rafmacalaba/datause-encoder-data huggingface dataset:
The results from the Logit poverty regressions confirm and strengthen the OLS regression results. Even though we determine the poverty status of the individual by the per capita expenditure of the household rather than by their individual incomes, these two concepts are strongly linked, particularly when the sample is restricted to income earners. Hence, as expected, being older and more educated reduces the chance of being poor. Larger households now increase the probability of being poor as does the number of children below the age of five. Being male _increases_ the probability of being poor for our sample. This may be because the females in our sample are income earners and households in which the woman is also earning an income are likely to be better off than those in which only the male is earning. Interestingly, this positive probability disappeared in 2000, suggesting either that males managed to increase their earning situation considerably or that women lost out relatively more during the crisis.

Surprisingly, living in an urban area increases the probability of being poor, and this effect increases over time. Similarly, the probability of being poor is generally higher outside Jakarta, although this effect is reduced in 2000 due to the improving relative position of the provinces with respect to Jakarta. Employment outside of agriculture reduces the probability of being poor, particularly in the 1993 sample.

The level regressions above confirm many of the links expected from the literature between personal, household, sectoral and regional characteristics and _level_ of income and poverty. However, they do not tell us much about the _dynamics_ of income and poverty. For this we need to exploit the panel nature of the dataset to identify the determinants of income and poverty changes.

# THE DETERMINANTS OF INCOME AND POVERTY _CHANGES_

To identify the determinants of income change we estimate a difference regression on both initial characteristics and changes in those characteristics. In particular we are interested to see if a change from the farm to the non-farm sector has an important impact upon income growth. In other words we estimate: _d_ ln _y_ = β 0 + β 1. _INDCHARS_ + β 2. _HHCHARS_ + β 3. _GEOG_ + β 4. _SECTOR_ + _d d d d_ β 1 . _dINDCHARS_ + β 2 . _dHHCHARS_ + β 3 . _dGEOG_ + β 4 . _dSECTOR_ + ε 34
legal system, which vary significantly across countries. With a small and homogenous economy, a country can adjust its legal and financial systems to the strengths of its economy more economically than a large country. DMS (2005) find that, despite apparent significant economic benefits from reform, there is very little time variation of creditor rights over the past twenty-five years around the globe. This suggests that the costs of improving the legal system are, in fact, very high for many countries. On the other hand, the success of India’s SME sector demonstrates that alternative mechanisms have substituted for formal mechanisms based on legal protection and supported the growth of non-state, non-listed firms in large and diversified economies such as India. It is possible that similar mechanisms/substitutes have also worked well in other countries, including developed countries (e.g., during their early stage of economic development when legal institutions were not as yet well developed).

# **VI. Summary and Conclusions**

In this paper, we examine the legal and business environment surrounding various types of Indian firms and their financing channels and governance mechanisms, and compare our results to those from other countries. Unlike many cross-country studies that focus on one or two dimensions of a country’s legal and financial systems, we study _all_ aspects of the financial system in the second largest developing country and find that many results based on existing cross-country studies do not apply to India. We also provide both aggregate and firm-level evidence. Our paper thus complements cross-country studies and advances the understanding of growth mechanisms.

With one of the largest and fastest growing economies in the world, India is unique among the countries studied in the law, institutions, finance, and growth literature: Despite its English common-law origin and British-style judicial system and democratic government, corruption within the legal system and government weakens legal protection of investors in practice. Financing of firms has been dominated by internal and alternative sources of financing, while listed firms have 39
# **1. Introduction**

Can business training programs that show short-run gains in profitability result in long-lasting entrepreneurial success? The return on investment from the billions spent by governments, aid agencies, microfinance organizations, and NGOs on training small businesses in developing countries will vary dramatically depending on the answer, yet most evaluations only track firms for one to two years (McKenzie et al. 2023). A lasting and constant long-term impact could arise if training improves the “A” term in the production function, increasing the long-term steady state size of the firm. The impact could increase over time if training results in growth through innovation and self-reinforcing psychological impacts. But conversely, impacts may be short-lived if training provides information that only helps solve immediate problems, just speeds up convergence to an existing steady state by helping firms over capital constraints, or if there is knowledge and skill decay and a reversion to previous business practices and mindsets. Psychological transfer studies show substantial decay over longer time periods (Blume et al, 2010).

Recent literature looking at the long-term impacts of cash transfers and multifaceted ultra-poor programs that involve asset transfers along with other components have found mixed results on business outcomes. Some studies have found lasting impacts, perhaps due to behavioral reasons and cash helping smooth shocks (de Mel et al. 2012), or to very poor households accumulating enough assets to potentially overcome threshold effects and sustain agricultural businesses (Balboni et al, 2022). However, others find fade-out due to control group catch-up and convergence (Barker et al, 2024; Blattman et al, 2020; Blattman et al, 2022; Brudevold-Newman et al. 2024). Bouguen et al. (2019) examine the emerging evidence on the long-run effects of development interventions and note that “human capital interventions appear to be particularly effective in boosting long-run economic outcomes” (p.529), although this finding comes from health and education programs, with no such evidence for entrepreneurial human capital. They also note that impacts can often differ substantially by gender, and by the population being targeted.

We examine the long-run impacts of a personal initiative training program in Togo, which is a psychology-based program designed to develop a proactive entrepreneurial mindset (Frese, 2024). In an experiment with 1,500 small business entrepreneurs, we found that this personal initiative approach resulted in a 30 percent improvement in profits over two years, approximately three times that of a traditional business training approach, with similar impacts for both male and female 2
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Mali has also made significant improvements in the nonresidential market. According to enterprise surveys in 2003 and 2007, the share of firms relying on their own generator fell from 45 percent in 2003 to just over 23 percent in 2007, and the share of power consumption supplied by own generation fell by half to 16 percent over the same period. This was a result of Mali improving not only electricity coverage but also the quality and reliability of the service. In fact, for those Malian firms with access to power, the quality of service is relatively good compared with neighboring countries. Losses due to power outages are less than 2 percent of sales, which is among the lowest on the continent. The duration of power outages is also below that of most of neighboring countries (table 11).

**Table 11. Performance of the electricity sector in West African countries**

||Burkina Faso (2009)|Cape Verde (2006)|Côte d'Ivoire (2009)|Ghana (2007)|**Mali (2007)**|Mauritania (2006)|Niger (2006)|Nigeria (2007)|Senegal (2007)|**Simple average**|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of power outages in a typical month|10.8|12.5|4.5|9.7|**4.4**|3.7|20.7|26.8|11.8|14.4|
|Average duration outages (hours)|3.3|5.3|4.6|12.6|**3.9**|2.9|0.5|8.2|6.2|6.6|
|Losses due to outages (% of sales)|5.8|8.9|5|6|**1.8**|1.6|2.5|8.9|5|6.4|
|% of firms owning/sharing generator|11.6|39.8|6.5|26.6|**23.8**|28.6|24.8|85.7|55.4|43.1|
|Electricity from generator (%)|10.9|4.6|15.1|29.5|**16**|9.2|10.5|60.9|24.7|30.6|

Source: Enterprise Survey database available online at http://www.enterprisesurveys.org, year of the survey in brackets.

Mali successfully introduced a rural electrification program in 2003 with the creation of AMADER<sup>2</sup> . At the time, rural electrification was only about 3 percent of the population, and average household monthly spending on energy was as high as $4 to $10. As of 2008, the program had granted access to 36,277 households (box 2). However, AMADER has been hindered by insufficient and uncertain funding for providing capital cost grants, which are essential for the success of the program given the high cost of connections ($776).

> 2 Agence Malienne pour le Développement de l’Énergie Domestique et l’Électrification Rurale, or Malian Agency for the Development of Domestic Energy and Rural Electrification

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observation and pre-knowledge of realistic price ranges for each commodity during the time period. Outliers were identified with Tukey outlier detection method (Tukey, 1977; Dastjerdy, et al., 2023) by calculating the interquartile range and applying fence-rule to filter values. Spurious datapoints (1.3% of the entire dataset) were excluded from further analysis. Second, the intraday crowdsourced and enumerator-submitted prices were computed into daily, weekly, and monthly averages for each commodity type/sub-type. To assess the degree of closeness between the prices from both sources, we conducted pairwise Pearson’s correlation analysis, and fitted a linear trend between paired price datasets, as described in other literatures (Taylor, 1990 and Senthilnathan, 2019). While the correlation coefficient (R) determines the existence and direction of a relationship between the prices, the coefficient of determination (r<sup>2</sup> ) indicates the pattern and strength of the relationship, based on the co-variability of both prices (Equation 1 and 2). R ranges from -1 to +1, indicating strongly negative to strongly positive relationship, respectively; whereas r<sup>2</sup> ranges from 0 to 1, directly indicating the degree of variance explained between the prices. The correlative and regression analysis were performed between the crowdsourced and enumerator prices at daily, weekly, and monthly timesteps, using relevant R packages and functions.

Also, following previous literature (Xu, et al., 2017; Snedecor & Cochran, 1989), we conducted paired sample student-t tests to assess if the differences between price means were significant between the data sources during the period. The tests were conducted based on the null hypotheses that mean prices from one data source is not significantly different from the other. Inferential test statistics were based on a confidence level threshold (α) of 0.05, and the acceptance or rejection of the null hypotheses was based on final _p_ -values.

# **Data Availability**

Generally, the datasets for the analyses were previously published and accessible through two (2) major public repositories. The overall crowdsourced price data was published on the data portal of the European Commission Joint Research Center (EC-JRC, 2022; SolanoHermosilla, et al., 2021), while the AI-estimated prices data is available through World Bank’s microdata library.<sup>2</sup> The initial correlative analysis between crowdsourced and ground truth

> 2 The search tags are, respectively, RTFP, RTEP and RTFX for Real-Time Food Prices, Real-Time Energy Prices, and Real-Time foreign eXchange Rates. Links to the dedicated data sets have been provided in

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capital more effectively than less developed markets. In the economic structure of Southeastern European countries, foreign direct investments (FDIs), remittances, international borrowings, and national reserves form material economic constituents. In this empirical testing, we seek to track the broad statistical relevance in the indicator movement on the performance of capital markets.

# **4. Empirical Results and Sensitivity Analyses**

This paper uses a panel PMG pooling of a wide range of data from the capital markets and macroeconomies in Southeast Europe to test for inherent relationship dynamics in the short and long run. It also uses the panel PMG estimation efficacies of fixing long-run coefficients for more reliable group data results on the existence of a relationship over the short and long run. In addition, this research utilizes further PMG estimation efficacies in allowing the intercept of short-run associating relationships as well as error-variance variability on the individual country basis, making it possible to enable sensitivity testing on short-run results dynamics for each individual country.

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and the estimated hazard rate retrieved from the first stage. The two stages can also (and more efficiently) be estimated simultaneously by maximum-likelihood.<sup>11</sup> The key issue is of course to find good instruments. We follow the approach suggested by Besley and Case (2001) and use political instruments taken from the World Bank’s worldwide governance database. Specifically, we use Kaufman’s governance index (the WB- Worldwide Governance Indicators (WGI) project<sup>12</sup> ) and its square, based on the idea that very corrupt and very honest governments would be reluctant to use inspection programs, the first because it would reduce corruption opportunities and the second because inspection would not be needed. This suggests that the probability of using an inspection program (the dependent variable in the first-stage probit) is a concave function of governance, something which can be captured by a second-degree polynomial. In some specifications, we also use IMF structural adjustment programs that were adopted simultaneously with PSI, as well as the official amount of OECD aid per country per year.

When no good instruments are available, the alternative route consists of using the Generalized Method of Moments (GMM), and in particular Blundell and Bond’s system-GMM (Blundell and Bond 1998). The system-GMM estimator runs two simultaneous equations, one in levels and one in first differences, in which endogenous RHS variables in levels are instrumented by their lagged first differences in the level equation, and by their lagged levels in the differenced equation. Suitable instruments can of course be added in the levels equation if available. The number of lags used as instruments is the experimenter’s choice, and it has been argued that this latitude sometimes makes results unstable. There is however a rule of thumb which states that the number of lags should not be larger than the number of individuals in the panel. In our case, this rule of thumb is of course not binding because the number of lags is anyway severely limited by the panel’s short duration. We report GMM results and compare them with IV results for robustness.

> 11 Both options are available in Stata’s treatreg command, and results reported in this paper are based on the second method. 12 http://info.worldbank.org/governance/wgi/index.asp Page | 11
corporations (MNCs). And the Internet helps to send materials to a global audience (Wu, 2000). In the beginning of the new millennium, Beijing and Shanghai each found an opportunity to create new image: Beijing will hold the 2008 Olympic Games, while Shanghai will be the site of the World Expo 2010.

In support of the efforts on new image promotion, investment has been funneled into urban renewal. Beijing and Shanghai are competing with each other in attracting the MNCs to establish regional headquarters. A new term “headquarter economy” ( _Zongbu Jingji_ ) was invented to describe this phenomenon, which typically refers to the most luxury and modern district with numerous skyscrapers, modeled on Manhattan in New York.

For example, the municipal authority of Beijing launched an urban renewal project in the heart area of Zhongguancun in 1999. Just along the former “Electronics street”, over 15 billion _Yuan_ was invested in an area of about 0.5 km<sup>2</sup> boosting the high-tech CBD of the new Zhongguancun High-tech Park<sup>8</sup> . The urban renewal project will totally change the landscape in the core of Zhongguancun. However, firms in Zhongguancun have responded variously. Most of small and medium technical firms complained that the project pushed by the government raised the housing price around this area and firms are finding that they cannot afford the soaring business costs. According to a human resource survey in Zhongguancun, sponsored by the real estate developers, over 70% of the respondents said they were thinking of relocating to a cheaper area. If the local SMEs cannot afford the increasing business cost in this area, the real estate developers involved with the renewal project have to rely on inducing MNCs to come to the area.

Taking the international city as the object of urban development, Shanghai has also invested heavily in urban renewal, but to some extent, in a different way. As most of manufacturing has been shifted to industrial parks in suburbs, abandoned plants and lofts in the inner city were left empty. The city had the choice of pulling down these old buildings to build new ones or transforming them into new space for new industries. Shanghai chose the latter course, renewing these old buildings by developing “urban industries ( _Dushi gongye_ )”. At present, nine former factories in the inner city and several old depots along the Suzhou River, have been refurbished to host the new urban industries, including software development,

> 8 The new Zhongguancun Science and Technology Park has been much larger than the original Zhongguancun in geographical sense to cover 5 industrial parks with different location in the city or suburb, see Zhou and Tong, 2003.

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As a point of comparison, respondents in the control group were randomly allocated to be asked one first-stage question that was identical to a question asked in a corresponding treatment group. The first stage treatment effects are measured by comparing the answers between the subset of respondents in the control group who received the same question as the corresponding treatment group. Importantly, even though the first-stage questions are based on the treatment, they are still framed neutrally to minimize the likelihood that they prime respondents in the control group.

Unconditional support for reform of energy subsidies is measured by asking respondents their views about reducing or eliminating energy subsidies as well as by asking about their support for a reform that resulted in a specific increase in energy prices.<sup>9</sup> The first two of these questions indicate people’s unconditional support for energy subsidy reform in general and were based on similar questions asked by Dechezleprêtre et al. (2022) about respondents’ support for policies addressing climate change, such as carbon taxes. The final unconditional support question measures levels of support for a specific increase in energy prices that is due to a reduction in energy subsidies; this question is somewhat similar to questions asked by Dechezleprêtre et al. (2022) about respondents’ support for policies addressing climate change.

Conditional support for subsidy reform is measured by providing respondents with a list of seven policies that could be immediately funded with the resources saved from reducing subsidies, and then asking respondents which they would support; and by an open-ended question allowing respondents to propose what accompanying policies could be funded. The former question lists policies that are commonly implemented in conjunction with subsidy reforms (e.g., cash transfers), and have been referred to in related studies (e.g., see Dechezleprêtre et al. [2022] and Vieites et al. [2023]). Importantly, the ordering of the specific accompanying policy options was randomized to minimize the risk of respondent fatigue impacting the overall results. The open-ended question provides qualitative insights into the

> 9The answers to these questions are self-reported, and the possibility exists that survey responses may not reflect real world behavior. However, this limitation exists across most studies that rely on survey experiments (Stantcheva 2022).

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Although it is estimated that around 82 percent of installed capacity in the fish processing sector complies with EU requirements as assessed by the Kenyan Competent Authority, a number of fish processing facilities were unable to up-grade their facilities and subsequently stopped operating. Currently, six processing facilities are idle (Figure 26).

It is evident that non-recurring costs of compliance borne by individual companies varied significantly, reflecting the varying standards of hygiene that prevailed within the sector in 1998 (Table 11). Thus, for example Plant G had made an investment of US$128,000, whilst Plants E and H had only minimal costs. In general, costs were generally greatest where factories had undergone major structural change in order to improve the general fabric of the facility, achieve segregation of operations and/or implement effectual pest control. These facilities generally had lower existing standards of hygiene and had not previously made the necessary investment to upgrade hygiene standards. Further, they were generally housed in older buildings, often converted from some alternative previous use, and/or had severe space constraints.

The total cost of compliance for the fish processing sector is estimated at US$557,000. This implies an average cost per plant of US$39,785. Whilst this is not a large amount given current exports valued at US$33.52 million, given that six plants are not operational, a significant proportion of the sector has clearly derived no pay-off from this investment at all. Further, there is no clear relationship between size of fish processing operation and non-recurring costs of compliance; some of the smaller processors incurred costs of the same order of magnitude as the larger processors.

# **Figure 26. Capacity and operating volumes of operational fish processing facilities, 2002:**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>G B A Z F Q E D H N C R M O P<br>Capacity Operating volunes<br><!-- End of picture text --> _Source: Abila and Jansen (1997)._ A number of plants that are not currently operational had made some investment in the up-grade of their facilities, in some cases significantly so (for example Plants I and J). Ironically, a number of these were prevented from completing the necessary investment because of the loss of EU markets; many were almost entirely dependent on exports of fresh Nile perch fillets to the EU.

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