rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4: Relationship between the extent of firm innovation and the sources of funding of fixed assets**

|**Variables**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|**% Fixed assets funded by banks**|0.0645**|0.0643**|0.0707**|0.0660***|0.0820***|0.0717***|
||(0.0274)|(0.0259)|(0.0298)|(0.0229)|(0.0268)|(0.0235)|
|**% Fixed assets funded by NBFI**|0.0156|0.1004*|0.2196***|0.1470**|0.2325***|0.1518*|
||(0.0494)|(0.0569)|(0.0711)|(0.0740)|(0.0739)|(0.0826)|
|**% Fixed assets funded by other**|0.0258|0.0539*|0.0749**|0.0812***|0.0693**|0.0764***|
||(0.0343)|(0.0310)|(0.0309)|(0.0247)|(0.0321)|(0.0266)|
|**Log (employees)**||0.0513***|0.0493***|0.0463***|0.0522***|0.0487***|
|||(0.0085)|(0.0088)|(0.0075)|(0.0088)|(0.0078)|
|**log (labor productivity)**||0.0229***|0.0327***|0.0226***|0.0304***|0.0235***|
|||(0.0052)|(0.0082)|(0.0064)|(0.0081)|(0.0066)|
|**Age**||0.0001|0.0000|0.0002|-0.0002|0.0002|
|||(0.0007)|(0.0007)|(0.0005)|(0.0007)|(0.0005)|
|**Percentage of domestic ownership**||-0.1111***|-0.1136***|-0.1059***|-0.1270***|-0.1100***|
|||(0.0389)|(0.0389)|(0.0317)|(0.0425)|(0.0374)|
|**Country FE**|||Yes|Yes|Yes|Yes|
|**Year FE**||||Yes||Yes|
|**Sample**|MRES|MRES|MRES|Full|MRES|Full|
||||||No HInc|No HInc|
|**Observations**|11,335|11,335|11,335|17,276|10,263|15,572|
|**Number of countries**|103|103|103|104|91|92|
|**R-squared**|0.003|0.0753|0.1249|0.1447|0.1260|0.1471|

Note: This table reports the results of the estimation of Equation (2). The dependent variable is the innovation score, as described in Table 1. In columns (1), (2), (3) and (5) we include data on the last survey available for each country. In columns (4) and (6) we employ the full sample. In columns (5) and (6) we exclude high-income countries. In the full sample (column 4) we can include in the analysis Ethiopia for which we do not have all the relevant information for MRES. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

We run regressions in Table 4 including as a dependent variable a firm adoption score. Results are in line with Table 4 and denote a strong association between a firm financial structure and innovation (see Figure 3). Once more the magnitude of the coefficients on the NBFI funding suggests that the development of financing from non-bank financial intermediaries could bring larger benefits than other sources of funding.

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|**Country**|**Formal**<br>**(% of**<br>**the**<br>**labor**<br>**force)**|<br>**Informal**<br>**Modern**<br>**(% of the**<br>**labor**<br>**force)**|**Informal**<br>**Rud.**<br>**(% of the**<br>**labor**<br>**force)**|**Modern**<br>**Informal**<br>**Wage /**<br>**Expected**<br>**Wage in the**<br>**Modern**<br>**Economy**|**Modern**<br>**Informal**<br>**Wage/**<br>**Min.**<br>**Wage**|**Informal**<br>**Rud. Wage /**<br>**Min. Wage**|
|---|---|---|---|---|---|---|
|Panama|62%|10%|28%|0.42|0.39|0.51|
|Peru|22%|32%|46%|0.61|0.39|0.35|
|Philippines|27%|34%|39%|0.59|0.39|0.37|
|Poland|77%|6%|17%|0.41|0.39|0.53|
|Portugal|86%|‐1%|16%|0.38|0.39|0.56|
|Paraguay|14%|44%|41%|0.72|0.39|0.30|
|Romania|65%|5%|30%|0.40|0.39|0.53|
|Rwanda|5%|18%|77%|0.75|0.39|0.29|
|Sudan|12%|37%|51%|0.72|0.39|0.30|
|Senegal|10%|37%|52%|0.74|0.39|0.29|
|Singapore|60%|30%|9%|0.49|0.39|0.44|
|Sierra Leone|7%|5%|88%|0.53|0.39|0.40|
|El Salvador|24%|39%|37%|0.62|0.39|0.34|
|Sweden|84%|10%|6%|0.41|0.39|0.52|
|Chad|6%|11%|83%|0.64|0.39|0.34|
|Togo|7%|12%|81%|0.64|0.39|0.34|
|Thailand|22%|25%|53%|0.57|0.39|0.37|
|Tunisia|62%|14%|24%|0.44|0.39|0.49|
|Turkey|57%|14%|30%|0.44|0.39|0.49|
|Tanzania|5%|12%|83%|0.70|0.39|0.31|
|Uganda|12%|10%|79%|0.54|0.39|0.40|
|Uruguay|75%|4%|21%|0.40|0.39|0.54|
|United States|87%|7%|6%|0.41|0.39|0.53|
|Vietnam|21%|18%|61%|0.54|0.39|0.40|
|South Africa|17%|75%|8%|0.77|0.39|0.28|
|Zimbabwe|22%|12%|66%|0.50|0.39|0.43|

_Note_ : All initial conditions correspond to averages from 2008‐2012, when available. The ratio of the modern informal wage to the minimum wage is constant as it is a function of parameters _a_ , ζ, and �, which are assumed to be constant across countries in the initial period. For sources and descriptions, see Table 1.

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that DrumNet will automatically deduct from each future marketing transaction. Maximum loan size is four times their balance in the TIF. The initial TIF amount depends on the specific crop the farmer wants to grow and the area under cultivation.<sup>4</sup> To ensure repayment, DrumNet organizes farmers into groups of 5 members each who are jointly liable for the individual loans taken out. At harvest time, DrumNet negotiates price with the exporter and arranges the produce pick-up at pre-specified collection points. Usually, there is a collection point for every 4 or 5 SHGs. In each collection point, a transaction agent is appointed among the members to serve as liaison between DrumNet and the farmers.<sup>5</sup> At these collection points, farmers grade their produce and package it, although the exporter has the final word on the grading.<sup>6</sup> In the credit-treatment group, DrumNet also works with local agricultural retail stores to coordinate the in-kind loans. The retailers are trained in basic DrumNet record keeping and submit receipts to DrumNet to receive payment.

Once the produce is delivered to the exporter at the collection points, the exporter pays DrumNet who in turn will deduct any loan repayment, pre-specified TIF percentage and credits the remainder to individual bank savings accounts that each farmer opened 4 For example, passion fruit in one quarter of an acre requires an investment of Ksh 5,000 (USD 67) but does not bear fruit for 6 months. The initial TIF for passion fruit is Ksh 1,250. French beans and baby corn only require an investment of Ksh 3,000 per one quarter of an acre and harvesting takes place after 3 months. In Kirinyaga, both French beans and baby corn can be grown and harvested all year.

5 Transaction agents are responsible for coordinating activities within farmer groups. The number of these agents has expanded from approximately 10 in early 2004 to 35 in January 2005. One member of each new farmer group is nominated as the transaction agent, receives additional training, and serves as the main point of contact for DrumNet, facilitating the market transactions. These farmers communicate frequently with the DrumNet staff, both in person in the office and via mobile phones. They are an important conduit of information about pickup schedules, market prices, approved field practices, and shifting grading standards.

> 6 Anecdotal evidence suggest that some export buyers arbitrarily change the rejection rate especially in periods of oversupply (Okello and Swinton, 2007), but we have no evidence that the buyer from DrumNet engaged in such practices.

7
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||||_level either alone or combined_<br>_with competitive dummy_<br>The variables are used to create a<br>number<br>of<br>individual<br>and<br>interaction terms.|||
|**Zelner and**<br>**Henisz (2000)**|_H0: the effect of political_<br>_constraints on investment:_<br>_-when facing strong interest_<br>_group competition, a utility_<br>_will invest more (less) in the_<br>_presence of higher (lower)_<br>_political constraints, ceteris_<br>_paribus._<br>_-as the level of interest_<br>_group competition declines,_<br>_the positive (negative) effect_<br>_of political constraints on_<br>_investment becomes smaller_<br>_and may become negative_<br>_(positive), ceteris paribus._<br>_H1: the effect of interest_<br>_group competition on_<br>_investment:_<br>_-when operating in an_<br>_environment with relatively_<br>_low political constraints, a_<br>_utility will invest more (less)_<br>_in the presence of weak_<br>_(strong) interest group_<br>_competition, ceteris paribus._<br>_-as political constraints_<br>_increase, the positive_|-each country’s annual<br>growth rate of megawatts of<br>generation capacity per<br>capita (this is the measure<br>of investment used)<br>(PI)|**-political constraints variable**<br>(CL) – constructed from<br>information on the number of<br>independent branches of<br>government, adjusted to take into<br>account the extent of alignment<br>across different branches and of<br>preference heterogeneity within<br>each legislative branch<br>_Result: mostly significant at the_<br>_5% level_<br>**-interest group competition**<br>**variable**– simply the ratio of<br>industrial to total electricity<br>consumption (SE)<br>_Result: mostly significant at the_<br>_5% level_<br>**-existing penetration level**<br>**variable**– megawatts of<br>generation capacity per capita in<br>the previous period (PI)<br>_Result: mostly significant at the_<br>_10% level_<br>**-government’s capital**<br>**investment budget**(CL) –<br>measured as real annual gross US<br>dollar public investment on<br>capital spending per capita;<br>_Result: insignificant_|n.a.|Country-level panel data of<br>78 developed and developing<br>countries over the period<br>1970-1994. The countries of<br>the sample are grouped in<br>three categories: countries<br>facing high political<br>constraints, countries facing<br>low political constraints and<br>weak interest group<br>competition, and countries<br>facing low political<br>constraints and strong interest<br>group competition.|

56
but they are slightly more likely to be a government worker at 0.6 increased percentage points— the estimate is statistically significant at the 10 percent level. Eligible mothers are not more likely to be a private worker but are significantly more likely to be an unpaid family worker. An additional public preschool per 1,000 children increases the likelihood of being an unpaid family worker by 4.1 percentage points, which is statistically significant at the 1 percent level. On the other hand, we find that an additional private preschool per 1,000 children increases both the probability of holding a second job and being self-employed by 0.1 and 0.2 percentage points, respectively.

To account for multiple hypotheses testing, we employ the Benjamini-Hochberg step-up method to control for the false discovery rate (FDR) (Benjamini and Hochberg 1995). A regular p-value of 0.05 suggests that 5 percent of all tests result in false positives. An FDR adjusted q- value of 0.05 instead suggests that 5 percent of _significant_ tests result in false positives. We note that the government worker outcome is not robust to the simultaneous inference correction, but the unpaid family worker outcome is—it is still significant at the 1 percent level. None of the statistically significant effects of private preschool are still statistically significant with the FDR adjusted q-value.

We turn to Table 7 for occupational changes. We find that higher public preschool access leads to a 2.3 percentage point increase in the likelihood of being employed as an agricultural worker.<sup>19</sup> Being the only statistically significant outcome in seven tests, we find that it is almost robust to the Benjamini-Hochberg correction, with an adjusted q-value of 0.108. Meanwhile, higher private preschool access suggestively leads to the higher probability of being employed as a manager and a production worker. The first, however, does not stand the test of false discovery, while the latter does.

Table 8 lists the top 3 occupations per occupation category.<sup>20</sup> Agricultural and animal husbandry workers, planters and farmers, and forestry workers make up a 97.74 percent share of agricultural workers. Tailors, food and beverage processors, and spinners make up an 84.7 percent share of production workers. These occupations represent traditional occupations in the informal sector that do not require a full-time commitment from preschool-aged eligible mothers. Better

> 19 Agricultural workers do not have to be paid. In fact, 49.39 percent of females working as agricultural workers are unpaid family workers.

> 20 IFLS collects open-ended description of occupation and categorizes them in 2-digit occupation codes. We aggregate them to the 1-digit occupation headings to reduce the number of tests.

20
# **Appendix: Regression Results**

||**Primary E**<br>All countries|**nrollment**<br>Initial<br>enrollment<br>lower than 70<br>percent|**Child Mortal**<br>All countries|**ity (under 5)**<br>Initially higher<br>than 50 per 1000<br>births|
|---|---|---|---|---|
|_t_|0.040***|0.088***|0.042***|0.044***|
||(0.008)|(0.010)|(0.001)|(0.002)|
|_Constant_|1.838***|-0.123|1.123***|0.291***|
||(0.079)|(0.098)|(0.013)|(0.019)|
|_Country-fixed effects_|Yes***|Yes***|Yes***|Yes***|
|R-squared within|0.10|0.49|0.70|0.67|
|Number of observations|367|125|723|434|
|Number of countries|116|38|145|87|

Source: Author’s calculations. Notes: Standard errors in parentheses. * p<0.05, ** p<0.01, *** p<0.001.

32
percent for females), and to have a more significant role in the household decision making. Women’s spouses were much more likely than men’s to be in wage employment (30 percent versus 5 percent).

At baseline, over 60 percent of firms saved money in some form of an account, with 57 percent using a bank account. This is considerably higher than the average bank account usage of 22 percent in a national survey of MSME owners in Malawi (Finscope, 2012). However, almost all of these bank accounts were personal accounts, as only about 2 percent of the firms (selfreported) had access to a business bank account at baseline (which is consistent with the fact that business registration is almost always a pre-condition for opening an account in the name of the business). In our sample, women were more likely to use saving mechanisms than men, including bank accounts (60 percent for women vs 55 percent for men), but also informal mechanisms such as ROSCAs and SACCOs<sup>13</sup> (12 percent vs 5 percent). Mixing of household and business finances is common, with 78.5 percent saying they take business money whenever required for household needs<sup>14</sup> .

Although use of a bank for (personal) savings is relatively common, the use of bank loans is rare, with only 7.3 percent of entrepreneurs having had a bank loan used for business purposes in the past. On average, the most recent loans had an initial maturity of less than five months for both male and female-owned enterprises. For firms that obtained credit in the past, 42 percent of the most recent loans did not require collateral. When collateral was needed, business owners primarily used cash deposits, followed by household assets and group-lending. These findings confirm that most loans were small in size. The proportion of entrepreneurs having been denied credit was similar for men and women - 19 and 17 percent respectively of male and female entrepreneurs that have applied in the past 12 months. Taken together, these baseline data do not suggest that women are more disadvantaged than men when it comes to access to finance, especially given that female-owned businesses are smaller on average than male-owned firms.

> 13 ROSCA - Rotating Savings and Credit Association; SACCO - Savings and Credit Co-operative.

> 14 The question asked was: “Do you take money whenever needed (every day, every other day, etc) from the business to pay your own or your family expenses?”.

12
whom we have information on sex, age, marital status, etc.? What types of assets are included? How is ownership defined? Which rights are identified? Does the survey allow us to distinguish individual and joint ownership? How were the assets acquired? And finally, does the survey collect data on the value of assets?

# **3.1 Gender Asset Gap Project**

The Gender Asset Gap project was initiated in 2009 to demonstrate the importance and feasibility of collecting nationally representative, individual-level data on physical and financial assets. The project collected data that were nationally representative for Ghana and Ecuador and representative at the state level for Karnataka, India. The primary goal was to measure asset and wealth gaps between men and women. The data collection included both qualitative fieldwork and a household asset survey. In the qualitative phase, the key themes of focus group discussions were the accumulation of assets over the individual life cycle, the importance of assets, the market for assets, and household decision-making over asset acquisition and use. In combination with key informant interviews and a literature review of legal, marital, and inheritance regimes, this work formed the basis for adapting the household survey template to the three specific contexts.<sup>7</sup> The household asset surveys, conducted between May 2010 and January 2011, included two survey instruments. The first instrument created a roster of all assets owned by anyone in the household, including dwelling, agricultural land, other real estate, livestock, agricultural implements, non-farm businesses, consumer durables, and identified the owners of each listed asset.<sup>8</sup> In Ghana and Karnataka, the person who was the most knowledgeable about the assets owned within the household was chosen as the respondent for the first instrument. In Ecuador, the primary couple was interviewed together whenever possible. The second instrument was administered separately to two adult members of the household and collected additional information including ownership of financial assets and rights and decision-making over assets.

The unit of analysis was the asset, with information collected on the owner or owners of each asset. Up to three owners could have been listed for each asset or it could have been coded “owned by all household members.” Because the owners are identified with an ID number that can be matched to the household roster, it is possible to compile the information at the individual-level, identifying the assets owned by each household member and associating them with the owner’s characteristics.

> 7 For detailed information on the survey methodology and the lessons learned, see Doss et al. (2011). 8 Additional information was collected in each of the surveys, but the placement and content varied across the three countries and is less relevant to this analysis.

8
the other debts actually decreased as a percent of GDP, being responsible for the remaining

# 5.0% of the federal bonded debt growth.<sup>20</sup>

We now turn to the decomposition of each of these three factors: the federal deficit, the assets accumulation, and the repayment of other debts.

# **_111.1_** _Financing of thefederal government (+ Central Bank) deficit_

In order to make the net debt variation of the period (R$165,422 or 13.1% of the GDP) compatible with the nominal deficits registered during the same period, it is necessary to make three adjustments. The first is to add the states', municipalities' and state-owned enterprises' net debt variation.

The second adjustment recognizes the privatization revenues. Since privatization revenues occur only once, they are not included as current revenue in the public deficit computation. Nevertheless, they are financial inflows public revenues which, _ceteris paribus,_ would lower the net debt (the state-owned enterprises that were sold were not previously included in the public sector assets). Under the hypothesis that everything else stayed constant, and assuming that all the privatization revenues were used for public debt redemption, the gross debt would diminish by the exact amount of these privatization revenues. Therefore, we have to add these revenues to the variation of the total net debt in order to obtain the debt variation concept that best conforms to the public deficit statistics.21 20 In Table 111.3, all figures are in R$, while in Table 111.4, the figures are in percent of GDP. Therefore, given that nominal GDP grew during the four-year period, the fact that "Other Debts" increased in nominal terms, while they decreased as percent of GDP is an indication that the increases occurred more to the end of the period relatively to the decreases.

**21** In reality, the relation between privatization and public debt is much more complex for at least two reasons. The first is that when a state-owned enterprise is sold, its debts are transferred to the private sector, diminishing the net debt by a value greater than the revenue of the privatization. The second reason is that

18
the likelihood of protection being successfully implemented is low (McMichael et al., 2020). We did not include sea level rise in our review because much analysis on this topic makes use of _modeled_ projections of exposure rather than retrospective empirical analyses using historical data (Neumann et al., 2015b; Davis et al., 2018).<sup>6</sup> Fourth, future research should look at alternative outcomes, such as survival or risk management migration, voluntary vs involuntary migration, jointly rather than separately, to improve our understanding of the response heterogeneity with respect to wealth and income. More generally, gaining a more systematic understanding of the irreducible heterogeneity of the climate-migration nexus should be considered as a primary task for future research (Hoffmann et al., 2020). Fifth, more research is needed on the role played by institutions and policies in ‘interfering’ with the decisions to migrate in response to climatic stress. Development policies can affect migration outcomes in a way which is difficult to know _a priori_ , as they could either facilitate or inhibit migration depending on the type of intervention and the related welfare outcome. For instance, while local investments in climate-resilient infrastructures or in the development of early-warning systems may reduce the need to migrate and improve _in-situ_ adaptation, social protection interventions or emergency responses alleviating weatherinduced liquidity constraints may make voluntary migration possible. This is especially important in order to provide evidence-based recommendations on national and international climate migration policies.

# _2.3. A closer look at Sub-Saharan Africa_

Finally, bearing in mind the major insights from the main review, we take a closer look at recent studies with an exclusive focus on Sub-Saharan Africa (SSA), one of the parts of the world indisputably more vulnerable and exposed to climate change (IPCC, 2014) and the area where LSMS-ISA surveys (which we use as benchmark in the assessment of data gaps in household survey data to understand the climatemigration nexus) are currently implemented.

A work by Lilleør and Van den Broeck (2011) in the northern highlands of Ethiopia revealed that environmental stress shapes migration primarily through impacts on household production. Di Falco, Veronesi and Yesuf (2011) carry out a study based on a survey conducted on 1,000 farm households located within the Nile Basin of Ethiopia in 2005. They find that about 58% and 42% of farm households had used no adaptation strategies in response to long-term changes in temperature and rainfall, respectively, and that migration is one among many adaptation strategies, adopted by less than 5% of

> 6 For a recent review of the literature on population exposure to sea level rise and migration, see <mark>McMichael et al. (2020).</mark>

13