rafmacalaba/datause-encoder-data huggingface dataset:
the informal sector have a 7.2% lower probability of making a loan application. Results do not change when we use the within firm estimator (column 4) and when we estimate the model using the conditional logit (column 5): being exposed to competition from informal firms reduces the loan application by formal firms.

Table 5: Competition from informal firms and loan application

|Dependent variable:|||Loan applicati|on||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Constrained by informal_t−_1|-0.0483**<br>[0.0206]|-0.0530**<br>[0.0209]|-0.0715***<br>[0.0239]|-0.354***<br>[0.0895]|-3.371**<br>[1.491]|
|Account_t−_1||0.0223|0.00938|-0.0777|-1.310|
|||[0.0253]|[0.0287]|[0.0990]|[1.348]|
|No need_t−_1||-0.0399**|-0.0399*|0.170**|-0.0892|
|||[0.0185]|[0.0216]|[0.0755]|[1.441]|
|Age_t−_1|0.00682|0.00778|0.00566|-7.575***|1.724|
||[0.0145]|[0.0147]|[0.0170]|[2.094]|[1.191]|
|Size_t−_1|0.0348***|0.0307***|0.0372***|0.810|0.889|
||[0.00735]|[0.00762]|[0.00909]|[1.166]|[0.778]|
|Export_t−_1|0.00865|0.0111|0.0171|0.178|0.787|
||[0.0229]|[0.0232]|[0.0316]|[0.171]|[1.354]|
|Number of competitors_t−_1|||0.00204<br>[0.00639]|||
|Model|Logit|Logit|Logit|Within|Cond. logit|
|Time FE|yes|yes|yes|yes|yes|
|Geographic area FE|yes|yes|yes|no|no|
|Firm FE|no|no|no|yes|yes|
|Additional controls|yes|yes|yes|yes|yes|
|Observations|2065|2008|1443|1446|64|
|Pseudo R2 (R2)|0.197|0.199|0.222|0.262|0.481|

_Notes:_ logit marginal effects, within estimator, and conditional logistic models. Unreported controls follow the specification in Table 5. Variables are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

**Robustness checks** We perform a number of robustness tests to check the validity of our results. To begin, we take care of selection issues by employing Heckman-type selection models (Heckman, 1976; Lewis, 1974; Gronau, 1974). In essence we model a firms’ probability of belonging to the panel (i.e., being interviewed in two consecutive waves of the WBES survey) adding as an excluded regressor firms’ belonging stratum (the intersection of sector and country). We then augment the original specification with the inverse Mill’s ratio of the selection regression. As shown in Table A4, our main conclusions are largely unchanged.

One possible concern with our results is that firms that report to be constrained most by informal competition are the ones that operated as informal firms in the past and only recently switched to a formal form of business. If this were the case, the lower connectedness with the banking sector may only capture a matter of timing whereby constrained firms are those having had a shorter periods to establish a banking connection. To assuage this concern we augment our baseline specification with a direct indicator about whether the company was originally operating informally, together with a measure for number of years since the formal registration. Results for both loan availability (reported in Table A5 column 1) and loan 13
Table 14. Effect of the school readiness intervention and CLRW treatment by <u>gender</u>

||1 year of sch<br>interventi|ool readiness<br>on sample|2 years of school<br>readiness<br>intervention<br>sample|
|---|---|---|---|
||end of 1st grade<br>average TEGRA|end of 2nd grade<br>average TEGRA|end of 1st grade<br>average TEGRA|
||score|score|score|
|girls||||
|in a CLRW control school|0.17|0.3**|0.29**|
||(0.15)|(0.12)|(0.13)|
|in a CLRW treatment school|0.18|0.09|‐0.09|
||(0.14)|(0.12)|(0.11)|
|difference|0.01|‐0.21|‐0.38**|
||(0.21)|(0.17)|(0.17)|
|boys||||
|in a CLRW control school|0.12|0.28*|0.01|
||(0.13)|(0.15)|(0.1)|
|in a CLRW treatment school|0.08|0.1|‐0.09|
||(0.13)|(0.14)|(0.08)|
|difference|‐0.04|‐0.17|‐0.1|
||(0.18)|(0.21)|(0.12)|
|sample size|1049|925|1140|

Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively. Effects are estimated by equation (9), presented in Annex Table 9.

# _Discussion_

The school readiness intervention positively affected student reading outcomes for girls in the CLRW control group and, in one case, boys. This reflects the findings in Table 4 showing that girls benefited more broadly than boys from the intervention. The effect size for girls in a CLRW control school exposed to two years of the school readiness intervention and one year of schooling is 0.3 standard deviation, and it is higher than the effect size for girls exposed to one year of the school readiness intervention and one year of schooling. This suggests that the 39
cycle, notably Argentina maintained its export duties as a regular source of taxation even after the economy fully recovered from 1999-2002 crisis and after the end of the commodities super cycle, providing annual fiscal revenue for more than 1.7 percent of the GDP (on average) since its implementation.

# **4.2.Results**

As stated before, the CAPB considers only the structural components of tax receipts and public expenditures. **Figure 22** exhibits the Federal Government observed primary balance from 1993 to 2021 as well as the estimated CAPB. Accordingly, the chart breaks down the difference between the noncommodities cyclical components of the budget and the cyclical contribution of commodity-related revenues.

**Figure 22. Cyclically adjusted primary balance as share of GDP (1993-2022)**

<!-- Start of picture text --> 4,00%<br>2,00%<br>0,00%<br>-2,00%<br>-4,00%<br>-6,00%<br>-8,00%<br>Cyclical Commodities Structural Budget Balance Observed<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Source:_ Own estimates based on INDEC, Min. Economy, IMF.

During the "convertibility plan"<sup>25</sup> (1991-2001), the Federal Government successfully achieved a balanced primary fiscal position. This accomplishment was a result of both domestic fiscal policy reforms and favorable economic conditions. Specifically, positive cyclical resources played a significant role, contributing over 1.4 percent of GDP at the peak of the economic cycle in 1998.

However, the outbreak and immediate aftermath of the 2001/2002 crisis had a considerable impact on tax receipts. This can be observed from the negative cyclical component experienced during the downturn from 2001 to 2005. To mitigate the effects of the crisis, the government introduced export duties, which not only helped stabilize public accounts but also led to an improvement in the cyclically adjusted primary balance (CAPB).<sup>26</sup>

> 25 The Convertibility plan was the monetary reform initiated in 1991, consisting in the implementation of a hard peg between the Argentine peso and the US dollar (AR$ 1 equals US$ 1).

> 26 The considerable improvement observed in the fiscal accounts after 2002 was the result of export duties introduced and a substantial fall in the current primary government expenditure which went down in real terms by 19.6% in 2002 versus the 1999 figures.
**<u>Appendix table 4: Characteristics of smallholder production</u>**

||**Total**||**By region**||**Locat**|**e on ag. e**|**state**|
|---|---|---|---|---|---|---|---|
|||**North **|**Center **|**South **|**No**|**Yes**|**Ttest**|
|**Land endowment and topography**||||||||
|Land area (ha)|1.06|0.86|1.52|0.76|0.99|1.78|***|
|% of mountain slope|15.16|23.23|13.74|13.56|15.52|11.37|***|
|% of dregs|8.00|6.02|6.42|9.91|8.16|6.25|***|
|% of plain|72.47|64.69|76.62|71.80|71.79|79.71|***|
|% of other topography|4.37|6.06|3.22|4.72|4.53|2.67|***|
|**Land use**||||||||
|Cultivated area (ha)|0.70|0.65|0.90|0.56|0.67|1.05|***|
|% of land under maize|69.25|60.01|72.05|70.25|69.18|69.90||
|% of land under rice|2.70|5.63|1.40|2.73|2.74|2.31||
|% of land under sorghum|1.90|0.04|0.11|4.10|2.05|0.32|***|
|% of land under beans|2.14|1.81|3.06|1.49|2.25|1.05|***|
|% of land under pigeon peas|3.63|0.09|0.06|7.95|3.89|0.97|***|
|% of land under ground nuts|5.60|3.74|8.84|3.54|5.32|8.51|***|
|% of land under cassava|5.36|18.85|4.05|1.54|5.46|4.40|**|
|% of land under tobacco|2.04|3.79|2.92|0.66|1.63|6.15|***|
|% of land under other crops|7.37|6.04|7.51|7.74|7.47|6.40|**|
|**Input and labor use**||||||||
|Value of fertilizer (2006 US$/ha)|39.44|62.22|50.50|47.24|50.75|52.83||
|Value of improved maize seeds (2006 US$/ha)|11.07|11.15|17.17|14.26|15.19|11.25||
|Value of manure (2006 US$/ha)|2.78|1.20|5.97|1.63|3.21|1.45||
|Value of other seeds (2006 US$/ha)|4.33|3.64|4.77|6.18|5.46|2.75|*|
|% attended extension activities|19.19|31.48|17.44|16.23|19.20|19.08||
|% used exchange labor|20.54|29.45|24.18|14.43|20.31|22.92|***|
|Value of ag. assets (2006 US$/ha)|318|996|233|144|327|223||
|**Output and profit**||||||||
|<br>Total crop yield (2006 US$/ha)|253|263|247|254|253|252||
|<br>Maize profit (2006 US$/maize ha)|199|171|187|218|201|179|***|
|<br>Rice profit (2006 US$/rice ha)|397|368|404|409|403|329|*|
|<br>Tobacco profit (2006 US$/tobacco ha)|636|749|553|737|646|606||
|No. of obs|20,677|3,379|7,732|9,566|18,873|1,804||

_Source:_ Own computation from 2006/07 NACAL. _Note:_ Other crops includes millet, soya beans, ground beans, cow peas, sun flower, sweet potato, irish potato, cotton, tea, sugar cane, and coffee.

30
# **3. Growth regression**

## **3.1 Framework**

Cross-country specifications explain real per capita GDP growth using as explanatory variables the initial level of real per capita GDP and other additional factors such as physical investment, human capital, etc.<sup>8</sup> Infrastructure capital can be included in the specification, which then takes the following form: where _gi_ is the growth rate of real per capita GDP for country _i_ , _yi0_ is initial income (possibly in log form), _K_<sup>_I_</sup> _i_<sup>is a measure of infrastructure capital, and</sup><sup>_Z_</sup> _i_<sup>is a vector of covariates as mentioned</sup> above.<sup>9</sup>

## **3.2. Data**

As in Straub, Vellutini and Warlters (2008), we use physical infrastructure indicators. As detailed there, this choice is motivated by the weakness and lack of availability of public investment data, as well as by the fact that we want to use similar data to allow direct comparisons with the results from the growth accounting exercise.

Physical indicators for four different sectors (telecom, energy, transport and water) are taken from the World Development Indicators database, covering the 1971-2006 period. Specifically, we use the following series:

> 8 See for example Levine & Renelt (1992) and Sala-i-Martin (1997) and (Romp & Haan 2007) for a discussion.

> 9 See Straub (2007) for a discussion of the limitations of such estimations.

13
<!-- Start of picture text --> Figure 16. How trade expansion would effect long-run marginal costs<br>a. Percent reduction on LRMC b. Monetary reduction on LRMC<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>- -<br>number of countries percentage of countries<br>Increase No Change 0-20% 20-40% 40% < Increase No Change 0-3 cents 3-5 cents 5 cents <<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008 _Note:_ See details in annex 11.

# **b. Do existing average effective tariffs cover LRMC?**

It is relevant to ask whether existing average effective tariffs are high enough to cover LRMC even if they may not be high enough to cover average historic costs as was demonstrated above. The analysis shows that 38 percent of the sample countries have already achieved average effective tariffs that are high enough for full capital cost recovery (figure 17a). Compare this with only 30 percent of countries that can fully recover historic costs.

<!-- Start of picture text --> Figure 17. Cost-recovery capabilities of residential effective tariffs vis-à-vis LRMCs<br>a. LRMC recovery, trade stagnation b. LRMC recovery, trade expansion<br>30<br>80%<br>70%<br>25<br>60%<br>50% 20<br>40%<br>15<br>30%<br>20% 10<br>10%<br>5<br>0%<br>0<br>0 5 10 15 20 25 30<br>LRMC, trade expansion, US cents/kWh<br>level of LRMC recovery<br>cents/kWh<br>percentage of countries<br>Effective Residential Tariffs US<br>cost<br>0%- 30% 30%-50% 50%-80% 80%-99% recovery<br><!-- End of picture text --> _Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

27
-1.62 while it is -1.39 for Machinery. A simple mean test supported the hypothesis that Metal import demand is more elastic than that of the Machinery, with a t-statistics of -13.02. Figure 2 presents the two Kernel density distribution of the estimated import demand elasticities for Metal and Machineries lines across all countries.

To properly test the homogenous vs heterogenous goods hypotheses, we use Rauch’s (1999) classi fi cation. Rauch groups four digit SITC goods into three categories: differentiated, reference priced and homogenous goods. By matching our HS six digit products to the SITC schedule, we are able to classify our products according to Rauch’s schedule. Table 2 provides the sample averages, medians and standard deviations of the estimated import demand elasticities according to the three categories of goods. It is clear that the average elasticity is larger in magnitude for homogenous goods, follows by the reference priced and differentiated goods. Simple mean tests supported the hypotheses that homogenous goods are more elastic than reference priced goods, and reference priced goods are more elastic than differentiated goods. The t-statistics of the two tests are 7.23 and 19.50 respectively. Similarly, the median elasticity for differentiated goods is smaller in magnitude than both reference priced and homogeneous goods. A simple rank test shows that the median elasticity of differentiated goods is statistically smaller in magnitude than the rest, with a p-value close to 0, while the difference between the median elasticities of reference price goods and homogenous goods is not statistically signi fi cant. All this suggests that differentiated goods are less elastic than reference priced and homogeneous goods, which con fi rms our fi rst a priori.

To test the second hypothesis, we reestimated import demand elasticities at the industry level, through a concordance linking HS six digit classi fi cation to ISIC three digit industry classi fi cation. Table 3 provides the average elasticity by country at the different levels of aggregation. It con fi rms that elasticities are smaller in magnitude when estimated at the industry level than at the tariff line level. On average elasticities estimated at the six digit level of the HS are 39 percent higher than those estimated at the three digit level of the ISIC.

In order to test the last two hypothesis we run the average elasticity at the country level on log of GDP and GDP per capita. The conditional plots of these relationships are provided in Figures 3 and 4, as well as the estimated coefficient and its standard error. They con fi rm that import demand is more elastic in large and less developed countries. Thus, the last two hypotheses cannot 17
For Egypt to seize the benefits of trade, it requires deeper reforms to incentivize substantial growth in the export sector, especially in favor of labor-intensive industries. It will be important, therefore, for Egypt to improve its business environment by: (i) lowering barriers to investment, especially foreign direct investment (FDI), (ii) promoting private sector attractiveness relative to the public sector in terms of wages and job security, and (iii) lowering costs for firms to formalize.

# **References**

- <mark>Abbey, E., Gyeke-Dako, A., D Oduro, A., Turkson, F., & Baffour, B. (2017). The Employment Generating Effects of Exporting: Firm level evidence of Micro, Small and Medium Enterprises (MSMEs) in Ghana.</mark> _<mark>Revista Swiss Programme For Research On Global for Development. Recuperado el</mark>_ <mark>,</mark> _<mark>11</mark>_ <mark>.</mark>

- Artuc, Erhan, Gladys Lopez-Acevedo, Raymond Robertson, and Daniel Samaan. 2019. _Exports to Jobs: Boosting the Gains from Trade in South Asia._ South Asia Development Forum. Washington, DC: World Bank. doi:10.1596/978-1-4648-1248-4. License: Creative Commons Attribution CC BY 3.0 IGO

- <mark>Bartik, Timothy J. (1991).</mark> _<mark>Who benefits from state and local economic development policies? Kalamazoo, MI: WE Upjohn Institute for Employment Research</mark>_ <mark>.</mark>

- <mark>Bernard, Andrew B., J. Bradford Jensen, and Peter K. Schott. (2009) “Importers, exporters, and multinationals”</mark> _<mark>Producer Dynamics</mark>_ <mark>: 513.</mark>

- <mark>Bernard, Andrew, B., J. Bradford Jensen, Stephen J. Redding, and Peter K. Schott. 2007. "Firms in International Trade."</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 21 (3): 105-130. DOI: 10.1257/jep.21.3.105</mark>

- <mark>Brambilla, Irene, Nicolas Depetris Chauvin, and Guido G. Porto. (2015)</mark> _<mark>Wage and employment gains from exports: Evidence from developing countries</mark>_ <mark>. CEPII, Centre d'etudes prospectives et d'informations internationals.</mark>

- <mark>Daily News Egypt (2020) “Egyptian building material exports increase 20% during 8M 2020” October 25, 2020.</mark>

- <mark>Dessing, Maryke. (2002) "The Impact of Trade on Employment in Eighteen Less-Industrialized Countries: An Econometric Analysis."</mark> _<mark>Canadian Journal of Development Studies/Revue canadienne d'études du développement</mark>_ <mark>23, no. 4: 743-774.</mark>

24
presence of qualified female teachers who show up for work on a somewhat regular basis is critical to increase girls’ enrollment (The World Bank Group 2005).

The enrollment rate has increased for rural girls in the years examined, but the increase can not attribute too much to wealth or economic growth. Instead, other institutional changes contribute more to the increase in rural girls’ school enrollment. Data can not provide direct evidence on causality of institutional changes on girls’ school enrollment but the fact is that dramatic changes have occurred in the educational landscape in Pakistan since 1990s. One of the most important changes is the emerging and booming market of the private schools in both urban and rural areas. The number of private schools increased from 32,000 to 47,000 from 2000 to 2005 and by the end of 2005, one in every 3 enrolled children at the primary level was studying in a private school (The World Bank Group 2008). The private schools are not just targeting on high income families but more on general population because the fees are quite affordable by the general population. A nationwide census of private schools shows that the fee in the median rural private school was about Rs.60 per month in 2000 (The World Bank Group 2008). The increase in the number of affordable schools provides more opportunities for parents to invest on children’s education, especially for rural girls.

# **8. Policy Implications**

The study suggests the demand side intervention should not just focus on the poorest of the poor, but rather have a wider targeting strategy. The reasons are as follows. First, the study finds strong negative relationship between per capita expenditure and the likelihood of child working or being inactive. It suggests that poverty is still a key factor that keeps children out of school. Second, the study also finds that ownership of productive agricultural assets and head 19
product and country on a bilateral trade basis.<sup>17</sup> Documentation of the steps we have taken, a brief explanation of the methodology and a detailed explanation of our aggregation methodology are in appendix C.

# **4.4 Estimates of the Ad Valorem Equivalents (AVEs) for Non-Tariff Measures (NTMs) for the Regions of our Model**

Our estimates of the AVEs of NTMs are based on the estimates of Kee _et al_ ., (2008; 2009), which in turn are based on the theoretical developments of Anderson and Neary (1996; 2003). Kee _et al_ . estimate the AVEs of NTMs for 105 countries at the 6 digit level. These estimates, as well as aggregated estimates for manufacturing and agriculture for the 105 countries, are available on the World Bank website.<sup>18</sup> The measure we use from Kee _et al_ . is the uniform tariff equivalent that generates the same level of import value for the country in a given year.<sup>19</sup> Kee _et al_ . provide estimates based on both applied and MFN tariffs; the measure we use is based on applied tariffs, which take into account bilateral trade preferences. At the six digit level, the estimates of Kee _et al_ . are sometimes subject to a substantial margin of error that may lead to misleading results in a CGE model policy analysis. Consequently, we have chosen to use the aggregated estimates of Kee _et al_ . at the sector level, i.e., for each country, we have two AVEs for each country: one AVE of the NTMs in manufacturing and one AVE of the NTMs in agriculture. We then further aggregate these values for 93 countries to the regions of our model. Details are available in appendix B.

# **4.5 Tariff Data**

**4.5.1 Kenya.** Most-Favored Nation (MFN) tariff rates at the eight digit level were taken from the website of the Kenyan government: www.kra.go.ke/customs/customsdownloads.php. These tariff rates were then aggregated to the 55 sectors of the model of Balistreri, Rutherford

> 17 We thank Peter Minor for his cooperation with us in this process. The estimates are subject to a margin of error for various reasons, including that the procedure relies on the Doing Business database and thus assumes the same number of days for all products in a country throughout the year.

> 18 The data set is available at

> ~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22574446 pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html</u>

> 19 Specifically, we take the difference between the Overall Trade Restrictiveness Index (OTRI) and for the Tariffonly OTRI (OTRI_T), which gives us the AVE of the NTMs.

14