rafmacalaba/datause-encoder-data huggingface dataset:
**Table S1.1.** OIC Countries and Donor Pool  Selection

|**Country**|**Joined**|**EFW data**|**Country**|**Joined**|**EFW data**|
|---|---|---|---|---|---|
|Afghanistan|1969||Malaysia|1969|*|
|Albania|1992||Maldives|1976||
|Algeria|1969||Mali|1969|*|
|Azerbaijan|1992||Mauritania|1969||
|Bahrain|1972||Morocco|1969|*|
|Bangladesh|1974|*|Mozambique|1994||
|Benin|1983||Niger|1969|*|
|Brunei|1984||Nigeria|1986|*|
|Burkina Faso|1974||Oman|1972||
|Cameroon|1974||Pakistan|1969|*|
|Chad|1969||Palestine|1969||
|Comoros|1976||Qatar|1972||
|Côte d’Ivoire|2001||Saudi Arabia|1969||
|Djibouti|1978||Senegal|1969||
|Egypt|1969|*|Sierra Leone|1972|*|
|Gabon|1974||Somalia|1969||
|Gambia|1974||Sudan|1969||
|Guinea|1969||Suriname|1996||
|Guinea-Bissau|1974||Syria|1972|*|
|Guyana|1998||Tajikistan|1992||
|Indonesia|1969|*|Togo|1997||
|Iran|1969|*|Tunisia|1969|*|
|Iraq|1975||Turkey|1969|*|
|Jordan|1969|*|Turkmenistan|1992||
|Kazakhstan|1995||Uganda|1974||
|Kuwait|1969||UAE|1972||
|Kyrgyz Republic|1992||Uzbekistan|1996||
|Lebanon|1969||Yemen|1969||
|Libya|1969|||||

_Source_ : Organisation of Islamic Cooperation (OIC) and Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) report. _Note_ : Listing of sample OIC countries with complete EFW data prior to 1990. * denote inclusion in the donor pool used to construct Synthetic Jordan.

**Table S1.2.** EFW Index and Components, Summary Statistics

||**Mean**|**Std. dev.**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|
|0. EFW index|5.802|0.910|3.010|7.650|315|
|1. Size of government|6.424|1.332|1.529|9.170|315|
|2. Rule of law|4.035|1.142|1.368|6.393|308|
|3. Sound money|6.947|1.498|0.059|9.679|315|
|4. International trade freedom|5.652|1.668|0.000|7.955|315|
|5. Regulation|5.928|1.123|2.722|8.678|312|

_Source_ : Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) report. _Note_ : Summary statistics for the Gwartney, Lawson, and Hall (2017) EFW index and its components, 1990–2015.
productivity gains in domestic work release time that can now be dedicated to income generating activities.

Our paper complements the seminal work of Furukawa (2014) who studies the effects of Pico‐PV lamps on children’s learning outcomes in rural Uganda. We extend the scope by examining the effects of Pico‐PV kits on various in‐house activities of all household members, not only those of school children. We find that households use the kits intensively in spite of the zero price and the novelty of the product. Furthermore, the kit considerably reduces consumption of kerosene, candles, and dry‐ cell batteries and, in consequence, energy expenditures. The reduction of kerosene improves household air quality and the reduction of dry‐cell battery consumption plausibly leads to environmental benefits. Moreover, we find that children shift part of their homework into the evening hours. Primary school boys even increase their total study time. While parts of these effects are clearly internalized benefits, other parts are important externalities, which may provide the cause for public subsidies, in particular if it turns out that households are simply too poor to raise the upfront costs alone.

The role of public policy in the promotion of Pico‐PV technology is not defined so far. The expectation of the World Bank’s Lighting Global program, for example, as well as other donors is that Pico‐PV kits make inroads to African households via commercial markets, implying that end users pay cost‐covering prices (see Lighting Global 2016). This might in fact work out for the relatively well‐off strata in rural areas but is much more uncertain for the rural poor. In fact, the major target group of Pico‐PV kits within
credit by deposit money banks increases by 1.9% if we use the full sample vs. 1.8% when we eliminate the outliers. The discrepancies are much larger if we examine the effects of an analogous increase in liquid liabilities (4.1% vs. 3.6%) and stock market capitalization (22.7% vs. 20.5%).

Table 15 shows the _sensitivity of our estimates to changes in the measures of institutional quality (IQ)_ . Here we use the different components of this aggregate index such as index of political institutions ( _IQ1_ ), index of the quality of institutions ( _IQ2_ ), index of socioeconomic environment ( _IQ3_ ), and the index of conflict ( _IQ4_ ). We perform this exercise both for foreign assets ( _FA_ ) and foreign liabilities ( _FL_ ) and for a selected set of financial development indicators. _Ceteris paribus_ , we find that the coefficient estimates of the interaction between _FA_ and the components of _IQ_ are positive and significant when estimating the regression for bank credit, bank deposits, bank assets, and liquid liabilities of the financial system. An analogous result holds for the interaction between _FL_ and the components of _IQ_ in the activity and size of financial intermediaries. The results are not as robust for net interest margin, stock market capitalization and value traded. The interaction coefficient between financial openness (either proxied by _FA_ and _FL_ ) and the different measures of _IQ_ is positive and (in most cases) statistically significant. Thus, evidence on the positive impact of financial openness on financial development with strong institutions is robust for the size and activity of banking intermediaries and the depth of private bond markets.

We evaluate the _sensitivity of our results to changes in the measure of investor protection (IP)_ and the results are reported in Table 16. We use the components of the investor protection index from the Doing Business report: disclosure index, director liability index, and shareholder suits index. For the measures of size and activity of bank intermediaries such as bank credit and deposits, assets of deposit money banks, and liquid liabilities, we find that the interaction of the different _IP_ measures and _FO_ (approximated by _FA_ or _FL_ ) has a positive and statistically significant coefficient. Unfortunately, we do not find a robust coefficient estimate for this interaction while evaluating stock and bond market development.

We also perform _sensitivity analysis of the coefficient estimates to different measures of trade openness (TO)_ . Rather than using trade openness we incorporate in the regression analysis trade in manufacturing and in non-manufacturing goods and the interaction of the corresponding measure of financial openness (say, _FA_ or _FL_ ) with manufacturing and nonmanufacturing trade. Table 17 shows the interaction coefficient of financial openness ( _FA_ , _FL_ or 24
27 TABLE 3

|DISTRIBUTION OF EDUCATIONAL SUBSIDY I<br>BY COUNTRY A<br>Author Year of Data Countrl|N SELECTE<br>ND REGION<br>I<br>T<br>_Prilmay_|D DEVELO<br> <br>Secondar=|PING COUNT<br>I~~~~~Hi<br>I Education|RIES,<br>gher<br>T<br>Total|
|---|---|---|---|---|
|, ,<br>**Total Con**|<br>**untrv**|||**-**|
|Meerman- 1974- Malaysisi(eediturelb|T -<br>+||||
|Selowfsky.1974.Colomina (ependiture).|**_+._**|+|**_-_**||
|Jaflade. 1970. Colombia (expenditu)|_NA_|NA|**_NA-_**||
|Fnxlev.et aL. 1969. Chile (expenditurefincome)|**_4._**|4<br>**.**|+|+|
|Castenada- 1996. Chile (expenditure)|+||**_-_**_|+|
|Petrei (contribution to Gini Coefficient)|||||
|Argentina|+|+|**_-_**|+|
|Costa Rica|+|+|**_-_**|+|
|Chile|+|+|**_-_**|+|
|Dominican Republic|+|_|_|_|
|Uruguay|+|4|_|+|
|Meesook, 1978, Indonesia (Share ofrecufrent<br>expenditure compared to share of school-age children)|+<br>_|?|_|**_-_**|
|van de Wagle, 1987, Indonesia (expenditure)|+|**_-_**|_|0|
|Dasgupta and TIlak, 1977, Andbra Pradesh India<br>(expenditure)|+<br>_<br>_|+|_|+|
|Selden and Wasylenko, 1985, Peru (expe_ dture)|_|4-|+|_NA_|
|Hammer, Nabi, and Cerone, 1989, Malaysia<br>|+||**_-_**|+|
|(expenditure)|||||
|Urban||_|||
|Selowsky, 1974, Colombia (expenditure)|_<br>**i.**|_|NA|+|
|jallade, 1970, Colombia (expenditure)|7|?|_|-|
|Petrei (contribution to Gini Coefficient)|||||
|Costa Rica<br>Uruguay|+<br>+|+<br>+|_|0<br>+|
|Meesook, 1978, Indonesia (Share of recurrent<br>expenditure compated to share of school-age childreo)|+|+|_||
|van de Walle, 1987, Indonesia (expenditure)|+|_|_|**+**|
|Dasgupta andrldak,1977, Andhra Pradesh India<br>(expenditure)|+|+|_|+|
|Selden and Wasylenko, 1985, Pemr (expenditure)||+-|+|NA|
- 10 -

# _Tb._ 1: exsxs AM POLICY tF1' _AM PMW4ANU -_ UWS1IAT

||I.|IAL|EFECS||||II.<br>OL|IC= EFFBCS|||
|---|---|---|---|---|---|---|---|---|---|---|
||ERct<br>Term of<br>Trade|enul Siwd<br>(AveEW<br>Export<br>Volume|k as % GN<br>1979-82)<br>Interest<br>Rate|P<br>Tbtal|Oe<br>Real W<br>Average<br>198D-3|Index of<br>han<br>R<br>1984|<br>Ate b/<br>1985|FAtfiu<br>Defici<br>Averap<br>1980-83|ted Gov<br>t (Z G<br>1964|ernumnt<br>DP) ci<br>1985|
|Pldistui|1.7|0.3|0.8|2.8|100.0|98.5|91.9|5.7|6.0||
|Philipplims|2.4|2.3|t.7|6.4|100.0|94.0|102.6|2.9|1.8||
|s.enpl|-3.2|3.1|1.2|1.1|100.0|96.0|105.5|7.3_e._|4.1||
|tdle|1.8|4|4.0|'2|100.0|79.9|61.4|0.1|4.4||
|1Brazil|3.3|0.4|1.9|5.7|100.0|83.6|82.6|2.6 e/|4.7||
|Modco|-3.9|0.7|2.7|-0.4|100.0|<br>94.1|101.1|8.3|8.3||

tITI. FEVRE9 hMICA=S

||R<br>Growth|ea Wort<br>Rate (2)|<br>d/|irrest<br> <br>|Account<br>(% GDP)<br>|Bale<br>|Grow<br>|GP<br>th Rate (|2)|
|---|---|---|---|---|---|---|---|---|---|
||Aerae<br>198043|19E4|1985|Average<br>198043|<br>1964|1985|Averae<br>198043|1984|1985|
|Pdcistan|10.5|-12.5|14.2|-2.7|-2.2|-4.0|7.6|5.5|7.8|
|Philippirm|3.3|2.8|-3.9|-6.7|-3.9|0.0|3.3|-6.1|-4.4|
|Soutl|0.4|-3.0|2.7|-14.8|-11.3|-12.4|3.5|-4.5|3.8|
|Chtle|2.7|3.0|8.0|-9.2|-10.7|-8.3|-0.4|6.3|2.4|
|Brazil|11.3|24.0|-5.8|-4.8|0.0|-0.1|1.0|5.7|8.3|
|Mtido|21.0|9.2|-7.1|-2.5|2.5|0.7|2.6|3.7|2.7|

a/ Exteral g*drA statistics fmn Balssa and mbarthy (1964). An inrease in the tenr of trade index indileat a deerioration **In** the term of trade.

b/ A decrem in tiw index teflects a deluatou It shold be noted that alternative indicm give ratlhr differnit ustiites.

c/ ITe detinitions vary a gret deal. **f** _Mhwtde_ exports.

_Rf_ _Avers for Ssuesgl and Brazil are derived fra figuwes for 1980, 1982, and 1983. f/ Not avaIZable.

Sourc: B1las and MtCarthy (1964), World Bak Ata
|normal returns<br>ns 1 and 2), 10<br>ne if the stock s<br>rs are corrected<br>cantly diferent<br>IL-TOP5|(8)<br>0.005<br>(0.946)<br>-0.113<br>(-0.739)<br>0.028***<br>(2.712)<br>-1.631<br>(-0.269)<br>Stock<br>Yes<br>Yes<br>0.05<br>4150|
|---|---|
|e monthly ab<br>gest 5 (colum<br>ble equal to o<br>and 8). Erro<br>tes are signif<br>AFF|(7)<br>0.009**<br>(1.991)<br>-0.074<br>(-0.498)<br>0.019<br>(0.954)<br>-1.502<br>(-0.258)<br>Stock<br>Yes<br>Yes<br>0.05<br>4215|
|. _ARETs,t_ is th<br> among the lar<br>a dummy varia<br>ders (columns 7<br>efcient estima<br>Top 20|(6)<br>0.007<br>(1.357)<br>0.297***<br>(2.949)<br>0.003<br>(0.402)<br>-1.832<br>(-0.299)<br>Stock<br>Yes<br>Yes<br>0.05<br>4150|
|f in stock s<br>f the PFA is<br>_LLf,s,t−_1 is <br>p 5 sharehol<br> that the co|(5)<br>-0.003<br>(-0.769)<br>0.323***<br>(2.768)<br>0.032***<br>(5.026)<br>-1.779<br>(-0.300)<br>Stock<br>Yes<br>Yes<br>0.06<br>4215|
|pension fund <br> equal to one i<br>ompany. _AFI_<br>among the to<br>/**/* indicate<br>Top 10|(4)<br>0.003<br>(0.692)<br>0.188**<br>(2.159)<br>0.020**<br>(2.372)<br>-1.895<br>(-0.309)<br>Stock<br>Yes<br>Yes<br>0.05<br>4150|
|gs ∆_Hf,s,t_ of<br>my variable <br>ers in each c<br>e PFA is not<br>. Note: ***<br>enthesis.<br>|(3)<br>0.009<br>(1.624)<br>0.206**<br>(2.470)<br>0.011<br>(1.574)<br>-1.887<br>(-0.316)<br>Stock<br>Yes<br>Yes<br>0.05<br>4215|
|s monthly change in holdin<br>arket index. _TOP_ is a dum<br>columns 5 and 6 ) sharehold<br>ame business group and if th<br>clustered at the stock level<br>0% level. T-statistic in par<br>Top 5|(1)<br>(2)<br>0.012**<br>(2.070)<br>0.006<br>(1.028)<br>-0.300<br>-0.296<br>(-1.304)<br>(-1.586)<br>-0.020<br>(-1.044)<br>0.054***<br>(7.965)<br>-1.469<br>-1.525<br>(-0.254)<br>(-0.255)<br>Stock<br>Stock<br>Yes<br>Yes<br>Yes<br>Yes<br>0.05<br>0.05<br>4215<br>4150|
|The dependent variable i<br>calculated relative to a m<br>(columns 3 and 4), or 20 (<br>and fund f belong to the s<br>for serial correlation and <br>from zero at the 1%/5%/1<br>_GROUP_ =|_ARETt−_1<br>_ARETt_+1<br>_GROUP_<br>_ARETt−_1_× GROUP_<br>_ARETt_+1_× GROUP_<br>CONSTANT<br>Cluster<br>Controls<br>Fixed Efects<br>_R_2<br>No. Obs.|

29
although exports fell sharply (by 9.1%), imports fell even more (by 12.6%), so the current account played a strong role in moderating the drop in GDP.

Real government spending _on goods and services_ rose by 0.9% in 2009, but Figure 1 shows that the contribution to economic growth was rather modest, despite the stimulus programs of 2009 and 2010. However, this understates the potential role of government in maintaining GDP, since it also transfers resources to households directly, thereby helping to maintain private consumption spending.

<!-- Start of picture text --> 10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>-8%<br>2006 2007 2008 2009 2010 2011<br>C I G X-IM GDP World GDP<br>% change p.a. (lines) or decomposition (bars)<br><!-- End of picture text -->

**Figure 1.  Decomposition of GDP growth into its macroeconomic components** Source: Data reported by Bank of Thailand; IMF (2012).  Note: C = consumer spending; I = investment spending; G = government spending on goods and services; X-IM = net exports.

The annual data that are summarized in Figure 1 are not fine-grained enough to allow us to date the onset of recession very accurately. Figure 2 presents quarterly data on real GDP growth (at annualized rates). GDP growth was negative for four quarters, starting in the last quarter of 2008. Compared to the crisis of 1997-1998, this recent downturn was substantially smaller and shorter.

One of the ways in which the global economic slowdown was transmitted to Thailand was through a dramatic reduction in the dollar value of merchandise exports, which dropped from US$170 billion in 2008 to US$151 billion in 2009. This was a far stronger reduction in exports Losers and Winners in Recession: Thailand 2008-09 6
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE lower than Ethiopia’s and Sudan’s, these countries would be able to export a sizable share of their domestic consumption (figure 3.3b, 3.4a). As a result, Egypt would decrease its power production substantially. Burundi and Kenya, which currently import minimal amounts of power, would become significant power importers.

Under the trade **Figure 3.3 . Existing and simulated patterns of future power trade in EAPP, 2015 (TWh)** expansion scenario, a. Existing flows (2005, TWh) several countries in EAPP would be able 0.30 to rely heavily on 0.25 power trade by 0.20 importing a large share of their 0.15 domestic power 0.10 Import needs. Figure 3.5 0.05 Export presents the absolute 0.00 trade flows between Egypt Tanzania Rwanda Uganda countries under the trade stagnation and trade expansion scenarios. Under trade expansion, b. Simulated flows (2015, TWh) power exporters 5 would export as little 0 as 22 percent (in the -5 case of Tanzania) and -10 as much as 200 -15 -20 percent of their -25 domestic -30 consumption. Egypt, Burundi Ethiopia Kenya Sudan Tanzania Uganda Kenya, and Burundi would import power. Egypt and Kenya would import Trade Expansion Trade Stagnation significant volumes of power—between Source: Derived from Eberhard and others 2009. 20 and 30 percent of their domestic consumption. Burundi would import almost all of its power needs under trade expansion, making it fully dependent on traded power.

The possibility of accelerating regional power trade in EAPP depends critically on the ability of Ethiopia and Sudan to deliver the massive investments in hydropower that would be needed. A host of technical, financial, and political challenges will make this difficult. First, from a technical perspective, the envisaged scale-up is around 10 times the installed generation capacity of Ethiopia and 1.5 times the generation capacity of Sudan. Increasing installed capacity to such an extent would be a huge technical 41
learning outcomes are comparable to those obtained for school readiness across each of the model specifications (i)– (iii).

In specification (iv), contemporaneous developmental outcomes are included in the model, and the wealth gradient ceases to be significant. These results suggest that the school-age developmental outcomes account for the remaining associations between learning outcomes and wealth, suggesting a close mapping of our school-based outcomes with our contemporaneous developmental outcomes (sustained attention, receptive vocabulary, and cognitive composite).

Table 6: Magnitudes of SES gradient for school learning outcomes (math and literacy)

|||Base|(i)|(ii)|(iii)|(iv)|
|---|---|---|---|---|---|---|
|Math|Q5 vs Q1|1.315|0.904|0.684|0.485|0.213|
|||[0.979, 1.651]|[0.569, 1.239]|[0.319, 1.05]|[0.126, 0.845]|[-0.074, 0.5]|
||R<sup>2</sup>|0.11|0.148|0.163|0.173|0.26|
||N. obs|1003|997|909|906|906|
|Literacy|Q5 vs Q1|1.317|0.74|0.519|0.395|-0.039|
|||[0.954, 1.68]|[0.372, 1.108]|[0.116, 0.921]|[-0.015, 0.806]|[-0.401, 0.323]|
||R<sup>2</sup>|0.125|0.165|0.194|0.199|0.273|
||N.obs|960|955|878|876|876|
|Demogra|phics|Yes|Yes|Yes|Yes|Yes|
|Lagged o<br>early con|utcomes &<br>ditions|No|Yes|Yes|Yes|Yes|
|Maternal<br>& endow|education<br>ments|No|No|Yes|Yes|Yes|
|Home en|vironment|No|No|No|Yes|Yes|
|Concurre|nt ability|No|No|No|No|Yes|

Note: The dependent variable is the standardized score of each test, normalized to have mean zero and standard deviation of one by 2-months age groups in each wage of the full sample. Estimation is done using censored least absolute deviations estimator (CLAD) and bootstrap estimates of its sampling variance. We present the estimated coefficient (and 95% confidence intervals of bootstrap estimates using the normal approximation) on quintile 5 relative to quintile 1, and the pseudo R-squared for each model. Coefficients on quintiles 2-4 are not reported for parsimony. Demographics include age (in years) dummies, gender, birth order dummies, urban and regional dummies. Lagged ability and early conditions include all lagged z-scores for developmental measures as assessed in 2007, as well as lagged stunting indicators and low birth weight indicators. Maternal endowments include the log of maternal height, maternal education defined as completed primary, secondary or higher (relative to no schooling), the maternal vocabulary score, and the crowding indicator. Home environment includes the total HOME score. Concurrent ability include the concurrent developmental measures as assessed in 2011.

.
**Table 5. Proportion of undergraduate and masters level courses using the following textbooks (Cond. on using textbooks)**

||||||Under<br>|graduate le<br>|vel<br>|||Masters level<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||LAC|SSA|MENA|EAP|ECA|SA|All<br>developing<br>countries|Top 20 US<br>economics<br>departments|Non-Top US<br>economics<br>departments|All<br>developing<br>countries|
|Michael Todaro and<br>Stephen Smith -|||||||||||
|Economic<br>Development|0.180*|0.629***|0.556***|<br>0.778***|<br>0.583***|0.500***|<br>0.428***|0.000|0.471***|0.284**|
|Debraj Ray -<br>Development<br>Economics|0.475|0.229***|0.222*|0.444|0.250*|0.500|0.379*|0.625|0.176***|0.395*|
|Abhijit Banerjee and<br>Esther Duflo - Poor<br>Economics|0.148**|0.057***|0.000**|0.222|0.083**|0.063**|0.110***|0.438|0.353|0.148***|
|Perkins et al. -<br>Economics of|||||||||||
|Development|0.018|0.348***|0.167|0.000|0.100|0.083|0.108|0.000|0.133|0.082|
|Banerjee, Benabou and<br>Mookherjee -<br>Understanding Poverty|0.066***|0.029***|0.000**|0.000**|0.083**|0.438|0.090***|0.438|0.059**|0.086***|
|William Easterly - The<br>Elusive Quest for<br>Growth|0.098|0.057|0.000|0.111|0.167|0.000*|0.076|0.188|0.118|0.062*|
|#countries|17|14|5|4|9|4|54|1|1|34|
|#courses|55|22|6|5|10|12|110|15|15|61|

*,**,*** indicate statistically different from non-top 20 US economics departments at the 10, 5 and 1 percent level respectively.

31
McKenzie and Woodruff (2017). _θ_ represents district-level fixed effects and _ε_ is the error term. For analysis using endline data, we further include all treatment indicators from Dalton, Ruschenpohler, Burak, and Zia (2018) to absorb experimentally induced variation in outcomes.

# **IV Results**

## **A Determinants of Aspirations**

Tables 3 to 6 report the results of OLS regressions of entrepreneurial aspirations for business size (Table 3), number of employees (Table 4), number of customers (Table 5), and sales (Table 6). For each dimension, we present regressions of short-term aspirations in the first four columns and aspirations gap in the fifth column. Columns (6)-(10) repeat the same pattern for long-term aspirations.

Several key findings emerge. For business size, Table 3 shows that younger firms and those with higher marketing sub-scores are significantly more likely to report higher short- and longterm aspirations. Aspirations gaps are strongly positive and significant for businesses with higher marketing subscores, with 23% higher short-term and 51% higher long-term aspirations for shop size. These findings are consistent with an expansionary entrepreneurial mindset. Indeed, we find corroborating evidence in entrepreneur characteristics where higher systematic thinking style and perceived agency are associated with higher aspirations gaps. Other entrepreneur characteristics, such as being younger and male also predict higher aspiration levels as well as higher aspirations gaps in the long-term. These results on entrepreneur’s age and gender hold true for all other dimensions of business aspirations as well. More educated entrepreneurs, however, have lower aspirations gaps which is indicative that these shop owners are already significantly closer to their ideal business size.

For employee aspirations, Table 4 shows that business factors play an important role in determining aspirations gaps. Specifically, entrepreneurs who run businesses on premises outside their household have significantly higher short- and long-term aspirations gaps for their number of employees. Similar to shop size, entrepreneurs with higher perceived agency aspire for more employees and have higher aspirations gaps. On business characteristics, we find that business age is again a significant determinant of employee aspirations, with younger businesses having higher aspiration levels. This association holds true for aspirations for number of customers (Table 5) and sales (Table 6) as well.

Turning to aspirations for customers and sales, proxies for the level of formality, specifically whether the business has a tax ID and whether the business premises are separate from the en15