rafmacalaba/datause-encoder-data huggingface dataset:
been only one study to our knowledge (Gosavi, 2015) that has utilized firm-level data that has been representative of the private sector and allows for cross-country comparisons. However that study explores the adoption of mobile money and does not explore the relationship between mobile money use and firm outcomes.

The goal of this study is to empirically investigate the relationship between the use of mobile money and private investment using firm-level data in three countries in East Africa –Kenya, Tanzania and Uganda. This study adds to the literature in the following ways: (i) it is one of the first studies to explore mobile money use by firms and the likelihood of investments, (ii) it uses nationally-representative firm-level data that follow a consistent methodology across countries allowing for cross-country comparisons, which is a rarity in the literature, and (iii) it establishes a positive relationship between mobile money use and investment that is robust across a number of specifications as well as different measures of mobile money use. The study finds that an increase in the adoption of mobile money leads to a 16 percent increase in the probability of investment by the firm. A further in-depth analysis is conducted of the different purposes of mobile money use and their relationship with the likelihood of investment. The intensity of mobile money use by each type of purpose and the link with firms’ decision to invest is also explored. The findings have important implications for policy makers aiming to improve private investment in East Africa.

The paper is organized as follows. The conceptual framework, including theoretical underpinnings of mobile money use and the state of the empirical evidence is presented in section 2. Section 3 describes the empirical strategy, section 4 presents the main results and section 5 concludes.

# **2. Conceptual Framework**

From a theoretical standpoint the relevance of mobile money and its effect on investment can only be explained by relaxing the assumption of perfect capital markets in traditional investment models. In the neoclassical investment model, for example, in equilibrium investment equals the rate of depreciation, and in the accelerator model, investment is proportional to the rate of growth in output. In both cases, capital stock is adjusted instantaneously to its desired level. In the Q theory of investment, an adjustment cost function is added to the firm’s profit maximization problem with the resulting outcome that investment is defined by the optimal path to the optimal capital stock.

4
<u>Figure 3: Scatter plots of the elasticities against income per capita</u> <!-- Start of picture text --> A. Sigma (σ)<br>2.8<br>2.4<br>2.0<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>5 6 7 8 9 10 11 12 13<br>lny<br>B. Omega (Ω)<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>5 6 7 8 9 10 11 12 13<br>lny<br>Source: Authors' calculations.<br> Note: lny = natural log of GDP per capita, U.S. dollars, 2015.<br>Omega<br>Sigma<br><!-- End of picture text --> Table 3 provides additional summary descriptive statistics for the two main income groups. The median trade elasticities are close to the simple averages in all cases. In developing countries, the upper range is also less but close to one for both the Armington and CET elasticities. Countries with elasticities near the value of one are usually those close to the boundaries of the high-income group, such as emerging economies like Brazil and South Africa and some Latin American countries like Costa Rica and 17
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# **Table 9: Tests on the significance of each variable**

|**Variable**|**F-test Value**|**[Prob]**|
|---|---|---|
|**rm2**|**F(18,71) = 7.3925**|**[0.0000]****|
|**rgdp**|**F(18, 71) = 6.3452**|**[0.0000]****|
|**INFL**|**F(18, 71) = 3.1743**|**[0.0003]****|
|**Interestrt**|**F(18, 71) = 1.7739**|**[0.0462]***|
|DISC|F(18, 71) = 1.4601|[0.1007]|
|**Dexrtend**|**F(18, 71) = 3.2265**|**[0.0002]****|
|FFUNDSRT|F(6, 25) = 1.3779|[0.2620]|
|**dumm1976**|**F(6,30) = 5.4211**|**[0.0007]****|
|**dumm1986**|**F(6, 25) = 2.5657**|**[0.0448]***|
|**dumm1990**|**F(6, 25) = 4.0413**|**[0.0058]****|
|**dumm2008**|**F(6, 25) = 3.7728**|**[0.0082]****|
|dumm2009|F(6, 25) = 1.0373|[0.4251]|
|**dumm2013**|**F(6, 25) = 4.7097**|**[0.0025]****|
|Constant|F(6, 25) = 0.78440|[0.5903]|

P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *  Significance level at 10%

65
20 disinflation program appears to have weakened the link going from shocks to key macro variables to the process of inflation.

# C. Cross-Sectional Variabilities

The conclusion, based on Tables 1 and 2, that it appears that there is a less important role of nominal vs. real shocks after the program is now further verified by turning to cross sectional variabilities. We discussed in the previous section the predictions of imperfect information models regarding the direction of comovement between price and output cross sectional variabilities. Figure 8a plots three measures of variability: of relative prices (across 10 broad categories in the CPI), relative growth rates of output (across 6 main sectors such as industry, agriculture, construction, etc.), and relative rates of growth of real wages (across 9 main sectors). We consider the latter two measures at applying to real variables. In each case, variability is measured by weighted cross sectional variances.10 There are two salient features of Figure 8a. First, there has been a decrease in cross sectional variabilities after the 1985 program. In particular, the index of dispersion of relative prices after the disinflation has become about one-third of its value before the program. This finding is in line with the observed positive co:relation between inflation and relative price dispersion that has been documented for several countries. Similarly, there has been a decrease of about 20 percent in the variability of real wage growth rates after 1985. There has been a somewhat slower downward adjustment 10 The data source for our calculations is Bank of Israel's Annual Report (various issues).
EBTANAS score. This strongly suggests that it is indeed attendance at SMK per se that adds less value to academic achievement (Table 8).

**Table 8: IV estimates of senior EBTANAS score/EBTANAS2 and COLLEGE entry: (IFLS3, n=708, 681, 823 & 823)**

||EBTANAS|2|COLLEGE||
|---|---|---|---|---|
|||||IV<br>estimates|
||OLS||||
|Independent variable|estimates|IV estimates|IV estimates|omitting<br>EBTANAS score|
|EBTANAS1|0.709***|0.614***|||
||(0.042)|(0.053)|||
|RURAL|-0.785|-1.695**|-0.033|-0.145***|
||(0.567)|(0.697)|(0.042)|(0.034)|
|FEMALE|0.054|-0.375|0.008|-0.010|
||(0.490)|(0.516)|(0.032)|(0.032)|
|AGE|0.488**|0.465**|-0.017|-0.014|
||(0.196)|(0.204)|(0.013)|(0.012)|
|EDU_HH|0.230***|0.066|0.024***|0.024***|
||(0.069)|(0.094)|(0.006)|(0.006)|
|HH INCOME|0.065|0.004|0.006**|0.002|
|(million IDR)|(0.043)|(0.050)|(0.003)|(0.003)|
|SMK|-4.468***||||
||(0.531)||||
|PRED_ SMK||-11.096***|0.226|-0.490***|
|||(2.553)|(0.164)|(0.131)|
|PRED_EBTANAS2|||0.032***<br>(0.005)||
|Intercept|-2.249|6.378|||
||(4.355)|(5.239)|||

21
Policy Research Working Paper 9296

# **Abstract**

This paper studies the cross-country patterns of risky innovation and growth through the lens of international trade. It uses a simple theoretical framework of risky quality upgrading by firms under varying levels of financial development to derive two predictions. First, the mean rate of quality growth and the corresponding cross-sectional variance of quality growth in a country are positively correlated. Second, both the mean and variance of quality changes are positively correlated with the country’s level of financial development. The paper tests these two hypotheses using data on disaggregated (Harmonized System 10− digit) bilateral exports to the United States. The patterns in the data are consistent with the theory. The mean and the variance of quality growth are strongly positively correlated with each other. Countries with greater financial depth are systematically characterized by higher mean and higher variance in the growth of product quality. The findings suggest a mean-variance trade-off in product quality improvements along the development path. Increases in financial depth do not imply lower variability of changes in the product space.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice Group. 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 wmaloney@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
# **4. IMPLICATIONS FOR CENTRAL ASIAN COUNTRIES: SUMMARY OF THE MAIN RESULTS**

Table 3 reports the quartile values for our variables of interest, while Table 4 summarizes the disadvantages of being landlocked and having a weak infrastructure for a representative (i.e. median) Central Asian country.

**Table 3: Quartile values (computed on average between 1992 and 2004)**

||||Percentile<br>|||
|---|---|---|---|---|---|
|||25th|Median|75<sup>th</sup>|obs.|
|**K**|Total sample|0.072|0.403|1.098|167|
||Coastal countries|0.091|1.190|1.996|132|
||CA countries|0.064|0.127|0.168|5|
||Other landlocked countries|0.031|0.264|0.533|30|
|**K**<sup>**TRANSIT**</sup>|CA countries|0.108|0.108|0.119|5|
||Other landlocked countries|0.064|0.600|0.801|30|
|**D**<sup>**PORT**</sup>|CA countries|2950|3100|3600|5|
||Other landlocked countries|597.5|870|1206|30|
|**B**<sup>**TRANSIT**</sup>|CA countries|5|5|5|5|
||Other landlocked countries|2|3|4|30|

Note: CA= Central Asian. _Source: Authors’ calculations._ We compute how the volume of trade of this median Central Asian country will change depending on the levels of its own and/or of its transit countries’ infrastructure, on the extra transport costs due to overland transport distances and on the ability of a landlocked country to negotiate access to the sea.

28
# **ITT)** Implications of a p2ositive theory for educational policy

We have shown that the educational output optimizing model of expenditure allocation, in all its variants is inconsistent with the evidence and that a simple model in which teacher utility receives direct concern in expenditure choices is consistent with the stylized facts. Having a positive model of educational spending is important for the analysis of policy options. As mentioned in the introduction, there are two kinds of educational policies currently proposed: more of the same (higher enrollments, higher expenditures, better teacher training, etc.) and fundamental reform of incentives (decentralization, community involvement, school choice, etc.). Guidance on the choice between these requires modeling the underlying public sector decision and control mechanisms as fundamental reforms are about changing the incentives within the sector.

# A) More hudget versus reform

As mentioned above, one of the puzzles in the literature on educational production functions is that it is often difficult to demonstrate a positive impact of increased spending on educational outcomes<sup>24</sup> . The literature is criticized precisely because the results seem so implausible to educators and educated alike: of course "money matters" and on some level 24 The two best arguments against this are the meta-analysis of studies in the US (Hedges, Laine and Greenwald, 1994) and the studies of the impact on subsequent wages of budgetary resources while in school (Card and Krueger, 1996), also done exclusively in the US. Both of these are reasonably convincing that there is _some,_ but small, connection between expenditures and outcomes, which of course under a positive maximizing model must have been the case. But neither of these methods speak to the question of whether a different budgetary allocation could have had a larger impact.

34
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|_Type of estimation_<br>This table presents stress tests to estim<br>outliers out, and US Holdings are norm<br>opposed to the 03/2008-03/2009 crisis<br>any local stock market returns. *, **, *<br>US Treasury.|_Time du_<br>ations in Didier et<br>alized by substract<br>period used in this<br>** Denote significa|_mmies, Lehman inte_<br>al., 2010. First three c<br>ing its sample averag<br>paper. In turn, column<br>nce at the 1%, 5%, an<br>**Financial globali**|_raction_<br>olumns presents<br>e and dividing it<br>s 4-10 are prese<br>d 10% levels, re<br>**Ta**<br>**zation and fin**|data from Didier<br>by its sample sta<br>nted for the sam<br>spectively. In ita<br>**ble 9**<br>**ancial recoupli**|_Rep_<br>et al., and their<br>ndard deviation<br>e data but with o<br>lics appears the<br>**ng around the**|_lacing time dummie_<br>methodology. Return<br>. Additionally their c<br>ur methodology. Ret<br>correspondent the p-<br>**crisis**|_s by S&P retu_<br>s are normal lo<br>risis period are<br>urns are dlog(s<br>value. Source:|_rns_<br>cal returns, a<br>defined as 6/<br>tock_index) a<br>Didier et al,.|
|---|---|---|---|---|---|---|---|---|
||**Full Sample**||||||||
|**Variable/Sample**|<br>**(Didier et al.,**<br>**2010)**|**EM+Developed**|**Non-EM**<br>**developing**|**Full Sample **|**Full Sample**|**EM+Developed**|**Non-EM**<br>**developing**|**EM**|
|S&P returns*US Holdings*Pre-<br>|0.2783***|-0.002|0.4869***|0.0141*|||||
|Lehman|_0.000_|_0.983_|_0.001_|_0.068_|||||
|S&P returns*US Holdings*Post-|0.2149***|0.072|0.3234***|0.0113|||||
|Lehman|_0.000_|_0.397_|_0.018_|_0.106_|||||
|S&P returns*US Holdings|||||0.012*<br>_0.091_|-0.004<br>_0.617_|0.0382**<br>_0.027_|-0.019<br>_0.192_|
|S&P Returns*US Absolute Flows|||||||||
|US Absolute Flows|||||||||
|||||0.8549***|0.8549***|1.080***|0.7516***|1.201***|
|S&P returns||||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|R-squared|0.558|0.682|0.467|0.347|0.347|0.554|0.274|0.504|
|Observations|1539|651|802|1628|1628|682|858|273|
|Time dummies|Yes|Yes|Yes|No|No|No|No|No|
|Countries|74|31|39|74|74|31|39|21|

This table presents stress tests to estimations in Didier et al., 2010. First three columns presents data from Didier et al., and their methodology. Returns are normal local returns, ar outliers out, and US Holdings are normalized by substracting its sample average and dividing it by its sample standard deviation. Additionally their crisis period are defined as 6/2 opposed to the 03/2008-03/2009 crisis period used in this paper. In turn, columns 4-10 are presented for the same data but with our methodology. Returns are dlog(stock_index) a any local stock market returns. *, **, *** Denote significance at the 1%, 5%, and 10% levels, respectively. In italics appears the correspondent the p-value. Source: Didier et al,. 2 US Treasury.
firms with a municipal license indicated that having a RUC—which allows to issue receipts for tax purposes—was the main benefit of obtaining the license. The ability to issue receipts can attract new customers or facilitate additional sales to existing customers, in either case increasing firm revenue. Regression results, using the same set of independent variables as in Table 1, confirm that more formal firms tend to have higher sales (columns 2-3 of Table 2), therefore lending support to the above hypothesis. In addition, more formal firms are significantly more likely to sell their products to larger clients (firms with more than 10 employees) and the government (including states and municipalities).<sup>19</sup>

**<u>Table 2: Impact of formality on sales and access to finance</u>**

||ln(S|ales)|P(Forma|l finance)|
|---|---|---|---|---|
|Formality index (broad)||0.735***||0.512**|
|Formality index (narrow)|0.526***||0.366**||
|Observations|1,110|1,110|1,112|1,112|
|R<sup>2</sup>|0.580|0.560|||
|Number of firms with access to finance|||265|265|
|Waldχ2|||150.2***|158.2***|
|**Identifying variables**|||||
|Distance to nearest tax office|-0.124***|-0.075**|-0.124***|-0.075**|
|Distance to municipal office|-0.184***|-0.131***|-0.184***|-0.131***|
|Firm had an inspection during the past|||||
|year|0.343***|0.268***|||
|*** p<0.01, ** p<0.05, * p<0.1.|||||
|Note: All specifications are estimated with<br>Confidence intervals calculated with robust<br>Log of sales is estimated using 2SLS, while<br>finance is estimated with IV probit.|a full set of<br>standard err<br>the probabi|control varia<br>ors.<br>lity of access|bles listed i<br>to formal s|n Table 3.1.<br>ources of|

There are also additional spillover benefits of having a tax identification number. In addition to issuing receipts, a related but less commonly mentioned benefit of having a RUC is the ability to take advantage of the VAT credit on purchased inputs (Figure 9). Focus group participants also mentioned than a further advantage of operating with a RUC is an improvement in their accounting practices; in other words, issuing receipts and keeping track of input purchases encourages better overall book-keeping which has a positive impact on firm performance. Finally—and this point applies more broadly to other dimensions of formality—focus group participants mentioned that after having taken the steps to formalize they no longer have to hid from authorities and can advertise their business openly. This could be an additional explanation for the positive impact of formality on sales shown in Table 2.

> 19 This result is a simple two-tailed test of the means of the formality index for groups which sell to large clients and the government, respectively, and groups which do not. However, the latter represent an overwhelming majority of the EMES sample (8 and 3 percent of the total, respectively). Therefore, although the differences are significant at the 1 percent level, these results are only indicative and should be interpreted with caution.

17
concavity of the non-horizontal section of the curve translates into the degree of _inequality_ among the poor.

|0<br>4<br>8<br>12<br>16<br>20<br>0<br>Cumulative Poverty Gaps|Figure2.1. <br>Table 2.3.<br>10<br>20<br>30<br>40<br><br>1996<br>Cumulative Percent|A Pictu<br>A Profile<br> <br>50<br>60<br>70<br>age of the Populati|re of Po<br>of Pov<br> <br>80<br>90<br>10<br>on|vertyinCameroon,1996<br>erty and Inequality, 1996<br><br>0<br>0<br>2<br>4<br>6<br>8<br>10<br>12<br>14<br>0<br>10<br>20<br>30<br>40<br>5<br>2001<br>Cumulative Poverty Gaps<br>Cumulative Percenta|-2007<br>-2007<br>0<br>60<br>70<br>80<br>ge of the Population|<br><br>90<br>100<br>2007||
|---|---|---|---|---|---|---|---|
|||Overall||Urban||Rural||
||1996|2001|2007|1996<br>2001<br>2007|1996|2001|2007|
|Headcount|53.26|40.18|39.90|41.39<br>17.88<br>12.17|59.62|52.08|55.04|
|Poverty Gap|19.09|12.79|12.31|14.67<br>4.28<br>2.81|21.46|17.32|17.50|
|Squared Poverty|Gap<br>9.00|5.55|5.03|6.92<br>1.59<br>0.96|10.12|7.67|7.24|
|Watts|26.66|17.38|16.11|20.55<br>5.48<br>3.51|29.94|23.72|22.99|
|Atkinson (1)|23.84|24.00|21.94|28.72<br>24.31<br>18.59|17.81|16.63|15.35|
|<br>Atkinson(2)|38.16|38.82|35.82|45.63<br>38.25<br>31.85|30.38|29.72|25.93|
|Gini|40.63|40.41|38.96|44.91<br>40.71<br>35.19|34.60|33.15|32.23|
|MLD|27.23|27.45|24.77|33.86<br>27.85<br>20.56|19.61|18.19|16.66|
|Theil|31.75|33.75|27.88|37.64<br>35.39<br>22.87|21.61|19.36|18.76|

Source: Authors’ Calculations (MLD stands for Mean Log Deviation).

Figure 2.1 is consistent with the poverty outcomes presented in table 2.3, showing that poverty incidence dropped from about 53.3 percent in 1996 to about 40.2 percent and 40 percent in 2001 and 2007 respectively. The other three measures reported in that same table (the poverty gap, the squared poverty gap and the Watts measure) show a similar decline. These other three measures are members of the _additively decomposable_<sup>10</sup> class of poverty measures.

> 10 This class of poverty measures is defined by the following expression: ������|���������� where z is the poverty line, f(y) is the frequency density function of the welfare indicator y, and ���|�� is a convex and decreasing function measuring individual deprivation. The indicator of individual deprivation is equal to zero when the welfare level is greater or equal to the poverty line. The poverty measures are _additively separable_ because the deprivation felt by an individual depends only on a fixed poverty line and her/his level of welfare and not on the welfare of other individuals in society. When the population is divided exhaustively into mutually exclusive socioeconomic groups, this class of measures allows one to compute

8
production of French beans, but first-time adopters do both. Both prior growers and new adopters increase their production of baby corn, since as mentioned before, baby corn was introduced by DrumNet. Interestingly, only prior growers perceive a reduction in marketing costs. This could be explained by the fact that first-time adopters were only selling at the farm-gate, while old adopters where hauling their produce to be exported to markets.

Most importantly, we find here that income is significantly larger for first-time exporters, an increase of 31.9 percent for the pooled treatment group. Panel B shows this broken down for the credit and no-credit group, but the difference between these two groups is not significant statistically (although the point estimate is higher for the noncredit group).

Using the marketing transaction data also collected at the time of the survey, we also tested whether treatment SHGs benefited from an access to higher prices than they would otherwise (note that whereas a large intervention of this sort may actually shift market prices, DrumNet, relative to the market as a whole, was too small to realistically cause general equilibrium shift in overall market prices). To examine prices available to farmers in the study, we use all transaction data available, including those conducted at farm-gate as well as at a local or distant market. The dependent variable is the price per relevant unit of the crop: Kg for French beans and coffee, 90 Kg bag for maize and beans and bunches for bananas. We run a pooled regression which includes crop fixed effects and a crop by crop specification for the main crops grown. Analogous to the impact Tables 3 and 4, all regressions include SHG fixed effects and all household baseline controls of Table 1b. Standard errors are also clustered at the SHG level, our unit of 17