rafmacalaba/data-use-mentions-extended huggingface dataset:
.146|
|Anderson/Kleibergen-Paap Statistic|107.203|46.157|107.203|107.203|
|<br>Anderson/Kleibergen-Paap Chi2|0.000|0.000|0.000|0.000|
|Cragg-Donald/Kleibergen-PaapStatistic|9.181|4.127|9.181|9.181|
|**Coefficient tests**|||||
|γ +β= 0|14.58***|14.29***|14.45***|14.46***|
|<br>θ +β= 0|0.90|1.08|0.90|0.90|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Contract choice (sharecropping only, output, or input sharing) is instrumented using landlord characteristics, in particular plot distance from homestead, land ownership, household composition (number of dependents, number of male adults, number of female adults), assets (no. of oxen, value of other livestock, iron roof), and characteristics of the head (age, sex, literacy level, and farming as a primary activity). First-stage regressions are reported in appendix table 6.

23
7 methodologies, there is broad consistency between the results of the enterprise surveys and the MENA survey.<sup>3</sup> There seems to be significant scope for further SME lending in MENA, as shown by the large differences between the long-run targets and the actual shares of SME lending reported by the banks (Table 3 and Figure 2a). This is true in both the GCC and the non-GCC regions, although targets are significantly lower in the GCC (about 12% of total lending), revealing that the banks themselves have concluded that there are “natural” limits to profitable SME lending in oil-based economies. In the case of non-GCC countries, the long-run target is much higher and around 27% of total lending. It is interesting to note that this target is very similar to the actual share of SME lending in developed economies. This could be more than a coincidence, and reflect expectations by the banks that the enabling environment and market conditions in MENA will eventually converge to those already prevailing in developed countries, creating the conditions for further and profitable SME lending.

State banks play an important role in SME lending in several MENA countries, as indicated by an average share of SME loans of 9% of total lending. This share is very close to the average share of private banks (Table 5 and Figure 2b). The long-run targets for SME lending for State and private banks are also similar. Moreover, while the differences between the average shares of SME loans in GCC and non-GCC countries are large and statistically significant, the differences between state and private banks are not statistically significant, as shown in Table 5. The important role played by state banks could reflect their mandates to fill a gap in SME lending, especially where the enabling environment remains weak and the private banks are still reluctant to take more risk, despite the potential profitability of the SME business. This result will be further explored below.

In addition to having a larger SME portfolio overall, non-GCC banks also extend a larger
bias introduced by measurement error in our proxy for human capital.<sup>13</sup> . Given that we are interested in determining the possible effect of natural resource abundance on economic growth, we extend traditional growth regressions by incorporating the share of natural resources exports in GDP and in total exports as proxies of resource abundance ( _Natural_ ).<sup>14</sup> As control variables we use human capital, measured by average years of schooling among the over-25 population ( _H_ ); government expenditure as a fraction of GDP ( _G_ ); openness, measured as exports plus imports divided by GDP ( _OPEN_ ); terms-of-trade shocks ( _TT_ );<sup>15</sup> investment as a fraction of GDP ( _I_ ); and initial income ( _y_ ). All the variables are measured at the beginning of each period of the panel. However, as a robustness test, we also estimate regressions using average values of some variables for each period. All the estimations use either period dummies and regional dummies for Africa and Latin America, or fixed effects, depending on the estimation technique ( _DREG_ ).<sup>16</sup> Our benchmark regression for the rate of growth, _γy_ , is the traditional growth equation extended by the inclusion of natural resources, as estimated by several authors and as implied by our model.<sup>17</sup> This regression can be written as where _i_ is a country index and _t_ indicates the number of the cross-section regression of the panel.

In a second stage we include an interaction effect between human capital and such as the Generalized Method of Moments, as proposed by Caselli, Esquivel, and Lefort (1996). 13For a revision of this point see, for example, Krueger and Lindahl (1999).

> 14As in most of the recent literature, we use the World Tables CD-ROM as a data source and define natural resources exports as the sum of exports of fuels and nonfuel primary products.

15We
the total amount of housing _H_<sup> ̃</sup> and commercial floor space _Z_<sup> ̃</sup> in each location, I use the area in square kilometers of buildings in each census tract from the Global Human Settlement Layer (GHSL) in 2000 weighted by the total number of employees and residents. To calibrate the fixed costs, I use the log-linear relationship between the total number of firms and the workforce in each sector from the model,and find _FI_ = 0 _._ 15, and _FF_ = 1 _._ 2. Section C.2 in the appendix specifies the details for this estimation. This result is consistent with the fact that for a firm, it is more difficult to produce in the formal sector. In addition, I use the estimate of the elasticity of substitution across sectors _ξ_ = 2 from Edmond et al. (2015), which is similar to the estimates of other papers (Asturias et al., 2016). Also, I compute the counterfactuals using a value of _η_ = 1 _._ 50 which is the lowest value of the migration elasticity that Tsivanidis (2019) finds for Bogotá, a similar context to Mexico City. This value is consistent with the assumption that _η ≤ κ_ from the theoretical framework.<sup>52</sup>

# **5.6 Model Inversion**

In this section, I recover the fundamental parameters _Bn, Bns_ , which capture differences in amenities that attract residents to each location and sector; and the parameters _Ais_ , which represent differences

> 52In section 6, I show that the results are robust to different values of the migration elasticity and the elasticity of substitution across sectors.

31
# **6.    Conclusions**

In this paper we explore the short-run consumption expenditure dynamics and the interaction of public and private arrangements using a randomized sample of 749 rural households in Malawi. The context of rural Malawi represents an extremely interesting setting due to the high HIV/AIDS prevalence that has inevitably shaped the social structure of many households. Moreover, the cash transfer pilot project used in the analysis represents one of the first experiments of social protection policies based on unconditional cash transfers in Sub-Saharan Africa. We exploit the unique source of exogenous variation provided by the randomized component of the program in order to isolate the effect of cash transfers on consumption expenditures as well as the net crowding out effect of cash transfers on private arrangements. We find a large effect in the level of variation of consumption expenditures for those households receiving the cash transfer. The effect is statistically significant across all specifications. The magnitude of the coefficient varies on the basis of the distribution’s quantile, showing that households in the .25 quantile of the variation of consumption’s distribution (i.e. those households that, in the absence of the cash transfer, would have shown a smaller variation in consumption expenditures) are those who register the highest increase in their consumption expenditures, while households in the .75 quantile increase their expenditures less (as they would have probably increased it in any case). These findings confirm the rejection of the perfect risk sharing hypothesis and suggest that there might be an important role for public interventions that might help households to pool risk more effectively. Consequently, the government’s initiative of implementing a public cash transfer program seems to be well justified by the type of risk sharing arrangements and market imperfections existing at the village level.

However, in order to test how such public interventions interact with pre-existing private arrangements at village level we have also looked at the effects of cash transfers on private transfers in a context where the social fabric is already heavily compromised by the presence of high HIV/AIDS rates,
>|0.05<br>0.06|0.11|0.08|0.11|0.13|

_Note:_ Data for 41 countries from 2010-18. _t_ statistics in parentheses. Dependent variable is wife's housing ownership (sole and joint). Coefficients after OLS estimation. Standard errors clustered at the country level.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. _Source:_ DHS.

20
Henderson, J.V. (2003) ‘The urbanization process and economic growth: the so-what question’, Journal of Economic Growth 8: 47-71.

Holl, A. (2004).’Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain’, Regional Science and Urban Economics, Elsevier, vol. 34(3), pages 341-363, May.

Hsieh, C, Klenow, P. (2009) ‘Misallocation and Manufacturing TFP in China and India’, The Quarterly Journal of Economics 124(4):1403-1448.

Hsieh, C, Klenow, P. (2014), ‘The Lifecycle of Plants in India and Mexico’, Quarterly Journal of Economics 129(3):1035-1084.

Hulten, C.R. (1996) ‘Quality Change in Capital Goods and Its Impact on Economic Growth’, NBER Working Papers 5569, National Bureau of Economic Research.

Jacobs, J. (1969) The Economy of Cities. Cambridge: MIT Press.

Jedwab, R. and Moradi, A. (2013) ‘Transportation Technology and Economic Change: The Impact of Colonial Railroads on City Growth in Africa’, CSAE Working Paper Series 201317, Centre for the Study of African Economies, University of Oxford.

Klapper, L. ,Laeven, L. and Raghuram, R. (2004) ‘Business environment and firm entry : Evidence from international data,’ Policy Research Working Paper Series 3232, The World Bank.

KPMG (2012-2013) Ethiopia Fiscal Guide 2012-13.

Krishnan, P. and Shaorshadze, I. (2012) ‘Technical and Vocational Education and Training in Ethiopia’ International Growth Center.

Lall, S.V., Shalizi, Z. and Deichmann, U. (2004) ‘Agglomeration economies and productivity in Indian industry’, Journal of Development Economics 73 (2): 643-673 Marshall, A. (1890) Principles of Economics. London: Macmillan Marshall, A. (1919) Industry and Trade. London:
15 Despite pessimistic expectations for the apparel sectors post-MFA, both countries continued increasing apparel exports, though growth slowed somewhat. Sri Lanka expected that exports would decrease by half and that 40 percent of firms would close in 2005 (Kelegama and Epaarachchi 2002). Sri Lankan apparel exports, however, grew 6 percent annually on average, and their value increased by approximately US$1 billion over 2005–08. In Cambodia immediately after the MFA removal, total apparel exports increased to US$2.7 billion in 2005 and to US$5.6 billion in 2011, a rise of almost 14 percent annually. The fact that these two countries continued to be important apparel producers makes them interesting cases for our study.

# **3.2. Unit Values**

In the neoclassical model presented in section 2, output prices drive wages. We use unit values as a proxy for output prices,<sup>15</sup> following Harrigan and Barrows (2009). Our unit value data come from both OTEXA and Comtrade, two well-known official sources of apparel trade data. We compute weighted averages of unit values by dividing total value by a common quantity measure (square meter equivalent in the United States data and kilograms in the European data). We use data from both the United States and the EU because the end markets of both areas are highly concentrated, with 87 to 90 percent of total Cambodian and Sri Lankan apparel exports going to those two destinations. Export products in both Cambodia and Sri Lanka are highly concentrated in a few items. The Sri Lankan apparel industry focuses on higher value-added products, such as lingerie. Unit values of apparel exports from India and Sri Lanka to the EU are higher than those of Asian competitor countries, including Bangladesh, Cambodia, China, Pakistan, and Vietnam (Tewari 2008).
selected are cataracts, pneumonia, coronary bypass, appendectomy, and hip replacement. These 5 procedures were selected because they were common procedures, a mix of different types of cases, and the procedure codes existed through all the different payment methods. Four are surgical, and pneumonia is a medical procedure. Table 2 shows how the procedural codes correspond across the different payment systems from 20002009.<sup>5</sup> Hospitalization claims data over 10 years (2000-2009) were collected from HZZO for

> 4 A few studies have used a differences-in-differences approach between treatment and comparison providers such as Yipp and Eggleston (2001) and Dafny (2005). Serra and Wagstaff (2010) use a differences-indifferences approach to examine treatment and comparison countries.

> 5 Pneumonia did not have a code in the 2002-2005 PPTP period, so codes for pneumonia were based on the World Health Organization International Classification of Diseases and Related Health Problems (ICD). Fortunately, our analysis focuses on the impact of the PPTP period beginning in 2005, when all five procedures had PPTP codes.

5
, are controlled. Noteworthy, the specific product-market-related factors which appear as significant differ between the two country groups: In Eastern and Southern Europe, these are Investment Freedom and Trade Freedom, whilst in Western and Northern Europe - Business Freedom and Monetary Freedom; when all countries are pooled together, it is Business Freedom which matters.

The share of non-contracted employees is negatively affected by perceived quality of public services (represented by Government Effectiveness, Satisfaction with the Government,

> 30 A sign in parentheses refers to a marginally insignificant effect ( _P_ -values between 0.10 and 0.13).

> 31 Table 10 also provides comparisons with the results of four other studies where the dependent variable is one of the following proxies for informal employment: (i) share of labor force not contributing to the pension system; (ii) share of self-employed in the labor force; (iii) share of workers without a contract or with a temporary contract. The results reviewed in Table 10 refer to: Fialova and O. Schneider (2011) – for a panel (2006-2007) of 25 European countries, using all three proxies;  Perry et al. (2007) and Loayza et al. (2009)  - for cross-sections of 57 to 70 world countries, using proxies (i) and (ii); Loayza and Rigolini (2006) – for a panel (ending in 2004) of 42 world countries and a sub-panel of 18 developing countries, using proxies (i) and (ii).

29
# **1. Introduction**

The informal sector provides livelihoods and jobs to millions of individuals around the globe. According to ILO (2018), about 70 percent of the labor force in the emerging and developing economies is employed in the informal sector. Despite this contribution, there is concern that competition from informal sector firms may hurt the growth and development of the formal sector firms. For instance, it is argued in the growth literature that more investment in R&D stimulates technological progress, which provides the foundation for the bulk of economic growth around the world (Grilliches 1998, Acemoglu 2009). A systematic and rigorous analysis of the impact of informal sector on R&D activity by formal sector firms, however, is lacking.

The present paper attempts to fill this gap in the literature by estimating the impact of competition from informal sector firms faced by formal sector firms (henceforth, informal competition) on the formal firms’ likelihood of spending on R&D activity. It does so by using firm-level survey data on small and medium enterprises (SMEs) in the formal manufacturing sector of a large cross-section of mostly developing countries. Our results show that greater informal competition leads to a significantly higher likelihood of firms spending on R&D activity. This is consistent with the broader literature that suggests that greater competition forces firms to innovate to differentiate their product and cut costs in order to survive. Further, we find that the positive impact of informal competition on the likelihood of R&D spending by formal firms (henceforth, R&D activity) is much larger (more positive) in countries where the cost of operating in the formal vs. the informal sector is higher and the benefits lower. This finding is along expected lines and as per the predictions of the “parasite” view of informality. As we argue below, it serves as an important check against endogeneity concerns with our estimation.

1
|**_Depend_**|**_ent variable:_**<br>(1)|**_Standardiz_**<br>(2)|**_ed PPVT_**<br>(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Panel A. OLS and IV second-stage estimations_|OLS w<br>variables|ith PDS-se<br>and full reg|lected<br>ressor set|IV with P<br>and f|DS-selected<br>ull regress|variables<br>or set|
||MHI-1|MHI-2|MHI-3|MHI-1|MHI-2|MHI-3|
|Maternal Mental Health (-)|0.006|0.006|0.008|-0.065|-0.032|-0.034|
||(0.0471)|-0.0233|(0.0253)|(0.501)|(0.245)|(0.265)|
|Observations|1,095|1,095|1,095|1095|1095|1095|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|
|_Panel B. First-stage estimation_||||MHI-1|MHI-2|MHI-3|
|Shock: Crop or livestock loss||||0.338***|0.689***|0.639***|
|||||(0.091)|(0.184)|(0.172)|
|Observations||||1095|1095|1095|
|Mother
top-left graph. Such configuration is rare in our sample, as we shall further discuss in Section V.<sup>14</sup> At the other extreme, in economies with very low poverty rates (bottom-right graph), the effect of raising inequality on poverty is virtually negligible, and its impact on growth, which is shown to be slightly negative, occurs only through the decreasing returns to scale in the production function.

In the intermediate case of poor (but not extremely poor) economies shown in the top-right and bottom-left graphs, the impact of inequality on poverty is always positive, and hence its indirect contribution to growth is negative, more so the higher the prevailing poverty rate. In turn, the direct effect of inequality on growth (i.e., the second term in (11)) is almost zero, due to the mutually opposing effects discussed above. As a consequence, the overall impact of inequality on growth is negative, due to the indirect effect of inequality that accrues through poverty. Finally, it is worth noting that these conclusions hold regardless of the prevailing level of inequality, at least for values of the Gini coefficient in the range between 0.2 and 0.8 – which covers 99% of our empirical sample, as shown in Section III.

# **III.- Growth, inequality and poverty: Empirical implementation**

We turn to the empirical strategy for analyzing the relationship between growth, inequality and poverty. First, we describe the data we use. Second, we present the baseline growth equation and examine the correlation between the key variables in the model.

# **III.1. Data**

We use an extended version of the poverty database constructed by López and Servén (2009). Details are given in the Appendix. In brief, given the limited availability of survey-based poverty data, we construct the headcount ratio, P0, the poverty gap, P1, and the squared poverty gap, P2, using a lognormal approximation
2013; Verduzco-Gallo, Ecker, & Pauw, 2014). Even with a low maize prices and higher incomes that can support wider food purchasing, there is still a lack of variety in consumption diets (Ecker & Qaim, 2011; Matita et al., 2023). Similarly, production patterns have not changed much with incentives for growing nutrient-rich crops such as legumes (Ortega et al., 2016; Waldman et al., 2017). Addressing hunger often takes precedence over diversifying diets which is linked to micronutrient deficiencies and ensuing health effects (Nuss & Tanumihardjo, 2010; Poole et al., 2021; Verduzco-Gallo et al., 2014). For instance, 37 percent of children under the age of five are stunted, only 25 percent meets the minimum dietary requirements, and female obesity is on the rise, a contributing factor to non-communicable diseases (NSO & ICF, 2017). Depending on context and the measure used, the evidence regarding the effects of AISP on food and nutrition security has also been mixed. While some studies (Chakrabarti et al., 2024; Harou, 2018; Mason et al., 2020; Matita et al., 2022; Mwale et al., 2022; Snapp & Fisher, 2015; Tione et al., 2022) show positive effects, others (Hemming et al., 2018; Karamba, 2013; Ragasa & Mazunda, 2018; Jacob Ricker-Gilbert & Jayne, 2017; Walls et al., 2018) report weak and inconsistent results. Increasingly, more marginal lands are being cleared for the cultivation of maize and soil health is at risk because the subsidies are not tied to any initiative to improve the environment (Holden, 2018). Thus, the effectiveness of standalone AISP is in question. This study therefore examines whether integrating input subsides with other interventions, such as irrigation schemes and social transfers, can result in improved food and nutrition security indicators (dietary diversity, food consumption scores, coping strategy index).

The combined effects of
Missing weather data combined with the way in which treatment variable is defined results in a fuzzy RD, so the treatment effect is estimated via two-stage least squares. Specifically, we estimate the following equations: Equation 1 estimates the first stage by regressing treatment, _Tmct_ , on _Zmct_ = **1** _{Xmct <_ 0 _}_ , an indicator for falling below the threshold amount of rainfall. Equation 2 is the reduced form, which estimates the effect of falling below the threshold on the outcome of interest in the following year, _ymct_ +1. Thus, the coefficient of interest is _β_ 2 _SLS_ =<sup>_<u>β</u>_1</sup> . For regressions where we have observations _β_ 0 at the crop level, we include crop fixed effects. The analysis is restricted to insurable crops (i.e., maize, beans, sorghum and barley) unless we explicitly note otherwise. Household level regressions using ENIGH data are estimated with a similar set of regressions, where observations are no longer indexed by crop _c_ , but are now indexed by _h_ to indicate an individual household. In keeping with the regression discontinuity design, these regressions are restricted to a sample of data within a narrow bandwidth of the normalized threshold of zero.

# **4 Results**

## **4.1 Main results**

We begin by looking at some descriptive statistics for the analysis sample in table 1. The analysis sample for the agricultural outcomes is composed of 976 unique municipalities over 8 years with 4,311 total observations. As mentioned before, the way in which the ENIGH sample is constructed results in a smaller number of unique municipalities for our economic regressions

> (192)<sup>5</sup> but multiple observations per municipality for a total sample size of 5,879. In panel A, we

> 5Given the large number of municipalities that get dropped from the ENIGH panel, we present summary statistics for the