rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> West Bank and Gaza: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Syria: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Yemen: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Libya: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text -->

# **C- Western countries and Japan**

<!-- Start of picture text --> Norway: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Netherlands: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 32
21 the consequent freed-up funds in the MOH budget are used efficiently and effecltvely upon the poor, there will be taxpayer resistance and noncompliance, over and above that which would have occurred with a well-designed formal health care insurance. Likewise if the provlders of health care believe that they are not treated fairly and reasonably in the transfer of financLal risk, they will not respond to the incentives offered by the health insurance in an optimal fashion. Assuming that the formal health insurance is designed at the Ministry of Health, it will not become a reallty **and** function smoothly for its redistributive and efficiency objectives, if it does not find approval at the Mlnistry of Finance, in the Parliament and at the Presidential leiel.

# VII. Summary and Conclusions

This paper has attempted a survey and analysis of health insurance in 23 Sub-Saharan countries. Perhaps the most useful way to approach the subject of health insurance in Sub-Saharan Africa is to think about it as a phenomenon of income class and income distribution. Put clearly and simply, the larger percentage of MOH budgetary expenditures in these countries (with the possible exception of Tanzanla and Ethiopia) is skewed towards a well-defined smaller population. In all of these countries, the well-to-do pay for the "best" 9Due to the initial amount of opposition to the Medicare health insurance program for the elderly by physicians and the American Medical Association, and by the American Hospital Association, there was a great deal of apprehension on the part of the U.S. Department of Health, Education and Welfare on July 1, 1966, when the program went into effect. Officials in the Department were afraid that physicians and hospitals would refuse to treat the elderly under the new insurance program. Because physicians and hospitals quickly realized how generous Medicare payments were, given the manner in which reimbursement was done, they quickly adjusted to it. In effect, they bore no financlal risk in the program.
2 Thir paper is written in honor of Bela Balassa, an economist who has had an enormous influence on the developing countries pcli Z dcoate and, above all, on the movement towards more open policies. For many years his incredible productivity has illed me and many others with awe. His work is always thoroughly down to earth and highly policy-relevant. In this respect the **present** paper is on attempt at emulation.

On **the** subject matter itself Bela's most comprehensive contribution **is** Balassa (1987), though he has touched on some of the issuis in many other writings. It is interesting to look at his **seveiAl** collected volumes (Balassa, 1977, 1981, 1985, 1989a, 1989b) from this point of view. It is clear that he is a "real targets" man, praising countries for adjusting their nominal exchange rates to bring about desirable real exchange rate changes, for example in response to external shocks, or to avoid undesiratle ones, and criticizing countries for failures on this - front. A principal theme of Balassa (1987) backed typically with a ccmpact presentation of the relevant empirical evidence - from many studies by himself and others is that elasticity pessimisr with regard to the real exchange rate is not<sup>justified.</sup> In Balassa (1983) he examined in detail the Turkish experience of 1979-83. In that case a reversal of a 22% real appreciation led to a doubling of exports between 1980 and 1983.

others - Mexico, Costa Rica, Morocco, Turkey, Kenya, Nigeria, India, Indonesia, Pakistan, Sri Lanka, and Thailand. The project is entitled "macroeconomic policies, crisis and growth in the long run."
# **Informality and the Life Cycle of Plants**<sup>*</sup>

Furkan Sarıkaya<sup>†</sup> M. Nazım Tamkoç<sup>‡</sup> Jesica Torres<sup>§</sup> December 2024

**JEL classification:** E23, J24, L25, O41, O33 **Keywords:** Informality, Life-cycle, Development, Productivity, Distortions

> *We would like to thank Edgar Avalos for his superb research assistance. We thank participants at LACEA/LAMES 2024 for comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Tamkoç and Torres thank DECRG for providing Research Support Budget. INEGI has reviewed our results to ensure that no confidential plant-level information is disclosed.

> †Arizona State University

> ‡The World Bank. Corresponding author, mtamkoc@worldbank.org.

- §The World Bank
has characterized the region’s spending habits in recent decades and explained, in part, the declining competitiveness vis-à-vis East Asian economies.

The decision to increase infrastructure investment at the outset of a financial crisis illustrates a venture into the policy realm of stimulus via public investment—even as low commodity prices affected reserves around the region. This may test the limits of the region’s rapidly declining fiscal space and rapidly increasing cost of capital. Finally, the reaction of the public sector may be viewed as more realistic with regards to outside sponsorship of projects. Whereas the last response to crisis was to expect the private sector to compensate for decreasing public expenditures, it is now clear that the public’s retreat was never replaced in the 1990s.

While the World Bank Private Participation in Infrastructure (PPI) Database finds the rate of private project closure in LAC to be lower in the last quarter of 2008 than in the same period in 2007, an overall decline in the trend of private deals coming to closure is not yet detectable. Project Finance’s DealLogic Database, which tracks private flows into all infrastructure projects, shows little change quarter to quarter up through the end of 2008. Despite this uneven overall rising and slight 1-quarter decline, interviews with IFC and banking staff working in the region suggest that private projects are just now beginning to freeze and that debt financing is simply unavailable for most projects. While first quarter 2009 data show continued growth in infrastructure finance, projects appear to be concentrating in fewer countries and fewer sectors. Nearly all investments by value have been in Brazil hydropower projects. There have been practically no utility-level investments in the region, no water sector investements and decreasing transport sector investments as well, aside from the expansion of the Panama Canal which closed in Q42008.<sup>7</sup> The expectation is that PPI levels will drop considerably through the rest of 2009 in LAC as they have in every other region of the world.<sup>8</sup>

**Figure 1: Private Financing to LAC Infrastructure, Quarterly from 2004 to 2008**

<!-- Start of picture text --> 9<br>LAC US$ Mill (excluding commodity-oriented<br>8 infra and Panama)<br>4 per. Mov. Avg. (LAC US$ Mill (excluding<br>commodity-oriented infra and Panama))<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Source: DealLogic Database, Project Finance, Authors’ Calculations<br>2008 US$ (Billions)<br>2004-1Q 2004-2Q 2004-3Q 2004-4Q 2005-1Q 2005-2Q 2005-3Q 2005-4Q 2006-1Q 2006-2Q 2006-3Q 2006-4Q 2007-1Q 2007-2Q 2007-3Q 2007-4Q 2008-1Q 2008-2Q 2008-3Q 2008-4Q<br><!-- End of picture text -->

> 7 Projectware Database, Dealogic, as of April 2009.

> 8 See also Izaguirre (2009) which identifies a modest decrease in quarter to quarter PPI projects in LAC coming to closure from 2007 Q4 to 2008 Q4.

4
Appendix 1: Description and summary statistics of the covariates used in this paper, household level, 2010-2013, rural Malawi

|Variables|Definition|Overall<br>Mean|SD|
|---|---|---|---|
|Age of the household head|Age of the household head|44.04|16.52|
|Male headed-household|Male headed-household|76.16|42.62|
|Highest level of formal education acquired by household head||||
|None|The household head never attended formal school|77.17|41.98|
|PSLC|PSLC is the highest formal level of education of household head|10.03|30.05|
|JCE|<br>JCE is the highest formal level of education of household head|6.87|25.30|
|MSCE|MSCE is the highest formal level of education of household head|4.68|21.13|
|Non-Univ Diploma and above|<br>Non-Univ Diploma and above|1.25|11.10|
|Highest level of formal education acquired in the household||||
|None|No member of the household ever attended formal school|63.41|48.17|
|PSLC|PSLC is the highest formal level of education of the most educated<br>|15.20|35.91|
||household member|||
|JCE|JCE is the highest formal level of education of the most educated household<br>member|12.22|32.75|
|MSCE|MSCE is the highest formal level of education of the most educated|7.66|26.61|
||household member|||
|Non-Univ Diploma and above|Non-Univ Diploma and above|1.50|12.16|
|Size of the household|Number of people living in the household at the time of the interview|5.22|2.42|
|Number of infant (<5yo) in HH|<br>Number of infant living in the household at the time of the interview|0.84|0.85|
|Number of children (5-14yo) in the household|Number of children living in the household at the time of the interview|1.62|1.41|
|Number of prime adults (15-60yo) in HH|Number of prime adults living in the household at the time of the interview|2.46|1.39|
|Number of elderly (60yo+) in HH|Number of elderly living in the household at the time of the interview|0.29|0.57|
|Household access to loan (0/1)|A member of the household received a loan in the year prior to the interview|16.63|37.24|
|Normalized wealth index|Principal component analysis estimate of asset index|0.05|0.08|
|Normalized TLU index|<br>Total livestock unit index tlu=cattle*0.5+pigs*0.2+sheep*0.1+goats*0.1|0.01|0.03|
|Total land area owned by HH in Acres|<br>Total land area owned by HH in Acres|3.35|49.13|
|Household was affected negatively by some income Shock During<br>the last 12 months (0/1)|Household was affected negatively by some income Shock During the last 12<br>months (0/1)|86.37|34.31|
|<br>Household has a migrant network (0/1)|<br>A member of the household is living outside of the EA|35.65|47.90|
|Rain variability|Rain - EA level Coefficient of Variation of Dec-Jan rainfall from 1983/84 -<br>2012/13|0.25|0.04|
|Rainfall (mm)|Average 12-month total rainfall (mm)|850.32|89.24|
|Temperature (∞C * 10)|Annual Mean Temperature (∞C * 10)|216.31|19.12|

36
on the cost that students have to sustain for uniforms, materials and school contributions.<sup>12</sup> This information is not reported for indigenous and community schools, since materials and uniforms are provided by the government either in the form of in-kind or cash grants to the schools. The average costs of the different items are provided by a school representative at least three years after the end of the PAL evaluation, thus ruling out the possibility of selective miss-reporting. We use information from the 2003 Formato 911 to assess whether treatment and control localities differed in terms of school supply prior to the rollout of PAL. For each student merged with the ENLACE test score, we have the unique school identifier ( _clave de centro de trabajo_ CCT) associated with each student test. Therefore, using information from the Formato 911 2007-2013, we can test whether the program affected the characteristics of the school where students attended primary education. In particular, we can potentially track the results of students whose families moved within Mexico after the follow up survey. In order to disentangle whether potential changes in distance are driven by increased willingness to spend on commuting or changes in residential location, we test the effect of PAL on 4 discrete measures: 1) attending a school within 5km from the community, 2) attending a school between 5 and 10km, 3) attending a school between 10 and 30km, 4) attending a school more than 30km from the community. Changes in the last outcome are possibly driven by changes in the household residential location.

# **3.2 ENLACE take-up**

In the follow-up survey of the experimental evaluation there were 5,444 children in PAL villages born between 1996 and 2005, and we match 69% of them write at least one ENLACE test. Thus, our final sample includes 3,773 children from 200 villages for whom we observe a total of 11,006 ENLACE tests; Table 1 shows how these observations vary across academic grades and years.

There are several potential reasons why we do not observe a child from the PAL survey taking the ENLACE test. First, children’s school attendance and writing of the ENLACE test could be differentially impacted by the PAL transfers; however, as we show in the results below, there is fact no difference in these outcomes across treatment groups. Second, the child’s family could have migrated abroad before the child reached the end of the third grade.<sup>13</sup> Migration information is not available for the PAL sample, but surveys of participants in

> 12According to the 2008 General Law of Education _,_ parents are supposed to contribute on a voluntary basis according to their economic possibilities. However, school principals often require the payment and parents perceive the contributions as compulsory. In order to reduce this phenomenon, in 2018 the Senate asked the Ministry of Education to officially forbid the school principals to claim school contributions.

> 13We observe students whose families moved within Mexico as the ENLACE was applied nationwide.

9
<!-- Start of picture text --> ar yy<br>a2 La<br>as Loo<br>a4 Lo<br>as Loo<br>0% 20% 40% 60% 80% 100%<br>ME Drugs and medicine in cash<br>PY Other medical goods and supplies in cash<br>Outpatient medical and paramedical services in cash<br>GME inpatient services<br><!-- End of picture text -->
While the dataset we received includes the universe of firms that ever benefitted from the 6 points subsidy, for this analysis, we focus on firms that have started benefitting from the subsidy only after 2016, when firms with fewer than 10 employees also became eligible for the subsidy. More precisely, we exclude from our dataset all firms that were eligible in 2013 when the subsidy first became available, but only for larger firms. The final dataset used in this analysis is a monthly panel of firms from January 2004 to September 2018, including firm demographics, total employment, and subsidy status, as detailed below.

# 4.2. Descriptive statistics

The final dataset includes the following variables:

- **Employment:** This variable includes the total number of employees under the same firm during that month.

- **Firm Age:** We calculate firm age as the difference between the firm closure date and establishment date. If the firm is not closed, we calculate the age as the duration of months between September 2018 and the establishment date.

- **Treatment:** This variable is defined as firms that ever benefitted from the 6 additional points subsidy, irrespective of the duration of benefitting.

- **After2016:** This is a dummy variable taking on the value 1 from March 2016 onward when the minimum employee condition for benefitting from the subsidy is removed.

- **Region4, Region5, and Region6:** Dummy variables taking on the value 1 if the firm operates in Region 4, 5, or 6, respectively.

- **Nuts2:** This variable defines which of the 26 NUTS-2 regions that the firm operates in.

- **Province:** This variable defines which of the 81 provinces that the firm operates in.

- **County:** This variable defines the county within each province that the firm operates in.

- **Sector:** This variable is defined in 4-digit NACE Rev.2 and later aggregated under 19 broad sectors.

11
# **Table of Contents**

|1.|Context .................................................................................................................................................. 3|
|---|---|
|2.|Methodology ......................................................................................................................................... 5|
|3.|Regulatory Reform ................................................................................................................................ 7|
|4.|Restructuring Reform .......................................................................................................................... 11|
|5.|Competition Reform ........................................................................................................................... 19|
|6.|Private Sector Participation Reform ................................................................................................... 28|
|7.|The Overall Reform Picture ................................................................................................................. 41|
|8.|Conclusion ........................................................................................................................................... 63|
|Ann|ex 1A: Developing Country‐Level Scores in the Power Sector Reform Index, 1990–2015 .................. 65|
|Ann|ex 1B: Adoption of Washington Consensus Reforms in the Power Sector, 1990 – 2015 .................... 67|
|Ann|ex 2:Cases of Failed Private Sector Participation in the Power Sector, 1990 ‐ 2015 ........................... 71|

2
# Table 1: THE CILSS QUESTIONNNAIRE

-FIRST ROUND-

|Qu|estionnaire<br>Section|Obtained Information|
|---|---|---|
|*|Composition<br>of the<br>Household|Identification<br>of the members of the<br>household. Demographic<br>information.<br>Information on the parents of the<br>household members.|
|*|Housing|Type of housing, tenancy status, housing<br>expenditures. Source of water and 1b<br>type of fuel used, other amenities.|
|*|Educstion|Literacy and educational attainment of<br>household members 5 years and older.<br>Schooling expenses in the last 12<br>months. Education of children who no<br>longer live in the household.|
|*|Health|Number of days of illness or injury, time<br>lost in regular activities. Health<br>expenditures. Utilization<br>of health<br>services in the last four weeks.<br>Preventive health care and associated<br>expenditures<br>in the last 12 months.|
|*|Activities|Main and secondary activities of<br>household members 7 years or older, in<br>the last 7 days and in the last 12<br>months. Type and sector of activities,<br>time devoted to activities, income and<br>social security benefits. Job search,<br>search for additiotal work, unemployment<br>spells. Work history. Domestic<br>activities.|
|*|Migration|Changes in residence of household members<br>age 15 or above. Reasons for the<br>migration.|
|*|Respondents for<br>the Second Round|Id& :ification<br>of the household members<br>that must be interviewed<br>during the<br>second round.|
|*|Housing Characteristics|Construction<br>materials and dimensions of<br>the living quarter.|

|-continued-|
|---|
<!-- Start of picture text --> 8<br>J can<br>ro me GER, RL<br>i na ey ONK = mo ithe eis<br>= PRT POL AT BRL ms47 ne4° = _ seas<br>38 KOR<br>ROU — — -CE- ~<br>z Rea eo TM ow<br>3.7 fey"the BeuBaD ona IDN me vn<br>Hy CHM<br>& row<br>S<br>a rag ROU<br>[=]<br>64 Wié 1a 1<br>Percentof U.S. imports (normalized, CHN« 4)<br><!-- End of picture text -->
Graph 3: Real Lending Rates of East.European Countries, Germany and the U.S. (In Percent per Quarter) Hungary <!-- Start of picture text --> 10<br>5 -o S . ...... . .....- . -<br>-10 _ . _... _,_,,__..,,,, .. ......... . .. _,____.... .......................... . .......<br>Jan-90 Apr-90 Jul-90 Oct-90 Jan-91 Apr-91 Ju1-9,<br>Months<br>Hungary *-*-- Germany -a-- U.S.<br>Poland<br>30<br>L 10 '.' '- '' '-<br>-1 0 I.. ....... ....... ............<br>-30 -1 20 ::-, ^_1 a......... .....<br>-30Apr-90 Jul-90 Oct-90 Jan-91 Apr-91 Ju1-91 Oct-91<br>Months<br>|-Poland _.-- Germrany _-_U S<br><!-- End of picture text -->

CSFR <!-- Start of picture text --> 4 - ^-...... ' ; ..-.... .<br>4 - ---- . . .. ..... .,;: ............... .. ....... ......... .......... ......... ........... . ...... , .,........ ..........<br>-8 .. . .. . .. . ..... . .<br>-10<br>Apr-91 Jun-91 Aug-91 Oct-91 Dec-91<br>Months<br>| CSFR ~-h-* Germany --- U.S l<br><!-- End of picture text -->
15 rates are revised several times per year and changes usually, but not always, follow the corresponding South African rates fairly closely. However, the prime rate has generally been held below the South African prime rate. The deposit rates offered by the CBL are also somewhat below RSA rates. Since it began to be regulated in 1986, the minimum rate on savings deposits, whil, it is set at about the same level as the rate on large savings deposits in RSA, it is much higher than that offered by RSA banks and building societies for small savings deposits. There are neither floors nor ceilings on the bank lending rates (despite the administered 'prime lending rate", which mainly serves as an indicative rate) or on the bank deposit rates other than the savings rate, The question to be addressed here is whether the RSA rates or the Central Bank administered rates are more relevant in determining the non-administered rates in Lesotho.

# Lending Tnterest Rates

The average lending rate in Lesotho has at all times exceeded the prime rate, usually by two to three percentage points, but exceptionally by as much as 7 points during 1984 (Figure 6). On the other hand, the average lending rate in Lesotho fell short of the South African prime rate throughout 1984.

The prime rate in Lesotho is set administratively: to what extent does it determine the average lending rate? Even if the prime rate is out of line with market conditions, it may persist because banks can adjust the average lending rate above prime. One hypothesis is, then, that the banks counter any discrepancies between the prime rates in Lesotho and South Africa by adjusting their average lending rates relative to the prime rate in Lesotho. The following equation, estimated on quarterly data from 1984 to 1988 suggests that such an hypothesis is at least partially true.

dAVG1.. - 1.222 + 0.990 dPRM,. - 0.552 GAP(-l) + 0.397 (PRML.a-PRM1,,) (3.4) (6.7) (4.0) (3.1) RSQ - 0.749 SEE - 0.848 DW - 2.19 where AVG is the average lending rates PRM is the prime rate, GAP(-l) is the lagged difference between AVG and **PRM..,** and "d" denotes quarterly change.