rafmacalaba/data-use-mentions-extended huggingface dataset:
Global estimates of different groups’ economic performance, whether countries or household income deciles, can provide a useful viewpoint on key global trends. However, the strength of these metrics is the general nature of the estimates they generate, while a major weakness derives from their general nature, namely overlooking high degrees of heterogeneity that may exists across countries or groups for which the trend estimates are derived.

Although there are standard methodologies to categorize countries according to their income level or their economic performance, it is hard to say that each country in each group is the same or that it grows at the same rate. Therefore, global estimates and trends tend to be reduced to single directional conclusions and struggle to capture the shifting patterns beneath aggregates of the data. They therefore face limitations for addressing policy questions. For example, a result in support of global beta convergence since 1960 or 1990 sounds fantastic but does not imply business-as-usual will finish the job of economic catch-up. We argue that is does not.

# **The devil in the details**

As previously discussed, there are some bottom billion economies that are growing faster than their peers, and these might be catching up faster with other slow growing economies outside of this group. For example, the Lao People’s Democratic Republic and Uzbekistan – both bottom billion countries – have achieved higher GDP levels compared to other non-bottom billion countries, such as Pakistan and Honduras (Figure 19).

# **Figure 19. Average GDP growth rates by decade, bottom billion vs emerging market selected countries**

Source: based on World Bank national accounts data.

# **Heterogeneous trends matter**

25
across "natural regions" and departments.2 But INEI has combined variables which are common to both the 1993 census and one such survey conducted in 1995 to develop imputed district-level measures of income and poverty (INEI 1996).

This procedure is conceptually similar to that used to estimate infant mortality at the district level. INEI estimated income in 1995 on the basis of the household survey, and then regressed income in every department on its correlates--household composition, education levels, access to basic services such as water, sewerage and electricity, ownership of durable goods, such as television, radio and refrigerator, and other variables included in both the census and the survey. The coefficients from the 24 department-level regressions were then used to impute average income in every district, and the fraction of the population in each district below an income-based poverty line.

# **3. The analytic framework**

# _Measures ofperformance_

The simplest measure of targeting focuses on leakage and undercoverage rates (Grosh, pp. 16-17; Baker and Grosh 1994). A poverty line is chosen to separate "poor" from "non-poor". Leakage rates are then defined as the fraction of total program resources which go to the nonpoor, and undercoverage rates as the fraction of the poor who do _not_ benefit from the program. By this measure, better geographic targeting indicators result in lower leakage and lower undercoverage rates.

A second approach simply ranks individuals by an indicator of welfare-say, per capita expenditures-and then cumulates the fraction of households and the fraction of resources transferred by different indicators. The results are often presented in terms of so-called "concentration curves" (see, for example, Milanovic 1995), and I follow this practice below. By this measure, the best targeting indicator is that whose concentration curve is above all others at every point. The concentration curve method does not require use of a poverty line, which may be an advantage given the fact that setting poverty lines can be contentious.

Alternatively, one might want to compare the
in developing countries actually have a bank account. As shown in the following chart, on average less than 40% of adults in developing countries have a savings or deposit account at a bank or micro-finance institution. As discussed below in Sector 4, the lack of a bank account prevents remittances beneficiaries in developing countries from using the remittances funds they receive regularly to access other financial products that may help them to improve their living conditions, such as consumer loans, mortgages, life and non-life insurance products, pension plans, etc.

<!-- Start of picture text --> Chart 6. Percentage of Population (15 - 65 years) with Savings<br>Accounts in Banks or Micro-finance Institutions in Selected<br>Countries<br>60%<br>40%<br>20%<br>0%<br>Bangladesh Bolivia Costa Rica Croatia Ecuador Egypt Guatemala Guyana Honduras India Indonesia Pakistan Philippines Slovenia Tanzania Thailand Tunisia Turkey Uganda Uruguay<br><!-- End of picture text --> Source: With data from CGAP (2004) and World Bank’s World Development Indicators (2004).

# **Access to Central Banks’ Clearing and Settlement Systems**

Another proposal to lower the cost of remittances has been to grant non-bank financial institutions direct access to the clearing and settlement systems that commercial banks use for their international operations – which in most cases are administered by central banks. This survey found that in practically all countries money transfer companies and other non-bank financial institutions – such as credit unions -- do not have direct access to the central banks’ clearing and settlement systems. They must clear and settle their remittances transactions through commercial banks, which allegedly increases the cost they face in their remittances operations. By granting non-bank financial institutions direct access to clearing and settlement systems, it is believed that remittances fees would be reduced.

This survey found enormous skepticism among developing countries about the feasibility of this proposal. 35 out of 40 countries are not interested in
<mark>Zander, K. K., Richerzhagen, C., & Garnett, S. T. (2019). Human mobility intentions in response to heat in urban South East Asia.</mark> _<mark>Global Environmental Change</mark>_ <mark>,</mark> _<mark>56</mark>_ <mark>, 18-28.</mark> **[1]** <mark>Zickgraf, C., & Perrin, N. (2016). Immobile and trapped populations.</mark> _<mark>The atlas of environmental migration</mark>_ <mark>.</mark> **<mark>[</mark> 1]** 50
Table 5: Market <u>Conditions By State Capital</u>

|**_Market_**|**_Cereal_**<br>**_Production_**|**_Cereal_**<br>**_Surplus_**|**_Area_**|**_Consumption_**<br>**_PC_**|**_Consumption_**|
|---|---|---|---|---|---|
|_Aweil_|60,457|-36,780|30,543|60|48,600|
|_Bentiu_|19,210|-38,793|37,837|72|38,500|
|_Bor_|65,177|-74,615|122,581|98|118,000|
|_Kajok_|107,239|-8,976|45,567|67|80,500|
|_Juba_|100,103|-43,474|43,033|127|140,000|
|_Malakal_|41,044|-45,598|77,283|144|153,000|
|_Rumbek_|70,370|-13,382|43,595|110|75,000|
|_Wau_|42,666|-2,814|91,076|114|35,500|

Note: All figures refer to the states where the towns are located. Cereal production in net, cereal deficit is the difference between production and consumption (and it is reported as the average over 2008-2013), area is expressed in squared kilometers, consumption expresses total consumption in thousands, as indicated in the Household survey for year 2012-2014, consumption per capita is the ratio of consumption and population. Source: Authors’ calculations based on FAO/WFP. 2012-2014

# **3.6. City Characteristics and Price Differentials**

When markets are integrated and well-functioning
of the results, with listing by firm, sector and region, and the data sources are presented in appendix B.

# **Share of Expatriate Labor Employed by Multinational Service Providers**

The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use Kenyan labor less intensively than their Kenyan competitors, if multinationals use mostly Kenyan labor, their expansion is likely to increase the demand for Kenyan labor in these sectors.<sup>25</sup> We obtained estimates of the share of expatriate labor or specialized technology not available to Kenyan firms that is used by multinational service providers in Kenya from the survey mentioned above. We found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or specialized technology. Our estimated share of foreign inputs used by multinationals in Kenya is presented in the table on sensitivity analysis.

# **Estimates of the Dixit-Stiglitz Elasticities of Substitution for Goods**

It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. Among the 73 countries, there were four in SubSaharan Africa: the Central African Republic, Madagascar, Malawi and Mauritius. We judged

> 25 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement.

24
networks and speed assumptions from de Soyres et al. (2019). In doing so, we limit the analysis to road and maritime routes for shipments within Africa, while considering rail and maritime connections for shipments to the rest of the world.<sup>4</sup> Two main reasons justify this approach. First, road is by far the main transport mode for trade within Africa, while most international trade in the rest of the world travels by sea and by rail.<sup>5</sup> Maritime shipping accounts for 80% in volume and 70% in value of global trade and rail is the second transportation mode used (International Transport Forum 2017). Second, most transport reforms in Africa included in the PIDA, as discussed in detail in section 3, consist of road and maritime infrastructure investments. This approach allows us to incorporate most of PIDA’s proposed interventions.

We then integrate the land transport network by including delays at (land and sea) borders and cargo loading and unloading times at seaports. For this purpose, we first use the World Bank’s Doing Business indicators, which provide country-level information on the ease of crossing land or sea borders by measuring the number of documents required to trade, days to cross the border, and costs associated with exporting and importing.<sup>6</sup> We limit our analysis to waiting times to import and export for the latest year available in the dataset. If a country has a maritime gateway, we consider both delays at the land border and delays at the maritime border.

For each country, we define both land and sea border delays if the country is coastal and only land border delays if the country is landlocked. Because data are reported only for the most used gateway per country, which for coastal countries is generally a port, we assume that unobserved land-border times are a fraction of reported sea-border times.<sup>7</sup> In addition to delays at the maritime
*<br>Resource poor Resource rich<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (IFPRI); Bolivia 2008; Cambodia 2010; Ethiopia 2011;<br>Indonesia 2010 (RKD); Nepal 2011; Peru 2012; Zimbabwe 2010. *Stunting for under 36 month olds. The rest under 24 month olds.<br>50<br>40<br>30<br>20<br>10<br>Percentage of children with HAZ < -2 SD<br>0<br><!-- End of picture text -->

# **3.2 Adequacies in Food Security, Environment, Health, and Care Practices**

Given the indicators available in the surveys, the determination of whether children have access to an adequate or inadequate level of each specific underlying determinant of nutrition is carried out using accepted international standards regarding infant and child feeding practices, food security, improved water and sanitation, and pre‐ as well as post‐natal care practices. Specifically, the WHO (2008) standards are used for assessing infant and young child feeding practices, the USAID (2012) 21
- 4 -

**Figure 1. Existing road network and districts covered by our sample**

<!-- Start of picture text --> Nepal Roads<br>Districts covered by our sample<br>Nepal DISTRICTS<br>/<br>Source: Benamghar and Iimi (2011).<br><!-- End of picture text --> The public contracts in the sample are relatively low-value, but the contract performance has not been satisfactory. Many delays and cost overruns have been experienced. In the sample, the projects were delayed on average about 100 days. This is significant compared to an average contract duration of 185 days. About 10 percent of the projects delayed more than one year. Cost overruns are also prevailing. Cost underruns occurred in some cases, but half the contracts underwent cost overruns. The average cost overrun rate is about 4 percent with some cost overruns offset by cost underruns.

Ex post adjustments depend on internal and external factors.<sup>2</sup> Weather is among the most important factors in Nepal. For instance, it rains a lot particularly during summer. In the sample, the district average precipitation is about 200 mm per month (Figure 2).<sup>3</sup> Heavy rains are likely to interrupt civil works and cause project delays, because contractors may not be able to convey the necessary road work equipment, such as pavers, and other materials on time.

Road procurement often has a cyclical pattern every year. In the case of Nepal, road works are started throughout the year but tend to be launched from April to July (Figure 3). This

> 2 See Benamghar and Iimi (2011) for further detailed discussion.

> 3 The precipitation data are based on only 20 weather stations that are close to our surveyed roads. There may be a certain measurement error, since the data are district-specific. In each district, there may be a variation in precipitation, depending on
Table 8 presents the results of an analysis of the probability of receiving supplier credit in our four countries. We begin by estimating a probit regression to look at the effects of networks on access to credit. We then examine the determinants of access to supplier credit for networked SMEs and indigenous-African SMEs separately.

The role of the network in facilitating access to credit is considered in model I, where firm size (log empt) is included to control for the fact that larger firms have better access to credit. The results show that networked SMEs are much more likely to receive supplier credit than indigenous-African SMEs. As expected, the coefficient on firm-size is positive and significant, indicating that larger firms are more likely to get supplier credit than smaller firms. These findings confirm those of other researchers using the RPED data who have noted an ethnic bias in supplier credit access in SSA. (Biggs, Raturi and Srivastava 2002; Fisman 2002; Fafchamps 2004).<sup>10</sup> Adding the length of relationship with the supplier (log yrssrel) to the probit in model II, we see that the number of years a firm has known its supplier is significant in determining access to supplier credit. This validates the notion that information gained from repeated interactions plays an important role in the willingness of suppliers to extend credit. However, the fact that the network coefficients change only marginally in this regression indicates that the information provided by repeated interactions and firmsize is not enough to explain credit access. SMEs in networks are still much more likely to receive supplier credit than indigenous-African SMEs because of network externalities.<sup>11</sup> 10 Fafchamps (2004) also analyzed financial case study data from about 40 firms and suppliers, collected by RPED teams in Zimbabwe and Kenya as part of the survey work in those countries. He tries to explain why ethnic networks matter so much in getting access to supplier credit by using a "socialization" variable in his regressions, which measures the degree of networking among “insiders
|Lower Urban|0.00858|0.0435***|
|---|---|---|
||(0.00298)|(0.00211)|
|Lower Rural|0.0205|0.0639***|
||(0.00238)|(0.00168)|
|Upper Urban|-0.0193|0.0217***|
||(0.00307)|(0.00218)|
|Upper Rural|0.00783|0.0375***|
||(0.00271)|(0.00182)|
|Borders Urban|0.0168|0.0825***|
||(0.00802)|(0.00603)|
|Borders Rural|0.0363|0.100***|
||(0.0111)|(0.00782)|
|Constant|0.586|0.592***|
||(0.00652)|(0.00430)|
|Observations|11,091|23,466|
|R-squared|0.324|0.383|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 **Source** : Authors’ calculations based on HIECS 2015 and 2017/18. **Note** : Regression run using total expenditures.

Table A2 contains the value of coefficients for the regression for the hedonic regressions in 2015 and 2017/18.

_Table A2: Values of coefficients for the hedonic regression (dependent variable log of rents)_

||(1)|(1)|
|---|---|---|
|VARIABLES|2015|2017/18|
|HH size|0.00121|-0.00637***|
|
|(13.98)|(18.08)|(13.97)|
|December x [Northern Hemisphere]|2.442***|1.918***|2.432***|1.946***|2.319***|1.858***|2.529***|2.092***|
||(24.75)|(19.10)|(42.34)|(33.63)|(56.87)|(45.38)|(39.49)|(32.35)|
|Constant|254.3***|254.7***|255.0***|255.3***|255.4***|255.7***|255.5***|255.7***|
||(736.01)|(775.48)|(1395.77)|(1472.50)|(1971.11)|(2080.00)|(1181.10)|(1235.86)|
|Observations|37,788|37,788|129,851|129,851|247,592|247,592|89,157|89,157|

Absolute value of t statistics in parentheses

- significant at 5%; ** significant at 1% *** significant at 0.1%

22
liabilities, which is estimated from the government’s presumptive tax code and ESS data on business annual turnover and activity type. The effective tax rates using self-reported data represent the tax burden based on what businesses claim to have paid, while the imputed effective tax rates represent what the tax burden would be using the tax code.

The self-reported tax payment in the ESS data measures the taxes paid on income and profits. It is possible that the information provided by respondents could include the _turnover tax_ , which is different from the presumptive tax that businesses pay instead of the VAT as discussed in section 2. This means that the self-reported tax payment could overestimate the presumptive tax payment if it also includes the _turnover tax_ . The analysis compares the self-reported tax payments on income and profits with the imputed presumptive tax liabilities using the tax code, keeping in mind that the self-reported payments may overestimate the presumptive tax because it could include payments for the _turnover tax_ .<sup>15</sup> The imputation method makes the following assumptions. First, it assumes that there is 100% tax compliance. Second, because there are 99 different business activity types in the tax schedule, it is not always clear which tax rate should be applied for tax assessment. For example, since there is no specific rate for electricians, it is not clear whether they should be taxed as “electrical lift works and repair” or as “building contractors”, which has a different tax rate. There is also no clear business activity in the tax schedule for individuals who brew and sell local alcohol, so it is assumed that they are taxed as those in the general merchandise, trade, and grocery activity, which has an exemption threshold for incomes below 50,000 birr. The difficulty in determining the tax rates for each activity highlights how the Revenue Authority tax assessment teams could easily apply different rates for firms engaging in similar activities. Third, detailed information on business activities is required to impute the presumptive tax, but some businesses in the
characteristic, an industry is classified as “high,” “medium,” or “low”’ based on its position in the distribution, which is derived from the aggregation of firm-level data within and across countries. Third, the role of intermediate and final demand in explaining services value added growth is assessed through a growth decomposition exercise, based on input-output tables for China and India, to shed light on the extent to which services can develop without a manufacturing core.

Three main findings emerge from our analysis. First, the contribution of increased ‘contracting out’ from the manufacturing sector to the services sector to annual services value-added growth between 2000 and 2014 did not exceed 10 percent. Second, to the extent that premature deindustrialization is therefore not notional but real, it might matter less than before because firms in ICT services, for example, are not very different from those manufacturing electronics across a range of international trade, learning-by-doing and innovation characteristics. Yet, a given service subsector is unlikely to provide opportunities for productivity growth and job creation for unskilled people simultaneously. ICT and telecommunication services, on the one hand, and hotels and restaurants and construction, on the other hand, typify this dichotomy. Wholesale and retail trade is an exception – neither skill-intensive, nor less amenable to the potential productivity benefits associated with international trade, foreign technology, and on-the-job learning programs. Third, among these productivity-enhancing services, the growth of ICT and other professional, scientific and technical services can be driven by final demand or by intermediate demand which derives from sectors beyond manufacturing. In contrast, the growth of wholesale and retail trade, in contrast, might be more dependent on a manufacturing core.

The remainder of the paper is as follows. Section 2 analyzes the importance of contracting out of manufacturing to services as an explanation for the latter’s growth. Section 3 compares the manufacturing and services subsectors along a set of characteristics that matter for productivity growth and job creation. Section 4 analyzes the role of intermediate and final demand in
an average increment of 4,000 individuals in each municipality and year) and previous results from other studies that use the same data in Colombia.<sup>18</sup> The literature that studies the impacts of immigration on firm performance generally finds

> 17The number of observations drops for column (3) because all the additional controls are not available for all the municipalities in the manufacturing survey. However, these controls are available for the largest municipalities that host the bulk of the economic activity in Colombia. The same reduction in our sample occurs for the estimates presented in Tables II and III.

> 18For instance, Camacho and Rodriguez (2013) uses the same sample to study the effect of conflict on firm exit. The authors find that a one-standard deviation increase in the number of guerrilla and paramilitary attacks in a municipality increases the probability of plant exit in 5.5 percentage points or .28 SD.

17