rafmacalaba/datause-encoder-data huggingface dataset:
borrower from another. Inevitably, there are also unobservable influences that cannot be captured in survey data, and it is entirely possible that we have not been able to control satisfactorily for the fact that borrowers of microcredit have little education and are inherently poor, especially in the less-affluent regions of the country.

# **Costs Compared with Benefits**

Even if a program has an impact, it may not be worth undertaking if it is too costly. The results of a first pass at measuring the cost effectiveness of SFD programs (except for microcredit) are shown in Table 15. The procedure that we followed involves measuring the total impacts of each program, using only the statistically significant coefficients from Tables 8-13, and dividing into the total cost. The numbers are in Egyptian pounds, and are for 2004. For instance, in order to measure the cost per one fewer illiterate person, using the regression method and comparing A vs. B, we divided the cost of the program (LE 200m) by (-0.038 × population in A × adults as a share of the population). This gives a value of LE 1,842, which is the cost of pulling one person out of illiteracy, on the (not very reasonable) assumption that the eradication of illiteracy was the only goal of SFD educational spending.

The available information on the cost of each program is approximate, and represents a raw total of spending from 1991 to 2007; in the absence of annual numbers it was not possible to inflate earlier figures to reflect inflation, or to cut off the spending totals in 2004, the year for which the household and community survey data are available. Nonetheless, the spending totals do reflect the correct orders of magnitude involved.

As with the measures of the impacts themselves, the estimates of cost-effectiveness are not completely consistent. For instance, the provision of potable water or sanitation is associated with a reduction in poverty, at a cost (per one fewer poor) that averages somewhere between LE 24,684 (kernel estimates of A vs. B for sanitation) and LE 63,161 (regression estimates of A vs. B for potable water). Yet road schemes and literacy programs are associated with _more_ poverty – either the econometric results are not adequately controlling for program placement, or roads and reading allow the less-poor to leave, reducing the poverty rate of those who remain – and the effects of health interventions on poverty are either positive or negative, depending on the comparison that one favors.

The cost-effectiveness analysis, however interesting, is incomplete, because it does not try to balance benefits with costs, and it does not provide a mechanism for adding up the multiple benefits for any given _Impact Evaluation: Egyptian Social Fund for Development Version of June 29, 2009_ _Page 24 of 36_
relationship disappears when industry dummies are included, indicating a spurious effect in the previous regression. However, the strong negative relationship with firm size persists, providing evidence in favor of our framework. Separate regressions by industry (not reported) yielded similar results.

# _4. Firm dynamics_

To fully test the dynamics of our framework would require longitudinal data. Though the ENAMIN is the most comprehensive data source available to date, it lacks this dimension. Nonetheless, the cross sectional evidence it does offer is consistent with our framework. First, though the data is truncated at a firm size of five employees (fifteen in manufacturing), long lived firms exist across the revenue distribution, reflecting the underlying distribution of 0. Remarkably, average firm age is roughly the same across all deciles of the revenue distribution, ranging from a low of 7.9 years for the seventh decile to a high of 9.0 years for the second decile; the first and last deciles have respective means of 8.8 and 8.7 years.<sup>22</sup> Moreover, the relatively uniform revenue-age distribution is not an artifact of the upper limit on number of employees: average firm revenue in the last revenue decile is more than eighty times larger than the first revenue decile. This suggests that this population of firms may be in a steady state, with entry and exit rates roughly uniform across the revenue distribution.<sup>23</sup> Second, the observed patterns of firm entry and exit are consistent with those predicted by our framework and with those observed in the U.S. and in other developing countries. Numerous studies have documented high entry and failure rates among startups that decline with size and age of the firm.<sup>24</sup> Evans and Leighton's (1989) study of self-employment dynamics in 22 The difference in average firm age between the second and seventh deciles is statistically significant at a five percent level of confidence. The difference between the first and last deciles is not.

23 Note that "exit" could happen for two reasons. The upper size limit on number of employees means that firms would leave the sample frame either if they failed or if they added too many employees. Smaller firms undoubtedly are more likely to fail; whereas larger firms are more likely to grow their way out of the sample frame.

24 Mansfield (1962) shows that smaller firms have higher and more variable growth rates. Dunne, _et al._ (1989) demonstrate that U.S. manufacturing plant failure rates decline steadily with the age of the plant. Davis, _et al._ (1994) find that net job creation in small U.S. manufacturing firms is not high relative to large businesses, despite 16
At the state level, HP, which has the highest EDIs, also received the maximum financial allocations per child. In Uttarakhand, where the PCA (2004-05) under SSA were quite high, also had the District Primary Education Program (DPEP) funds in six districts till March 2006. Thus the PCA in Uttarakhand were actually even higher than the figures included in this analysis. States like Chhattisgarh and MP, both somewhat are at the bottom in EDI rankings had also allocated more than Rs.1000 per child under SSA in the past couple of years. Interestingly, PCAs of AP, Bihar, Gujarat, Kerala and UP were below Rs.500/- – though they were all at different levels in terms of EDI rankings – Kerala at the top, AP and Gujarat in the middle and Bihar and UP at the bottom. There was really no pattern in the per-child SSA financial allocations across states.

There was also a clear disconnect between the ‘real investment needs’ of the districts and the actual allocations made on an annual basis under SSA in 2004-05. The correlation analysis between various EDI components and the district-wise per child allocations and expenditures for the 500 districts demonstrates this complete disconnect between needs and financial allocations / expenditures. Also see the spread of districts with no clear patterns in terms of PCA and EDI in graph 1.

|Indices of:|Access|**T**<br>Infrastru<br>cture|**able 4 C**<br>Teacher|**orrelation**<br>**INPUT**|**Matrix**<br>OUT-<br>COME|EQUIT<br>Y|EDI|PCA|PCE|
|---|---|---|---|---|---|---|---|---|---|
|Infrastructure|0.3336|1||||||||
|Teacher|0.4202|0.2943|1|||||||
|Outcome|0.4069|**0.5784**|0.4027|**0.6302**|1|||||
|Equity|0.4135|0.4672|0.3387|**0.5454**|**0.6537**|1||||
|PCA|0.2096|-0.0632|0.4167|0.2239|-0.0342|0.0254|0.042|1||
|PCE|0.2038|0.0135|0.4142|0.2612|-0.0045|0.0501|0.075|**0.896**|1|
|PCE as % of PCA|-0.021|0.1617|-0.0267|0.0661|0.0551|0.0615|0.063|-0.098|0.311|

**Graph 3. EDI (2003-04) and Per Child Allocations (2004-05)**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>PCA_2004-05<br><!-- End of picture text --> 14
**TableA3. Factors affecting the non-poor household's probability to live in the Bangweulu Park System GMA and, once in the GMA, the probability to participate in the** **<u>VAG/CRB</u>**

||Bangweulu|Probitmode|lsfortheh|ousehold's prob<br>Participate|ability to<br>in the|
|---|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CR|B<sup>_a_</sup>|
|||(1)||(2)||
|hage|Age of the household head in years|-0.014|**|-0.018|**|
|||(0.006)||(0.008)||
|fhhh|Female-headed household dummy|0.709|***|0.061||
|||(0.112)||(0.286)||
|maxedu|Education of the most educated household|-0.081|***|0.018||
||member in years|(0.028)||(0.036)||
|c0to14|Number of children below 15 years|0.011||0.108|**|
|||(0.033)||(0.045)||
|f15to60|Number of female members 15-60 years old|0.197|**|0.167||
|||(0.083)||(0.125)||
|m15to60|Number of male members 15-60 years old|0.233|**|0.282|**|
|||(0.092)||(0.111)||
|m61plus|Number of members older than 60 years|0.017||0.188||
|||(0.162)||(0.235)||
|kroad|Distance to the nearest main road in km|0.012||0.015|***|
|||(0.008)||(0.004)||
|kbsch|Distance to the nearest basic school in km|0.018||-0.021||
|||(0.022)||(0.020)||
|kheal|Distance to the nearets health centre in km|-0.002||0.009||
|||(0.006)||(0.007)||
|vacast|Value of consumer durable assets in ZMK|0.161||0.023||
|||(0.155)||(0.054)||
|dcoop|Participation in cooperatives dummy|-0.540|***|0.741|***|
|||(0.177)||(0.201)||
|nproj|Number of projects in the community|0.017||0.183|**|
|||(0.029)||(0.081)||
|dcfnd|CRB funded dummy variable|0.413|**|1.190|***|
|||(0.171)||(0.177)||
|npart|Number of participants in the CRB/VAG|0.015||0.185|***|
|||(0.056)||(0.048)||
|cons|Intercept|0.769|*|-2.942|***|
|||(0.425)||(0.387)||

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

18
<mark>Crawfurd, L., & Pugatch, T. (2020). Teacher labor markets in developing countries. GLO Discussion Paper No. 473. Retrieved from: https://www.econstor.eu/handle/10419/213877</mark> Cruz-Aguayo, Y., Ibarrarán, P., & Schady, N. (2017). Do tests applied to teachers predict their effectiveness?. Economics Letters, 159, 108-111.

Dal Bó, E., Finan, F., & Rossi, M. A. (2013). Strengthening State Capabilities: The Role of Financial Incentives in the Call to Public Service _The Quarterly Journal of Economics, 128_ (3), 1169-1218. <mark>da Conceição, L. (2017, September 12). Moçambique: Professores de Tete em greve por falta de salários.</mark> _<mark>DW</mark>_ <mark>. Retrieved from</mark> <u><mark>h</mark> ttps://www.dw.com/pt-002/mo%C3%A7ambique-professores-de-tete-emgreve-por-falta-de-sal%C3%A1rios/a-41723964</u> da Silva Filho, G. A., & de Xavier Pinto, C. C. (2014). Higher Salaries, More Teaching, Better Performance? _Unpublished manuscript._ Retrieved from <u>https://editorialexpress.com/cgibin/conference/download.cgi?db_name=SBE36&paper_id=94</u>

- Das, J. (2017, July 21). Teachers’ salaries: Too many bucks for the bang? _Future Development at Brookings._ Retrieved from <u>https://www.brookings.edu/blog/futuredevelopment/2017/07/21/teachers-salaries-too-many-bucks-for-the-bang/</u>

- <mark>De Ree, J., Muralidharan, K., Pradhan, M., & Rogers, H. (2018). Double for nothing? The effect of unconditional teachers' salary increases on performance.</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>. 133 (2): 993–1039.</mark>

- Dolton, P., Marcenaro-Gutierrez, O., Pistaferri, L., & Algan, Y. (2011). If you pay peanuts do you get monkeys? A cross-country analysis of teacher pay and pupil performance. _Economic Policy, 26_ (65), 5, 7-55.

- <mark>Duflo, E., Dupas, P., & Kremer, M. (2015). School governance, teacher incentives, and pupil–teacher ratios: Experimental evidence from Kenyan primary schools.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>123</mark>_ <mark>, 92-110.</mark>

<mark>Elacqua, G., Hincapié, D., & Mariana Alfonso, E.V. (with Montalva, V., & Paredes, D.). (2018). Profesión: Profesor en América Latina ¿Por qué se perdió el prestigio docente y cómo recuperarlo? Banco Interameri - cano de Desarrollo. Retrieved from: https://publications.iadb.org/publications/spanish/document/Profesi%C3%B3n-Profesor-enAm%C3%A9rica-Latina-Por-qu%C3%A9-se-perdi%C3%B3-el-prestigio-docente-yc%C3%B3mo-recuperarlo.pdf</mark>

- <mark>Evans, D. K., & Mendez Acosta, A. S. (2021). Education in Africa: What Are We Learning?</mark> _<mark>Journal of African Economies</mark>_ <mark>, 30(1), 13-54.</mark>

- Ferraz, C., & Finan, F. (2009). _Motivating Politicians: The Impacts of Monetary Incentives on Quality and Performance_ . NBER Working Paper No. 14906.

Finan, F., Olken, B. A., & Pande, R. (2017). The personnel economics of the developing state. In _Handbook of Economic Field Experiments_ (Vol. 2, pp. 467-514): Elsevier.

25
of being formal (e.g. taxes, pension contributions, and minimum wages).<sup>3</sup> Another obstacle formal sector firms might face is the high level of corruption among law enforcement authorities, financial agencies, and regulators. This can increase the cost of creating a new business and staying in business in the formal sector and can make working in the informal sector an attractive alternative (Dutta et al. 2011). The informal sector can also provide buffers against economic uncertainty and underdevelopment in the formal sector as it can provide livelihood to a large number of people. Moreover the incomes generated in the informal sector, insofar they would not have been generated otherwise when spent in the formal sector can provide a boost to the formal economy (Frey and Schneider 2000).

Whether one views the informal sector as a burden or takes it as a sector that contributes positively to the overall economy, there is no doubt that informality is a pervasive and substantive phenomenon in developing countries. It is thus important to explore different aspects of informal economies, especially the working of informal firms. According to the IFC (2012), around 80 percent of total micro, small and medium sized enterprises (MSMEs) are informal today. Firms in the informal sector face a number of challenges that can negatively impact their operations and growth. These challenges can be related to public infrastructure (power, land and water); they can represent weak institutions (property rights, legal protection, and corruption) or lack of access to benefits associated with participation in the formal sector (access to credit, new technologies, and greater visibility of business through publicity). According to data collected by the World Bank through Informal Enterprise Surveys, firms in the informal sector identify lack of access to finance as the biggest obstacle they face (Figure 1).

A lot of recent research around informal firms has focused on understanding different aspects of the business environment that discourages these firms from formalizing. These can include rule of law, property rights, corruption and regulatory requirements for formalization. Previous research has shown that countries with more burdensome entry regulations have larger informal sectors (Djankov et al. 2002). However, many recent randomized experiments which made the formalization process easy, by providing firms with information on how to register and the potential benefits of registration and by lowering costs associated with registration found very few informal firms formalize as a result (de Mel et

> 3 The impact of start-up costs on aggregate productivity is unclear, since high start-up costs will prevent the entry of inefficient firms in the first place (Bartelsman et al. 2004).

4
# **3.4 Summary statistics**

Table 3 presents correlation coefficients between our regional enforcement capacity measure (inspectorate density) and key regional characteristics such as number of firms and gross regional product (GRP). The relatively high positive correlation (0.6) between enforcement and the regional unemployment rate stands out. The fact that regions with stronger enforcement capacity have higher rates of unemployment is consistent with theoretical predictions about the effect of stringent labor laws; however this is not a hypothesis we can convincingly test given the data and setting.

Regions with stronger enforcement capacity are somewhat less ‘industrial’: they have fewer firms, a smaller share of manufacturing and a larger share of services in total output. This is consistent with our claim that labor inspectorate density is determined by an ‘exogenous’ population based rule, as that rule would automatically leave more industrialized regions with lower inspectorate density (unless regional population size is correlated with industrialization).

Regions with stronger enforcement capacity are (weakly) less urbanized. Other attributes like regional GDP per capita and nominal wages are uncorrelated with enforcement capacity. Thus, with the exception of unemployment, urbanization and industrialization, regions with high and low inspectorate density look broadly comparable.

Table 4 summarizes the cleaned RUSLANA panel data by year, showing the number of firms, how many of those are “entering” RUSLANA that year, and the breakdown of firms by sectors in surges and slumps. We note, again, that “entry” into RUSLANA is not an accurate measure of firm entry into the market. Regarding surges and slumps, the data show that 2006‐2009 were volatile years. A large share of firms were in surging sectors in 2006 and 2007, and then slumping in 2008 and 2009; the latter reflects the onset of the global recession in those years.

Table 5 summarizes the panel of incumbent firms that we use for most of our analysis; the statistics for each year _t_ include only those firms that existed in the panel and have data in both year _t‐1_ and _t_ . In a given year, the mean employment level in a firm is around 100, and about 2‐5 jobs are added per firm, on average. But there is significant cross‐sectional variance in these variables. The mean age of firms ranges between 8‐10 years across the years.<sup>14</sup>

> 14 It is notable that the mean age rises between 2004 and 2010. We suspect it is because RUSLANA has a procedure of dropping firms that stopped operating in early years from the panel altogether.  That is, if a firm exited in 2005, it may not exist in the panel at all. This rule is not clearly documented, but we suspect that it results in systematic underrepresentation of older firms in earlier years.

9
<!-- Start of picture text --> MIGRATION TO SOUTH AFRICA INTERNAL MIGRATION<br>A L L M A L E FE M A L E A L L M A L E FE M A L E<br>2004 2009 2014 2004 2009 2014<br>18%<br>17%<br>16% 16%<br>15% 14% 15% 14%<br>12% 13% 13% 13%<br>11% 11%<br>8% 8%<br>7%<br>4%<br><!-- End of picture text --> The decline in mining jobs for men and the relative difficulty for women to work in South Africa led to a spike in internal migration from 2004 to 2009, which has largely been maintained in 2014. As internal migration has become the dominant livelihood strategy in Lesotho, Basotho men and women moving to urban areas tend to migrate individually, leaving their families behind, hence reproducing the historical migratory patterns of cross‐border miners. While half of the migrants are young and unmarried (see Table 2 below), approximately a third of both male (39%) and female migrants (34%) are married. Interviews conducted for this report revealed that people are often motivated to move away for work in order to cover education costs and other basic needs for their dependents. The responsibility to provide for children is particularly salient for women, 13% of whom are widowed, divorced or separated, and therefore the sole caretakers. Notwithstanding the significant economic benefits of migration, segregated migration patterns of men and women have altered the social fabric of Lesotho, with detrimental effects on health, family structures, and the institution of marriage.

**_Table 2_ . Characteristics of male vs. female internal migrants (DHS, 2014)**

||**MALE**|**FEMALE**||**MALE**|**FEMALE**|
|---|---|---|---|---|---|
|**Age group**|||**Highest educational level**|||
|15‐19|24.88|31.38|No education|10.42|1.73|
|20‐24|22.53|22.01|Primary|43.16|29.29|
|25‐29|15.70|16.51|Secondary|36.76|54.47|
|30‐34|12.11|9.81|Higher|9.67|14.51|
|35‐39|8.88|7.00|_Total_|_100.00_|_100.00_|
|40‐44|6.45|4.75||||
|45‐49|4.36|_4.71_|**Household wealth level**|||
|50‐54|3.32|2.43|Poorest|22.50|20.44|
|55‐59|1.76|1.40|Poorer|_21.79_|20.03|
|_Total_|_100.00_|_100.00_|Middle|20.49|_20.42_|
||||Richer|18.68|18.53|

16
Figure A1: Survey period in context–contd.

<!-- Start of picture text --> LBR MDA<br>0<br>80 80<br>0<br>-20<br>-20 60 60<br>-40<br>-40<br>40 40<br>-60<br>-60<br>20 20<br>-80 -80<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>MDG MLI<br>100 0 80<br>0 80 -20<br>60<br>-20<br>60 -40<br>-40 40<br>40 -60<br>-60<br>20<br>-80 20 -80<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>MNG NER<br>90 0 60<br>80 -20<br>0 40<br>-40<br>-20 70<br>-40 -60<br>20<br>-60 60<br>-80<br>-80<br>-100 50 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>NIC NPL<br>0 16 0 100<br>-20 -20 80<br>14<br>-40 -40<br>60<br>12<br>-60 -60<br>10 40<br>-80 -80<br>-100 8 -100 20<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

38
# **Potential Forest Loss and Biodiversity Risks from Road Improvement in Lao PDR**

Stephen Danyo* Senior Environmental Specialist, World Bank Susmita Dasgupta Lead Environmental Economist, Development Research Group, World Bank David Wheeler Senior Fellow, World Resources Institute

**Keywords:** Deforestation; Biodiversity Loss; Infrastructure; Road; Green Growth, Forest, Protected Areas, Lao PDR

## **JEL Classification:** Q23; R49; Z3

* Authors’ names in alphabetical order.

Corresponding Author: Susmita Dasgupta. Email: sdasgupta@worldbank.org; Telephone: +1-202-473-2679 We are thankful to Qinghua Zhao, Senior Information Officer, Development Research Group, World Bank, for development of the new World Bank biodiversity database used in this paper.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.