rafmacalaba/datause-encoder-data huggingface dataset:
## **6.2 Implications for the Revised Network**

Figure 8 and Table C1 summarize how alternative assumptions about informal firms affect the distribution of outlinks across counties. If we assume informal firms source half of their inputs from the formal sector but have no formal buyers, inequality in county-level outlinks declines by 16% compared to our model-predicted network. This represents an 8.5 percentage point larger reduction than our default scenario where small formal and informal firms share similar linking patterns. The share of total outlinks accounted for by Nairobi- and Mombasa-based firms now drops down to 69%, down from 80% in the baseline network and 75% in the first scenario with informal firms (see Table 5).

Allowing informal firms to sell 25% of their output to formal firms, instead of none, has little implication for both the overall decline in inequality and Nairobi’s share of outlinks. Allowing for a larger share of informal firms’ inputs to be sourced from the formal sector results in a smaller reduction in inequality (11%) and a higher share of Nairobi- and Mombasa-based links (73%), more closely matching the initial network with informal firms. This pattern is driven by the greater reliance of informal firms on formal suppliers in this scenario, which in turn tend to locate in counties with larger formal economies.

Our simulations of domestic and international trade shocks under these alternative assumptions reinforce our earlier findings, showing even stronger relative effects in sectors and counties with high informality (Figure C6). This more pronounced impact stems from increased intra-county linkages among informal firms. While more prominent local linkages reduce spatial inequality and urban concentration, they amplify the impact of domestic shocks through stronger withincounty multiplier effects.

# **7 Conclusion**

Firm-to-firm transaction-level data sourced from tax records have become a valuable tool for mapping domestic firm networks and trade flows. Aside from studying the micro dynamics of firm-to-firm relationships, they allow researchers to compute often hard-to-observe flows of goods and services across regions within national economies, which can be leveraged to estimate the welfare implications of policy interventions. This paper shows that applying such data in settings with high informality can pose important challenges. We find evidence that formal sector trade patterns provide a skewed representation of overall economic activity: they are more spatially concentrated and underestimate intra-regional trade in favor of trade with urban hubs.

We incorporate informal firms into a structural model of network formation leading to a revised 35
It is found that all estimates of the model parameters make economic sense (have expected signs). Given the controlled variables, ethnic minorities still have lower per capita expenditure than Kinh and Hoa people. Households of large size are more likely to have lower per capita expenditure than households of small size. As expected, assets are positively correlated with per capita expenditure. Households who have more working members or members with vocational training tend to have higher expenditure. Finally, the R-squared values are quite encouraging with the range from 0.43 to 0.7.

# _IV.3 Poverty estimates_

# _Regional estimates_

Table 1 presents the estimates of the poverty incidence of the eight rural regions. It shows that the estimates from the small area estimation exercise are very close to the estimates based on the 2006 VHLSS (both for the large and small models). While we observe a noticeable difference for the Central Highlands, the difference is not statistically significant. The standard errors for the Central Highlands estimates based on the 2006 VHLSS are rather large due to the small number of observations.

The poorest region is the North West with a poverty rate of above 50 percent. In regions with low levels of poverty, about 10 percent of the rural population lives below the poverty line.

**Table 1 The poverty incidence estimates of regions**

|Region<br>VHLSS 2006|Small area estimation|
|---|---|
|Red River Delta<br>11.0|11.3|
|[1.1]|[0.9]|
|North East<br>29.9|31.6|
|[1.8]|[1.6]|
|North West<br>56.4|57.3|
|[3.7]|[2.6]|
|North Central Coast<br>33.1|32.9|
|[2.4]|[1.7]|
|South Central Coast<br>17.1|17.8|
|[2.1]|[1.2]|
|Central Highlands<br>34.4|39.9|
|[3.7]|[2.0]|
|North East South<br>9.9|10.1|
|[1.5]|[0.9]|
|Mekong River Delta<br>11.8|12.6|
|[1.0]|[1.3]|
|_Standard error in the brackets_||

_Source: Authors’ estimation from VHLSS 2006 and ARFC 2006_ 10
~~~~~~~~63 ______

# TABLE 4: Dlstributlon of Farm Enterprises by Tenure Category, 1981.

## (a) Number of farms

Private Ejido Mixed Total All farmn 1,003,374 2,099,038 189,668 3,292,100 Farms of 5 hectares or less 565,846 1,220,058 120,824 1,906,728

## (b) Percent of farms by tenure category

||Private|Ejldo|Mixed|Total|
|---|---|---|---|---|
|All farms|30.5|63.8|5.7|100.0|
|Farms of 5|||||
|hectares or less|29.7|64.0|6.3|100.0|

(c) Percent of farms In each tenure category that occupy five hectares or less Private Ejido MIxed Total 56.4 58.1 63.7 57.9 SOURCE: VI Censos Agricola-Ganadero y EJidal,1981, (Cuadro 01) INEGI-SPP: Mexico D.F., 1988.
<!-- Start of picture text --> { . 4 We hee 7 paiorine<br>i R\ i thallens (> - ae RAR 7 ae<br>‘é wh i Sed 4 a. p<br>. ne Me ASS 2 y ‘% 5<br>. teU Wes : # r: TY ,<br>.\SS4 pasA sna eenGee 1 Prin a y. “hele ° oe :<br>Sansta ag 4 * sng’ AIFa2 (Poo.<br>ae Sue sagteek” PA eo —c<br>e* at M4 x Karimnatae— . oy i, > i" et oe. Se . Veer ey :<br>Loray, StelFh ial RES ,<br>ggg INDONESIA Susie oe I vty<br>ane cn Se & ca ‘é& 7 ( or<br>fRT Seine ne, 7 S eea *¢ a‘arpentariaIf of =)a<br>J Fi : ; Northetn bs %<br>land),Mapmyindia,© OpenSteetMap contributors, and the GIS User Communit met ong Kong), Esti<br><!-- End of picture text -->
Equity Bank was more likely to expand to underserved districts than other types of banks between 2006 and 2009.

In our _second_ main set of results, we find that the presence of Equity Bank branches has a strong positive impact on the residents’ likelihood of having access to both bank accounts and credit. For example, our regressions imply that the penetration of Equity Bank into a new district increases the probability of a household having a bank account between 4 and 9 percentage points. Since the proportion of households that had a bank account rose from 14 percent in 2006 to 23 percent in 2009, the impact of Equity Bank on financial access appears to be economically meaningful. The positive effect of Equity Bank on financial access is particularly strong for individuals with low income and less education, without their own permanent house, and without a salaried job. These findings support the argument that the business model of Equity Bank, which targets underprivileged segments of the population _and_ bring in sustainable profits, has paid off in terms of greater financial inclusion.

We also find that the presence of domestic private banks (including Equity Bank), government banks and foreign banks is positively related to use of bank accounts at the district level. Interestingly, the positive impact of the foreign banks is the smallest among the three groups of banks. A potential explanation for these differences is that foreign banks traditionally have targeted the high-income segment of the population. These strategies are reflected in the differential requirements for opening a bank account. For instance, Equity Bank only requires an ID and a photo (which can be taken in a bank branch) to open a bank account. By contrast, foreign banks, such as Barclays, require a minimum balance of 20,000 Ksh. (about US$ 222), a prohibitive amount for the lowest-income segment of the population.

We undertake a number of steps to ensure that our main results are not driven by alternative factors and potential endogeneity biases. We include individual and household characteristics to control for potential changes in (survey) sample composition between 2006 and 2009, and district fixed effects to control for time-invariant district characteristics. We also include the number of bank branches, which can be viewed as a parsimonious control for time-varying district characteristics, such as economic development and bank competition. The most important, potential endogeneity bias stems from the fact that banks’ branch penetration is not a random event. In fact, we show that between 2006 and 2009 Equity Bank shifted its branching policy moving more toward less 4
Policy Research Working Paper 9305

# **Abstract**

This study investigates the impact of financial incentives and role of information and communication in textbook availability (especially those in local languages) in Zambia. It uses the difference-in-difference estimation method to identify the causal links among the factors. The data used for the study were collected in 2017 and 2019 for baseline and end-line information. The study shows that providing financial incentives to schools increases the likelihood of a school receiving textbooks by 0.126. This improvement is mainly driven by raising the likelihood of a school collecting the books from zone-center schools and District Education Board Secretaries offices, by 0.356 and 0.158, respectively.

Providing financial incentives to District Education Board Secretaries does not have any impact on the likelihood of a school receiving textbooks. This is partly because the current textbook delivery practice in districts and communities relies heavily on the action of schools and less on District Education Board Secretaries. Thus, providing incentives directly to schools seems to intensify the current last-mile textbook distribution practice and has better results. In addition, providing information to schools on the availability of textbooks at District Education Board Secretaries offices improves the outcomes significantly with minimum cost.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at xcao@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
at an EGS wage rate of Rs 40 the annual cost represents 3.7% of GDP. This rises to 4.9% at a wage rate of Rs 50. (The labor supply to the scheme and hence cost was virtually identical for the augmented specification.) However, we found that our estimates of the cost are quite sensitive to the cut-off point in predicted probability of doing casual labor used in identifying those likely to supply casual labor. While a cut-off of 0.5 is a natural choice (given that it identifies whether a person is more likely than not to do such work), it is worth checking sensitivity to a different cut-off. We find that lowering the cut-off point to a predicted probability of 0.4 raises the cost to the equivalent of 4.9% of GDP at the Rs 40 wage rate, and 6.8% at Rs 50, while raising the cut-off to 0.6 lowers the cost to 2.8% and 3.6% of GDP at the Rs 40 and Rs 50 wage rates, respectively.

What would the impact on poverty be if the same budgetary outlay was allocated on a uniform basis? We have seen that the EGS is targeted to the poor; in particular the mean gain rises as household expenditure per person of a household falls (Figure 1). By contrast, a uniform transfer gives the same amount to everyone, whether poor or not. So the EGS has a clear advantage in terms of targeting. This can be seen in Figure 4, which plots the mean percentage gain against household rank by expenditure per capita. However, this targeting advantage of EGS comes with extra costs, in the form of foregone incomes for participants and non-wage costs to the government. So the outcome of this comparison is unclear on _a priori_ grounds.

As described in section 2, to simulate an un-targeted transfer scheme we take the same estimated gross cost of the EGS and allocate it uniformly to all, whether poor or not. We assume that 10% of the budget available for transfers must be set aside to cover administrative costs.<sup>15</sup> We provide calculations for two assumptions about the extent of cost recovery from the assets

> 15 In a survey of administrative costs for transfer programs requiring individual assessment in Latin America, Grosh (1995) reports a median administrative cost of 9% of the budget.

20
# Mediating Variables

These are variables that might facilitate improvements in business performance as a result of improved mental health and well‐being of entrepreneurs. They include counterproductive work behaviors (CWB), social networking, perceived social support, individual entrepreneurial orientation (autonomy, risk taking, proactive decision making, information seeking, and so on), as well as overall work‐life balance. A brief description of each is provided below.

- **Perceived social support** was measured using the Multi‐dimensional Scale for Perceived Social Support (MSPSS). MSPSS measures perceived social support in three domains: family, friends and significant others (4 items each).

- **Psychological capital** was measured using the Psychological Capital Questionnaire (PCQ) 12 items version. The PCQ‐12 measures psychological capital across four domains (hope‐4 items, optimism‐2 items, resilience 3 items and self‐efficacy‐3 items).

- **Individual Entrepreneurial Orientation (IEO)** was measured using an adapted Individual Entrepreneurial Orientation (IEO) Scale (Sang and Lim 2009). Data were collected regarding the autonomy, innovativeness, risk taking, competitive aggressiveness, information seeking (pro‐active behavior) and impulsiveness to reflect the degree of change in behaviors over time.

- **<mark>Social networking</mark>** <mark>was measured using the composite scores of social networking with other business circles, as measured by the 4‐item Social Networking Questionnaire. The questionnaire tapped into 4 areas of entrepreneurial networking: 1) sharing of business ideas, new techniques or suppliers; 2) formation of connections with new potential customers, markets or other entrepreneurs; 3) gain of new customers, or financial support from other entrepreneurs; and 4) Sharing of tools, inputs, equipment or employees with other entrepreneurs.</mark>

- **<mark>Work‐life balance (WLB)</mark>** <mark>was measured using the composite scores of work‐life balance as measured by the 8‐item Work‐life Balance Questionnaire. WLB is described in the current study as the balance that an individual requires between the time allocated for work and other aspects of life.</mark>

- **<mark>Counterproductive work behavior (CWB)</mark>** <mark>was measured using the composite score of counterproductive work behaviors as measured by 4 counterproductive work behavior questions. CWB is non‐productive behavior of entrepreneurs that goes against the legitimate interests of an organization. As such, it can harm organizations or people in organizations, including employees and clients, customers. Entrepreneurs were asked whether they thought they should have stayed home, had angry outbursts, complained about insignificant things at work, or thought of leaving the business in the previous three months.</mark>

Business Performance Variables: Sales, asset turnover, employee turnover etc.

Since improvements in mediating variables and business performance are likely to take longer to manifest, these outcome variables will only be measured at endline.

28
strategy. In section 5, we present and discuss our estimation results, and section 6 concludes.

# **2. Data and Measurement**

The China Urban Labor Survey (CULS) was conducted at year-end 2001 by the Institute for Population and Labor Economics at the Chinese Academy of Social Sciences (CASS-IPLE), working with provincial and municipal government offices of the National Bureau of Statistics. The authors collaborated in the design and execution of the survey. The CULS was conducted in five cities: Fuzhou, Shanghai, Shenyang, Wuhan, and Xian. The cities were chosen to be broadly representative of China’s different regions. Fuzhou and Shanghai are coastal cities, Shenyang is in the northeast, Wuhan is in central China, and Xian is in western China. Three of the cities are among China’s six largest cities by population, and another ranks tenth. In each of the five cities, a representative sample of 700 households whose members were urban permanent residents (holding urban resident permits of the surveyed city) were surveyed.<sup>2</sup> Each household head was asked questions about the household, and then all household members above age 16 and no longer in school were interviewed individually. This resulted in 8,109 individual observations. Of these 8,109 individuals, 5,787 were younger than mandatory retirement age, and 4,076 were employed or reported labor earnings at the time of the survey. We exclude self-employed workers because their reported earnings reflect returns to capital as well as labor. We also exclude individuals born after 1978, because many could have still been in school at the time of the

> 2Within each city, a three-stage proportional population sampling approach was used to sample an average of 15 registered urban households in each of 70 neighborhood clusters (for details, see Giles, Park, and Cai, 2006) or a discussion of survey protocol on Giles’ website (http://www.msu.edu/~gilesj/Protocol.pdf ).  The survey had a non-response rate of 16.5 percent, of which 6.5 percent of households could not be found, 4.9 percent had moved, and 5.1 percent refused to be interviewed. This refusal rate compares favorably with the first round refusal rates of two influential surveys from transition and developing countries: the Russian Longitudinal Monitoring Survey (RLMS) and the Indonesia Family Life Survey (IFLS).

6
Treasuries, about 30 bps above the overall EMBI+ spread at end-2006.<sup>33</sup> Table 2 also presents the EMBI spread for Africa and the EMBI+ spread, which have been on a declining trend since 2002, attesting to the improved global interest rate climate, from which Kenya has also benefited. What is exceptional when Kenya’s experience is stacked against that of even emerging market countries is its ability to issue nominal (i.e., un-indexed) local currency debt at single-digit interest rates when countries such as Brazil and Turkey have had great difficulty in doing so in spite of running significant primary surpluses (far greater than Kenya’s) for several years in a row (Brazil since 1999). This indicates a high degree of macro policy credibility and the absence of “debt intolerance” (Reinhart, Rogoff and Savastano 2003). Further, Kenya’s debt structure is less vulnerable to external shocks as a result of the falling share of external debt over the past decade. Finally, Kenya’s debt dynamics are favorable and it has been growing impressively over the past three years in contrast to the debt sustainability problems and slow growth in many of the emerging market countries.

In January 2008, following post-election violence, S&P downgraded Kenya’s long-term local currency rating to B+ from its earlier level of BB- and long-term foreign currency to B from B+, five levels below investment grade.

5.2 Survey Evidence from the Investment Climate Assessment What would be the implications of greater confidence and lower country risk? One would expect to see firms more willing to reinvest profits instead of depositing these in an overseas bank account. One would also expect investment horizons to lengthen, so that the hurdle rate for investments goes down.<sup>34</sup> Indeed, the rising political and macroeconomic stability

> 33 The Emerging Markets Bond Index Plus (EMBI+) tracks total returns for traded external debt instruments in the emerging markets. In addition to serving as a benchmark, the EMBI+ provides investors with a definition of the market for emerging markets external-currency debt, a list of the instruments traded, and a compilation of their terms. The EMBI+  tracks instruments mainly from the three major Latin American countries (Argentina, Brazil, and Mexico), reflecting the size and liquidity of these external debt markets. The non-Latin countries are represented in the index by Bulgaria, Morocco, Nigeria, Philippines, Poland, Russia, and South Africa.

> 34 See section 2.

25
disproportionately better o ff than their counterparts in the LI region compared with the extent to which poor in the I region has been better o ff than their counterparts in the LI region. The curve showing the I-LI gap in 2005 rotated around the 55th percentile, making the gap almost fl at across all of the expenditure quantiles. Compared with 2000, the I-LI gap in 2005 has increased for all of the quantiles below the 55th percentile and decreased for all of the quantiles above the 55th percentile. For instance, the gap is about 16.4 percent at the 20th percentile and 16.2 percent at the 80th percentile. This implies that the poor (rich) in the I region has experienced a faster (slower) rate of consumption growth compared with their counterparts in the LI region.

The I region, being home to two main metropolitan areas in Bangladesh, is more urbanized than the LI region. One possibility for the observed change in the I-LI gap is that the poor in the urban areas may have gained more than proportionately in terms of consumption growth compared with the poor in the rural areas. Figure 3 illustrates the urban-rural gap in LRPCE in 2000 and 2005. As opposed to the I-LI gap, the urban-rural gap is monotonically increasing in consumption quantiles for both 2000 and 2005. More importantly, the gap has shifted downward in 2005 for all consumption quantiles.<sup>14</sup> This means that consumption of the rural population grew at a faster rate compared with urban population in both the I and LI regions narrowing the urban-rural di ff erences. Despite the narrowing of the urban-rural di ff erences, the I-LI gap widened at the lower end of the LRPCE distribution.<sup>15</sup>

# 4 Empirical Results

For the estimation of equation (5), the vector of explanatory variables Z is constructed using the household and individual level information collected in the HIES 2000 and 2005. The demographic e ff ects are controlled in the regression by including household size, the percentage of children (less than 13 years) among household members, the age of the household

> 14 A detailed analysis of the urban-rural gaps within each region shows similar downward and parallel shift in the gaps in 2005 in both regions, though extent of the downward shift was more pronounced in the I region.

> 15 Appendix Table A.2 reports the I-LI and urban-rural gaps in LRPCE from OLS and quantile regression results for a selected number of quantiles. The results con fi rm the overall trend observed in Figure 2 and 3. We omit discussion of the Table for the sake of brevity.

13