rafmacalaba/data-use-mentions-extended huggingface dataset:
Clinton was ignorant of the facts on the ground or chose to disregard them, her implication that China has been extracting Africa’s wealth for itself is utterly wide of the truth.”<sup>26</sup> This study uses data gathered from the home and host countries as well as individual companies to suggest that neither the US Secretary of State nor China’s Xinhua News Agency were correct in their statements. The fact is that Chinese investment in Africa is far from monolithic and is growing increasingly diverse and dynamic. The study shows an underlying factor behind this evolution: the rising role played by the private sector. Whereas large SOEs continue to be important players in areas of infrastructure and resource-based activities, the private sector has indisputably led the way by contributing to the non-traditional manufacturing sector in many parts of Africa.

This diverse trend has had a wide-ranging impact on Africa’s development. The injection of private capital, technology and entrepreneurial ideas offers host countries significant economic opportunities, especially in early industrialization and job creation. For these reasons, they are particularly welcome by African host countries. Nevertheless, private Chinese firms invest in Africa based on pure self-interest, i.e., seeking profits. This is what fundamentally distinguishes them from official aid or many investment projects led by their SOE compatriots. However, profit-driven investment does not bring one-sided benefits only. Well utilized, it can generate significant economic benefits for host countries – the reason one sees countries around the world competing for foreign direct investment.

An interesting finding of this study is that not only the motives but also the basic behavior of private Chinese investors are similar to those of other international investors. They make decisions on where to invest by pretty much the same determinants, i.e., market gain, production cost saving, resource and raw materials access, and risks perceived, including those imbedded in the investment environment. In fact, one may argue that the surge in private Chinese investment overseas reflects less the rising economic power of China
, interest rate, yet it varies negatively with inflation, and the level of financial development (M2 to GDP).

The following equation represents the regression equation with expected signs shown between brackets based on theory:

### **_Real Gross Private Savingst =_**

_f(Growth in real per capita incomet-k, M2 to GDPt-k, Inflationt-k, Real interest ratet-k)_ (+/-) (-) (-) (+) This paper will investigate short and long run relationships between the variables of interest using the above mentioned econometric procedure.

## **Real Gross Private Savings Per-Capita**

Gross private saving is generally computed as the difference between gross national saving and the relevant definition of gross public saving (Loayza, Schmidt-Hebbel and Servén 1999). The applicability of this definition is challenged in developing economies that suffer from a weak national accounting framework. Accordingly, saving behavior in developing countries has been predominantly examined using multi-country cross-sectional data, which has produced dubious results given vast disparities among countries with respect to the nature and quality of data. This makes cross-country comparisons and policy recommendations difficult as it is fraught with danger. (Athukorala and Sen 2001). This was the main limitation for studying private savings behavior in Egypt in the past. For example, Al-Mashat (2006) used non-government time deposits to GDP as a proxy for private savings to assess the probable relation between financial sector 11
In Thailand, a minimum wage was introduced in 1972 for Bangkok and the metropolitan area. Currently, the minimum wage in Thailand differs by province and by economic sector. The level of the minimum wage relative to the overall average wage is high, at a ratio of approximately 0.5 (Del Carpio and Pabon, 2014). Del Carpio et al. (2014) reported that changes in the regional minimum wage between 1998 and 2010 had negative repercussions on employment (with an elasticity of −0.05) and the effect was disproportionally higher for workers who were the least productive (older workers between 50 and 60 years of age and workers with lower levels of education). In terms of hours worked, an increase was observed for male workers, suggesting that less productive workers more apt to lose their job were being replaced by working‐age male workers.

For African countries, the literature on the effect of the minimum wage is relatively recent. Most of the analyses are based on the experience of South Africa, which has been gradually instituting a minimum wage policy since 1999 in specific sectors of the economy. For example, Bhorat et al. (2013) studied the effect of introducing the minimum wage in five low‐skilled job sectors: retail trade, domestic work, forestry, security, and transportation (taxi service), based on panel data from household surveys for the period 2000‐2007. This analysis found that the minimum wage had no significant negative effect on employment in four of the sectors. The exception was public transportation, which included unionized taxi drivers. In fact, the retail trade, domestic work, and security sectors saw increases in employment following introduction of the minimum wage. However, these sectors also saw a reduction in the number of hours worked, which suggests that employers adapted by cutting back on their workers’ schedules.

An adverse effect on employment has been seen in the agricultural sector (Bhorat, Kanbur and Stanwix, 2011). According to estimates by Bhorat et
is known in the criminological literature as justice reinvestment. Using data procured from existing World Bank and other justice sector analytical products may assist in maximizing identification of the specific elements of community policing capacity that demand prioritized support. Identifying armed groups’ deterrence or incentives when designing justice interventions may be even more complex. To consider prioritization of policy in such circumstances is to consider that, firstly, criminal organizations’ control of

> 8 Our understanding of local context informs the degree of control over local civic life (criminal consolidation) and the nature of relations between non-state actors and the state (proximity between armed actors and the state). Where civic groups play important roles in mediating relations with the state, an opening to employ those groups as sensitizing agents exists. Where closer relations between the state and the armed actors exist, the state should take the lead in sensitizing the armed actors as to the intended focus of punitive state action. See: Enrique Desmond Arias. (2013). The impacts of differential armed dominance of politics in Rio de Janeiro, Brazil. Studies in Comparative International Development. 48:3, 263-284, p. 265.

12
When , the absolute value of the derivative is bigger in (5b). This is an important implication of accounting for MR.

As we generalize to agreements with a small number (>2) of countries, MR becomes more important for the actual comparative static but also for the comparison between South-South and North-South agreements.<sup>9</sup> When countries 1 and 2 are part of an agreement involving 4 countries – 2 Northern and 2 Southern – the analogue to (5a) is where 1 and 2 are a Northern importer and a Southern exporter (or vice versa). When all 4 countries are Southern, the analogue to (5b) is Thus, with estimates of Beta from a gravity model and data on the GDP shares, we can conduct appropriate comparative statics. Table 1 gives illustrative values for the values of the terms in brackets, which we refer to as the multipliers. Based on 2000 real GDP values, the average share is 1.04%. This is dominated by a handful of large countries. For example, the United States and Japan have a combined GDP share of 45% with a resulting multiplier that is well below unity. While specific agreements with specific multipliers could be analyzed, our application will be to generic groupings. For this purpose, the table also has information on the average size of a Northern country and multipliers involving only Northern countries. Our main comparison of interest is between South-South and North-South agreements, where we see the former multiplier does not stray far from unity but the NS multiplier is below 0.9 once four countries are involved.

||Share of World GDP<br>(θ)|Multiplier<br>(2 countries)|Multiplier<br>(4 countries)|
|---|---|---|---|
|Average Pair|1.04%|0.979|0.938|
|US; Japan|27.8%; 17.
# **1. Introduction**

Small and medium-size enterprises (SMEs) provide jobs and incomes to millions of individuals in the developing countries (see ILO 2019, ILO and GIZ 2013, Ayyagari et al. 2011, 2014). According to the World Bank’s Enterprise Surveys data on formal firms in 125 developing countries, SMEs (5-99 workers) account for about 47 percent of the total formal sector employment and 52 percent of the total output on average across countries. The rest is accounted for by large firms (>99 workers). Ayyagari et al. (2014) report that over 95 percent of the new formal sector jobs are created by SMEs in the developing world and only 5 percent by large firms. SMEs are also expected to promote inclusive growth (see OECD 2019). The contribution of SMEs is even higher if we include the self-employed, micro firms (fewer than 5 workers), and the unregistered or informal firms (see ILO 2019). Thus, it is not surprising that most countries have implemented policies to promote SMEs.

However, a lingering concern with SMEs is that they may be unduly affected by economic downturns and crises, such as the COVID-19 pandemic. That is, SMEs are less resilient to crises than large firms. This may happen for several reasons that are discussed in detail in Section 2. The relative underperformance of SMEs in periods of crises implies that SMEs act as destabilizers, magnifying the adverse impact of crises. Furthermore, while it may not be a surprise that SMEs were considerably affected by the COVID-19 pandemic, what is interesting is uncovering the potential contributors that favored large firms while debilitating SMEs. A rigorous comparison and quantification of SMEs’ and large firms’ performance during a crisis in the context of developing countries is lacking (see Section 2). This study attempts to fill this gap in the literature. It analyzes the difference in the decline in sales due to the COVID-19 pandemic between SMEs and large firms (henceforth, the “sales decline gap
services such as bank facilities and general financial development. Barriers to Entry is measured with the barrier of access to land; we also control for competition from informal firms. For each category of the business environment, we find the relevant variables in the Enterprise Survey, _Doing Business_ , and Global Competitiveness data set, respectively. Table 1 reports the name and definition of each variable.

For Enterprise Survey (ES) indicators, the logic of constructing the variables is the same as in Reyes et al. (2017). We measure an aspect of the business environment for a firm with the mean of the response of other firms in the same city and in the same industry category. Specifically, (1) Government Protection is measured with “Corruption Obstacle” (the extent to which corruption is viewed as an obstacle by firms in the city-industry cell) and “Security Costs” (the city-industry average of a firm’s expenditure on security as a share of its sales). (2) Infrastructure is measured with “Power Outage” (the city-industry share of firms that experienced a power outage in the survey year) and “Web” (the city-industry share of firms that use websites to conduct business). (3) Human Capital is captured by “Skill Labor Obstacle” (the extent to which a shortage of skilled labor is viewed as an obstacle by firms in a cityindustry cell). (4) Access to Finance is reflected by “Overdraft” (the city-industry share of firms with an overdraft facility from a financial institution). (5) Barriers to Entry is measured by “Land Access Obstacle” (the extent to which access to land is viewed as an obstacle by firms in a city-industry cell). For all the variables based on subjective perceptions of the degree of obstacles (Corruption Obstacle, Skill Labor Obstacle, Land Access Obstacle),<sup>6</sup> we transform the ranking to a dummy variable indicating moderate or more serious obstacles. In total, we
testing the VARs than by using DSGE models.

**Table 2:** Correlation of VAR-identified technology shocks with true DSGE-generated shock

|Identifcation|**Max-Share**|**Long-Run**|**Spectral**|**Limited Spectral**|**NAMS**|
|---|---|---|---|---|---|
|**Correlation**|0.94|0.62|0.93|0.93|0.93|
||(0.86, 0.98)|(0.19, 0.85)|(0.85, 0.97)|(0.84, 0.97)|(0.77, 0.97)|

Note: 5th and 95th percentile values shown in brackets The high performance of the VARs is likely to be in part due to the shock processes driving the DSGE model. The highly persistent technology shock in this and other similar DSGE models tends to drive the vast majority of the variance of labor productivity. In this calibration, over 99% of the volatility of labor productivity is driven by _εa_ , the technology shock.<sup>11</sup> Therefore, several of the methodologies find a similar result given how insignificant the non-technology driving processes are in the variation of productivity. In (Francis et al., 2014), the technology shock was a true unit-root process, which dominates the variance of technology by even more than the persistent AR(1) chosen here.

> 10As shown in Appendix section 9.2, up to 25% of the standard deviation of the initial non-technology shocks are still present in the labor productivity data at period 40.

> 11Based on resimulating the model over 100,000 periods one shock at a time.

13
unemployment insurance, wage subsidies, and deferral of tax obligations, utility payments and social security contributions, have helped to mitigate the socio-economic impacts of the crisis on households and workers. Governments in EMDEs, however, have been able to spend far less on relief measures because of limited fiscal space. Spending on above the line fiscal measures by September 2020 amounted to 3.8 percent of GDP in emerging markets and 2.4 percent of GDP in low-income developing countries. As a result, though the economic downturn was on average less severe in lower-income countries, the impact on households and individuals may have been far worse, especially for the poor and vulnerable.

The vast gap in adequacy of relief measures between advanced and developing economies is apparent from social protection expenditures that are critical for protecting households, even though many developing countries have extended existing safety net programs and/or introduced temporary new ones. Low-income countries have spent an average of US$4 per person in COVID-19 social protection responses, compared to US$30 in lower middle-income and US$156 in upper middle-income countries (Annex, Figure A-4). Spending on social protection measures is especially low in South Asia and Sub-Saharan Africa (Gentilini et al., 2020). In the HFPS data, the share of households who report receiving public assistance in April-July 2020 rises with per capita GDP, with coverage rates of less than 10 percent seen for most low-income countries in Sub-Saharan Africa (Figure 10a). Coverage among those who lost jobs during the pandemic is also strongly correlated with per capita GDP – a large share of those affected in low-income countries did not receive public assistance, at least during the first few months of the crisis (Figure 10b).

Thus, by all indications, public social assistance in the early months of the pandemic was highly inadequate in most developing countries and particularly so in low-income countries. Within countries, those receiving assistance were more likely to have reported job and income loss during the pandemic and have low socioeconomic status (lower education level
compared to the 1.9 percent average in the business-as-usual baseline.<sup>5</sup> We also simulate the long-term growth under a global “green transition” scenario where the increased demand for copper, if the world achieves Net Zero Emissions (NZE), results in higher copper prices, higher copper investment, and greater exploration. The estimated GDP growth is roughly one percentage point higher compared to the baseline, which would help Peru considerably in achieving its growth targets. The combined scenario of the global green transition and ambitious domestic reforms accelerates Peru’s GDP growth significantly and the country reaches the target of HI status by 2042.

The rest of the paper is organized as follows. Section 2 presents past growth dynamics. Section 3 discusses the assumptions used in the baseline simulation, presents the baseline growth path to 2050 and the contribution of each growth driver to the slowing growth rate. Section 4 presents the combined impact on GDP growth due to reforms on each growth determinant. It shows Peru’s standing in comparison to structural and regional peers on five separate TFP determinants and quantifies the impact from converging to frontier countries on TFP growth. Section 5 presents the global green transition scenario, which analyses the effects of higher copper prices that would result from global NZE, a shock to the copper sector TFP, higher investment, and long-term production. It then combines the domestic reform and the global green transition scenario to quantify the combined impact of GDP growth. The conclusion provides a summary of the main findings and policy implications.

> 3 The LTGM-HC tracks the years of schooling and the quality of education and health across age cohorts to determine the human capital of the workforce. In the LTGM-TFP, the TFP growth rate is calculated as the compositive effect of TFP determinants: innovation, education, market efficiency, infrastructure, and institutions. See the Appendix for details.

> 4 Future demographic projections are taken from the United Nations (UN).

> 5 Ambitious reform is the mix of shocks to the investment
**|10.062 **|-2.864 **|
|||||||(3.700)|(5.331)|(4.481)|(4.481)|(4.569)|
|DEBT|||||||-0.001|-0.002|-0.001|-0.002|
||||||||(0.001)|(0.000)|(0.000)|(0.000)|
|RENT||||||||-0.012 ***|-0.008 ***|-0.015***|
|||||||||(0.002)|(0.002)|(0.002)|
|Obs.|2,177|2,177|1,242|978|962|970|901|883|883|883|
|Countries|120|120|72|71|70|70|61|68|68|68|
|Hansen J|0.275|0.300|0.240|0.132|0.163|0.163|0.139|0.167|0.153|0.265|

Bootstrapped standard errors in parenthesis (100 replications) * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 Table 3: Estimation results: addressing endogeneity

# **7 Robustness Check**

In this section, we provide a robustness check for our empirical findings. In order to check the consistency of our empirical results, we use an alternative variable of institutional quality from a source other than ICRG. We employ the _government effectiveness_ index from the Worldwide Governance Indicators, 2015 Update database of the World
# **1. Introduction**

Vietnam’s current labor market policies have evolved in the context of its long and ongoing transition from a mainly planned to a modern, market-led economy with a fast-growing middle class. This transition started in the mid-1980s with the _doi moi_ (“renovation”) reforms. Over the last 30 years, it has led to significant shifts of employment away from agriculture toward manufacturing and services; away from household enterprises toward registered and regulated businesses; and away from state owned enterprises (SOEs) toward private domestic and foreign-owned firms (cf. McCaig and Pavcnik, 2013).

So far, Vietnam has been remarkably successful in managing its transition. Observed through an employment lens, regional disparities persist but unemployment has been low, wage growth pronounced and – at least among private sector workers – wage inequality has been declining (cf. Pierre, 2012, Imbert, 2013, and Sakellariou and Fang, 2014). Moreover, there are few signs that Vietnam’s labor market policies have thus far constrained the pace of the country’s growth. In fact, results from World Bank investment climate surveys show that a smaller proportion of firms identifies labor market regulations as a constraint to doing business in Vietnam than in Cambodia, Malaysia, Thailand and many other countries in the East Asia and Pacific (EAP) region (cf. World Bank, 2014a).

At the same time, Vietnam’s transition from central planning to a market economy is far from complete. In many sectors SOEs remain dominant and workers with access to state jobs continue to enjoy rents. In 2008, wages were 12 to 14 percent higher in the public than in the private sector even for workers with comparable observable characteristics (cf. Phan and Coxhead, 2013). Moreover, Vietnam’s transition from an agrarian, rural and informal economy, to an urban, manufacturing and services-based and formal economy is not complete either (cf. World Bank, 2014b). According to an investment climate survey,
straints find larger effects that we do: for example, Pickering et al. [2015] find a 30 percentage point (pp) increase following an intense CLTS program in Mali, while Clasen et al. [2014] and Patil et al., 2014 achieve increases of 28 pp and 19 pp using CLTS-like activities combined with subsidies in Orissa and Madhya Pradesh, India respectively. On the other hand, Guiteras et al., 2015 found no significant effects of a CLTS-inspired intervention in Bangladesh, unless it was paired with subsidy provision in which case the uptake was 7 percentage points. Similarly, Cameron et al., 2013 and Abramovsky et al., 2018 find small or no average impacts of CLTS interventions in Indonesia and Nigeria respectively.

The rest of the paper is structured as follows. The next section describes the context of the study and the sanitation loan product. Section 3 discusses the experimental design, and data. Thereafter, Section 4 presents a theoretical model outlining how the sanitation loan intervention influences household borrowing and investment decisions. Section 5 outlines our empirical strategy, and is followed by a presentation of our empirical findings in Section 6. Section 7 concludes.

# **2 Context and intervention**

## **2.1 Context**

Our study concentrates on 5 blocks of 2 districts, Latur and Nanded, in the South-Eastern area of Maharashtra, India (see Figure 1). Maharashtra, with its capital Mumbai, is one of the largest Indian states, counting approximately 100 million people living in almost 44,000 villages (GoI, 2011a). While this is the second richest state in the country in terms of per capita income, incidence of poverty remains close to the national average, implying severe inequalities within the state (GoM, 2012). This is exemplified by the 2011 Human Development Index, which averaged 0.752 for the state, different districts however ranging from 0.604 to 0.841, with Latur and Nanded falling among the low-ranked districts with