rafmacalaba/datause-encoder-data huggingface dataset:
significant deterioration of the poor state of infrastructure was also observed during the crisis period (World Bank [1996]).<sup>41</sup>

**Figure 2: Distribution of marginal propensity to save across economic agents**<sup>**42**</sup>

<!-- Start of picture text --> 70<br>60<br>Saving<br>50<br>National Average<br>40<br>30<br>20<br>10<br>0<br>Rural non Rural Traded Urban Informal Central Urban formal Urban skilled Capitalists Nonfinancial Commercial<br>Traded Government Unskilled Companies Banks<br>Marginal propensity to save (%)<br><!-- End of picture text --> Source: Authors’ calculations.

The distribution of credits in the changes in assets and liabilities accounts across economic agents in the financial side of the SAM shows an improvement in the capital accounts of economic agents. The sum of saving and gross borrowing is positive across all economic agents, reflecting the positive saving and credit allocation. However, the flows of domestic credits to the economy (public and private credits), which is US$75.1 million (equivalent to less than 0.8% of GDP) is unevenly distributed. Most of it is allocated to nonfinancial companies and the general government, which together receive well over 83% of domestic credit allocated by commercial banks. Credit to households account for about 16.7 percent of total credit and less than 1% of GDP. The geographical distribution of domestic credit allocation is also biased. Rural households engaged in the production of traded and non traded goods received less than 1 percent of total credit allocated by commercial banks and financial institutions (see col. 53 of the financial SAM in Annex II).

The low saving rate, particularly among the rural households, has implications on the distribution of asset ownership and accumulation (physical and financial asset accumulation). Most of the physical and financial asset accumulation accrues to non financial companies and

> 41 Tracking the direction of causality between public saving and growth in the case of Cameroon where both variables have been subject to large fluctuations, will be an important research topic on its own.

> 42 Note that capitalists refer to households which primarily derive their income from investment in capital shares and physical capital; whereas nonfinancial companies refer to formal enterprises, which unlike commercial banks and insurance institutions are not engaged in financial transactions and operations.

18
# <u>Table 5:  Types of Financing, by Firm Type, Means (Percentages)</u>

See Table 1 for variable definitions. The minimum age of firms in our sample is 4 years. Bank financing includes both domestic and foreign banks. “Other” includes and other sources of financing. In Panel B, ***, ** and * indicate significance of the mean difference at the 1%, 5%, and 10% level, respectively.

# _<u>Panel A: All Firms</u>_

||**Obs.**|**Bank**|**Trade**<br>**Credit**|**Family & Informal**<br>**Sources**|**Retained Earnings**<br>**& Equity**|**Other**|
|---|---|---|---|---|---|---|
|**_Allfirms_**|1,664|29.16|2.43|7.98|17.13|43.30|
|**_By age_**|||||||
|4-7|452|24.31|1.61|13.08|18.73|42.28|
|8-13|597|25.80|3.82|7.33|20.20|42.85|
|>13|615|35.99|1.68|4.87|12.98|44.48|
|**_By size_**|||||||
|_< 50_|478|14.94|1.17|12.29|17.32|54.28|
|_51-100_|280|21.08|1.84|12.20|18.60|46.28|
|_101-250_|353|32.82|3.29|5.91|19.09|38.90|
|> 250|553|43.21|3.27|3.45|14.97|35.10|
|**_By Ownership_**|||||||
|_Foreign_|125|26.38|8.30|2.42|16.29|46.61|
|_State_|362|39.11|0.25|2.94|9.32|48.39|
|_Domesticprivate_|1,177|26.40|2.48|10.13|19.62|41.38|

_<u>Panel B: by Extension of Trade Credit</u>_

|||<br>**_A_**|<br>**_R_d = 0_**<br>|<br>**_Family &_**||<br>**_AR_**|**__d = 1_**<br>|**_Family &_**|
|---|---|---|---|---|---|---|---|---|
||**Obs.**|**_Bank_**|**_Trade_**<br>**_Credit_**|<br>**_informal_**<br>**_sources_**|**Obs.**|**_Bank_**|**_Trade_**<br>**_Credit_**|<br>**_informal_**<br>**_sources_**|
|**_Allfirms_**|969|27.97|1.14|8.19|626|30.37|4.43<sup>***</sup>|7.88|
|**_By age_**|||||||||
|4-7|260|25.15|0.82|11.81|170|23.16|3.02<sup>**</sup>|15.25|
|8-13|322|23.68|1.51|8.77|256|28.45|6.34<sup>***</sup>|5.67|
|>13|387|33.43|1.03|5.27|200|38.96|3.18<sup>**</sup>|4.44|
|**_By size_**|||||||||
|_< 50_|311|14.85|0.43|12.21|137|14.80|2.58<sup>**</sup>|13.13|
|_51-100_|162|21.46|1.01|10.50|109|19.83|3.23|14.48|
|_101-250_|199|32.00|1.84|7.33|142|33.83|4.89<sup>**</sup>|4.41|
|> 250|297|42.56|1.47|3.29|238|42.10|5.77<sup>***</sup>|3.91|
|**_By Ownership_**|||||||||
|_Foreign_|52|22.00|1.60|4.81|70|29.57|13.64<sup>***</sup>|0.74|
|_State_|226|36.71|0.20|2.26|114|39.37|0.39|4.84|
|_Domesticprivate_|691|25.56|1.41|10.38|442|28.18|4.01<sup>***</sup>|9.79|

41
(Wetterberg 2002). At the same time, customary _adat_ (customary law) institutions might be indicative of an increased adherence to traditional laws and customs. As such, they may also clash with centrally mandated conflict resolution mechanisms, providing the potential for conflict escalation.

Inadequate policing, ineffective courts and judicial system that allow for high levels of crime tend to also create space for violent conflicts to take place; incidents of crime, when severe, may also constitute conflicts in themselves.<sup>4</sup> Public and communal security arrangements may therefore reduce the likelihood of conflict. However, their existence may also be in response to a worsening security situation.

# **3. Empirical Evidence on Local Conflict**

The Government of Indonesia’s Village Potential Statistics survey (PODES) offers perhaps the first opportunity to look at the incidence and causes of conflict across Indonesia on a nationwide scale. This section summarizes conflict incidence based on the data from PODES, and examines reporting issues. Our comparison to a detailed qualitative study of local conflict (Barron _, et al._ 2004b) suggests that key-respondent (i.e., village heads) based approaches like the PODES are subject to reporting problems. Treated as a complement to more in-depth studies, however, they provide an opportunity to map levels of conflict and to test some of the

> 4 Qualitative fieldwork in over two dozen villages (see Section 3) provided some insight into the linkages between conflict and crime, as well as the underreporting of crime.  In three districts studied, the Bank study followed conflicts which involved rape as a trigger.  In these cases, rapes were public knowledge; indeed, it was the fact that the act had been brought to public attention – rare, given the norm of treating such matters as a family concern, and keeping it within the household as a result – that had led to the triggering of a broader social conflict.  Likewise, whereas none of the thirteen villages in East Java reported having experienced arson in the previous year, conflicts in a number of these villages had resulted in the burning of property (privately- and publicly-owned).

11
8

# **Descriptive Analysis**

Figure 1 depicts trends in a number of inequality measures over the period of interest. As the first panel illustrates, overall per capita income inequality at the national level has declined significantly irrespective of the measure of inequality considered (the Gini coefficient, as well as the GE(0) and GE(1) measures are depicted). Median inequality across states has also fallen over the period. The Figure also illustrates that for the GE(0), GE(1) and GE(2) indicators, the “between dimension” accounts for only 3 to 8 percent of total inequality in Brazil and remains relatively constant over time. Thus, our focus on “within-state” inequality in the analysis of the macroeconomic correlates of inequality presented in subsequent sections of the paper is justified. Moreover, the 2000s were years of fiscal adjustment for Brazilian states. The median primary balance moved to a surplus in the late 1990s, and varied around one percent of state GDP for most of the subsequent decade, before falling closer to zero after the global financial crisis (bottom-right panel of the figure).

<!-- Start of picture text --> Figure 1. Income Inequality and sub-National Primary Balance: 1995-2011<br> Median (across states) inequality measures have<br>Overall decrease in inequality at the national level.<br>also fallen.<br>1995 2000 2005 2010 1995 2000 2005 2010<br>year year<br>gini ge0 medgini medge0<br>ge1 medge1<br>Stable share of “between-states” inequality for Primary Balance/GDP, median, 75 th and 25 th pctles<br>different indicators (across states)<br>10.0%<br>9.0%<br>8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>1995 2000 2005 2010<br>between_ge0 between_ge1 year<br>between_ge2 between_gem1 median 75th percentile<br>25th percentile<br>.7 .7<br>.65 .65<br>.6<br>.6<br>.55<br>.55<br>.5<br>.5<br>.02<br>0<br>-.02<br>primary balance<br>-.04<br>Share of between State inequality<br>1995 1996 1997 1998 1999 200 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 -.06<br><!-- End of picture text -->
40 Table _I1._ Expenditure elasticities for 15 subsidized and nonsubsidized foods in urban and rural Egypt, 1997

|||Expenditure el|asticities||
|---|---|---|---|---|
||Ur|ban|R|ural|
|Food|Lower<br>40<br>percent of<br>population|Upper<br>60<br>percent of<br>population|Lower<br>40<br>percent of<br>population|Upper<br>60<br>percent of<br>population|
|Subsidized foods|||||
|Baladi bread|-0.11|-0.25|-0.05|-0.16|
|Baladi wheat flour|-0.08|0.05|0.04|-0.09|
|Nonsubsidized foods|||||
|Wheat|-0.01|-0.01|-0.02|-0.06|
|Fino bread|0.35|0.16|0.34|0.17|
|Rice|0.05|-0.02|-0.20|0.10|
|Other cereal|0.21|0.13|0.16|0.11|
|Oil|0.62|0.42|0.71|0.49|
|Sugar|0.55|0.45|0.63|0.48|
|Pulses|0.48|0.29|0.54|0.37|
|Vegetables|0.73|0.56|0.74|0.58|
|Fruits|0.73|0.56|0.73|0.58|
|Meat|0.86|0.63|0.93|0.68|
|Eggs, milk|0.77|0.57|0.83|0.60|
|<br>Condiments|0.80|0.59|0.83|0.63|
|Beverages|0.90|0.66|0.96|0.72|
|Nonfoods|||||
|Nonfoods|1.60|1.42|1.71|1.53|

Source: Howarth Bouis and Akhter Ahmed, "The Egyptian Food Subsidy System: Impacts on the Poor and an Evaluation of Alternatives for Policy Reforms", (Washington, DC: International Food Policy Research Institute, 1998), Table 15.
Table 1: Estimated threshold values of the impact of the increase in the share of foreign private investors ( _푓푝푖,푡_ −1) and the expected public debt (E _푡 퐷푒푏푡푖,푡_ + _푛_ <u>) on the interest rate</u>

||Threshold composition<br>|Threshold level|
|---|---|---|
|(1) IT|_푓푝푖,푡_−1= <sup>−</sup><sup>_훽_0</sup><br>_훽_1<br>|E_푡퐷푒푏푡푖,푡_+_푛_= <sup>−</sup><sup>_훿_</sup><br>_훽_1|
|(2) PSTR|_푓푝푖,푡_−1=<br>−_훽_0<br>_훽_1(_푓푝푖,푡_−1;_훾_1_,푐_)<br>|E_푡퐷푒푏푡푖,푡_+_푛_=<br>−_훿_<br>_훽_1<br>�_휕푔_(_푓푝푖,푡_−1;_훾_1_,,푐_)<br>_휕푓푝푖,푡_−1<br>�|
|(3) GPSTR|_푓_=<br>−_훽_0|E_퐷푒푏푡_=<br>−_훿_|
||_푝푖,푡_−1 <br>_훽_1(_푓푝푖,푡_−1;_훾_1_,훾_2_,푐_)|_푡푖,푡_+_푛_<br>_훽_1<br>�_휕푔_(_푓푝푖,푡_−1;_훾_1_,훾_2_,푐_)<br>_휕푓푝푖,푡_−1<br>�|

# **2.4 Data**

## **2.4.1 Data coverage**

Based on forecast data availability, we use bi-annual data of 25 countries: 11 advanced economies and 14 emerging markets during 2006H2-2018H2. Advanced economies include Australia, Canada, the Czech Republic, Denmark, Japan, the Republic of Korea, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Emerging markets include Bulgaria, Colombia, China, Hungary, India, Indonesia, Malaysia, Mexico, Peru, the Philippines, Poland, Thailand, Turkey, and South Africa. The Euro area countries are omitted from advanced economies for the following reasons. As discussed by Ichiue and Shimizu (2015) and Wright (2011), this is because their term structures had been highly correlated with those of Germany since the introduction of the euro. Therefore, it would be difficult to identify how much long-term interest rates in the Euro area have reflected the fundamentals of these countries. In this regard, the share of foreign private investors in the euro area has been higher relative to others (Arslanalp and Tsuda (2014a)). In the euro area, the zero percent risk weight has been a major driver of banks’ holdings of foreign debt securities denominated in the local currency due to Basel capital regulation. Bonner (2016) shows that capital regulation encourages banks to substitute other bonds with government bonds. Because of this regulatory impact, the impact of foreign investors on interest rates for the euro area could be different from that of other countries. Hence, euro area countries are omitted.<sup>12</sup>

> 12Brzoza-Brzezina and Kotlowski (2018) employing the PSTR model also use data from both advanced economies and emerging markets. However, they use actual data, not forecast data

10
**Table 5: Baseline Group Comparison on Student Characteristics**

|||3<sup>rd</sup>grad<br>|e<br>||5<sup>th</sup>grade<br>|<br>|
|---|---|---|---|---|---|---|
||WSD|Grant|Control|WSD|Grant|Control|
|Student age|10.20|10.20|10.10|12.73|12.59|12.64|
||(0.10)|(0.10)|(0.10)|(0.08)|(0.08)|(0.08)|
|Number of siblings|4.90|4.70|4.75|4.70|4.70|4.80|
||(0.13)|(0.13)|(0.13)|(0.13)|(0.12)|(0.12)|
|Ate breakfast today|0.69|0.71|0.73|0.67**|0.73|0.74|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Ate lunch yesterday|0.96|0.95|0.94|0.94|0.97|0.95|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Electricity at home|0.19*|0.21|0.24|0.20|0.17|0.20|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Radio at home|0.91|0.92|0.93|0.88|0.89|0.87|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|
|TV at home|0.37|0.38|0.38|0.40|0.36|0.36|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Telephone/Mobile at home|0.83|0.81|0.82|0.81|0.86|0.83|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Percent repeating the Class|0.09|0.09|0.09|0.08|0.07|0.08|
||(0.29)|(0.29)|(0.29)|(0.26)|(0.26)|(0.26)|
|Observations|462|462|445|423|458|447|

Notes: Standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. The mean comparison test contrasts each treatment group with the control group.

28
diseases, and child health. The plausible explanation for this evidence gap is that most of the infrequently covered domains predominantly concern LMICs, where very few DHI studies have been conducted.

# 3.1.5 Health outcomes

The impact of DHIs on summary or ultimate health outcomes such as mortality, quality of life and health care has been under-explored. The focus of most studies concerned short-term outputs and directly measurable health outcomes (e.g., number of clinic visits). In fact, there is a huge gap in the number of studies that either report intermediate or summary health outcomes.

The health outcomes reported by the studies are organized along the DHI theory of change (TOC) casual pathway from outputs through impact. For this analysis, outputs comprised process outcome (non-therapeutic) as well as knowledge and beliefs. Intermediate outcomes include behavior change, client/provider satisfaction, health status (natural units), process outcome (therapeutic) and health care utilization. The final outcomes consist of health status (aggregated/summary units) and quality of care. Based on this classification, most outcomes reported by the included studies are intermediate (n=2,306 outcomes), followed by impact (n=342) and outputs (n=296).

<!-- Start of picture text --> SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>SRs<br>IEs<br>0 50 100 150 200 250 300<br>Clients Healthcare providers Health system managers Data services<br>Others<br>Knowled ge and beliefs<br>Health status (aggreg ated/su mmary units)<br>Health status (natural units)<br>Quality of care<br>are n<br>Healthc utilisatio<br>utic)<br>Process outcome (therape<br>(non utic)<br>Process outcome therape<br>on<br>Client or provider satisfacti<br>ur<br>Behavio change<br>c s<br>Economi outcome<br><!-- End of picture text -->

**Figure 3 Outcomes reporting from the DHI Evidence base by intervention type**

# 3.1.6 Economic outcomes

Cost data for the interventions are infrequently reported. About 80% (n=632) of the studies did not report any cost data and out of those that reported more than half simply provided cost data without performing further analysis such cost-effectiveness analysis. A majority of
reported not applying for a loan because they did not need one. The firms that did need a loan but did not apply for one reported the following reasons for not applying: Complex application procedures for loans or line of credit, Interest rates are not favorable, Collateral requirements for loans or line of credit are unattainable, Size of loan and maturity are insufficient, Did not think it would be approved, and Other. Figure 5 shows that a large share of both SMEs and large firms cite unfavorable interest rates as the primary reason for not applying for a loan or line of credit. Following this, a large percentage of SMEs also cite complex application procedures, collateral requirements, and unlikely to be approved, in that order, as other factors for not applying for a loan or line of credit. The reasons for large firms are similar though a higher percentage of large firms report unlikely to be approved as a reason compared to application procedures or collateral requirements.

In Figure 6 we examine if firms are able to substitute the lack of bank finance with other sources of external finance in financing their fixed asset investment. Specifically, firms in the Enterprise Surveys were asked to estimate the proportion of the purchase of fixed assets that was financed from internal funds/retained earnings, banks, non-bank financial institutions, equity (issue of stock), trade credit (both credit from suppliers and advances from customers), and other sources including moneylenders, friends, and relatives. As expected, Figure 6 shows that small and medium firms make use of bank finance to a lesser extent than large firms. We also see no evidence that these firms disproportionately substitute bank finance with other sources of external finance such as trade credit or equity finance compared to large firms.

9
Soon after the trainings were completed, we conducted a short process evaluation in a randomly selected 96 projects to determine when funds were received relative to when the trainings were conducted. We found that 17 projects received their training after they started using their funds, with 11 receiving training within two weeks of using their funds. Four projects (4.2% of the randomly selected sample) began using their funds at least a month before they received training. We consider this late treatment to be non-compliance. Given the low rate of late trainings, we do not make corrections for non-compliance and so focus on the intention-totreat (ITT) estimates.

# **4 Data and balance**

# **4.1 Data**

The data for the analysis presented here come from several sources. Before the program began, we were given limited administrative data on what projects were to be funded by NUSAF2. From this list, we obtained information on the location, budget, and general types of projects.

We conducted a survey of local officials between January and March 2013 in which we included all 45 districts and 485 sub-counties in areas where NUSAF2 operated at that time. Subcounty officials interviewed in the survey include elected and appointed officials, as well as local NUSAF2 officers. We were interested in obtaining information on levels of corruption or mismanagement. To measure this, we asked each respondent the following question: “In your personal opinion, within your district, which sub-county has the biggest problem with corruption?” We then counted the number of times a sub-county was mentioned. Of the subcounties in the sample, 47% were never mentioned by an official and 20% were mentioned only once. We created an indicator if the sub-county was mentioned more than once.<sup>11</sup> As mentioned above, outcome data were obtained from two separate surveys: first, a project quality assessment captures effects on community project outputs, and second, a survey of individual households conducted six months later that captures effects on the households.

> 11 It is possible that communities select project types based on local prevalence of corruption. We in fact observe this. Communities that are in areas cited as corrupt choose livestock projects 58% of the time, while those in areas not cited as corrupt choose livestock 70% of the time.

14
perspective that “take[s] it as obvious that individuals’ preferences are formed by society and that society, so to speak, exists within persons.”<sup>2</sup> For the first time, research across disciplines is producing rigorous evidence that context, history, and culture affect decision-making. New techniques have expanded the ability to infer causality from non-experimental data. Research findings are that preferences are influenced by social networks and experience and exposure through which individuals learn mental models (such as categories, identities, and narratives) that they use to process information. Myopic preferences can explain why the wrong types of leaders emerge and well-intentioned reformers fail to obtain legitimacy (Khemani, 2017).

**Table 1:  Frameworks to explain economic development outcomes**

|**Focus on:**<br>**Approach**|**Rational choice**<br>**with fixed**<br>**preferences**|**Institutions**|**Culture**|**Empirical validation**<br>**of individuals’**<br>**intentions**|
|---|---|---|---|---|
|- Early economics|No|Yes|Yes|No|
|- Neoclassical growth theory<br>- Neoclassical price theory, including<br>the economics of asymmetric<br>information|Yes|No|No|No|
|- NIE* and the colonial origins of<br>underdevelopment<br>- Analytic narratives|Yes|Yes|Some<br>authors, not<br>others|No|
|- RCT-based evaluation of projects|No|No|No|Yes|
|- Behavioral development economics|No|Yes|Yes|Yes|

* Douglass North’s work beginning in the 1990s recognizes the influence of bounded rationality, ideology, and cultural mental models to process information, which makes it an outlier in the category of NIE.

> 2 Personal communication from the sociologist, Peter Hedstrom, cited in Fehr and Hoff (2011).  An early survey of work on endogenous preferences is Bowles (1998).  A history of behavioral economics is Heukelom (2014).

8
**Figure C1:** Conceptual Framework for Causal Pathways to Child Treatment Effects

> <sup>_T_</sup> _i_ : Treatment **<u>Mother:</u>**

_Sit_ ( _X_ , _T_ )  1 <u>Birth Outcome: Is a child born in this interval?</u> <!-- Start of picture text --> AitC ( T ): Birth Timing: Child Age Father<br>AitM ( T ): Birth Timing: Maternal Age Z i ( T ): Father Selection<br><!-- End of picture text -->

**Maternal Selection** effects that alter the identity of the woman who gives birth

- **Child Selection** effects that alter the identity of the child who is born

## **<u>Child</u>**

D1. Direct impacts

**Direct Treatment** effect on the child actually born

**Figure C2.** Height-for-age z-score by age in months (control group)

<!-- Start of picture text --> 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60<br>Age in Months<br>Males Females<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>-2<br><!-- End of picture text -->

# **References (for the Appendix)**

78