rafmacalaba/datause-encoder-data huggingface dataset:
complementarities. Karakaplan (2021) finds that conventional small business loans of up to $1 million went up on the order of magnitude of an additional dollar of conventional loans for a marginal dollar of PPP loans, particularly for small banks. Berger, Karakaplan, and Roman (2021) similarly find strong positive effects especially for smaller banks and smaller businesses. Marsh and Sharma (2021) also find significant increases in lending by small banks, while Lopez and Spiegel (2021) find increases in small business and farm lending by all bank sizes. In contrast, another study using the Federal Reserve’s Y-14 data on conventional loans over $1 million suggests strong substitution effects. Chodorow-Reich, Darmouni, Luck, and Plosser (forthcoming) find firms that receive PPP loans obtain fewer conventional credits over $1 million from very large banks. Thus, there may be key differences by firm and bank size that need more research to obtain a clearer picture of the answer to this important question.<sup>29</sup> Some research focuses on the financial institutions that issued the PPP loans. Several studies find that small, community banks played an outsized role in PPP, disbursing outsized shares of the PPP loans (e.g., Levine, Lin, and Xie, 2020; James, Lu, and Sun, 2021). Another study finds that FinTech firms also played a key role in distributing PPP funds, providing greater roles in local markets with fewer bank branches, lower incomes, larger minority shares of the population, in industries with bank small business lending (Erel and Liebersohn, 2020).

There may also be some long-term costs of PPP to the extent that the funds were misused or were allocated to unproductive firms that should otherwise not be kept alive. One theoretical study emphasizes the potential for misallocation of the PPP funds to firms by banks (Joaquim and Netto, 2021). Some empirical evidence suggests that some PPP funds were not employed as

> 29 Although it is not our focus here, some evidence suggests that another much smaller program to stimulate bank lending, the Main Street Lending Program, also increased bank lending outside the program (Minoiu, Zarutskie, and Zlate, 2021).

37
Summarizing the results from the analysis at the district and firm levels, the variation in the mining employment shock across districts corresponds to employment growth particularly in heavy manufacturing sectors in the south between 2003 and 2013. This process of structural transformation was facilitated by the entry of new firms in the heavy manufacturing sector in the mining districts as well as by an increase in the average size of employment in heavy manufacturing firms in the mining districts over time.

# **5. Conclusion**

Evidence based on regional I-O tables and census data for firms supports the occurrence of a mining shock in the south of Ghana. We find evidence that the mining productivity shock drove the differentiation of production networks and the structural transformation process in the north and the south of the country. We interpret the regional difference in the sectoral composition of economic activities to be possibly due to weak interregional linkages which prevent the propagation of the local sectoral productivity shock from the south to the north.

Regional differences in intersectoral linkages and the pattern of structural transformation have several implications for aggregate productivity growth in Ghana. First, a large mining output shock in the south creates scope for policy interventions to sustain productivity growth by maintaining a strong production network in the region. Industrial policies aimed at fostering stronger linkages between mining and other sectors can help not only to secure greater economic gains from mining in the south but also to redress the lack of access to modern energy services in other sectors, particularly in manufacturing, as suggested by Aryeetey and Ackah (2018). Second, allocating resources to strengthen intersectoral linkages in the north also appears crucial as geological preconditions for oil and gas deposits have been found in the Voltaic basin located in the north of the country (Skaten 2018).

Third, divergent patterns of structural transformation across regions promote regional specialization (for example, clothing industry in the north, heavy manufacturing in the south), which, with lower trade costs, could enhance aggregate productivity growth through stronger interregional linkages (Caliendo et al. 2018). Finally, with Ghana’s upstream mining activities and petroleum revenues starting to recover in the first half of 2021 in the wake of the pandemic, followed by a negative oil price shock, the country is better positioned to pursue placed-based development (Kline and Moretti 2014)—a strategy that has already been emphasized in the context of Ghana (Aragon and Rud 2013; Fafchamps et al. 2017).

Our study suggests two areas for further research. First, similar studies on other emerging oil and gas producers including Kenya, Mozambique, Tanzania, and Uganda could 17
# **2. Program Description**

Siem Reap, Cambodia is best known as the location of Cambodia’s majestic Angkor Wat temple. Over two million tourists travel to Siem Reap each year to see the temple, and as a result the area around the temple including the town of Siem Reap has experienced explosive economic growth. The economic impact of this growth has not extended beyond a few miles from Angkor Wat however. Jobs in the tourism sector require literacy and some ability to speak English and as such are unavailable to the poorest members of Siem Reap province. In a 2008 study, 14 percent of Siem Reap province residents were classified as very poor (ID Poor 1) and another 15 percent were classified as poor (ID Poor 2) despite the increase in tourism to Angkor Wat temple.<sup>7</sup> Siem Reap province’s rural poverty is readily apparent by casual observation a few miles outside of the Angkor Wat tourist region.

To address this persistent poverty in the rural areas of Siem Reap, the Cambodian government and the World Bank launched LEAP as a pilot project. LEAP was designed to meet three broad pro-poor objectives. These were captured by the three program components: 1) to build and strengthen SHGs among the poor to serve as intermediaries with the state and with lending institutions, 2) to provide the poor with better access to finance and 3) to forge better links between poor producers and important markets and value chains. The program hoped that through these activities the villages would accumulate social capital which would in turn strengthen villagers’ trust, trustworthiness and capacity for collective action in pursuing these goals.

Overall LEAP inputs included coordination activities, training programs, monitoring as well as cash in the form of “seed” grants (see LEAP, 2012). Under the _first component_ , SHGs were formed and trained (e.g. management, book keeping and meeting facilitation).

> 7 The Ministry of Planning in Cambodia, with the help of various foreign donors has devised a set of criteria for the identification of poor households for targeting programs. This program called Identification of Poor, or IDPoor for short, surveys each household to determine if they belong in one of two categories (IDPoor 1 and IDPoor 2) or neither. IDPoor 1 are the very poor, those unable to provide enough food for the household. IDPoor 2 are less poor, corresponding to those households that are between the food poverty line and the poverty line. If the household is in neither category we consider them non-poor. About 15 percent of the Cambodian population fall into each of the categories IDPoor 1 and IDPoor 2 (see Ministry of Planning, 2013a,b).

5
Demekas, Dimitri, Jimmy McHugh, and Theodora Kosma. 2002. “The Economics of Post- Conflict Aid.” _IMF Working Paper_ WP\02\198.

- Dar, Manzoor, Alain de Janvry, Kyle Emerick, David Raitzer, and Elisabeth Sadoulet. 2013a. “Flood-tolerant rice reduces yield variability and raises expected yield, differentially benefitting socially disadvantaged groups.” _Scientific Reports_ 3, Article number 3315, November 22.

- Dar, Manzoor, Alain de Janvry, Kyle Emerick, David Raitzer, and Elisabeth Sadoulet. 2013b. “A Flood of Investments: Flood-Tolerant Rice and Farm Investment in India.” University of California at Berkeley.

- de Janvry, Alain, Craig McIntosh, Felix Povell, and Elisabeth Sadoulet. 2013. “Utility, risk, and demand for incomplete insurance: Lab experiments with Guatemalan cooperatives.” University of California at Berkeley.

- de Schutter, Olivier. 2013. Right to Food. http://www.srfood.org/en/right-to-food. Elbadawi, Ibrahim, Linda Kaltani, and Klaus Schmidt-Hebbel. 2008. “Foreign Aid, the Real Exchange Rate, and Economic Growth in the Aftermath of Civil Wars.” _World Bank Economic Review_ 22(1): 113-140.

- Eswaran, Mukesh, and Ashok Kotwal. 1986. “Access to Capital and Agrarian Production Organisation.” _Economic Journal_ 96(382): 482-98.

- Ferraz, Claudio, and Frederico Finan. 2008. “Exposing Corrupt Politicians: The Effects of Brazil’s Publicly Released Audits on Electoral Outcomes.” _Quarterly Journal of Economics_ 123(2): 703-745.

- Fuchs, Alan,�and�Lourdes Rodriguéz-Chamussy. 2011. “Voters Response to Natural Disasters Aid: Quasi-Experimental Evidence from Drought Relief Payment in Mexico.” University of California at Berkeley.

- Fuchs, Alan, and Hendrik Wolff. 2011. “Concept and Unintended Consequences of Weather Index Insurance: The Case of Mexico.” _American Journal of Agricultural Economics_ 93(2): 505-511.

- Ghesquière, Francis, and Olivier Mahul. 2010. “Financial protection of the state against natural disasters : a primer.” The World Bank, Policy Research Working Paper WPS5429.

- HLPE-CFS (High Level Panel of Experts of the Committee on Food Security). 2012. _Social Protection for Food Security_ . FAO: Rome.

- Hoff, Karla, and Joseph Stiglitz. 2008. “Exiting the Lawless State.” _Economic Journal_ 118(531): 1474-97.

- Hornbeck, Richard. 2012. “The Enduring Impact of the American Dust Bowl: Short- and Long-Run Adjustments to Environmental Catastrophe.” _American Economic Review_ 102(4): 1477-1507.

- Hsiang, Solomon, Narita, Daiju. 2012. “Adaptation to Cyclone Risk: Evidence from the Global Cross-Section.” _Climate Change Economics: Special Issue on Adaptation_ 3(2)

- Ibarra, Hector, and Olivier Mahul. 2004. “Self-insurance funds as agriculture insurance providers: The case of the Fondos in Mexico.” The World Bank.

- Jayachandran, Seema. 2009. “Air Quality and Early-Life Mortality: Evidence from Indonesia’s Wildfires.” _Journal of Human Resources_ 44(4): 916-54.

- Jina, Amir. 2014. “Impacts of Climate Change on Low-Lying and Flood-Prone Areas: The Case of Bangladesh.” Goldman School of Public Policy, UC Berkeley.

13
|**Contents**|
|---|
|1.<br>Introduction ........................................................................................................................................... 3|
|2.<br>Adapting the multidimensional approach of informality analysis to Indonesia .................................... 4|
|2.1<br>Definitions of informality: Global vs Indonesia ........................................................................... 4|
|2.2<br>Informality data and estimation methods description: Cross-country and Indonesia-specific<br>databases ................................................................................................................................................... 6|
|3.<br>Levels and trends of informality: EMDEs vs Indonesia ....................................................................... 8|
|3.1<br>Informality in EMDEs .................................................................................................................. 8|
|3.2<br>Informality in Indonesia ................................................................................................................ 9|
|3.3<br>Informality at the regional level in Indonesia ............................................................................. 10|
|4.<br>Policy and development challenges associated with informality: EMDEs vs Indonesia .................... 12|
|4.1<br>Development challenges associated with informality ................................................................. 12|
|4.1.1<br>Underdevelopment, poverty and productivity gaps ............................................................ 12|
|4.1.2<br>Poor governance, unfriendly business climates, and heavy-handed regulations leading to<br>high-cost investment. .......................................................................................................................... 18|
|4.2<br>Policy challenges associated with pervasive informality ............................................................ 19|
|4.3<br>The COVID-19 shock and the informal sector in Indonesia ....................................................... 22|
|5.<br>Conclusion .......................................................................................................................................... 25|
|Reference .................................................................................................................................................... 27|
|Annex 1: Examples of common definitions of informality in the literature ............................................... 31|
|Annex 2: Definitions of informality from SAKERNAS ............................................................................. 33|
|Annex 3: Description of informality dimensions ........................................................................................ 34|
|Annex 4: MIMIC Model Estimation Results (1993-2021) ......................................................................... 36|
|Annex 5: The impact transmission channels of the COVID-19 shock on the informal sector ................... 37|

2
regulation. We used the standard curriculum, which is homogeneous across contexts and demographics. Thus, we can think of this curriculum as a blueprint that is cheaper and easier to replicate and scale up than other psychology-based programs, including _Virtue_ .

To measure the impacts of the ASPs on students’ outcomes and explore potential mechanisms, we collected data before the start of the intervention (baseline) and right after its completion (follow-up). To measure the main outcomes—namely, behavior at school, school dropout, and academic performance (math scores)—we rely on administrative records submitted as printed reports by teachers from the three countries. Data on behavior at school and math grades were collected at baseline and endline, whereas reports on school dropout are available only at endline.

Moreover, we undertook an innovative and exploratory investigation to provide evidence on three potential mechanisms underlying the effects of psychology-based interventions on students’ main outcomes—namely, socio-emotional skills, fluid intelligence,<sup>5</sup> and emotion regulation.<sup>6</sup> To gather measures of socio-emotional skills, we used taskbased games available in an application developed and used by Danon et al. (2023). We collected measures to proxy for perseverance, self-control, and risk-averse behaviors. To assess emotion regulation, we examined the impact of our intervention on indicators of emotional reactions, which we refer to as _emotion regulation_ markers. These markers were estimated using a software platform based on computer vision and AI called Reactiva (Amangeldiyev and Egana-delSol, 2018) and a platform designed to conduct research on behavioral economics in the field.<sup>7</sup> Last, we use a progressive matrices test (similar to a

> 5Fluid intelligence is consistent with Mullainathan and Shafir’s (2013) idea of mental bandwidth. The ASPs’ psychology-based curriculum may increase concentration capacity and, thus, improve fluid intelligence, which proxies for logical thinking.

> 6The literature defines _emotion regulation_ as a mixture of cognitive and emotional processes that affect a person’s disposition to act (Salzman and Fusi, 2010). Drawing from Kahneman’s (2011) theory of automaticity, which posits that individuals often act automatically and unthinkingly because of habitual responses, we argue that participants in both _Virtue_ and _Mindful_ may increase their emotion regulation by reducing their automatic emotional reactions to both positive and negative stimuli.

> 7 For the former, we leverage a software development kit, developed by a spin-off of MIT’s Affective Computing Group (Picard, 1995; McDuff et al., 2015), that measures different features of emotions and has been trained on millions of subjects. Regarding the latter, we construct an online platform with researchrelated protocols such as the ability to randomly show emotionally laden stimuli and present cognitive tests.

4
have liked more days of work and still had fewer than the 100 days stipulated by the Act. We have no choice but to ignore this aspect of the scheme‘s performance since the National Sample Survey (NSS) did not ask how many more days of work the household wanted; all we know is whether the household wanted work on the scheme.

Table 1 gives the participation rate, the demand rate and the rationing rate across states, as well as the rural headcount index ( _H_ )—the proportion of the population living below our updated versions of the Planning Commission‘s latest poverty lines. (The table also gives the female share of employment, to which we return later.) ―Demand‖ is defined as either getting work on the scheme or seeking work but not getting it. For India as a whole, 45% of rural households wanted work on the scheme. Of these, 56% got work—a national rationing rate of 44%. The rationing rate varied from 15% in Rajasthan to 84% in the Punjab. Only three states have rationing rates under 20%. There is clearly a large excess demand for work.

A striking observation about the data in Table 1 is that participation rates are only weakly correlated with the rural headcount index across states, as can be seen in Figure 1. If MGNREGS worked the way the Act intended then this weak correlation would be surprising, as one would expect the scheme to be more attractive to poor people, and hence have higher take up in poorer states. The same point holds for public spending on MGNREGS. Table 2 gives summary statistics on spending per capita for 2009/10 and 2010/11. The correlation between MGNREGS spending per capita and the poverty rate is -0.02 using spending in 2009/10 and 0.04 for 2010/11.

Why is MGNREGS not more active in poorer states? We postulate that being a poor state has two opposing effects on participation. First, there is an effect of greater poverty via a higher demand for MGNREGS work. Call this the ―demand effect‖ of poverty. We see confirmation of this in Figure 2, which shows the expected positive correlation between _D_ and _H_ (r=0.50). The second effect is that poorer states tend to have greater un-met demand for work on the scheme. Call this the ―rationing effect.‖ We suggest three reasons why the rationing effect would work in the opposite direction to the demand effect. First, poorer states will be less able to afford the share of the costs that are borne by the state and local governments. Second, poorer states will tend to have weaker capacity for administering such a scheme. Third, the poor may well be less empowered in poorer states. As we will see in the next section, both poor and non-poor people have a demand for work on the scheme, though the demand is greater amongst the poor. If poor 6
Both the welfare ratio WR defined in equation 15, and the money metric utility MMU defined in equation 10, satisfy the properties identified at the beginning of this section, and therefore represent strong candidates for proxying the living standards based on household budget survey data.

The common structure of the two measures facilitates their interpretation and practical implementation: they are both defined as the ratio between nominal total household expenditure to a true-cost-of-living index. They can both be approximated by replacing TCLI with a price index, a Paasche index in the case of MMU, a Laspeyres index in the case of the WR. The choice of the index (whether Paasche or Fisher) implies a different choice of the underlying price measure. In this sense, the question “which index is the best choice” is not a good question: No ‘best’ index exists. By choosing the price index, the analyst chooses the welfare measure: Paasche implies a MMU, Laspeyres implies a WR, Fisher, Törnquist and other indexes imply different welfare measures.

The analysis reviewed in this section hopefully helps to clarify the recommendation put forward in Deaton and Zaidi (2002) and confirmed by Mancini and Vecchi (2022), and sets the stage of the analysis carried out in the next section. The aim is not to contribute to the debate on the best welfare measure (MMU versus WR), but rather to focus on different price adjustment procedures, SI versus MI, _taking as given that the appropriate welfare concept has been chosen_ .

# **4.2 Can the SI and MI methods be ranked?**

Section 4.1 discusses two welfare concepts, _MMU_ and _WR_ , which we use here as benchmarks for assessing whether the practice of using one deflator (SI) is preferable than the practice of using two deflators (MI). Consider first the MMU approach. According to equation 9, the money-metric utility function for household _h_ , MMU<sup>h</sup> , can be written as the ratio between household _h_ expenditure and the true-cost-of-living index (TCLI) associated to the utility level _u_ achieved by household _h_ at the prevailing prices p<sup>h</sup> : where TCLIPKh is the Paasche-Konüs TCLI introduced in equation 8. The equation above, corresponding to equation 9, identifies our benchmark. While methods SI and MI can be assessed 17
_10_ _Price Support at Any Price?_ active agricultural policy as well, but seeks for alternative growth paths. Therefore, the analysis concentrates on evaluating effects of a different path of growth on the income relation, without any policy counteraction (the results should be interpreted as likely directions of changes rather than forecasts).

Economic growth assumed in the base solution (generally based on the long-term projections of the World Bank, 1994) reaches 4.4% yearly (see table 2.1), with growth of investment considerably higher than growth of consumption. A 3.7% annual increase of the personal consumption is not equal for all groups of goods and services. Consumption of food products grows only by 1.7% yearly and consumption of non-food goods and services by 4.6%. Structural changes in agriculture are moderate. The number of farmers falls by one quarter between 1991 and 2010 (average yearly fall of 1.6%) and the average farm area grows by more than 2 hectars. As growth of output per farmer reaches 3.3% yearly, and average wage grows only by 3% yearly, a 11 % increase of the income relation takes place.

**Tab.2.1 The base solution - main indicators**

||J<br>=<br><-<br>Index<br>1991|<br>2010|=<br>Average<br>growth<br>rate<br>l|
|---|---|---|---|
|**Gross Domestic Product**<br>_of which:_<br>Personal Consumption|**100.0**<br>100.0|**224.9**<br>200.2|**4.4%**<br>3.7%|
|<br>Fixed Capital Formation|100.0|363.2|7.0%|
|Income Relation (fanners' income/wage)|100.0|110.8|0.5%|
|<br>Number of farmers (in millions)|3.285|2.416|-1.6%|
|<br>Average farm area (in hectars)|6.3|8.6|1.6%|
|<br>Output per farmer|100.0|184.6|3.3%|

# _Source: Model POLAGR, simulation Jan. 1995_

In the sensitivity analysis, three factors that may have an important impact on the income relation were taken into account:

- _(a)_ the rate of growth of GDP in non-agricultural sectors;

- _(b)_ the scale of the real appreciation of zloty;

- _(c)_ efficiency gains in agriculture (we define efficiency gains as growth of value added exceeding growth of output).