rafmacalaba/datause-encoder-data huggingface dataset:
_JULY 2018_ _12_ coefficient on Loan ~~2~~ 016 in the ANCOVA regressions in columns (1) and (2)). The magnitude of treatment effects in the DID models is slightly larger than the ANCOVA and remain statistically significant even when applying PSM methods. Again, the similar effects for the unconditonal and conditional samples suggests that the impact of loans on employment is not being driven by the impact on business survival.

Table 6 and Table 7 report the impact of receiving a WEDP loan on the number of hours worked by the owner and by employees. The loans had a small and insignificant impact on the number of hours worked by the owner but a strongly significant positive impact on the number of hours worked by employees per week when measured 3 years after the program started. The magnitude of effects on the number of hours worked by employees is similar across all the regression models.

Overall, the results show that female-owned enterprises who are able to borrow through the WEDP program are able to exhibit a higher growth potential after 3 years than the non-borrowers. The most robust impacts of the loans are on employment generation; however, the impact on profitability is less reliable with the positive impact only significant for the models where matching methods were not utilized. A comparison of the unconditional and conditional results suggests that the impact of receiving a WEDP loan is not occurring through the extensive margin of allowing firms to continue to operate that otherwise would have had to close down.

# _B. LEVEL DIFFERENCES FOR EXISTING AND START-UP FIRMS_

Next, we report the level differences for firms that received a loan and did not across a wider array of outcomes measured in the 2016 follow-up survey. Tables 8 and 9 report the level differences for those that received a loan to those that did not (the treatment dummy is whether they reported receiving a loan in either the 2014 or 2016 surveys). We report results across three models: ordinary least squares (OLS), OLS with controls and a PSM kernel estimator where the ATT is computed averaging over the unit-level treatment effects of the treated where the control unit outcome matched to a treated observation is obtained as kernelweighted average of control unit outcomes. PSM Min computes the propensity score based on characteristics of the entrepreneur and PSM Max on characteristics of the entrepreneur plus a measure of firm size.

Table 8 reports the results comparing WEDP borrowers to those without any WEDP loans for firms that were already existing at baseline. In panel A, we report positive significant differences across a range of business performance outcomes (profits, sales, capital machinery and employees are all significantly higher for firms that received a WEDP loan). The PSM kernel matching models in columns (3) and (4) produces similar effects to the OLS regressions and the impacts remain statistically significant. The number of hours worked by the owner is slightly lower for firms that received a loan which is consistent with them passing some of the workload onto their employees since the owner, on average, already works long
# **4. Education and Wage Differentials**

Education is the single most important factor that contributes to wage differentials. Table 7 presents the wage equation using dummies of the highest degree completed as measures of education level. The results suggest that all factors carry the expected signs. Other things equal, females earn less than males; experience counts although returns to experience increase at a decreasing rate; education counts and the wage premium for higher education increases with the level of the highest attainment; manufacturing workers earn the highest wage, followed by services workers, while agricultural workers earn the least; occupation counts, officials, managers, executives, and professionals earn more than laborers. Comparing 2007 with 2003, wage premium for secondary and high education increased slightly; while wage gaps across industries declined. Comparing female with male workers, wage premium for secondary and tertiary education is higher for female; while that for primary education is lower, consistent with the findings in many other countries.<sup>8</sup> The wage gap across economic sectors of employment differs significantly between males and females. The daily wage level of males in the services sector is 30 percent higher than those in the agricultural sector, while the wage level of females in the services sector is virtually the same as those in the agricultural sector. The wage gap between executives, professionals and laborers is higher for females than males.

> Fields (2003).

> 8 See Psacharopoulos, G. and H.A. Patrinos (2004), and Schady (2000) and (2003).

10
- 13 -

To measure these types of transport connectivity, a variety of data sets are used from different sources. The most important data are road network data, which come from the individual EAC countries. The timing of these data vary from 2006 to 2010 (Rwanda, Burundi and Uganda in 2006, Tanzania in 2008, and Kenya in 2010).<sup>9</sup> Thus, there is a slight difference between the dependent variable and transport connectivity data. However, the difference is considered to be small, especially when taking into account the fact that the road network data are not changed dramatically in the short term.

Various assumptions are made to calculate the lowest transport cost from a city to the port. It is composed of vehicle operating costs, which are assumed to differ according to road characteristics, such as road class, surface and road conditions, opportunity time costs of drivers, waiting time at transport nodes, such as rail stations and ports, and nodal fees and charges, such as cargo handling and customs fees. An optimal route is selected based on the lowest transport cost. Not surprisingly, firms in inland cities bear much higher transport costs than those in coastal cities (Figure 10). For instance, the transport cost for firms in Mombasa is estimated at $78.33 per ton, which includes various fees paid at the port and time costs of waiting in and outside of the port. The transport cost of conveying goods from Nairobi to Mombasa is estimated at $119.50 per ton. The transport costs from Kampala and Kigali are much higher at $160.18 and $191.82 per ton, respectively.<sup>10</sup> Regarding other explanatory variables than transport connectivity, the city population is used to capture the local demand at the aggregate level ( _POP_ ). The optimal inventory level is expected to increase with the size of total demand. In East Africa, Dar es Salam is the largest metropolitan area with 4.3 million of population, followed by Nairobi with 3.1 million and

> 9 The new generation of BEEP after 2011 does not include data on firm inventory, which unfortunately does not allow us to use some recent data for the analysis.

> 10 These figures are broadly consistent with those in another freight cost survey, Doing Business by the World Bank. This is a cross-country and the data represent export and import freight costs from the country’s largest business city. For instance, the import freight costs are estimated at $112 per ton for Kenya, $153 for Uganda, and $237 for Rwanda (http://www.doingbusiness.org/data/).
from 53 percent to 63 percent (figure 4). Sub-Saharan Africa and Europe and Central Asia have the smallest absolute increase of learning poverty, 5 percentage points, while South Asia has the largest (17 percentage points), followed by Latin America (12 percentage points). Africa also has the smallest relative increase, 5 percent, while East Asia and the Pacific and Europe and Central Asia have the largest (more than 30 percent), suggesting that the children in the upper-middle-income and lower-middle-income countries are likely to become the new learning poor. This result only reinforces the understanding that Sub-Saharan Africa was already experiencing a massive learning crisis before COVID-19 in which children were not learning as much when schools were still open. (Table A1 in the annex has the full set of results in terms of learning poverty.)

**Figure 4 Learning poverty simulation results**

<!-- Start of picture text --> 18.0<br>16.5<br>15.0<br>12.0<br>11.5<br>10.5<br>9.0<br>7.6<br>6.0 6.5 6.4<br>4.8 4.6<br>3.0 3.6 3.1<br>2.1 1.5 1.9 1.7<br>0.0<br>WLD EAP ECA LAC MNA SAR SSF<br>52.7 21.2 63.3 58.2 86.7<br>Baseline 13.3 50.8<br>Intermediate Optimistic Pessimistic<br>Change in Learning Poverty<br><!-- End of picture text --> Source: Author's calculation. All underlying numbers can be found in table A.1 in the annex.

As previously discussed, a complementary and relevant set of measures are the gap and severity. In order to avoid the confounding of policies required to improve schooling, in what follows we focus exclusively on the learning deprivation (Table A1 in the annex in the annex has the same results in terms of learning poverty). Sub-Saharan Africa and the Middle East and Northern Africa are the two regions in which students are on average the furthest behind in respect to the minimum proficiency level, with a Learning Deprivation Gap of approximately 20% (figure 5). By comparison, this rate is double the global average (10.5%), four times larger than the East Asia and Pacific Gap (5%), and more than tenfold larger the Europe and Central Asia average gap (1.3%). This average learning gap is equivalent to what students are expected to learn, in the respective regions, in a full academic school. In the pessimistic scenario, the learning gap for the average student in low- and middle-income countries could increase by 30 percent; and East Asia and the Pacific and Latin American being the region with the largest relative increase, close to 40%.

The gap measures are not distribution-sensitive and cannot distinguish between an increase in the learning gap driven by students near the threshold and one driven by those at the very bottom of the learning distribution. The African and Latin American and Caribbean increases in the learning gap might be qualitatively different if, in one set of countries, the pandemic were pushing many children marginally below the deprivation threshold and in the other, it were further deepening the deprivation of those already far below the threshold.

27
# **Figure A1: Simulation of the pdf for Z**

<!-- Start of picture text --> 0.35<br>Through Simulation<br>Derived Mathematical Formulation<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>Z = B - V<br>PDF<br><!-- End of picture text -->

# **Figure A2: Plots of E(B-V) under parametric variation in H, when B and V are independent normal**

<!-- Start of picture text --> Expected Value of B-V with Change in EB = H<br>1.3<br>Through Simulation<br>1.2 Exact Mathematical Formulation<br>1.1<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2<br>EB = H<br>E[B-V|H]<br><!-- End of picture text -->

**Figure A3: Plots of Standard Deviation of (B-V) under parametric variation in H, when B and V are independent normal**

29
region (i.e. WMA). By contrast, nonagricultural production technologies are taken from the national supply-use table (StatsSA, 2004) and are thus the same across WMAs.

# _3.2.2 Domestic and international trade_

Producers in each region<sup>12</sup> supply their output to a national commodity market, where they are exported, sold domestically, and/or combined with imported goods. Substitution possibilities exist between production for domestic and foreign markets based on a constant elasticity of transformation (CET) function. Profit maximization drives producers to sell in those markets where they can achieve the highest returns. These returns are based on domestic and export prices (where the latter is determined by the world price multiplied by a flexible exchange rate and adjusted for any taxes). According to the 2002 SAM, relatively little of SA’s agricultural production is exported, with the exception of horticultural products (see Table 3). Rather it is mining and metals that generated almost half of total export earnings.

Substitution possibilities also exist between imported and domestic goods under a CES Armington specification.<sup>13</sup> The final ratio of imports to domestic goods is determined by the cost minimizing decision-making of domestic demanders based on the relative prices of imports and domestic goods (both of which include relevant taxes). Most of SA’s imported goods are chemicals, machinery and equipment. Agricultural imports are considerably smaller and are mainly for food crops, such as maize and wheat (shown as summer and winter crops in Table 3).

Under the small-country assumption, SA faces perfectly elastic world demand/supply at fixed world prices. There are, therefore, four endogenous commodity prices in the model: a single national supply price reflecting region-specific producer prices; an export and an import price based on world prices and the exchange rate; and a composite market price. The final market price is the same in all regions and includes transaction costs and indirect taxes. While observed prices do vary across SA, the assumption of a national commodity market avoids having to model physical trade flows between WMAs (for which there is no data). This implies that consumers can purchase commodities produced

> 12 Note that “region” and “WMA” are interchangeably used throughout this paper to mean the same thing. 13

> Trade elasticities are taken from the Global Trade Analysis Project (Dimaranan, 2006).

15
Table 9: Split IV regression of the effects of IT use on firm-level productivity: High competition with China

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|2.456***|2.289***|2.372***|2.097***|2.175***|2.692**|
||(0.651)|(0.704)|(0.731)|(0.727)|(0.803)|(1.055)|
|F-frst stage|22.5|19.2|22.7|21.6|21.6|11.03|
|Sargan|0.14|0.14|0.36|0.59|0.53|0.58|
|Share of labor with Internet|1.594***|1.784**|1.881**|1.745**|1.819**|2.045*|
||(0.579)|(0.718)|(0.780)|(0.778)|(0.840)|(1.070)|
|F-frst stage|40.1|26.0|26.7|26.9|26.9|14.7|
|Sargan|0.55|0.49|0.24|0.35|0.30|0.25|
|Share of labor with computer|3.072***|2.849***|3.331***|3.085***|3.338**|3.493**|
||(0.885)|(0.911)|(1.063)|(1.131)|(1.378)|(1.538)|
|F-frst stage|14.9|13.6|12.1|10.6|10.6|6.4|
|Sargan|0.29|0.28|0.78|0.96|0.84|0.87|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|730|730|730|730|730|730|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 24
More specifically, the work benefited from Khakhalin and Butler, “Privatization of Enterprise Land in the Russian Federation: 1992 – 2003”<sup>17</sup> , which clearly describes the legal history and economics of businesses trying to privatize the land under their buildings (which were mostly privatized during the early-mid 1990s). This paper describes the many changes in policy, pricing, and procedures for land privatization both before and after the enactment of the Land Code in 2001, and how the main stakeholders reacted to the shifting incentives and parameters affecting the land market.

The Khakhalin/Butler paper points out that while land rents are almost entirely at municipal discretion, both land purchase prices and land taxes (and the formulas for their distribution between Federal/regional/and municipal levels of government) are established by law. In particular, land purchase prices are currently set within minimum and maximum parameters, established as a multiple of the land tax rate (which itself is set administratively, based on cadastral values that are also established administratively).

While there is some evidence that some public sector bodies were more willing to privatize land when they were allowed to charge and keep the full revenues from the highest land prices (especially when they were facing enactment of new laws that would force them to accept lower revenues in the future), the more general pattern suggests that even the highest land privatization prices stipulated during the period between 1992 – 2003 “were apparently not high enough to induce localities to promote or even cooperate with further land privatization.”<sup>18</sup> Further, since land rental rates are much less constrained, most municipalities still seem to prefer it for the sake of “greater long term control over revenue.”<sup>19</sup> Finally, they note extensive anecdotal evidence that municipalities exploit vague administrative procedures to refuse or delay applications for land privatization, including designation of large amounts of land as being in “water protection” or “sanitary protection” zones or reserved by “master plans” for “future public use.”<sup>20</sup>

# **Sources of information**

The following sources of information were used in the course of this research:

1. Data collected by the Survey of Land and Real Estate Transactions in the Russian Federation<sup>21</sup> ; and

2.

- Data from open sources of information:

- Descriptive statistics on Russian regions from the Russian Statistical Agency (RosKomStat) web-site;

- Art. 2 of the Federal Law on Enactment of the Land Code;

- statistics of land transactions in Russia regions in 2004 year (Form #3-zem)

- obtained from the Russian Statistical Agency.

> 17 Khakhalin and Butler, op cite.

> 18 Ibid., pg. 13.

> 19 Ibid., pg. 26

> 20 Ibid., pg. 27.

> 21 The survey was conducted by the marketing agency Media Navigator on behalf of FIAS.

9
# **2 Data**

We use a unique data set by combining worker-level information from the World Bank’s International Income Distribution Data Set (I2D2), which contains harmonized household and labor surveys, with FDI project-level information from the fDi Intelligence database.

## **2.1 FDI**

fDi Intelligence has been tracking and verifying individual cross-border greenfield investment projects since 2003.<sup>5</sup> The database, available through fDi Intelligence, which is part of the Financial Times Group, provides project-level information on the value of investments and the estimated jobs created. Importantly, the data allow us to do our analysis both at the sub-national level and at the industry level, as well as to focus on the extensive margin of FDI, i.e. the number of greenfield projects.<sup>6</sup> Figure 1 shows the evolution of FDI projects in the seven emerging economies we study. Most of these countries saw a steady rise in the yearly number of FDI projects over the years, yet the last couple of years saw a slight reverse in the trend. Brazil saw a steady rise in FDI from 2006 to 2012, culminating with around 550 projects in 2011. Colombia saw a steady rise in FDI from 2003 to 2013, culminating in around 175 projects. As for Brazil, FDI inflows have been declining since 2013. South Africa and the Philippines saw similar increases until 2013 and 2014, whereas Vietnam peaked earlier in 2008 with around 350 projects. In Mexico and Ethiopia FDI projects have been growing since 2003 and have shown more resilience in the last years covered with no observed decline.

> 5The data are now a primary source of FDI information for UNCTAD, the World Bank, and the Economist Intelligence Unit.

> 6We aggregate FDI in 10 sectors, namely Agriculture, Extraction, Manufacturing, Construction, Utilities, Commerce, Transport and Communications, Financial and Business Services, Public, and Other Services. The number of regions varies by country: Brazil (27), Colombia (24), Ethiopia (9), Mexico (32), Philippines (42), South Africa (9), Vietnam (96). For most countries these regions refer to state-level information, following the disaggregation available in the I2D2 harmonization.

4
Overall, the results were encouraging, but additional efforts are needed to enhance data coverage. Figure B3.3.1 plots the outcomes of the exercise for five different years (December 2007–December 2011). It displays the buildup of imbalances in the run-up to the 2008, including rising household indebtedness, which took place against the backdrop of favorable macroeconomic and financial conditions and an upturn in the financial cycle. The model also illustrates how 2008 marked the turn of the financial cycle, with a marked slowdown in credit growth, deleveraging households and sharp deterioration in the macroeconomic outlook.

Obviously, data availability determines the range of indicators available for financial stability monitoring. The exercise in El Salvador highlights the importance of efforts to strengthen data coverage, particularly with regard to the household and corporate sectors. In addition, the lack of an indicator for housing prices is a pressing data gap.

# **Figure B3.3.1 The Salvadoran Cob Web Model**

<!-- Start of picture text --> Macroeconomic conditions<br>5.00<br>NBFIs 4.00 Financial conditions<br>3.00<br>2.00<br>Structure of the financial<br>1.00 Financial cycle<br>system<br>0.00<br>Liquidity and funding Enterprises<br>Capital and earnings Households<br><!-- End of picture text --> _Source_ : Banco Central de Reserva de El Salvador

# **4. Practical considerations**

Besides establishing the analytical underpinnings of financial stability monitoring, policymakers also need to give some thought to more practical aspects. Responsibilities for conducting macroprudential analysis need to be assigned to specific agencies. In addition, macroprudential analysis needs to be embedded in an institutional framework, aimed at ensuring that the 30