rafmacalaba/datause-encoder-data huggingface dataset:
capabilities are complementary factors for other investments, such as the adoption and use of Information Technologies (IT) (see, for example Bloom et al., 2012, 2016).<sup>18</sup> Figure 4: Informality according to normalized polychoric **PCA 5** vs. Management practices and digital readiness <!-- Start of picture text --> (a) Management vs. Informality index (b) Digital readiness vs. Informality<br>.7 2<br>1.8<br>.6<br>1.6<br>.5<br>1.4<br>.4 1.2<br>1<br>.3<br>-.2 0 .2 .4 .6 .8 -.2 0 .2 .4 .6 .8<br>PCA Informality PCA Informality<br>(c) Management score by informality type (d) Digital readiness by informality type<br>0.7 1.5<br>1.4 1.34<br>0.6 1.3<br>0 .5 4 1.2<br>0.5 1.1<br>1.0<br>0.4 0.37 0 .4 0 0.9 0.78 0 .8 8<br>0 .3 3 0.8 0 .6 9<br>0.7<br>0.3<br>0.6<br>0.5<br>0.2 0.4<br>0.3<br>0.1 0.2<br>0.1<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>Normalized Management score Initial Digital Readiness Score<br>Mean management score<br>Mean digital readiness score<br><!-- End of picture text -->

**Notes:** Both panels control for size, sector, age, and country. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

In line with the extant literature on management practices (see Bloom et al., 2019, 2022; Grover and Torre, 2019), we also observe a positive relationship between management and firm size (Figure 5, panel a). That is, the selection mechanism through which more capable firms survive and grow is at work; however, informal firms exhibit a weaker sizemanagement relationship indicating that there is some degree of misallocation perpetuated due to informality, particularly by parasite firms where this relationship is the weakest. A similar pattern is also observed with respect to digital readiness (Figure 5, panel b). This suggests that the faster churning in the informal sector is not necessarily linked to productivity improvements, as also evidenced in Vietnam (Mccaig and Pavcnik, 2021).

> 18In terms of educational distribution, fully formal firms exhibit a much higher proportion of managers with a college degree compared to informal firms (Figure A6).

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disaggregated and assumed to respond to the market in the same way as other investors. If PEMEX investment is, in fact, less responsive to changes in the cost of capital than other business entities in Mexico, then the tax externality will be lower, as can be seen in Table 16. For a lower (in absolute terms) price elasticity of demand for investments of −0.5 for PEMEX and a slightly higher one of −1.5 for non-PEMEX entities, we would still obtain a per unit capital externality of 1.4 percent.

## **Table 16 Sensitivity of the EOCK tax externalities/unit of capital to investment price** **<u>elasticities by PEMEX and non-PEMEX entities</u>**

|||Rest of t<br>|he economyinvestment<br>|elasticity<br>|
|---|---|---|---|---|
|||−0.5<br>|−1<br>|−1.5<br>|
|PEMEX|0.0|0.4%|0.6%|0.7%|
|investment|−0.5|1.7%|1.5%|1.4%|
|elasticity|−1.0|2.9%|**2.4%**|2.1%|

# **5 Conclusions: Lessons and considerations drawn from the Mexican case**

**The main weakness of the traditional top-down single value approach to the EOCK is that it has difficulty in accommodating the different capital mobilization transaction costs and, more importantly, the different systematic risk premiums required on capital funds supplied to different types of project investments.** While the average commercial project (essentially financed in the long run by market sales revenues and using average debt leverage) bears the market risk premium on its equity, projects bearing more or less market risk will face higher or lower systematic risk premiums, or, in other words, their EOCK will vary accordingly to reflect the changes in costs incurred on capital financing. By contrast, all routine projects (non-mega projects with modest unsystematic project risks) ultimately financed out of government revenues (largely taxes) will bear no systematic market risk premium, such that the costs of capital supplied in a competitive market will be lower than the commercial projects.

**Hence, there are arguments for having two discount rate regimes: one for public sector projects funded out of budget resources, and the other for public sector market-oriented projects.** For the latter, the EOCK would either be based on a weighted average cost of capital plus the economic externality per unit of capital or on the actual weighted average cost of capital for each type of commercial project. Nevertheless, the institutional arrangements and capacities in handling a high volume of public investment projects favor using a single EOCK to simplify the project selection process and bring greater transparency and control to the process.

**Based on the empirical results of this paper’s case study, we recommend that a single EOCK rate for Mexico be set at 10 percent.** However, it is also recommended that a degree of flexibility be allowed for the upward adjustments to the EOCK in the case of large-scale commercial projects in regulated sectors or PPPs, where above-average systematic market risks increase the costs of finance.

29
12

**Table 6: Spending by the Global Middle (Percent of Global GDP in $PPP)** **<u>(2009 to 2030)</u>**

||**2009**|**2030**|
|---|---|---|
|N. America|26|10|
|Europe|38|20|
|C. and S. America|7|6|
|Asia Pacific|23|59|
|SSA|1|1|
|M. East,N. Africa|4|4|

Source: Selected from Table 3, Kharas (2009).

Nothing guarantees sustained growth in the Asian Driver economies. The fall in consumption in the northern deficit economies may be so large that it undermines export-oriented growth in China and India, with a potential combination of negative multiplier effects on economic activity and political disruption as unemployment grows. It may also be that environmental externalities grow so substantially, exacerbated by changing and unpredictable climate, that output growth is not sustainable. And it may be that global political instability spills over into the AsiaPacific region, with a harmful impact on economic growth. So, as in the case of the analysis of likely growth paths in the northern economies (Section 3.1), there are clear uncertainties in projecting forward, particularly in the context of a disruptive global financial crisis. Nevertheless it is our judgment that just as growth is likely to be reduced or to stagnate in the northern economies in the future, so growth in Asia in general, and in China and India in particular, is likely to be sustained. If nothing else, the relativities in growth paths between these two worlds in the past two decades is likely to be sustained, and even to increase. If this is the case, then it is important to understand the nature of demand in these two large southern Drivers of growth, an issue which we now consider.

# **4. PATTERNS OF DEMAND IN SOUTHERN DRIVERS OF GROWTH**

Despite differences in country size and endowments, there are well-established paths of development through which most economies pass (Kuznets, 1966; Chenery and Syrquin, 1975). Low-income economies tend to be agrarian, with the primary sector dominating GDP. As incomes rise and manufacturing expands, the industrial sector takes over as the major Driver of GDP growth. Continued income growth leads to higher demand for services, and at higher income levels it becomes the dominant contributor to GDP. These structural shifts represent a well established pattern, observed in a large number of countries over time. What interests us in this analysis is that in the context of China (and India) becoming the major Driver(s) of global demand in the coming decades, what implications the structural shifts in these Asian Driver economies have for low-income country exporters in general, and for lowincome country exporters of commodities in particular? Here there are two major issues – the structure and the nature of import demand – and in both cases we will consider them in relation to the evolution of the Chinese economy.
There are seven factors in the 2004 SAM, which we aggregate to five factors - agricultural labor, land, unskilled labor, skilled labor, and capital. Table 9 presents the mapping:

**Table 9: Factor aggregation in the 2009 SAM**

|**Aggregate factors**|**Factors from the 2004 SAM**|
|---|---|
|Agricultural labor|Travail agricole<br>Travail des autres activités rurales|
|Unskilled labor|Travail du secteur informel<br>Travail non qualifié du secteur privé formel<br>Travail nonqualifié du secteurpublic formel|
|Skilled labor|Travail qualifié du secteur privé formel<br>Travailqualifié du secteurpublic formel|
|Capital|Rentiers|
|Land|Land is assumed to be 50% of the income reported to<br>capital in the two agricultural sectors|

In Niger, 86 percent of employment is in subsistence agriculture. The informal sector does not employ many skilled workers. Therefore we assign workers in the informal sector to the unskilled labor category.

# **_Structure of the Nigérien Economy_**

Using data from the 2009 SAM, we consider the structure of consumption, production and trade (see table 10) in Niger. We find that agriculture (reported as food agriculture and other agriculture) and services have the highest shares in total consumption (25.7 and 20.7 percent respectively). Likewise, these two sectors have the highest share of total production. Niger depends heavily on imported goods – 26.9 percent of total consumption is imported. Important import sectors include manufacturing goods (93.5 percent of total consumption is imported) and petroleum (100 percent of total consumption in 2009 is imported); manufacturing is the largest import sector, accounting for 65.1 percent of total imports. The next largest import good is petroleum, which accounts for 15.3 percent of total imports.

Mining is the most important export good – it accounts for 46.7 percent of total exports. The majority of mining is exported – 80.9 percent of production. Agriculture (reported as food production and other agriculture) is the next largest export, accounting for 38.4 percent of total exports.

Construction, public services, and education/health are purely non-traded goods.

As seen in Table 11, the service sector employs the majority of unskilled nonagricultural labor – 81.3 percent.<sup>5</sup> The sectors public service and education/health employ

> 5 Note, the category services is an aggregate of the following sectors represented in the 2004 SAM: transport and communication services (19.2 percent), financial services (2.7 percent), real estate and business services (30.3 percent), hotel and restaurant services (8.3 percent) other services (36.7 percent), and personal and

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for a ‘A-’ bond to 577 basis points for a ‘CCC+’ rated bond (box 1). There is a sharp jump in spreads (of 91 basis points in 2005) at the investment grade threshold.<sup>3</sup> When arms-length monitoring of investment projects is difficult, non-bank investors either do not invest, or take direct control of the investment project via FDI. Also, the cost of financing FDI projects is affected by sovereign risk ratings. In 2005, FDI constituted 85 percent of private capital flows in the unrated countries compared to 26 percent in the BBB rated developing countries (figure 1).<sup>4</sup>

**Figure 1: Composition of private capital flows in rated and unrated countries, 2005**

<!-- Start of picture text --> Lower rated countries have less diversified financing<br>Compostion of flows<br>100%<br>Portfolio equity<br>80%<br>Bond<br>60% Bank loan<br>FDI<br>40%<br>20%<br>0%<br>A BBB BB B CCC Unrated<br>Sovereign rating in 2005<br><!-- End of picture text --> _Source_ : World Bank, Global Development Finance 2006 database.

Several factors influence a country’s reluctance or inability to get rated. Countries are constantly reminded of the risks of currency and term mismatch associated with market-based foreign currency debt, and the possibility of sudden reversal of investor sentiment (Panizza, Eichengreen and Hausmann 2005, Calvo and Reinhart 1999). The information required for the 3 This sharp jump reflects the limitations or regulations that prevent institutional investors from buying subinvestment grade. Knowing where a country lies on the credit spectrum can give some idea about the cost of capital. For poor countries that are rated below investment grade, an improvement in rating (via financial structuring or proper accounting of hard currency flows such as remittances) could result in significant spread savings, in the range of 300-700 basis points depending on the initial rating and global credit market conditions.

4 Fernández-Arias and Hausmann (2001) argue that a higher share of FDI—and a correspondingly lower share of private debt flows—may be an indicator of poorly functioning markets, inadequate institutions and high risks. The authors find a higher FDI share in countries that are poorer, riskier, more closed, more volatile, less financially developed, and with weaker institutions and more natural resources. Loungani and Razin (2001) report a similar negative relationship between Moody’s country ratings and FDI share.

2
# **Table A8: The effects of temperature on poverty and inequality – Country level analysis**

# **using alternative data from WDI and SWIID**

# _Panel A: Poverty data from WDI_

|Poverty:|$1.90|/day|$3.2|0/day|$5.5|0/day|
|---|---|---|---|---|---|---|
||Panel FE|Long<br>differences|Panel FE|Long<br>differences|Panel FE|Long<br>differences|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|0.629***|0.217***|1.020***|0.381***|1.344***|0.501***|
||(0.098)|(0.028)|(0.153)|(0.042)|(0.220)|(0.055)|
|Precipitation|-0.019|0.047|-0.017|0.008|-0.010|0.060|
||(0.016)|(0.233)|(0.029)|(0.325)|(0.036)|(0.339)|
|Country FE|Yes|No|No|No|Yes|No|
|Year FE|Yes|No|No|No|Yes|No|
|Mean dependent var.|7.288|7.288|15.399|15.399|26.593|26.593|
|Number of countries|134|95|134|95|134|95|
|Observations|1,717|95|1,717|95|1,716|95|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Regressions are weighted by region population. Poverty data are taken from the WDI. Poverty and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1.

_Panel B: Inequality data from WDI and SWIID_

|Inequality:|Gini|– WDI|Gini –|SWIID|
|---|---|---|---|---|
|||Lon||Lon|
||Panel FE|g<br>differences|Panel FE|g<br>differences|
||(1)|(2)|(3)|(4)|
|Temperature|0.171***|0.194***|0.165***|0.255***|
||(0.023)|(0.033)|(0.037)|(0.040)|
|Precipitation|0.013**|0.022*|0.006*|0.018*|
||(0.005)|(0.013)|(0.003)|(0.010)|
|Country FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|38.295|38.295|38.445|38.445|
|Number of countries|128|90|128|90|
|Observations|1,505|90|3,781|90|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level. Regressions are weighted by region population. Inequality data in Columns (1)-(2) are taken from the World Development Indicators (WDI). Inequality data in Columns (3)-(4) are taken from the Standardized World Income Inequality Database (SWIID). Inequality and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on crosssectional data with a smaller sample size compared with panel data. *** p<0.01, ** p<0.05, * p<0.1 56
# **Grants and ODA Debt Forgiveness in Africa**

Grants to Africa have increased significantly since 1989. In this paper we distinguish between grant figures that include ODA debt forgiveness, which we call total grants, and those without the forgiveness element, which we call pure grants. This distinction is made because these two types of grants may have a different impact on the resource balance of recipient countries.

# **Recent trends**

Total grants and pure grants to African countries have been increasing since the late 1 980s, and especially during the early 1 990s. Mos of ODA debt forgiveness began in 1988 with very small amounts, increased in 1989, and peaked in 1990 at slightly more than $2 billion. Since 1991 it has been high and stable-between $500 million and $1 billion a year (Chart 1).

**Chart 1. ODA, Grants, and ODA debt forgiveness for 32 African countries, 1984-93**

<!-- Start of picture text --> USS milliom<br>'J,IKMI. ., ,, :i,i<br>f11 'r-Grani cF ding Tech. '. -.<br>~orgvne"s|. . /to<br>4 (ON)~ ~ ~ ~ ~ ~~~o-p<br>d . grant .- - - - -pur' - - -<br>_. I -. - - - - - - - - - -------- --. .-,<br>210<br>IY1 I'JIS l'9M I'J7 , 9)K 19#' 19'1 I'JYI 199'2 1IYJ<br><!-- End of picture text -->

Source: OECD/DAC. Note: ODA comprises grants plus otlier forms of assistance. Grants, in turn, comprise debt forgiveness and "pure" grants.

The 32 recipient countries in our sample differ in the size of their economy, their indebtedness, and the types of capital flows they have received. Within this context we analyze next how the mix of grants and ODA debt forgiveness is allocated across these countries; that is, we study whether there is a clear criteria or rationale for such allocation.

3
Table 3. Impacts of Refugee Camps on Local Economic Development

||Night Lig|ht Density|Defore|station|
|---|---|---|---|---|
||[0 to 63]|Sin*|Ha/_Km_<sup>2</sup>|Sin*|
||(1)|(2)|(3)|(4)|
|_A. Main Specifcation_|||||
|SID_×_I(Post 2017)|0.034***|0.017***|0.0002**|0.0002**|
||(0.006)|(0.002)|(0.0001)|(0.0001)|
|FDR q-values|[0.001]|[0.001]|[0.032]|[0.032]|
|R-squared|0.686|0.741|0.126|0.127|
|Observations|71,760|71,760|52,624|52,624|
|_B. Controlling for differ_|_ential linear_|_pre-trends_|||
|SID_×_I(Post 2017)|0.039***|0.026***|0.0004***|0.0004***|
||(0.008)|(0.003)|(0.0002)|(0.0002)|
|FDR q-values|[0.001]|[0.001]|[0.009]|[0.009]|
|R-squared|0.686|0.741|0.126|0.128|
|Observations|71,760|71,760|52,624|52,624|
|Mean Dep. Variable|0.732|0.509|0.003|0.003|
|_Controls (All Panels)_|||||
|Grid FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

_Notes:_ Panel A presents the results of the main specification outlined in equation 1. Panel B shows the results for the non-inferiority test proposed by Bilinski and Hatfield 2018. These results detail the main specification while controlling for an indicator variable representing differential linear pre-trends between the treatment and control groups. SID represents standardized values of the inverse distance from each grid to the refugee camps, and I(Post2017) is an indicator variable with a value of 1 post-2017, marking the onset of substantial Rohingya inflows to Bangladesh. Dependent variables include: (i) Night light density [0 to 63]: This variable represents the average light density at the grid level, obtained by averaging across pixels at the desired level of aggregation. Values range from 0 (no light) to 63 (maximum light density). (ii) Night light density Sin*: This variable is the inverse hyperbolic sine transformation of the original night light density variable, interpretable as a logarithm transformation. (iii) Deforestation _Ha/Km_<sup>2</sup> : This variable corresponds to landsat images from the Hansen Global Forest Change, focusing on identifying forest loss defined as the transformation of forested areas into non-forested ones due to stand-replacement disturbances. The measure is expressed in hectares per square kilometer. (iv) Deforestation (Sin*): This variable represents the inverse hyperbolic sine transformation of the original deforestation variable, also interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid level in parentheses. False Discovery Rate (FDR) q-values are reported in brackets. Significance levels are denoted as follows:<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

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amounts of production inputs, brings about effects similar to quantitative increases in resources, such as labor, land and capital. At the same time, the reduction of trading costs may bring about an effect that resembles removal of tax with its resulting distortion. Table 4 below summarizes the impacts on EV and real GDP of the economies of APEC, as well as non-APEC economies in the world.

**Table 4: Estimated Impact on Macroeconomic Indicators (Billion US dollar for EV, % change for real GDP)**

||_Case_|_1_|_Case_|_2_|
|---|---|---|---|---|
||_GDP_|_EV_|_GDP_|_EV_|
|_AUS_|0.0|1|0.0|0|
|_CAN_|0.0|-1|0.0|-1|
|_CHL_|0.0|0|0.0|0|
|_CHN_|6.3|110|4.7|80|
|_HKG_|0.0|6|0.0|3|
|_IDN_|4.4|11|3.1|8|
|_JPN_|0.0|2|0.0|2|
|_KOR_|1.1|8|0.8|6|
|_MEX_|3.1|22|2.7|19|
|_MYS_|8.0|13|6.6|12|
|_NZL_|0.0|0|0.0|0|
|_PER_|3.9|3|2.6|2|
|_PHL_|15.0|18|11.5|14|
|_RUS_|16.1|118|9.7|75|
|_SGP_|0.2|4|0.1|3|
|_THA_|22.4|61|18.3|54|
|_CTP_|0.0|3|0.0|3|
|_USA_|0.0|1|0.0|-2|
|_VNM_|31.3|22|24.8|19|
|_EU27_|0.0|-4|0.0|-10|
|_ROW_|0.0|8|0.0|3|
|_APEC_|--|402|--|297|
|_World_|--|406|--|290|

(Note) Authors’ calculation using the GTAP model and Gempack. The figures are on the prices in 2006.

Four points merit discussion in regard to our results. First, the reforming economies in APEC stand to benefit significantly in regard to GDP and welfare gains with the type of from technical progress, if the rate of reduction in trade costs is the same, because the former involves the simultaneous reduction in transfer income.

25
Based on the regression of Kang (2000) we may find the expected logarithm of the shipping margin<sup>28</sup> for each country of interest using the average logarithm of shipping margins with restrictions for each classification of countries (developed countries, and developing countries) in which the country of interest is classified; and the average logarithm of degree of restrictiveness index for each group of countries.: In 6.1 and 6.2, given that the equation was estimated based on Kang’s data, we calculate the average logarithm of shipping margins with restriction for industrialized countries as well as developing based on Kang (2000, table 11.1), not the entire sample. In the case of the restrictiveness indices, we calculate the average logarithm of TRI index for group of developed countries, and industrialized countries are -0.65, and -1.10, respectively (Kang 2000, table 11.2) for developed (or industrialized) countries29.We take the simple average of the natural log of the TRIs for each group of countries, i.e. we calculate ∑ ln(T for maritime transport sectors. Here, d refers to the dd number of countries at each level of development. Accordingly, the average logarithm of shipping ii=1 ii TT ) /dd margins with restrictions for developing countries and industrialized countries are 0.1777 and 0.0795, respectively. Further the average logarithm of TRI index for group of developed countries, and industrialized countries are -0.65, and -1.10, respectively.

One potential problem may occur due to the fact that in 6 and thus in 5.1 and 5.2 nothing forbids Mi to lie below 1, but it is inconsistent with economic logic of shipping margins, so in that case we set _E_ [ln( _M i_ )] = 0 .

28Mathematical expectation of logarithm of shipping margin.

59