-stage coefficients are highly statistically significant, and the instruments are comfortably strong in the econometric sense. The estimates are economically significant: moving from the 25th to the 75 percentile in a gender gap variable increases the export share in a sector at the 75th percentile

> 6Capital intensity is computed as the share of value added not going to labor compensation, and is built using the UNIDO database. Skill intensity is computed as the fraction of the total wage bill corresponding to workers with at least some college education in the US (originally obtained from Autor et al., 1998; and translated from the US Census Industrial Classification (CIC) into ISIC Rev. 3 industries using a correspondence built by the authors). Country-level skill and capital abundance are sourced from Hall and Jones (1999).

19
# **Introduction**

The flow of workers’ remittances to developing countries has increased dramatically in recent years. While in 1996 developing countries received $58 billion dollars in remittances, in 2003 remittance inflows amounted to $96 billion dollars (see Annex 2 for detailed data on remittances). It is expected that the volume of workers’ remittances will surpass $110 billion dollars in 2004. Workers’ remittances constitute an increasingly important source of income for many families, especially poor families, whose relatives have emigrated to other countries in search of better employment opportunities. Moreover, after foreign direct investment, workers’ remittances constitute the largest source of external financing for developing countries. For 25 developing countries, remittance flows represented more than 5% of their respective GDP in 2003.

In an effort to improve the developmental impact of remittances in recipient countries, the World Bank is currently conducting a series of studies to better understand the characteristics of remittances, as well as the financial infrastructure underpinning money transfers in both sending and recipient countries. In particular, the World Bank is talking to central banks, money transfer companies, and senders and recipients of remittances in a number of developing and developed economies.

The purpose of this paper is to present the findings of a survey conducted by the World Bank with central banks in 40 developing countries (other World Bank surveys with market participants in the remittance industry and senders and recipients in selected countries are currently under preparation). This survey focused on the following four topics: (i) coverage of national statistics on remittances, (ii) cost of transferring and delivering remittances, (iii) regulatory regime for remittance transactions, and (iv) recent initiatives in developing countries to channel remittance flows through formal financial institutions.<sup>1</sup> Most of the information contained in this paper was kindly provided to the World Bank by the central banks of participating countries between July and December 2004. The list of 40 developing countries in Asia, Europe and Central Asia, Latin America
# **3.3 Border Frictions Adjusted Routes**

It is difficult to take tariffs and NTMs into account when evaluating transport networks and market access because their effects depend on the value, type, and quantity of products being traded. In addition, AfCFTA has set clear aims to eliminate most inner-African tariffs in the foreseeable future. Regulatory and border frictions on the other hand are more uniformly applicable.

A critical question then is how the estimates from Table 3 can be sensibly added to road distance and travel time estimates. Surely time spent preparing documents or waiting at the border is different from driving time. To better understand the relative cost of these times, I collect data on domestic transport costs from the 2019 DB survey for a sample of 23 (mostly landlocked) African economies where exporting/importing involved domestic transportation of 100km or more. This information is not standardized across countries and thus not published in the DB indicators. For each country surveyed, the distance in km to the border, the time in hours to the border, and the cost in USD of exporting/importing a representative product are recorded.

To estimate the average cost of an hour spent on the road, I regress the cost of the trip on the travel time in hours while controlling for the log of the velocity and a dummy indicating whether the transport was import related.<sup>12</sup> I do the same for the distance traveled. Since I am more interested in converting times into each other rather than converting cost to time, I also regress the cost of border and documentary compliance from Table 3 on the respective time required using data for all 53 available African economies. Table 4 reports the results.

Table 4: Relative Cost of Exporting/Importing Times

|Cost in USD:|Tran|sport|Border|Documentary|
|---|---|---|---|---|
|Model:|(1)|(2)|(
<!-- Start of picture text --> Nonbanks are more focused on rural areas...<br>% of branches in urban and rural areas<br>Rural<br>Urban branches branches<br>A, SN<br>Commercial banks<br>oe<br>Specialized state financial institutions<br>Microfinance institutions<br>Cooperatives<br><!-- End of picture text --> :--but commercial banks are still the most prevalent financial institution in rural areas % of rural branches by type of institution <!-- Start of picture text --> f Other 1%<br>ly Microfinance institutions 3%<br>: Specialized state financial institutions 5%<br>y 15%<br>Commercial<br>banks<br>78%<br><!-- End of picture text --> Source: Financial Access database.
|
||(0.898)|(1.101)|(2.313)|(2.011)|(1.081)|(1.781)|(2.694)|(2.500)|
|Observations|17,346|17,930|3,666|4,242|8,014|8,482|1,507|1,763|
|R-squared|0.630|0.619|0.481|0.433|0.567|0.529|0.370|0.589|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999. _Note_ : Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All regressions use sample weights. National Industry Classification (NIC) uses 1987 codes while National Classification of Occupations (NCO) uses 1968 codes, with 100 where missing <u>(e.g.</u> students or women not in the labor force).
# **Figure 1: Financial Literacy Questions (Lusardi and Mitchell, 2011)***

- 1) Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?

**More than $102** Exactly $102 Less than $102 Do not know Refuse to answer

- 2) Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?

More than today Exactly the same **Less than today** Do not know Refuse to answer

- 3) Please tell me whether this statement is true or false. ―Buying a single company’s stock usually provides a safer return than a stock mutual fund.‖

True **False** Do not know Refuse to answer

* Correct answers in bold.

So far, no national surveys on financial literacy have been conducted in the lowest income country grouping as defined by the World Bank, although the World Bank is planning surveys in Malawi, Zambia, and other countries. However, the nationally representative FinScope surveys, which focus mainly on financial access and behavior but also measure a few aspects of financial literacy, have been widely implemented in the Africa region as well as in Pakistan.<sup>3</sup> Some of the findings from the most recent FinScope surveys in 14 countries are summarized in Table 2. They generally indicate low levels of financial access. For instance, even

> 3 The FinScope surveys are administered by the South African organization FinMark Trust and were launched with initial funding from the DfID.

5
# Figure 4. Sensitivity of inflation expectations to inflation shock

## A. Sensitivity of inflation expectations to inflation shocks

B. Sensitivity of inflation expectations to inflation shocks, all countries <!-- Start of picture text --> Percentage points Percentage points<br>0.8 1990-2018 1990-2004 2005-18 1.0<br>0.6<br>0.4<br>0.5<br>0.2<br>0.0<br>-0.2 0.0<br>All Advanced EMDEs<br>economies<br>C. Sensitivity of inflation expectations to D. Sensitivity of inflation expectations to<br>inflation shocks, advanced economies inflation shocks, EMDEs<br>Percentage points Percentage points<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies.

A.-D. Inflation expectations are five-year-ahead expectations of annual inflation. Inflation shocks are defined as the difference between realized inflation and short-term inflation expectations in the previous period.

Sensitivity is estimated using a panel regression of the change in five-year-ahead inflation expectations on inflation shocks, as described in Appendix 3. Bars denote medians and vertical lines denote 90 percent confidence intervals. Based on a sample of 24 advanced economies and 23 EMDEs during 1990H2-2018H1.

B.-D. Time-varying sensitivity is estimated by regressing the change in five-year-ahead inflation expectations on inflation shocks, as described in Appendix 3. Solid lines denote the median of estimates and the dotted lines indicate the median of 68 percent confidence intervals.

- B. Based on a sample
licensing of the third mobile telephone provider<sup>10</sup> and the ―Second National Operator‖ were primarily due to this 7 World Bank staff estimates.

8 In the hope of improved performance, Kenya’s railways were turned over to Rift Valley Railways, a South African company, in November 2006.

9 The Logistics Perception Index measures the perceptions of managerial level personnel of international freight forwarding companies. It is published by the Global Facilitation Partnership for Transportation and Trade and available at: www.gfptt.org.

10 Regarding the third mobile telephone operator, a consortium of a local investor (Kenya National Federation of Cooperatives, KNFC) and foreign investors (Econet Wireless) won the tender in February 2004. But the consortium was put together to meet the 30 percent local ownership requirement, not because of business reasons. Citing deals 9
section, we investigate this issue further using models that aim at identifying the determinants of TFP differences across firms. We start by estimating convergence equations for TFP to assess whether the observed dynamics in productivity lead to corrections in the observed differences in TFP. In particular, we estimate a model given by where a negative estimate of  indicates convergence. The presence of convergence dynamics would point to the fact that, on average, firms with low productivity levels tend to increase their productivity more than their high-productivity counterparts. We include a dummy variable for state-owned enterprises in the specification, whose parameter estimate informs us about whether state-owned firms converge in the long-run to higher, lower or similar productivity levels as compared to privately- and mixed-owned firms.<sup>13</sup> Since we are interested in unveiling convergence dynamics between firms, we do not include firm-specific fixed effects in the specification at this point.<sup>14</sup> The results of the estimation of (6) are presented in Table 7 for the different TFP data obtained using the methods described above.

Table 7: Convergence equations: TFP

||OLS|FE|OP|LP|
|---|---|---|---|---|
|Lagged TFP|-0.099**|-0.025***|-0.008**|-0.027**|
||(0.042)|(0.009)|(0.003)|(0.011)|
|State-owned|-0.159**|-0.113*|-0.159**|-0.160**|
||(0.068)|(0.063)|(0.072)|(0.070)|
|Intercept|0.377*|0.297|0.552**|0.483**|
||
generation since land values form the basis for activities such as property tax collection and land sales or leases. A credible system that helps “discover” and “disseminate” the value of land makes it difficult for buyers to defraud existing landowners. In the absence of such information, there is considerable scope for undervaluation of land during the acquisition process.

In countries where land valuation is successful, techniques are standardized to enable appraisers to arrive at uniform and transparent valuations. In the United States, most states require that assessors and appraisers be certified. Appraisers generally work for private clients to determine the market value of property in the event of real estate transactions, while assessors generally work for the government to determine values of properties for tax purposes.<sup>9</sup> Both agents must abide to the same regulations in valuating real property. However, for practical purposes and to avoid overestimation of prices, property prices for tax purposes are usually set at a fraction of actual market values. For example, it is generally recognized in the United States that tax assessors’ calculation of property value is about 33 percent lower than market value. In Bogotá, Colombia, property values have usually been set at between 70 and 80 percent of estimated market values.

Public land valuation in developing countries is especially fraught with challenges. These include the cost of hiring private assessors since these countries lack standardized public methods, the need to maintain and constantly update land price data and the fact that inter-governmental transfers of public land are often recorded as a zero value transaction. However, there are a

> 9 United States Department of Labor, Bureau of Labor Statistics.  Occupational Outlook Handbook, 2010-11. http://www.bls.gov/oco/ocos300.htm#oes_links

4
**Table 9: Identifying the $1.90 poor based on covariates of poverty**

_<u>(Pooled data across countries with each country weighted by millions of poor)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|-0.007***|-0.216***|0|-0.257***|-0.465***|0|683,533|
|Asset index|-0.052***|-0.185***|0|-0.529***|-0.661***|0|431,436|
|Asset ownership|-0.010***|-0.053***|0|-0.154***|-0.197*
et al. (2022b) to supplement the World Bank BOS database.

# 4. Empirical Methodology

**This section presents the empirical methodologies behind the main results** . It presents the econometric approaches used to (a) assess the differential performances between the varied ownership and control levels of SOEs and POEs during the 2011-2019 period, (b) examine whether SOEs acted as buffers during the

> 7 The competitive and excluded sectors are not listed in this paper due to the large number of sectors but for the full list see Dall'Olio et al. (2022b).

7