rafmacalaba/datause-encoder-data huggingface dataset:
had reopened (even among those schools whose opening was delayed). It was still before Sierra Leone was officially declared to be Ebola free (November 2016). The endline covered the same topics as the baseline survey with an additional module related to the crisis and experiences during it, such as if respondents’ households were contacted by tracer teams, or quarantined.

The timing of the endline survey means that outcomes are measured post-crisis. Hence we do not claim that the _onset_ of any changes in behavior always started during the crisis. Rather, the results should be interpreted as capturing _persistent_ impacts into the post-crisis period of the severity of the shock, the provision of ELA and their interaction.

A concern is that respondents migrate in response to the crisis and then attrit. Encouragingly, 83% of our respondents were tracked from baseline to endline (4 _,_ 790). Among tracked women, 81% (3 _,_ 865) resided in the same village, while others were tracked to a different (but typically nearby) village. Hence although geographic mobility is high, it does not lead to severe attrition. In the Appendix Table A2 we present correlates of attrition and show that treatment assignment and the intensity of Ebola-related disruption do not predict attrition, nor does their interaction, and nor is there differential attrition on observables with treatment or disruption.

# **4.2 Randomization and Implementation**

The ELA program was randomly assigned to 150 villages, with the other 50 remaining as control. Districts were the randomization strata. The original evaluation design was to randomly divide treated villages in three arms: ELA clubs in T1 would only provide life skills training; T2 villages would be as T1 but additionally provide vocational training, and T3 would be as T2 but additionally provide microfinance. This design was meant to unpack the impacts found in our earlier work on ELA in Uganda [Bandiera _et al._ 2018]. Given the crisis and imperfect program compliance with the design, we pool T1, T2 and T3 arms throughout. Common to all treatment arms is that ELA clubs provide alternative time uses and life skills to young girls and women.

A concern is whether ELA clubs were rolled out to any meaningful extent during the crisis. This is especially so given that BRAC’s core field activities in microfinance were curtailed during the epidemic.<sup>24</sup> To provide evidence on the matter, Figure 3 summarizes the findings from our survey to ELA club mentors, conducted in June/July 2015 when weekly cases had dropped far from their peak. Panel A provides time series evidence on ELA club openings. This shows: (i) 70% of clubs opened on time (by September 2014) and by January 2015 all clubs in treated villages had opened; (ii) the majority of clubs were continuously open through the crisis. Panel B details ELA 24Concerns were first raised in March 2014 on BRAC field activities, with microfinance activities being suspended in August 2014. They restarted in March 2015. BRAC found huge demand for microfinance in response to the Ebola shock, credit being considered critical for household recovery.

14
# **#4: Power utilities often impose a substantial fiscal burden and contingent liabilities on government budgets**

36. Estimates of the quasi-fiscal deficit of power utilities—or hidden losses in the case of privatelyowned utilities—provide a strong reminder of the macroeconomic significance of the under-recovery of the cost of electricity service. The QFD of the power sector stand at 0.93 percent of GDP on average (see Figure 7a). Again, these figures are broadly in line with the literature (see Annex 1), suggesting that the sample is sufficiently representative of the situation in developing countries overall.

**Figure 7: Quasi-Fiscal Deficit/Hidden Cost of the Power Sector, broken down by Source and Type of Utility**

<!-- Start of picture text --> a) By Source of Deficit/Hidden Cost<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>-1.00%<br>-2.00%<br>Collection losses T&D losses Underpricing<br>b) By Ownership Type of Utility<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>-1.00%<br>Public Mixed Public/Private Private<br><!-- End of picture text --> _Source:_ World Bank staff.

20
<!-- Start of picture text --> $ billion percent of EM GDP<br>1400 China 10.5<br>DEM Asia ex. China<br>1200 BMENA VK: 9.0<br>4000 DLatin America ‘Y Y y G 7.5<br>800 BEM Europe [| Y Y) r/ Z<br>Total, Percent of AY Y V/A. | 6.0<br>600 Y V/ ZG ~~ Z, Y<br>4 EM GDP | i | 1 [| 4.5<br>400 a % A | a ape 3.0<br>200 ~Ceoeeuudne 1.5<br>ae+= wy |<br>0 = 0.0<br>-200 -1.5<br>02 03 04 05 06 07 08 09 10 11 12 13<br><!-- End of picture text -->
**Table 5:** Rainfall and Rural Non-Farm Employment Categories

||||**Panel A:**<br>**Broad**<br>**Categories**||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)<br>|(4)<br>|(5)<br>|(6)<br>|
||LFP|Unemployed|Non<br>Farm|Self<br>Employed|Casual<br>Work|Salaried<br>Work|
|Positive Shock|-.000|-.004<sup>_∗∗∗_</sup>|-.023<sup>_∗∗_</sup>|-.012<sup>_∗∗_</sup>|-.008|-.005<sup>_∗_</sup>|
||(.009)|(.001)|(.009)|(.005)|(.006)|(.003)|
|Zero Shock|-.003|-.001|-.011<sup>_∗∗_</sup>|-.001|-.007<sup>_∗_</sup>|-.004<sup>_∗_</sup>|
||(.005)|(.001)|(.005)|(.002)|(.004)|(.002)|
|Positive Shock, Lag1|-.015|.002|.012|.003|.002|.003|
||(.011)|(.001)|(.010)|(.005)|(.009)|(.003)|
|Zero Shock, Lag1|-.005|.001|.016<sup>_∗∗_</sup>|.006|.005|.003|
||(.009)|(.001)|(.008)|(.004)|(.007)|(.003)|
|Positive Shock, Lag2|-.015<sup>_∗_</sup>|-.004<sup>_∗∗∗_</sup>|-.044<sup>_∗∗∗_</sup>|-.006|-.028<sup>_∗∗∗_</sup>|-.006<sup>_∗_</sup>|
||(.009)|(.001)|(.009)|(.005)|(.007)|(.003)|
|Zero Shock, Lag2|-.011<sup>_∗_</sup>|-.002<sup>_∗∗_</sup>|-.013<sup>_∗∗_</sup>|-.001|-.010<sup>_∗∗_</sup>|-.003|
||(.006)|(.001)|(.006)|(.003)|(.005)|(.002)|
|Observations|710787|453161|453161|453161|453161|453161|
|R<sup>2</sup>|.34|.03|.19|.08|.09|.11|
|Dep Var Mean|.68|.01|.23|.08|.06|.06|
||||**Panel B:**<br>**Sectors**||||
||(1)|(2)<br>Farm<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|
||Farm|Labour|Manufacturing|Trade|Construction|Service|
|Positive Shock|.029<sup>_∗∗∗_</sup>|.032<sup>_∗∗∗_</sup>|-.008<sup>_∗∗_</sup>|.001|-.005|-.009<sup>_∗∗_</sup>|
||(.009)|(.010)|(.003)|(.004)|(.005)|(.004)|
|Zero Shock|.012<sup>_∗∗_</sup>|.012<sup>_∗_</sup>|-.003|-.001|-.009<sup>_∗∗∗_</sup>|.003|
||(.005)|(.006)|(.002)|(.002)|(.003)|(.003)|
|Positive Shock, Lag1|-.016|-.006|.007<sup>_∗∗_</sup>|.009<sup>_∗∗∗_</sup>|-.002|-.000|
||(.010)|(.012)|(.003)|(.003)|(.007)|(.004)|
|Zero Shock, Lag1|-.019<sup>_∗∗_</sup>|.002|.005<sup>_∗_</sup>|.010<sup>_∗∗∗_</sup>|.001|.003|
||(.009)|(.011)|(.003)|(.003)|(.006)|(.003)|
|Positive Shock, Lag2|.051<sup>_∗∗∗_</sup>|.008|-.005|-.007<sup>_∗_</sup>|-.027<sup>_∗∗∗_</sup>|-.006|
||(.009)|(.011)|(.003)|(.004)|(.005)|(.005)|
|Zero Shock, Lag2|.017<sup>_∗∗∗_</sup>|-.011|-.001|.000|-.012<sup>_∗∗∗_</sup>|-.001|
||(.006)|(.008)|(.002)|(.002)|(.004)|(.003)|
|Observations|453161|453161|453161|453161|453161|453161|
|R<sup>2</sup>|.21|.28|.08|.05|.06|.09|
|Dep Var Mean|.75|.32|.07|.04|.03|.08|

_Notes_ : This table shows the impact of rainfall shocks on rural non-farm employment by employment type and sector. The unit of observation is rural individuals aged 15-65. Except for column (1) of Panel A, the sample is restricted to individuals who participate in the labour force. Positive Shock is a dummy equaling 1 if the district’s annual rainfall incidence falls in the top 20th percentile of the district’s historical rainfall distribution; Zero Shock is a dummy equaling 1 if the district’s annual rainfall incidence falls between the 20th and the 80th percentile of the district’s historical rainfall distribution. Standard errors in parentheses, clustered by district.

46
**Do Procurement Rules Impact Infrastructure Investment Efficiency? An Empirical Analysis of** **_Inversão das Fases_ in São Paulo State**

By: <u>Luis Blancas</u><sup>1</sup> <u>, Laura Chioda</u><sup>1</sup> <u>, Tito Cordella</u><sup>1</sup> <u>, Alexandre Oliveira</u><sup>1</sup> <u>, Felix Várdy</u><sup>2,3</sup> <mark>T</mark>

> 1 The World Bank, 1818 H Street N.W., Washington DC 20433.

> 2 University of California Berkeley Haas School of Business, 2220 Piedmont Avenue, Berkeley, CA 94720. 3 The authors gratefully acknowledge participants at seminars at SABESP, COPASA, the Brazilian Ministry of Planning, and the state administration of Minas Gerais for helpful comments and discussion. They also thank those institutions for their generosity in sharing data and answering to a number of queries The are deeply indebted to Rafael Arantes, Estelle Cantillon, Carlos Carvalho, Aline Dieguez, Loreni Foresti, Francisco Gaetani, Ana Hirle, Carlos Henrique, Atsushi Imi, Álvaro Mendes, Alessandro Nirino, Loçanny Silva, Karen Silverwood-Cope and Maurício Soares for useful comments and constructive criticism; and to Jivago Ximenes for his outstanding research assistance.
# **5. Discrimination through social institutions and norms**

# **What are institutions and how do they discriminate?**

Exclusion is grounded in institutions. As the sites where norms are created and maintained, institutions determine the way in which people behave. Institutions are defined as the very ‘rules’ of how we live (North 1990), with ‘rules’ referring to the norms of behavior and to social conventions, in addition to formal rules such as laws and regulations. Laws can be seen as the formalization of norms, implying the use of formal, state sanctions when they are broken. Many norms however are not formalized or codified into law. Norms may be enforced through social sanctions, which can be as rigidly applied as the sanctions for breaking formal laws and regulations. For example, the enforcement of informal norms often takes place through displays of disapproval (e.g. hostile remarks, gossip, and ostracization. Sanctions may also take on more violent (and potentially illegal) forms, such as bullying, intimidation, and even extra-judicial killings (Helmke and Levitsky 2004). The substantive content of institutions is internalized within individuals, and when internalized, norms hold a privileged position in individuals’ cognitive structure.

Institutions, whether formal (such as the laws of a state) or informal (such as the caste system, patriarchy, culture, and other social hierarchies and structures) are widespread in all societies. Institutional norms structure many aspects of social life including what is important, who has access to opportunities and how access is obtained. Social exclusion occurs when the way in which institutions assign value and allocate opportunities systematically discriminates against particular groups of people, denying them the opportunities, resources, and recognition that would allow them to participate fully in social, economic and political life (Kabeer 2000).

Many anti-discrimination efforts have targeted formal institutions—the constitution, national laws, political and electoral systems, and organizations responsible for service delivery. For example many countries have legislation and policies which enshrine the right of every child to go to school. Yet ‘informal discrimination’ in education is widespread (UNESCO 2010; see case below). This is because the vast majority of social institutions are informal.

# **_CASE: Social exclusion and education outcomes: The interaction of ethnicity, language, and poverty_**<sup>**_19_**</sup>

Access to education opportunities can help people break out of social exclusion. Yet all too often experiences in school reinforce and perpetuate social exclusion. Children born to parents who are a member of an ethnic or linguistic minority, a particular racial group or a low caste emerge from school with less education and lower achievements than do children from other social groups. Research has found that while poverty matters a great deal in perpetuating educational disadvantage, the nonpoverty component is also very important because of the weight of social and cultural discrimination.

> 19 Source: UNESCO 2010

26
Most of the above studies have focused on public firms, which tend to be large and perhaps unrepresentative of firms across countries. Thus, the definitions of external financing focus on debt and equity, and do not take into account the possibility that in some countries firms may substitute other forms of financing. However, some progress has been made over the last 10 years largely due to the firm surveys conducted by the World Bank that have greatly expanded the information available about financing patterns of especially small and medium firms across countries. These sources include the Regional Program on Enterprise Development (RPED) studies for Sub-Saharan Africa in the 1990s; Business Environment and Enterprise Performance Surveys (BEEPS) for the transition economies; the World Business Environment Survey (WBES), conducted across 80 countries in 1999–2000; and the Enterprise Surveys (ES), conducted since 2002 and available for almost 100 countries.

The Enterprise Surveys use standardized survey instruments and a uniform sampling methodology to benchmark the investment climate of countries across the world and to analyze firm performance. They sample from the universe of registered businesses and follow a stratified random sampling methodology. These surveys include micro-, small, and medium enterprises that are not captured in data sets based on published financial statements. In addition to specific firm information, these surveys contain an array of questions on the business environment in which the firm operates, the proportion of investment and working capital that is financed externally, and also the source of external financing comes (i.e., debt, equity, suppliers‟ credit, leasing, and other sources such as development banks, moneylenders, public sector or other informal sources). The more recent surveys also contain sampling weights that allow us to draw inferences about the population of firms in each country.

Beck, Demirguc-Kunt, and Maksimovic (2008) use the World Business Environment Survey (WBES) and find that while the external financing of firm investment is not a function of institutions, the form of external financing is. Firms in countries with more developed institutions use bank and equity financing to a greater extent, whereas in institutionally underdeveloped countries trade credit finance and financing from residual sources substitute to offset the shortfall in external finance. Asset-based lending such as factoring, fixed-asset lending, and leasing are not dominant substitutes for bank finance in developing countries. They also find 40
Table 2.1 presents a summary of the distribution of per adult equivalent<sup>8</sup> expenditure based on the 1996, 2001 and 2007 household surveys conducted by the National Statistical Office. All these surveys follow the sampling frame of the 1987 population census. The samples are stratified and the 1996 survey has the smallest sample size with 1,728 observations 36 percent of which represent the rural sector. The National Statistical Office (2002) has noted this under-representation of the rural areas in the 1996 household survey. For the other two surveys, the sample size is 10,992 observations for 2001 and 11,391 observations for 2007.

On the basis of the means reported in the second column of table 2.1, we find that (see table 2.2) the average per adult equivalent expenditure grew 5.4 percent per year over the period of 1996-2007 in nominal terms. Looking within sub-periods, the mean per adult equivalent expenditure grew by about 9 percent per year between 1996 and 2001, and by about 2.5 per year between 2001 and 2007. In real terms, these average rates of growth fall respectively to 1.9 percent, 4.1 percent and 0.5 percent. National account statistics tell a different story. The real per capita GDP is believed to have grown only by 1.57 percent per year between 1996 and 2001, and by 0.57 percent between 2001 and 2007 (National Statistical Office 2002, 2008).

Table 2.2. Growth in Average per Adult Equivalent Expenditure in Cameroon

||(1996-2007)|
|---|---|
|Period|Average Growth Rate (percentage)|
||Nominal<br>Real|
|1996-2001|9.0<br>4.1|
|2001-2007|2.5<br>0.5|
|1996-2007|5.4<br>1.9|

Source: Authors’ Calculations

> 8 The underlying scale assigns weights to individual members of the household according to their age and gender.  However there is no gender differential for children up to the age of 10.  Thus children who are at most 1 year old get a weight of 0.255.  Those with age between 1 and 3 years get assigned a weight of 0.45. Between the age of 4 and 6, the weight is 0.62 while it is 0.69 for the 7-10 age group.  Starting from age 11, males get assigned the following weights: 0.86 between 11 and 14, 1.03 between 15 and 18, 1 between 19 and 50 and 0.79 above 50.  All females between 11 and 50 get a weight of 0.76 and those above 50 get a weight of 0.66.

8
# Appendix Table 18

Firm Indebtedness and Use of Public Programs, over Sales (2020)

||∆Debt / S|ales (2020)|∆Guaran<br>Sales|teed Debt /<br> (2020)|∆Non-Guar<br>Sales|anteed Debt /<br> (2020)|
|---|---|---|---|---|---|---|
||(1)<br>With<br>Previous<br>Loans|(2)<br>Without<br>Previous<br>Loans|(3)<br>With<br>Previous<br>Loans|(4)<br>Without<br>Previous<br>Loans|(5)<br>With<br>Previous<br>Loans|(6)<br>Without<br>Previous<br>Loans|
|Use Credit Program|0_._190|0_._182|0_._170|0_._160|0_._020|0_._022|
||(0.002)|(0.003)|(0.001)|(0.002)|(0.002)|(0.001)|
|Use Employment Program|0_._016|0_._000|_−_0_._003|_−_0_._002|0_._019|0_._002|
||(0.004)|(0.001)|(0.001)|(0.000)|(0.004)|(0.001)|
|Use Credit Program _×_ Employment Program|0_._005|0_._001|0_._025|0_._010|_−_0_._020|_−_0_._010|
||(0.006)|(0.006)|(0.002)|(0.004)|(0.005)|(0.003)|
|Increase in Sales Dummy|_−_0_._008|_−_0_._027|_−_0_._044|_−_0_._017|0_._036|_−_0_._010|
||(0.007)|(0.003)|(0.003)|(0.002)|(0.007)|(0.002)|
|Decrease in Sales Dummy|_−_0_._001|_−_0_._020|_−_0_._032|_−_0_._011|0_._031|_−_0_._008|
||(0.007)|(0.003)|(0.003)|(0.002)|(0.007)|(0.002)|
|Dependent Variable Mean|0.066|0.040|0.088|0.029|-0.022|0.011|
|Dependent Variable Std. Dev.|0.270|0.141|0.138|0.094|0.227|0.085|
|Number of Firms|62,041|50,468|62,041|50,468|62,041|50,468|
|R<sup>2</sup>|0.140|0.258|0.418|0.452|0.016|0.020|
|Industry FE and Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|

This table reports linear regressions of the change in firms’ leverage on a set of firm-level characteristics, separately for firms with and without previous loans, for the credit program eligible firms sample. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. The dependent variable is the change in the debt outstanding between December 2020 and December 2019, relative to 2020 sales. Use credit program and employment program are dummy variables for program participation. Increase (decrease) in sales dummy is equal to one if sales growth is greater (lower) than or equal to 2% (-2%), and zero if sales growth is between -2% and 2%. All columns include industry and municipality fixed effects. Standard errors, shown in parentheses, are clustered at the industry and municipality level. For change in debt over 2020 sales and change in guaranteed debt over 2020 sales, observations in the top and bottom 1% of the distribution are winsorized. Change in non-guaranteed debt over sales is constructed as the difference between change in debt outstanding and the change in guaranteed debt, over 2020 sales.

36
**Figure 5.2b. HDB Activi Fl ures**

|mm||||||
|---|---|---|---|---|---|
|_1985_|_657_|6.4|3,893|6,237|779|
|_1959_|1,355|5.6|15,442|8,562|747|
|**_2X9%>p_**|2,372|5.3|38,566|16,025|1,249|
|**_.i9$J1_**<br>r3,109||4.4|47,648|18,756|2,479|
||3,799|3.8|53,330|22,535|2,692|
||3,540|3.9|55,207|22,930|2,713|
|_E9_S|3,220|3.9|60,832|22,580|2,763|
|**_1E9C_**|3,248|3.7|65,076|21,825|2,314|
||3,037|_3.5_|54,769|19,018|2,028|
|_19p7_|3,949|4.0|88,112|25,303|2,763|
||_2,302_|_3.7_|_39,915_|_14,795_|_1, 711_|

* Shorter average lengths of stay (ALOS) are considered rough measures of improved efficiency.

** Ecocardiographs (ECGs) are routine tests carried out before most operations to examine the heart. Because they are routine they are good proxies for hospital activity.

Source: Author's construction using HDB data.

# _Explaining HDB 's decision rights complementarities in HRM_

By 1993, HDB had settled into a strong and logically allocated set of decision rights in HRM. An initial attempt to give the ALSM a screening and oversight role in the hiring of all staff was resisted by management according to results from the decision rights analysis interviews. By 1993, only senior administrative staff and attendant doctor HRM matters were cleared by the ALSM. The remaining decisions in hiring, promotion, discipline, and firing were taken by the director, who would inform the ALSM of his decisions _ex post._ The director was also granted some important decision rights in internal organizational matters - rights that complemented others in increasing HDB's service delivery and expenditure capacity. For example, the process of streamlining and organizing the stockroom, the pharmacy, and the kitchen involved the stripping of decision rights from one area of the administration and their reallocation to different, more appropriately trained staff members. The director enjoyed strong decision rights in this area and the ALSM supported him. Other, similar complementarities were created in HRM but will not all be listed here.

However by far the most important complementarity created in HRM is one that is most difficult to design in a public sector organizations: with the creation of the ALSM, HDB became a residual claimant of its own, newly created revenue. None of this revenue was claimed (nor was it technically "claimable") by the MOH, despite calls by doubtful central inspection and regulatory agencies to put a stop to all support committee-type activities and collect their funds. While this revenue could not be redistributed among the leaders (owners) of the innovation -- the ALSM members and the HDB director -- they were free to 27