rafmacalaba/data-use-mentions-extended huggingface dataset:
generates about $2.50 of those benefits for every $1 in tax incentives offered to foreign investors (República de Costa Rica, 2022). Ideally, this analysis should be completed by an independent government agency, like the Ministry of Finance or Treasury.

_5) Unique online portal for potential exporters and buyers._ Effective export promotion agencies have client relationship management tools that help them respond to the idiosyncratic needs of firms at scale. In Costa Rica, this system is built on web-based surveys available to potential exporters and investors that evaluate each group’s readiness to start exporting. Some

> 5 Evidence from Europe suggests foreign investment promotion agencies can also be effective in high-income countries, especially in attracting investment to less-developed regions of those economies (Crescenzi, Di Cataldo, and Giua, 2021).

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# ANNEX A Production function approach

The production function approach assumes that potential output can be captured by a Cobb-Douglas production function with constant returns to scale (Solow 1957):<sup>15</sup> Yt = AtKtaLt(1-a) , where Yt is potential output, At is potential total factor productivity (TFP), Kt is the potential capital stock, and Lt is potential employment. To extend the sample beyond 2019—the latest available data from Penn World Tables—TFP was recalculated as the Solow residual of output, employment (extended using data from Haver Analytics) and capital (extended using investment data from Haver Analytics and the perpetual inventory method; table 3). Labor and capital shares are the within-country averages of those reported in Penn World Tables. Human capital is not separately accounted for in the production function approach but affects TFP growth and labor supply growth, as described below.

Two of the three components of potential output—potential TFP and potential employment—are proxied by the fitted values from panel regression estimates. The third component, the contribution of capital to potential growth, is assumed to be the same as the contribution of capital to actual growth, as shown in the Penn World Tables (and extended using data from Haver Analytics). This approach yields an unbalanced panel dataset for 30 advanced economies and 64 EMDEs for 1998-2021 (table 4). The same approach, using appropriate assumptions, can be used to project potential growth into the future. These assumptions and the approach for projections for 2022-32 are detailed in Kilic Celik, Kose, and Ohnsorge 2023.

Capital stock data from Penn World Tables 10.0 is used until the latest available year in the dataset (2019 for most countries in the sample). For 2020-21, investment data are compiled from national statistical agencies and Haver Analytics, while the capital stock is estimated from investment data by the perpetual inventory method using historical average depreciation rates.<sup>16</sup> Potential TFP growth is defined as the fitted
First, it assumes that past the world avoids major crisis with impacts beyond past experience.<sup>17</sup> Second, if the increase in global inequality that we have seen recently (2005-2008) continues then this trajectory would be unattainable without a further acceleration in growth. (Later we will see how much inequality matters to attaining any given target.) Third, a constant annual percentage point decline cannot, of course, continue indefinitely—it must slow down, becoming nonlinear in time.<sup>18</sup> But when? It is worth considering this issue further.

In looking for clues as to when the nonlinearity might emerge, one can study the experiences of places that have seen substantial reductions in poverty to under 10% or so. Figure 5 gives the poverty rate over time in urban and rural China.<sup>19</sup> The nonlinearity only becomes evident below about 5% for urban China, but is not found yet for rural China. By contrast, Figure 6 gives the graphs for Argentina (after the 2001-2 financial crisis) and Brazil. In Argentina, we see the expected nonlinearity emerging around a poverty rate of 4%, while in Brazil a marked nonlinearity emerges below about 8%, but in the opposite direction, with lower poverty rates than one would have expected.

So there is no clear pattern, although these are just a few countries, each with their own special features. To look at this issue more systematically I used the data set described above of the changes in poverty across countries from <u>PovcalNet.</u><sup>20</sup> Here the focus is on those countries that started out with more than “negligible” poverty, which I define as more than 1% of the population living below $1.25 a day. This gave 82 countries.

Figure 7 plots the annualized change in the $1.25 a day poverty rate against the initial poverty rate across
it was in the summer for the _Eurobarometer_ . On the other hand, ESS samples are much larger, and, as mentioned above, the quality of ESS field work in the Southern Europe, as well as in Ireland and UK seems to be better.

<!-- Start of picture text --> Sectoral Composition of Formal and Informal Employees<br>In European Countries, by Region. 2004-2009<br>LV RU UA BG PL EE SK HU SI CZRO SE FI NO DK ES IT GR PT CY IL CH UK IE AT DE NL FR BE<br>East North South West<br>LV RU UA BG PL EE SK HU SI CZ RO SE FI NO DK ES IT GR PT CY IL CH UK IE AT DE NL FR BE<br>East North South West<br>Construction, Trade, Hospitality & Pers. Serv. Transport, Finance & Bus. Serv<br>Manufacturing Agriculture<br>Education, Health & Social Care, Public Administration, Utilities, Post & Communications<br>Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded<br>Source: Calculation with ESS data<br>100<br>75<br>50<br>25<br>0<br>100<br>75<br>50<br>25<br>0<br><!-- End of picture text -->

# **Figure 12 Sectoral composition of formal and informal employees, by country**

Figure 12 compares, for each country, sectoral distribution of informal employees and their formal sector counterparts (self-employed are not considered). Taking into account that the number of observations on informal employees for seven of the countries examined is less than 100, we report distribution among just five broad sectors. The ―usual suspects‖ (construction, trade, auto repair, hotels and restaurants, personal and household services) together account for [almost] 40% to
the aggregate measure for date _t_ : where _nit_ and<sup>_P_</sup> _it_<sup>are the population shares and poverty measures for sectors</sup> _i = u,r_ for urban and rural areas respectively. “Time” ( _t_ ) represents the ordering of the _T_ survey rounds in time, which can differ from real time given the uneven spacing.<sup>32</sup>

**_Demographic data:_** The population estimates are based on the Census population totals and assume constant growth rates for urban and rural populations between censuses. We use the NSSO’s urban-rural classification.<sup>33</sup> Over such a long period, some rural areas would have

> 30 See Datt and Ravallion (2011) for further details on these adjustments.

> 31 All three measures are members of the class of measures proposed by Foster, Greer and Thorbecke (1984).

> 32 Controlling for unequal time interval between survey rounds made little difference to the results.

> 33 The NSS follows the Census definition of urban areas which includes all places with a municipality, corporation, cantonment board or notified town area committee, and places that meet a number of criteria including a population

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**Figure 3. ICT use at work and at home**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.8 1<br>0.6 0.8<br>LKA<br>PER<br>TUR MEX 0.6<br>0.4 LAO<br>RUS ECU<br>GRC 0.4 GHA<br>LTU ITA KAZ<br>0.2 CHL<br>POL<br>HUN ESP 0.2 SLV<br>CZE FRA SVK<br>DEU 0 COL BOL<br>KEN<br>-0.6 -0.4 BELEST-0.2 AUT0 IRLJPN 0.2 0.4 0.6 0.8 1 -1 -0.8 -0.6 -0.4 -0.2 0 0 0.2 0.4 0.6 0.8 1 1.2<br>SWE CANSVN KOR-0.2 ISR GEO VNM<br>NLDFIN KOS ARM UKR -0.2<br>NOR GBR USA<br>DNK NZL -0.4 -0.4 PHL<br>SGP SRB<br>MKD<br>-0.6 -0.6 Low ICT at home<br>Low ICT at home<br>Low ICT at work Low ICT at work<br><!-- End of picture text --> Note: The vertical axis measures the Low ICT use index (a higher value means lower ICT use at work), while the horizontal axis measures the Low ICT at home index (a higher value means poorer internet access at home). The variable to measure internet connectivity at home is not available for El Salvador, thereby we use a different approach for this country. We consider that households have internet access at home if they have a computer and fixed telephone access.

When
and informal sector in each centro poblado as well as the total local population in each centro poblado, province and region. We use the 2007 data to construct both of our instruments discussed in more detail below as well as to create additional controls for the local economic environment. More specifically, in the first part of our analysis, we use information on the industrial distribution (using detailed four-digit codes) in each centro poblado, while in the second part, we use information on the total number of Venezuelans in each province in Peru.

To construct the _Trade shock_ instrument for the first part of our analysis, we also use trade data from the reports of TradeMap. From this website, we are able to identify export and import values for Peru on a monthly basis since 2006 at the HS 6-digit product revision. In addition, correspondence tables of HS 6-digit product revision to ISIC 3.1 revision (United Nations) are used to harmonize products with their corresponding industry sector in order 9
preferences and focusing exclusively on actual choices may leave an incomplete picture to understand wellbeing. (Kahneman and Krueger 2006)

**There is solid evidence in the economic literature that the most common measures of subjective wellbeing are consistent enough in time to be a reliable measure of well-being, and are not mainly driven by mood changes or personal distortions.** Krueger and Schkade (2008) analyzed the test–retest reliability of two types of measures of subjective well-being (a standard life satisfaction question and a measure based on recording of the satisfaction experienced at different times of the day)  and obtained a serial correlation of about .60 when assessed two weeks apart. This is lower than the reliability ratios typically found for education, income and many other common microeconomic variables. Nonetheless, the estimated degree of reliability of subjective well-being data is high enough to detect effects when they are present in most applications, especially if samples are large and the data are aggregated across people or activities. (Krueger and Schkade 2008)

**Even though the focus of this paper is on perceptions of economic mobility, we borrow from the literature on perceptions of economic well-being to identify factors behind the mismatch between objective and subjective measures.** For this, we followed the accounts of Selezneva (2011) and Dolan et al. (2008) about determinants of satisfaction with life (SWL). In her account, Selezneva (2011) reviews more than 70 studies that associate subjective well-being with income, work and family life, in the economic, psychological and political science literature, paying particular attention to transition countries. Dolan et al. (2008) reviews papers published in economics journals since 1990 as well as some key reviews in psychology that focus in finding individual-level, family-level and social-level determinants of subjective well-being. Both papers categorize the determinants roughly in income, personal characteristics, attitudes and beliefs, work conditions and macro environment.

**There is agreement in the literature that the correlation between income and subjective well-being is at most weak, leaving
- **Conducting a regional Regulatory Services Audit.** This would enable countries to better understand each other current regulatory regime in services and better enable them to engage in these negotiations effectively.  Such an audit involves a diagnostic of what concessions (offer) could be made at low (e.g., binding status quo) or high (e.g., major reform required) costs.  Frequently, lack of coordination or information-exchange within different branches or ministries of a government could serve as an impediment to realizing the constraints/concessions available in trade negotiations.  A regulatory audit is an exercise that would promote better information flow between a government in identifying the status quo and strategy in services trade. This would also help assess whether negotiated agreements with third parties are less restrictive than negotiations among members within PAFTA.  In the World Bank’s regulatory audit for Maghreb, it was apparent that rules applicable to services trade with the EU or the US are often less restrictive than among Arab countries.  This is particularly so for movements of persons and capital.

- **Negotiating a pan-Arab agreement in services.** Looking forward, the regulatory audit exercise will help to assess the areas of expeditious collaboration among PAFTA members and areas where a special work program may be required.  Harnessing the knowledge of the strengths/weaknesses of their respective regulatory frameworks would also help members better design basic principles for regional liberalization. Establishing a regional database of the regulations pertaining to each services sector and country could also help regional negotiations by increasing the level of transparency and identifying the status quo and feasible results. This transparency exercise could be accompanied by training and other technical assistance to help PAFTA countries translate the collected information into negotiation positions (requests/offers).

- **Reinforcing statistical tools.** Access to reliable data on trade flows is essential to the identification of offensive/defensive trade interests and development of a broader trade strategy.  In this region, it appears that some countries have not developed a data management system for
# **2. The Pacific Access Category and PINZMS Data**

The natural experiment we use is based on the Pacific Access Category (PAC) under New Zealand’s immigration policy. The PAC was established in 2001 and allows an annual quota of 250 Tongans to migrate to New Zealand without going through the usual migration categories used for groups such as skilled migrants and business investors.<sup>3</sup> Specifically, any Tongan citizens aged between 18 and 45, who meet certain English, health and character requirements,<sup>4</sup> can register to migrate to New Zealand.<sup>5</sup> Many more applications are received than the quota allows, so a ballot is used by the New Zealand Department of Labour (DoL) to randomly select from amongst the registrations. The probability of success in the ballot is approximately 10%. Thus, we have a group of migrants and a comparison group who are similar to the migrants, but remain in Tonga only because they were not successful in the lottery. Once their ballot is selected in the lottery, applicants must provide a valid job offer in New Zealand within six months in order to have their application to migrate approved and be allowed to migrate.

The other options available for Tongans to migrate are fairly limited, unless they have close family members abroad. Ninety-four percent of all Tongan migrants are located in New Zealand, the United States and Australia.<sup>6</sup> In the 2004/05 financial year New

> 3 The Pacific Access Category also provides quotas for 75 citizens from Kiribati, 75 citizens from Tuvalu, and 250 citizens from Fiji to migrate to New Zealand.

> 4 Data supplied by the DoL for residence decisions made between November 2002 and October 2004 reveals that out of 98 applications, only 1 was rejected for failure to meet the English requirement, and only 3 others were rejected for failing other requirements of the policy.

> 5 The person who registers is
Women entrepreneurs are more likely to lack information about the tastes, preferences, and price sensitivity of foreign customers (Campos et al. 2019), which could be linked to limited learning due to smaller access to networks. They are also more likely to view customs and trade regulations as major constraints, and since their businesses are smaller, they are more affected by non-tariff measures (International Trade Centre 2015). Possible interventions, yet to be evaluated, include marketing interventions to attract new customers and improve product design, export-readiness programs, virtual trade missions, participation in fairs, and information on and simplification of trade regulations. The interventions discussed above that encourage crossover to male-dominated sectors, which are typically less protected and more profitable, and access to trade finance can also make women-led businesses more likely to become exporters. More evidence on the effects of these policies is crucial since the literature finds a strong correlation between exporting and increases in productivity (Verhoogen 2023). Furthermore, if a lack of demand for their products is a binding constraint for women entrepreneurs (as shown in Ghana by Hardy and Kagy 2020), interventions on skills and capital alone might not be effective. Rather, interventions that help women access new markets (e.g., crossing over to tradeable industries, exporting, using digital technology to sell online, or accessing procurement) would be a priority.

# 5.3 Increasing access to public and private procurement

Some estimates indicate that women-led firms earn less than 2 percent of purchases made by large corporations and governments (Vazquez and Frankel 2017). A significant share of large corporations do not collect sex-disaggregated data on procurement, which is usually the first step in designing corporate supplier diversity programs (IFC 2023). Initiatives in several countries aim to connect women entrepreneurs with market opportunities through corporate procurement. Women-led businesses can benefit from these initiatives by expanding access to markets and internal value chain financing. Moreover, there is potential for external value chain financing even for smaller firms by linking microfinance with value chains.
# 3.1 Volume of trade

Trade openness is commonly measured by the volume of trade, defined as real exports plus imports, over GDP. Overall, the volume of trade has sharply increased in most selected regions from 1990-94 to 2010-14 —except for oil rich countries that already have a high volume of trade (about 94-95 percent in periods 1990-94 and 2010-14). Trade openness grew by 13 percentage points of GDP for the whole region over the past two decades. The growth of trade as a percentage of GDP among non-resource rich countries was comparable to the regional average whereas it was faster than average among countries that are abundant in minerals and metals (see Table 1).

# 3.2 Trade Diversification

Looking at the volume of trade is not sufficient to understand the linkages between international trade integration and growth. Looking beyond the volume of trade, we examine the degree of trade diversification, as proxied by the extent of trade concentration across products and across markets. Earlier evidence shows that high levels of diversification in export baskets may result in higher growth (Lederman and Maloney, 2007), and reduce risk to external shocks and output volatility (Haddad et al. 2010). Diversifying the export basket is an important element of the antipoverty agenda as low-income countries tend to be specialized in few export products in highly volatile sectors (Cadot et al. 2013). Having said this, it is typically recommended for these countries to implement policies that diversify their export baskets and/or raise the productivity of the reduced product space through lower trade costs.

We use the Herfindahl index to compute the concentration of exports across products and markets. Here, lower values of our concentration measures can be interpreted as greater extent of export diversification. The Herfindahl index is computed as follows: where _x_ represents the amount of exports, the subscript i stands for a specific product (or market) and n is the total number of products (or markets). When a single export product (market) produces all the revenues, _H_
not be explicitly controlled for due to data limitations. Additional analysis to validate a number of hypotheses on drivers of development outcomes presented in this report would be warranted. Assessing the extent of _seasonal_ and _sectoral_ influence, which will be possible in future iterations of World Bank Enterprise Surveys, is also essential in order to remove potential biases, improve the quality of information, and unbundle outcomes to what can and cannot be influenced by policy.

Future work can build on several directions to address certain gaps as well. First of all, this study draws on a large and diversified sample of developing economies, with only a few OECD countries represented in the sample. It would be worthwhile to extend these indicators to advanced OECD countries, where premia may well differ considerably, particularly as greater FDI is received from emerging economies with different motivations. In addition to the geographic scope, replicating these patterns over time would allow countries to monitor progress, and assess the impact of specific policy interventions on outcomes. Second, the scope of this work only addresses inward FDI. Future work could focus on differences between domestic multinationals and other domestic firms, highlighting development aspects of outward FDI. Finally, the outcomes assessed relate to economic growth and socio-economic inclusiveness, which are key pillars of development strategies. An extension to environmental outcomes would align the set of indicators more closely to the SDGs and add value to the existing framework.
42***|<br>0.32|
||(0.01)|(0.03)|(0.06)|(0.06)|(0.36)|(0.01)|(0.03)|(0.06)|(0.41)|
|Observations|15,462|15,508|15,464|15,464|9,008|15,462|15,508|15,500|9,032|
|R-squared|4,195|4,196|4,152|4,152|2,358|4,195|4,196|4,188|2,382|
|Department FE|No|No|No|Yes|Yes|No|No|Yes|Yes|
|Yeardummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Source:_ Author based on DHS 2000/2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Reference group for wealth quintile is poorest.

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