rafmacalaba/datause-encoder-data huggingface dataset:
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23
<!-- Start of picture text --> Fig. 1b: Urban shares in organized sector<br>0.80<br>0.70<br>Organized Output<br>0.60 Organized Employment<br>Organized Plants<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>1989 1994 2000 2005<br>Urban shares<br><!-- End of picture text -->
39

|Collection|Title in full|Type of collection|Institution|Geographic<br>focus|Type of survey(s)|Further information on data and access|
|---|---|---|---|---|---|---|
||Transitional<br>Economies||||||
|HIES|Household Income &<br>Expenditure Survey|Individual<br>country survey –<br>raw data|National<br>governments|Global|HIES|Survey-specific|
|LIS|Luxembourg Income<br>Study|Ex post<br>harmonized|Luxembourg<br>Income Study|Global, mostly<br>OECD|HBS, HIES,<br>multipurpose surveys|Details of the LIS and how to access the data<br>are at the LIS website.<sup>40</sup>|
|LSMS|World Bank Living<br>Standards<br>Measurement Study|Multi-country<br>survey initiative<br>– somewhat<br>standardized|World Bank|Global,<br>developing<br>countries|Multipurpose<br>surveys|The LSMS project is described at the LSMS<br>website.<sup>41</sup>Most microdata sets are publicly<br>accessible via the World Bank Microdata<br>Catalog.<sup>42</sup>|
|MCSS|WHO Multi-Country<br>Survey Study on<br>Health and<br>Responsiveness|Multi-country<br>survey initiative<br>– highly<br>standardized|World Health<br>Organization|Global|Health survey,<br>including out-of-<br>pocket expenses and<br>some data on<br>household<br>consumption|Details of the survey and how to access the<br>microdata can be found at the MCSS<br>webpage.<sup>43</sup>|
|MNAPOV|World Bank Middle<br>East & North Africa<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>Middle East<br>& North<br>Africa region||Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SARLF|World Bank South<br>Asia Labor Flagship<br>harmonized survey<br>collection|Variety of<br>surveys in South<br>Asia countries,<br>often raw data|World Bank|World Bank’s<br>South Asia<br>region|Multiple types, many<br>not relevant to the<br>current database|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SARMD|World Bank South<br>Asia harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>South Asia<br>region|HBS, HIES,<br>multipurpose surveys|Accessible only to World Bank staff via World<br>Bank Microdata Library.|
|SEDLAC|Socio-Economic<br>Database for Latin<br>America and the<br>Caribbean|Ex post<br>harmonized|CEDLAS and the<br>World Bank|Latin America<br>& Caribbean|HBS, HIES,<br>multipurpose surveys|Details of the SEDLAC project are at the<br>SEDLAC website.<sup>44</sup>Website gives no details<br>of how to access to microdata. World Bank<br>staff can access microdata via the World Bank<br>Microdata Library.|

40 The LIS website is at http://www.lisdatacenter.org/our-data/lis-database/. 41 The LSMS website is at http://www.lisdatacenter.org/our-data/lis-database/. 42 The relevant webpage is http://microdata.worldbank.org/index.php/catalog/lsms. 43 The relevant page is http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/mcss/about. 44 The SEDLAC website is at http://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/.
export profit line has shifted leftward, bringing more firms into the export market. That is, the productivity cut-off for joining the export market falls from 68.72 to 38.09.

In our simulation, we fix the total number of workers at 400,000 in 281 firms. Originally, 16,552 work in the heterogeneous sector _b_ and the rest work in the reserve sector _a_ . The model is full-employment, general equilibrium prior to the trade agreement. In the heterogeneous sector, there are 46 firms selling to the international market that employ a total of 9,300 workers, with an average firm size of 202 workers. The remaining 235 firms in this sector produce for the domestic market, employ 2,752 workers total, with an average size of 11.7 workers.

We model an increase in the international (export) price of 5 percent (Table 4). The results show that employment in the export sector increases by 2,451 workers. This increase is broken down into three groups of firms: (i) “always exporters” increase employment by 642 workers (7 percent); (ii) “never exporters” reduce employment by 9 workers (with a drop in average firm size of about 0.1 worker per firm); and (iii) firms that switch from domestic to exporting increase employment by 1,819, an increase in average firm size of 30.8 workers. The change in employment in these three groups matches the overall increase in employment in the export sector. In the next section, we turn to firm-level data from Egypt to estimate actual employment changes and compare them to the general results of the model simulation.

# **4. Data and Descriptive Analysis**

## **_4.1 Sources_**

Our empirical analysis relies primarily on the World Bank’s Enterprise Surveys (ESs) for Egypt, a small panel of firms followed over time. The ESs are conducted across all geographic regions and cover small, medium, and large companies and are representative of firms in the nonagricultural, formal private economy. The survey—stratified by size (small, medium, and large), 14
bank finance: leasing uses the quality of the asset and trade credit is usually based on the relationship between suppliers and customers.

All results are robust to the inclusion of country dummies (and the exclusion of rule of law or credit information variables). We have also investigated other factors affecting the business environment, such as creditor rights, cost of contract enforcement, and rate of debt recovery. The results on these indicators have consistent signs, but are not significant at conventional levels, and therefore are not reported.

Overall, our results suggest that better rule of law and credit information infrastructures are relatively more important for younger firms in increasing their use of formal bank finance and decreasing their use of informal finance.

# **5.  Robustness Checks**

Until now, we have used binary dependent variables, i.e. equal to one if the firm uses the type of financing and zero otherwise. However, the WBES also collects information on the proportion that each source of finance contributes to working capital and new investment. We use this information for further tests. In other words, we create dependent variables equal to the sum of the total proportion for each financing source for new investment: Bank financing (the sum of the percentage of total new investment financing from local or foreign banks); Trade Credit; and Informal Finance (the sum of new investment financing from informal sources and friends or family). We are unable to test the total percentage of leasing financing because of the high proportion of censored observations (equal to zero). We estimate regressions by Tobit model since our dependent variables are limited to between 0% and 100%.

17
Some countries have adopted capital controls as ways to isolate the domestic economy from the volatility of global financial cycles (see Kawai, **1998b;** World Bank **2000b,** Chapter **_5)._ 16** Restrictions on capital inflows usually have the objective of curtailing excessive short-term inflows through taxation of foreign loans with a short maturity (usually less than 12 months), higher reserve or liquidity requirements (see Dooley, **1996),** or imposition of outright controls. Chile had unremunerated reserve requirements (URR) in place from **1991** until September **1998,** starting with a 20 percent URR in a non-interest bearing account with the central bank on all newly entering portfolio capital for a maturity of up to one year. This rate was raised in May **1992** to **30** percent for foreign currency borrowing except **by** corporations and for all transactions in August **1992,** and then lowered to **10** percent in June **1998** before being zeroed out in September **1998.17** The effectiveness of the controls seemed to progressively diminish as investors learned of ways to circumvent the legislation, requiring successive efforts at expanding the comprehensiveness of the control regime. Several authors (Gallego et al., **1999;** Edwards, **1999)** found that the URR measures were effective in lengthening the contracted maturity of capital inflows, and the average maturity of newly contracted debt rose, though this had little impact on the volume of repayment associated with the stock of debt.

**A** similar type of control can be exercised through prudential regulation of banks (see Stiglitz and Bhattacharya, **1999).** Calvo and Mendoza **(1999)** studied the effectiveness of prudential controls on capital inflows in limiting foreign borrowing **by** banks. Prudential regulation is less distortionary and less subject to evasion, but less

> **16** Here we focus on controls to regulate capital inflows, while we consider controls on capital outflows under "Managing Crises and Contagion," Lesson **5.**

**29**
Table 5b: Decomposition of change in 8<sup>th</sup> <u>grade Science score at percentiles</u>

||**Characteristics**|**Coefficients**|**Total change**|
|---|---|---|---|
|**P10**||||
|Student and household|1.10|-3.72|-2.62|
|Schools|-0.52|4.91|4.39|
|Teachers<br>|-0.34|11.78<br>|11.44<br>|
|Constant|-|37.83|37.83|
|Approximation error<br>|0.22<br>|0.61<br>|0.83<br>|
|**Total **<br>**P20**|**0.46**|**51.40**|**51.86**|
|Student and household|1.18|-16.58|-15.40|
|Schools<br>|-2.46<br>|6.57<br>|4.11<br>|
|Teachers|0.48|5.65|6.13|
|Constant||60.33|60.33|
|Approximation error|--0.66|1.40|0.74|
|**Total **<br>|**-1.47**|**57.37 **|**55.90**|
|**P30**<br>Student and household|0.55|0.31|0.86|
|Schools|-3.06|7.42|4.36|
|Teachers|0.84|-3.96|-3.12|
|Constant<br>|-|51.89|51.89<br>|
|Approximation error|-1.37|1.90|0.53|
|<br>**Total**|**-3.04**|**57.56**|**54.52**|
|**P40**||||
|Student and household|-0.12|-14.70|-14.82|
|Schools|-4.06|10.64|6.58|
|Teachers<br>|0.89|-0.24<br>|0.65<br>|
|Constant|-|54.50|54.50|
|Approximation error<br>|-0.71<br>|1.68<br>|0.97<br>|
|**Total**|**-4.00**|**51.87**|**47.87**|
|**P50**<br>Student and household|-1.07|4.81|3.74|
|Schools<br>|-6.25<br>|31.22<br>|24.97<br>|
|Teachers|1.07|-5.47|-4.40|
|Constant<br>|-<br>|15.10<br>|15.10<br>|
|Approximation error<br>**Total**<br>|1.04<br>**-5.21**|-0.36<br>**45.29**|0.68<br>**40.08**|
|**P60**||||
|Student and household|-0.69|-32.79|-32.10|
|Schools|-1.98|-2.89|-4.87|
|Teachers<br>|1.01|4.19<br>|5.20<br>|
|Constant|-|67.73|67.73|
|Approximation error|-3.09|4.24|1.15|
|<br>**Total **<br>|**-4.75**|**40.49**|**35.74**|
|**P70**<br>Student and household|-0.31|-0.08|-0.39|
|Schools|-1.76|10.45|8.69|
|Teachers|2.51|-20.05<br>|-17.54<br>|
|Constant|-|42.65|42.65|
|Approximation error<br>|-1.10<br>|1.78<br>|0.68<br>|
|**Total**|**-0.66**|**34.75**|**34.09**|
|**P80**<br>||||
|Student and household|-0.47|-6.60|-7.07|
|Schools<br>|-0.11<br>|1.20<br>|1.09<br>|
|Teachers|1.17|-10.87|-9.70|
|Constant<br>|-<br>|44.62<br>|44.62<br>|
|Approximation error|-0.30|0.55|0.25|
|**Total**<br>|**0.29**|**28.90**|**29.19**|
|**P90**||||
|Student and household|-0.76|-21.75|-22.51|
|<br>Schools|<br>0.52|<br>-15.53|<br>-15.01|
|Teachers|2.51|-26.43|-23.92|
|<br>Constant|<br>-|<br>87.42|<br>87.42|
|Approximation error|0.58|-0.11|0.47|
|<br>**Total**|**2.84**|**23.61**|**26.45**|
|N: 2003<br>||5,100<br>||
|N: 2007||6,792||

29
<!-- Start of picture text --> 12,000 =© ¢el . 5<br>5 OS ; 4<br>8,000 Ei 7 =<br>an 3.8<br>2 ala<br>c oO<br>zs= 3o<br>= f 265<br>=<br>4,000 ”<br>1<br>__-e-eea ne<br>0 5 8 0<br>Jan. Feb. Mar. Apr May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.<br>> Credit Program -@® Employment Program<br><!-- End of picture text --> <!-- Start of picture text --> 25 g g<br>20 3 5<br>Ss©rr 15 - 4° on e@ e© e oe e<br>i Wz<br>ri Pal<br>£ 10 ~<br>5 / gs tt<br>/ a<br>0 / -<br>Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.<br>+> Credit Program -@ Employment Program<br>> Both Programs<br><!-- End of picture text -->
# **2.2. Currency Composition of Public Debt: Domestic Currency Debt on the Rise**

Alongside the recent increase in public debt, the currency composition of debt portfolios has been changing.<sup>6</sup> Foreign currency denominated debt still accounts for the larger part of total debt; however, domestic currency denominated debt has been increasing at a faster pace. For the whole region, the median general government gross debt denominated in foreign currency stood at 29 percent of GDP at the end of 2018 whereas that of public debt in domestic currency was 22 percent of GDP. Between 2013 and 2018, however, median domestic currency debt increased by 10 percentage points, and median foreign currency debt by 7 percentage points (figures 7 and 8).

**Figure 7. Domestic Currency Public Debt, by Income Group** _(% GDP)_

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2007 2013 2018 2007 2013 2018 2007 2013 2018 2007 2013 2018<br>Low-Income Countries Lower-Middle Income Upper-Middle Income Sub-Saharan Africa<br>Countries Countries<br><!-- End of picture text --> _Note: The columns represent group simple averages whereas red dots (diamonds) represent the group medians. Source: World Bank; IMF – World Economic Outlook Database, October 2018_ The increase in domestic currency debt was particularly pronounced in upper-middle-income countries ( **Figure 7** ). Median levels increased from 11 percent of GDP in 2013 to 29 percent in 2018. The median domestic debt of LICs and LMCs also increased although at a slower pace than UMCs, 8 and 14 percentage points of GDP, respectively. The heavier reliance on domestic currency financing in UMCs is not surprising, as these countries have deeper and more developed domestic debt markets and are therefore able to raise more domestic financing. Commodity

> 6 The set of countries used for the analysis of domestic and foreign currency denominated debt is smaller than the set of countries used for the total public debt analysis. For several countries, the split between domestic and external debt is not reported: Burkina Faso, the Democratic Republic of Congo, Gabon, Lesotho, Madagascar, Mauritius, Nigeria, São Tomé and Príncipe, and Zimbabwe.

8
<!-- Start of picture text --> Manufacturing employment in India<br>f-2)<br>E ia<br>& eo<br>5 ~ Po ae<br>= aan<br>Eanf.-----777° ae<br>~ ee<br>oO<br>1990 1995 2000 2005 2010<br>Year<br>~-@--~ Formal tradables — ®— - Formal non-tradables<br>——#-—- Informal tradables ——*— _ Informal non-tradables<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>Q<br>= eo -s<c--*<br>= a ae<br>© ne<br>So ae a<br>f | U7 ee<br>es ee<br>oO<br>1990 1995 2000 2005 2010<br>Year<br>“@--~ Formal tradables — ®— - Formal non-tradables<br>——@#-—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text -->

<!-- Start of picture text --> Manufacturing employment in India<br>Zao<br>Ss<br>°ON<br>< 7<br>e- at<br>5 oe enn<br>£ eae<br>Wig tl, ----<br>Oo<br>1990 1995 2000 2005 2010<br>Year<br>“@--- Formal tradables — ®— - Formal non-tradables<br>——# —- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text -->
Figure 8

# Business and Labor Regulations vs. Ouput Loss per Annum in Recessions

<!-- Start of picture text --> 0.00 0.00<br>-0.010.00 AUSCANSWE 5.00GBRESPFRA DEUITA10.00JPNPRT 15.00 20.00 -0.01 AUSCAN0 FRAITASWEGBR20 JPN 40DEU 60 PRT80 100 ESP 120 140 160<br>-0.01 USA -0.01 USA<br>HKG TWN COL HKG COLTWN<br>-0.02 NZL SGPMEX IDNVEN BRA -0.02 SGPNZL MEX VEN BRAIDN<br>-0.02 THAMYS ARG -0.02 MYSARGTHA<br>-0.03 CHL -0.03 CHL<br>-0.03 KOR -0.03 KOR<br>PER PER<br>-0.04 -0.04<br>Starting business - Number of procedures Starting business - Duration (in days)<br>0.00 0.00<br>-0.01 0 JPNCANGBRAUSESPDEUITA FRASWE2 PRT 4 6 8 10 12 -0.01 0 20 40ITAFRADEU 60 PRTSWE AUS80ESPGBRCANJPN 100<br>-0.01 TWNHKG USACOL -0.01 COL USA HKG TWN<br>-0.02-0.02 SGP MEXNZLMYSTHAARG VEN IDN BRA -0.02-0.02BRAVEN IDN ARG MYS THA MEX NZL SGP<br>-0.03 CHL -0.03 CHL<br>-0.03 KOR -0.03 KOR<br>PER PER<br>-0.04 -0.04<br>Closing Business - Duration (in days) Closing a Business - Recovery Rate (in cents of US$)<br>0.00 0.00<br>-0.01CANJPN0 AUSGBR 20 ITA SWEFRADEU40 PRTESP60 80 100 -0.01 0 AUSJPNSWEGBRCANFRA ITA50 ESP DEU PRT100 150 200<br>-0.01HKG USA COL TWN -0.01 USAHKG COL TWN<br>-0.02SGP MYSVENNZL BRAIDN MEX -0.02NZLSGP VENMEX IDNBRA<br>-0.02 THA ARG -0.02 THA MYS ARG<br>-0.03 CHL<br>-0.03 CHL<br>-0.03 KOR PER -0.03 KOR<br>-0.04 PER<br>Hiring & Firing Workers - Difficulty of Firing Index -0.04<br>Hiring & Firing Workers - Firing Costs (in weeks of wages)<br>0.00 0.00<br>-0.01 0 10AUS GBRJPNCANITA 20FRASWEESPPRTDEU 30 40 50 -0.01 0 2 FRAITA 4 PRTESP SWEJPN6 CAN DEU8 AUS GBR10 12<br>-0.01 HKGUSA TWN COL -0.01 COL TWN USA HKG<br>-0.02-0.02 NZL SGPBRATHA MYSARGIDN MEX VEN -0.02-0.02 BRAMEX ARG VEN THAIDN MYS NZL SGP<br>-0.03 CHL -0.03 CHL<br>-0.03 KOR -0.03 KOR<br>PER PER<br>-0.04 -0.04<br>Enforcing Contracts - Number of Procedures Getting Credit - Legal Right Index<br>Average output change Average output change<br>Average Output Change<br>Recession - Average Output Change<br>Recession - Average Output Change Recessions - Average Output Change<br>Recessions - Average Output Change Reccesions - Average Output Change<br><!-- End of picture text --> 32
in addition, an investment slowdown and increase in imports also impacted growth during the three quarter deceleration period of Q3, 2016–17 through Q1, 2017–18.<sup>33</sup> Second, many economic indicators now firmly point that these events had a transient impact as the economy has been slowly recovering from them.<sup>34</sup> Growth has since steadily accelerated to 6.3, 7.0, and 7.7 percent in the last three quarters spanning Q2, 2017–18 to Q4, 2017-18. Economic revival is also evident in high frequency indicators such as the Purchasing Manager Index (PMI) and the Index of Industrial Production (IIP). Both of these confirmed a sharp slowdown in the months surrounding the introduction of the GST, but have recovered and have registered a consistent expansion in recent months. While consumption and services continue to be the main drivers of growth in the last three quarters of 2017-18, the contribution of the public sector to GDP growth has declined, and growth in the manufacturing and construction sectors has accelerated. Even as the investment rate broadly remains burdened by stressed balance sheets of banks and corporates (twin balance sheet issues), investment growth has picked up in recent quarters. Credit growth, and to a lesser extent exports growth, has also recovered in recent months after a protracted period of deceleration (Figure 12).<sup>35</sup>

**Figure 12: High frequency data suggests some uptick in credit growth and exports**

<!-- Start of picture text --> 20 30<br>18<br>20<br>16<br>14<br>10<br>12<br>10 0<br>8<br>-10<br>6<br>4 -20<br>2<br>0 -30<br>Credit Growth: SCBs 3mma Export Growth 3mma<br>Apr-13 Aug-13 Dec-13 Apr-14 Aug-14 Dec-14 Apr-15 Aug-15 Dec-15 Apr-16 Aug-16 Dec-16 Apr-17 Aug-17 Dec-17 Apr-18 Apr-13 Sep-13 Feb-14 Jul-14 Dec-14 May-15 Oct-15 Mar-16 Aug-16 Jan-17 Jun-17 Nov-17 Apr-18<br><!-- End of picture text --> Note: Credit data is from the Reserve Bank of India; Exports data is from Ministry of Commerce and Industry. 3mma refers to three months moving average.

> 33 Due to the GST-related uncertainties, producers destocked their existing inventories, while exports declined, and gold imports nearly doubled, as buyers front-loaded their purchases. Once the initial uncertainties abated, economic activity recovered, and new orders, including in manufacturing, reportedly picked up.

> 34 In our analysis we see the transient impact of demonetization on financial, real estate and professional services, and on construction, but not so much on other sectors of the economy. On the uses side, deceleration was more evident in an already slowing rate of investment; and in an escalated level of import of gold (possibly due to capital flight).

> 35 Outstanding credit by only Scheduled Commercial Banks; we do not consider credit by non-scheduled banks or other financial corporations. The same applies to the discussion of bank credit under section 4.3.

26
<!-- Start of picture text --> Kazakhstan Niger<br>20%<br>55%<br>16%<br>45%<br>12%<br>35%<br>8%<br>25%<br>4% 1998 2000 2002 2004 2006 2008 2010 2012<br>1998 2002 2006 2010<br>Headcount Gap<br>Headcount<br><!-- End of picture text --> Figure 1b Examples of countries with gap and headcount moving in different directions <!-- Start of picture text --> Chad Nigeria<br>42%<br>44%<br>40%<br>37%<br>36%<br>32%<br>32%<br>28%<br>27%<br>1996 2004<br>2003 2004 2005 2006 2007 2008<br>Headcount Headcount<br><!-- End of picture text --> 9