rafmacalaba/datause-encoder-data huggingface dataset:
comes are constructed at the teacher-level, and given their rank-based construction, normality does not seem a helpful approximation to the distribution of error terms. As stated in our pre-analysis plan, we therefore estimate equations (5) and (6) with a round-school random-effects estimator to improve efficiency. The permutations of treatments used for inferential purposes mirror those above.

Results are reported in Table 4 and, to the extent available, are based on pooled data.<sup>34</sup> Consistent with the pooled results in Table 3, we see a positive and significant impact of experienced P4P on both the summary metric and the learning sub-component. The specifications with teacher inputs as dependent variables suggest that this impact on student learning is driven, at least in part, by improvements in teacher presence and pedagogy. Teacher presence was 8 percentage points higher among recruits who experienced the P4P contract compared to recruits who experienced the FW contract; an impact that is statistically significant at the 1 percent level and sizeable in economic terms given that baseline teacher presence was already 90 percent. Recruits who experienced P4P were 0.10 points more effective in their classroom practices than recruits receiving FW, although this impact is weaker in terms of statistical significance (and it is possible that this improvement occurred only during the observation). We find no evidence of impacts on lesson planning.

# **4.3 Dynamic effects**

Our two-tiered experiment was designed to evaluate the impact of pay-for-performance and, in particular, to quantify the relative importance of a compositional margin at the recruitment stage versus an effort margin on the job. The hypotheses specified in our pre-analysis plan refer to selection-in and incentives among placed recruits. Since within-year teacher turnover was limited by design and within-year changes in teacher skill and motivation are likely small, the total effect of P4P in Year 1 can plausibly only be driven by a change in the type of teachers recruited and/or a change in effort resulting from the provision of extrinsic incentives.

Interpreting the total effect of P4P in Year 2 is more complex, however. First, we made no attempt to discourage _between_ -year teacher turnover, and so there is the possibility of a further compositional margin at the retention stage (c.f. Muralidharan and Sundararaman 2011). Experienced P4P may have selected-out the low treatment. These variables are therefore not included as independent variables.

> 34As discussed in Section 3.4, FW schools only received unannounced visits to measure teacher inputs in Year 2.

30
In formulation **7** , we keep the same variables as in **6** except that we replace ethnolinguistic fractionalization with religious fractionalization to check whether the two fractionalizations have the same effect. _Religious fractionalization_ variable is calculated from the same source as ethnic fractionalization (see footnote 21 above). In most countries, ethnic fractionalization is greater than the religious.<sup>33</sup> The two fragmentation variables are barely positively correlated in the rest of the world although they are somewhat more correlated in Africa (Table 5).

Table 5. Ethnolinguistic and religious fractionalization in Africa and rest of the world

||Africa|Rest of the world|
|---|---|---|
|Mean(median) ethnic fractionalization|0.73 (0.79)|0.39 (0.39)|
|Mean(median) religious fractionalization|0.56 (0.62)|0.31 (0.25)|
|Correlation between the two|0.23|0.1|
|fractionalizations|||
|Standard deviation of ethnic fract.|0.19|0.24|
|Standard deviation of religious fract.|0.20|0.24|

Note: Maximum fractionalization 1: likelihood of two random individuals belonging to two different group is then 100 percent. Source: Annett (2000).

Unlike ethnic fractionalization, religious fractionalization alone has no significant effect, and the interaction of religious fractionalization and the Africa dummy, turns out to be similarly not significant. The Africa-specific intercept becomes greater than zero. So in the other formulations, we return to using ethnic rather than religious fractionalization.

It would seem that most of Africa’s “exceptionalism” (high inequality) is not accounted for by a greater ethnolinguistic fractionalization as such, but by stronger

> fractionalization and inequality (with higher levels of fractionalization having an additional positive effect), the squared variable is indeed significant but so is still the Africa dummy. Similarly, interaction between ethnic fractionalization and commodity dependence shows a significant positive effect for high commodity-dependent countries, but does not make the Africa dummy variable insignificant. (Results of both regressions available on request.)

> 33 This is not surprising because the number of ethnicities in the world surpasses by much the number of religions.

20
correct TFP obtained from OLS regression and employ this additional variable (TFP_ADJ) for further robustness checks.

Given the high correlation among the different estimates of TFP, we present our main results using one of the estimates (TFP_S) and use the others to perform robustness checks. We should also note that some recent research has highlighted that OLS and 2SLS TFP estimates do not differ substantially (see Eslava et al, 2004).

# **4. Access to credit and productivity**

We first present OLS estimates of main correlates of firm TFP and assess the impact, if any, of access to credit as proxied by the presence of a credit line or overdraft facility. We then discuss the econometric problems associated with OLS estimation in this context, discuss the validity of a set of instruments, and present 2SLS estimation. Finally, we discuss a number of sensitivity checks to assess the robustness of our estimation.

# _4.1 OLS  estimates_

We regress estimated TFP on a number of basic correlates and then add to this baseline specification our main variable of inference. The results are reported in Table 4. We find that large enterprises are overall more productive, particularly if compared to micro enterprises. As expected, companies that were previously government owned are overall less productive. Also, younger firms are on average more productive.

11
# **3. Simple measures of profits**

We motivate our initial two measures of business profits on the basis of the way these questions are asked in the Mexican National Microenterprise Survey (ENAMIN), one of the more comprehensive and regular microenterprise surveys carried out in developing countries.<sup>4</sup> The first measure of business profits is obtained by asking the firm owner for the revenue and expenses of the firm, and then calculating profits as the difference between reported revenue and reported expenses. We asked owners for the total revenues of the firm in the last month, and then for the total expenses in each of eleven categories (purchases of inventories; purchase of electricity, water, gas and fuel; interest paid; wages and salaries for employees; rent for machinery and equipment; rent for land and buildings; telephone or cellphone charges; taxes; maintenance and general repairs; traveling expenses; and other expenses).

The second measure asks owners directly for their profits, by means of the following question, illustrated here for the month of March: “What was the total income the business earning during the month of March after paying all expenses including the wages of employees, but not including any income you paid yourself. That is, what were the profits of your business during March?” Table 2 reports the mean, standard deviation, median, and percentage of profits which are negative, for each of these two measures of profits for July and August in the KMS, and for the first and second rounds of the SLMS. For comparison with the KMS, we also separate the second round measurements in the SLMS into measures for retail trade and manufacturing firms. The foot of the table reports the Pearson and Spearman (rank-order) correlations between the two measures, along with a p-value for a test of independence.

> 4 Less detailed versions of the same questions are also asked in the World Bank’s Living Standards Measurement Surveys (see Vijverberg, 1991 and Vijverberg and Mead, 2000).

6
necessarily as the result of bad _policy_ (e.g. tax _rates_ that are “too high”) but as the combination of policy with weak implementation so that the implementation of complex taxes results in substantial uncertainty.

The policy implementation interpretation links firms’ concerns about policy uncertainty and the suggestive evidence about the impact of uncertainty on firm employment growth with the growth literature, both the standard growth regression literature and the literature on episodic growth, generally (e.g. Hausmann, Pritchett, Rodrik 2005) and its application to Africa (Abarcha and Page 2007, Patillo et al. 2005)<sup>29</sup> .

# **V.B)  Programmatic responses:** **_de jure_ “Policy Reform”**

As raising economic growth rates in Africa has been a development priority for decades, there have been clear responses, both programmatic and in overall reform efforts, to the major growth obstacles identified in the macroeconomic and microeconomic literature. Obviously addressing “macro instability”—reducing inflation, macroeconomic imbalances, exchange rate crises, debt—has been a major agenda for decades. “Access to finance” has been addressed through both big picture reforms of the financial sector and through programmatic attention to micro-credit and SME financing. Infrastructure and its shortages as obstacles to growth are also again attracting enormous attention and investments. Our argument is that inadequate conceptualization of the problem of policy _implementation_ has hindered effective response to the problem of policy _uncertainty_<sup>_30_</sup> . It is not at all obvious that the typical approach to “policy reform” of

> 29 For instance, Abarcha and Page (2007) find that a variety of indicators of “institutional quality” (the World Bank’s CPIA and all six components of the KKM indicators) are lower during growth decelerations. 30 One reaction to the empirical finding of consistently high levels of firms complaining of “policy uncertainty” is that the question was dropped from the standard Enterprise Survey instrument precisely because it was difficult to interpret the results and translate them into programmatic action.

44
# **Tables**

Table 1: Variation in Grades Taught by Teachers and Number of Times Teachers are Observed

||(1)|(2)<br>Publ|(3)<br>ic Sector|(4)|(5)|(6)<br>Priv|(7)<br>ate Sector|(8)|
|---|---|---|---|---|---|---|---|---|
||Observed<br>Once|Observed<br>Twice|Observed<br>Three Times|Observed<br>Four Times|Observed<br>Once|Observed<br>Twice|Observed<br>Three Times|Observed<br>Four Times|
|Only Grade 3|235|37|14|14|346|35|2|0|
|Restricted Sample|235|33|8|11|346|32|1|0|
|Only Grade 4|166|14|1|0|274|8|0|0|
|Restricted Sample|166|12|0|0|274|8|0|0|
|Only Grade 5|0|0|0|0|0|0|0|0|
|Restricted Sample|0|0|0|0|0|0|0|0|
|Grade 3 and 4|31|235|53|12|29|83|19|6|
|Restricted Sample|31|31|1|0|29|11|1|0|
|Grade 3 and 5|13|37|8|0|11|14|6|0|
|Restricted Sample|13|35|5|0|11|9|1|0|
|Grade 4 and 5|25|110|18|0|27|31|19|0|
|Restricted Sample|25|26|1|0|27|10|0|0|
|Grades 3, 4, 5|8|48|214|83|3|28|25|28|
|Restricted Sample|8|14|9|1|3|6|2|0|

This table reports counts of the number of teachers who are observed teaching only grade 3, only grade 4, only grade 5, only grades 3 and 4, only grades 4 and 5, and grades 3, 4, and 5 by how many times they were observed. The restricted sample excludes teachers who are ever observed teaching two classes of students who appear to be the same in two consecutive years (90% or more of the students in year _t_ were taught by the same teacher in year _t −_ 1).

Table 2: Sources of Variation in Teacher Value-Added Calculations

|Nu|(1)<br>mber of Teachers|(2)<br>Number of Students|(3)<br>Teachers in Schools With<br>_>_1 Teacher With Tested<br>Students|(4)<br>Students in Schools<br>With_>_1 Teachers<br>With Tested Students|
|---|---|---|---|---|
|Public, Rd 1|486|8,340|4|131|
|Private, Rd 1|303|3,617|0|0|
|Public, Rd 2|593|9,327|214|3,290|
|Private, Rd 2|336|3,340|97|846|
|Public, Rd 3|1007|16,946|884|15,320|
|Private, Rd 3|579|6,777|524|6,247|
|Public, Rd 4|1103|15,357|812|12,610|
|Private, Rd 4|599|5,911|478|5,020|

This table presents the breakdown of the data used to calculate within and across school TVAs. Within school TVAs require teachers to teach in schools where more than one teacher has tested students (such that the mean school effect is not equal to the sole teacher’s TVA). The sample of students driving variation in the within school TVAs are the students who attend schools where more than one teacher has tested students.

35
markets, they typically make their purchase via mutual funds. In actively managed funds, it is the fund manager who ultimately determines the portfolio allocation, **by** choosing how the fund invests its assets (within the limits of the fund's defined scope). In index funds, the manager's role is passive, aimed at replicating a predetermined index.

Mutual funds have become one of the main instruments for investing in emerging markets. The first funds, in the 1980s, were closed-end funds; they are well suited to invest in illiquid markets, because their shares cannot be redeemed. With increasing liquidity in emerging markets, open-end funds have become the most widely used instrument. It is important to recognize that mutual fund investors include other types of institutions as well. For example, more than half of pension funds invest in emerging markets through existing mutual funds. This is considered better for liquidity reasons and is also less expensive than giving specific mandates to mangers. Therefore, **by** looking at mutual funds, one is counting much of pension fund investment in emerging markets as well. **A** survey published at World Bank **(1997)** estimates that pension funds hold around **_1.5_** to 2 percent of their portfolios **_($50_** billion-$70 billion) in assets from emerging markets.

Hedge funds are a newer type of institutional investor. They are still small relative to the other institutional investor types. It is estimated that hedge funds held total assets of around **$81** billion **by** year-end **1997,** only a small fraction of which is invested in emerging markets.'<sup>0</sup> Like other institutional investors, insurance companies are also believed to invest only a small proportion of their assets in emerging markets. However,

> to See Eichengreen and Mathieson **(1998)** for a detailed study on hedge funds.

**8**
liquidity for self-insurance and its dependence on the interest rate are thus more relevant when the advanced technology is more profitable or pledgeable. The same logic also applies when the advanced technology is more risky. This is because the mean-preserving spread described in part (iii) works to increase the expected return to liquidity _ρ_ ̄ _≡_ [ _ωl_ + (1 _− ωl_ ) _ρ_ ].

Notice that parts (ii) to (iv) above also allow a direct interpretation in terms of empirically observed outcomes. Industries where _ωlA_<sup>_j_</sup> , _ρ_ ̄ or _γ_ is higher will be more exposed to the advanced technology. This technology allows for higher expected returns, but is also more volatile due to the presence of idiosyncratic liquidity risk. The empirical hypothesis therefore is that the liquidity mechanism should be particularly relevant in fast growing and volatile industries, and in those with high income pledgeability.

# **4 Empirical analysis**

In this section, we use firm-level panel data from the World Bank Enterprise Surveys (WBES) to examine the model’s key predictions regarding the effects of inflation on corporate innovation and financing activity. To that end, the WBES are particularly useful as they provide harmonized cross-country data for a large number of developing countries with significant variation in

## **4.1 Data**

**_World Bank Enterprise Surveys._** The WBES are designed to provide internationally comparable establishment-level data for a large number of developing countries that are representative for the formal private sector economy (that is, establishments with at least five employees) for each country-year episode. They include information on innovation activities, sources of finance, and other key characteristics for over 100,000 establishments in 139 developing countries between 2006 and 2016. The harmonized surveys are conducted about every three years in each country. The surveys provide sampling weights for each country-year episode so that the weighted estimations are representative at the country level. Appendix A.3 provides of technology, the analysis is then per unit of initial investment, with unchanged comparative statics.

14
|Log<br>Total<br>(8)<br>3.33<br>(.05)_∗∗∗_<br>3.87<br>(.21)_∗∗∗_<br>.01<br>(.07)<br>-.41<br>(.24)_∗_<br>-.08<br>(.05)<br>-.11<br>(.17)<br>.21<br>(.07)_∗∗∗_<br>.34<br>(.19)_∗_<br>.<br>2,159<br>xpenditure<br>educational<br> and 7) are<br>Children in<br>at least one<br>d standard|
|---|
|ondary<br>Only<br>Fees<br>(6)<br>(7)<br>133.56<br>130.72<br>(3.71)_∗∗∗_<br>(3.79)_∗∗∗_<br>245.80<br>129.11<br>(11.45)_∗∗∗_<br>(14.24)_∗∗∗_<br>-63.47<br>-64.18<br>(4.81)_∗∗∗_<br>(5.00)_∗∗∗_<br>-60.43<br>-39.20<br>(13.20)_∗∗∗_<br>(16.19)_∗∗_<br>9.89<br>(3.69)_∗∗∗_<br>153.43<br>(11.57)_∗∗∗_<br>-2.53<br>(4.78)<br>-10.35<br>(13.20)<br>.<br>.<br>16,890<br>16,890<br>nt variable includes only e<br>nclude all categories of <br>vel. Columns 2 and 3 (6 <br>ndent variables labeled “<br> to households that have <br> it has a mean of zero an|
|Sec<br>Total<br>Spending<br>(5)<br>183.69<br>(4.21)_∗∗∗_<br>294.50<br>(12.98)_∗∗∗_<br>-33.43<br>(5.45)_∗∗∗_<br>-5.87<br>(14.98)<br>.<br>16,890<br>to 4 the depende<br>well as 5 and 8) i<br>ant education le<br> in 1997. Indepe<br>mple is restricted <br>dardized so that|
|Log<br>Total<br>(4)<br>.99<br>(.008)_∗∗∗_<br>1.28<br>(.05)_∗∗∗_<br>-.16<br>(.01)_∗∗∗_<br>.62<br>(.07)_∗∗∗_<br>.07<br>(.009)_∗∗∗_<br>.36<br>(.05)_∗∗∗_<br>.008<br>(.01)<br>.08<br>(.06)<br>0.000<br>11,708<br> In columns 1<br> 1 and 4 (as <br>) for the relev<br>2006 and zero <br>chool. The sa<br>schooling, stan|
|ly<br>es<br>(3)<br>4.20<br>(.76)_∗∗∗_<br>22.83<br>(4.60)_∗∗∗_<br>-3.54<br>(1.07)_∗∗∗_<br>14.58<br>(5.95)_∗∗_<br>2.18<br>(.82)_∗∗∗_<br>41.91<br>(4.19)_∗∗∗_<br>-2.56<br>(1.14)_∗∗_<br>25.80<br>(5.16)_∗∗∗_<br>0.000<br>16,890<br>US dollars. <br>. Columns <br>d transport<br> of one in <br>n type of s<br>n years of|
|Primary<br>On<br>Fe<br>(2)<br>4.01<br>(.77)_∗∗∗_<br>43.57<br>(4.18)_∗∗∗_<br>-3.66<br>(1.08)_∗∗∗_<br>50.26<br>(5.07)_∗∗∗_<br>0.000<br>16,890<br>penditure in<br>y education<br>s, board, an<br>takes a value<br>lled in a give<br>s measured i|
|Total<br>Spending<br>(1)<br>11.80<br>(.90)_∗∗∗_<br>64.12<br>(4.89)_∗∗∗_<br>-5.57<br>(1.27)_∗∗∗_<br>81.68<br>(5.94)_∗∗∗_<br>0.000<br>16,890<br>educational ex<br>ly on secondar<br>ooks, uniform<br>FPE variable <br>household enro<br>’s education i|
|n in public school<br>n in private school<br>Children in public<br>Children in private<br>duc. _×_ Children in public<br>duc. _×_ Children in private<br>duc. _×_ FPE _×_ Children in pub.<br>duc. _×_ FPE _×_ Children in priv.<br><br>_γp_<br>_private,F P E_<br>_γp_<br>_private_<br>_−_<br>_γs_<br>_private,F P E_<br>_γs_<br>_private_<br>= 0<br>ndent variable is total annual household<br>ry education, and in columns 5 to 8 on<br>re available in both surveys (i.e., fees, b<br> to primary (secondary) fees only. The <br>sure the total number of children in the <br>ither primary or secondary school. Head<br> of unity.|
|Childre<br>Childre<br>FPE _×_ <br>FPE _×_ <br>Head’s e<br>Head’s e<br>Head’s e<br>Head’s e<br>P-value:<br>Obs.<br>The depe<br>on prima<br>expenditu<br>restricted<br>. . . ” mea<br>child in e<br>deviation|

19
completion are not nearly as large as gender gaps in labor force participation. Across the 142 countries in the Barro-Lee data set, the average gender gap in primary school completion is only six percentage points and the average gender gap in secondary school completion is only four percentage points. This reflects the very high rates of primary school completion in many parts of the world: more than two thirds of the countries in the Barro-Lee data set have rates of primary school completion above 90 percent for both men and women. Moreover, many wealthy countries have compulsory schooling laws which tend to reduce gender gaps in educational attainment.

In Table 2, we examine the cross-country relationship between grammatical gender and primary school completion. As expected, the relationship is _positive_ and significant when continent controls are not included — reflecting the fact that primary school completion rates are highest in Europe, where gender languages are dominant. Once continent fixed effects are included, the estimated association is negative but not statistically significant. In Columns 4 through 6 of Table 2, we examine the relationship between grammatical gender and the gender gap in primary school completion. When continent fixed effects are omitted, we again find a positive association, though it is not statistically significant. After including continent fixed effects, we find a negative relationship that is marginally statistically significant. Coefficient estimates suggest that grammatical gender is associated with a 3.72 percentage point increase in the gender gap in primary school completion (Table 2, Column 6, p-value 0.089).

We observe an even more muted cross-country relationship between gender languages and secondary school completion (Table 3). In the absence of continent fixed effects, we again find a positive and significant association between grammatical gender and secondary school completion — reflecting in part the very high rate of female secondary school completion in Europe and the very low rate in Africa. After including continent fixed effects, the association between grammatical gender and female secondary school completion is never statistically significant. Moreover, we never observe a statistically significant association between grammatical gender and the gender gap in secondary school completion. Thus, 21