rafmacalaba/datause-encoder-data huggingface dataset:
_Five,_ our work confirms that better management practices is associated with superior firm performance. Firms with more structured management practices have higher sales per employee, profitability, are more innovative and more likely to adopt sophisticated technology. This result is highly robust to a numerous checks and controls as well as varying measures of firm performance.

_Finally_ , while explaining the differences in management practice across firms, it becomes clear that among the firm characteristic that explain at least a portion of the variation in management practices, firm size, foreign linkages and the education level of non-managers are the most relevant. Surprisingly firm age is negatively associated with better management practices, especially in services. This implies that unlike in the United States and Mexico, firms do not learn to be better managed as they age. Thus, it is possible that a lack of pro-competitive forces in Croatia and more so in the services sectors do not promote learning and possibly hinder market selection.

If management matters for firm performance, then why firms do not adopt better management practices? Our survey also includes a question relating to the firm’s own perception of its management practices, which was originally a part of WMS survey questionnaire. More specifically, we ask the following question: “ _Excluding yourself, how well managed is your firm on a scale of 1 to 10, where 1 is worst practice, 10 is best practice and 5 is average_ ”. Prior studies using WMS data that compared self-perception of management capabilities with the objective management score found a huge divergence between observed management capabilities and perceived one. In line with these findings, we uncover that firms in Croatia also have a higher perception on their management practices ( **Figure 9** ). Given that firms do not realize that there is a problem with respect to their management practices, it does not get the required attention. Our survey objectively measures management capabilities among firms, and also provides an understanding of the capabilities that these firms are weak at.

_Figure 9: Firms perceive their management to be better than it actually is_ <!-- Start of picture text --> Difference vs. Real Management Score<br>Croatian Firms (Manufacturing and Services)<br>0 .2 .4 .6 .8 1<br>Real Management Score<br>Diff = Perceived - Real Fitted values<br><!-- End of picture text -->
**Table 3 Predicted poverty rates with climate change**

||||**Low**|**Medium**|**High**|
|---|---|---|---|---|---|
||**Base Year**|**No Growth**|**Growth**|**Growth**|**Growth**|
||2004/05|2040|2040|2040|2040|
|**Rural**|48.8|54.8|35.8|18.3|2.1|
|**Urban**|31.1|32.3|15.7|5.8|0.2|
|**All**|44.5|49.4|31|15.3|1.1|

_Notes_ : Annual growth rates in mean consumption derived from several NSS rounds are drawn from Datt and Ravallion (2011). Low growth refers to 1958-1991 (0.58 rural and 0.79 urban). Medium growth rates to 1991-2006 (1.17 rural and 1.49 urban). High Growth simply doubles the growth rates under the medium growth rate scenario (i.e. 2.34 rural and 2.98 urban). Source: Jacoby, Rabassa, and Skoufias (2011) Taking into account average income growth up to 2040, the national poverty rate will rise by 3.5 percentage points compared to the counterfactual of zero warming (see Table 4). Given the current population projections, climate change is predicted to result in around 50 million more poor people than there otherwise would have been in that year.

**Table 4 Differences in poverty rates (in percentage points) due to climate change**

||**Low**|**Medium**|**High**|
|---|---|---|---|
||**Growth**|**Growth**|**Growth**|
||2040|2040|2040|
|**Rural**|5.9|4.4|0.7|
|**Urban**|1.1|0.6|0.1|
|**All**|4.8|3.5|0.6|

Source: Jacoby, Rabassa, and Skoufias (2011)

# **_3.3 The Impacts of Increased Climate Variability on Welfare and Poverty_**

Although there is a great deal of uncertainty over the exact magnitudes of the global changes in temperature and especially precipitation, it is widely accepted that significant deviations of the variability of climate from its historical patterns are likely to occur (IPCC 2007b). Erratic weather and increased climatic variability will affect agricultural productivity which, depending on how effective was the portfolio of _ex ante_ and _ex post_ risk management 17
technologies have a positive (even if small) health impact and seem to be an improvement to surface water and open defecation. According to our estimates, it is therefore not clear why simple technologies and the total lack of technologies are often both classified as unimproved water and/or sanitation. Moreover, it seems that there is not a statistical significant difference of the impact of various water technologies on child diarrhea, independent of where we make the technology cut-off (according to our or the WHO definition). The big difference seems to be whether households use surface water (left-out category) or ground water (extracted by simple or more advanced technologies).

**Table 5: Classifications and Positive Externalities**

|||**child diarrhea**||**c**|**hild mortality**||
|---|---|---|---|---|---|---|
||**odds ratio**|**95% confiden**|**ce interval**|**hazard ratio**|**95% confidenc**|**e interval**|
||||**Technological**|**Classification**|||
|Latrine|0.929***|0.911|0.949|0.954***|0.927|0.982|
|Flush|0.871***|0.844|0.899|0.861***|0.813|0.913|
|Well|0.925***|0.901|0.950|0.954***|0.922|0.988|
|Piped|0.927***|0.900|0.956|0.884***|0.847|0.924|
|Observations|753,239|||796,219|||
||||**WHO/UNICE**|**F Classification**|||
|Basic sanitation|0.937***|0.918|0.958|0.962|0.917|1.010|
|Improved sanitation|0.886***|0.863|0.910|0.949***|0.917|0.982|
|Basic water|0.937***|0.911|0.964|0.948***|0.913|0.984|
|Improved water|0.917***|0.893|0.943|0.873***|0.833|0.926|
|Observations|753,239|||796,219|||
||||**Private/Public**<br>|**Classification**<br>|||
|Public sanitation|1.006|0.975|1.038|0.967**|0.939|0.996|
|Private sanitation|0.916***|0.895|0.938|0.869***|0.833|0.908|
|Public water|0.927***|0.902|0.953|0.955***|0.921|0.991|
|Private water|0.861***|0.834|0.889|0.924***|0.890|0.921|
|Observations|595,661|||630,320|||
||||**Househ**|**old Level**|||
|Latrine/flush|0.922***|0.904|0.941|0.942***|0.916|0.970|
|Well/piped|0.926***|0.903|0.951|0.940***|0.909|0.972|
|Observations|753,239|||796,219|||
||||**Cluste**|**r level**|||
|Latrine/flush|0.837***|0.779|0.901|0.782***|0.727|0.841|
|Well/piped|0.958|0.879|1.043|1.017|0.934|1.109|
|Observations|61,605|||83,745|||

_Notes: Logit Model for diarrhea and Weibull Survival Model. Odds ratios and hazard ratios are reported. Yearcountry fixed effects. The set of control variables used in the estimation are identical to the ones specified in Table 3 and 4. For more details on the classifications see Table 1. The left out category in all specifications is surface water and open defecation.*** p<0.01, ** p<0.05, * p<0.10. Robust standard errors underlying confidence intervals are clustered at the cluster level._ 25/36
Now we turn to individual level data and estimate the effect of earnings differentials on the occupation choice of teachers versus non teachers, using the empirical framework laid out in the previous section. Table 3 is the descriptive sample statistics.

**Table 3: Sample summary statistics:**

|Variable<br>name|Definition|Mean|Standard<br>error|
|---|---|---|---|
|Dependant<br>variables:||||
|_Dteacher_|0-1 dummy variable, =1 if individual is a teacher|0.163|0.370|
|_Earnings_|Monthly earnings in Rupiah (Rp)|1,262,190|2,038,909|
|_lnW_|Log earnings, =ln(_Earnings_)|14.106|0.757|
|Explanator<br>y variables:||||
|_Age_|age|37.173|10.183|
|_Dfemale_|0-1 dummy variable, =1 if individual is female|0.396|0.489|
|_Drural_|0-1 dummy variable, =1 if individual lives in rural area|0.153|0.360|
|_Dmarried_|<br>0-1 dummy variable, =1 if individual is married|0.725|0.447|
|_D2001_|0-1 dummy variable, =1 if individual is from 2001 sample|0.069|0.253|
|_D2002_|<br>0-1 dummy variable, =1 if individual is from 2002 sample|0.110|0.313|
|_D2003_|0-1 dummy variable, =1 if individual is from 2003 sample|0.140|0.347|
|_D2004_|0-1 dummy variable, =1 if individual is from 2004 sample|0.149|0.356|
|_D2005_|0-1 dummy variable, =1 if individual is from 2005 sample|0.108|0.310|
|_D2006_|<br>0-1 dummy variable, =1 if individual is from 2006 sample|0.133|0.340|
|_D2007_|0-1 dummy variable, =1 if individual is from 2007 sample|0.141|0.348|
|_D2008_|<br>0-1 dummy variable, =1 if individual is from 2008 sample|0.150|0.357|
||<br>0-1 dummy variable, =1 if individual lives in Nanggroe Aceh|||
|_D11_|<br>Darusalam|0.026|0.158|
|_D12_|0-1 dummy variable, =1 if individual lives in Sumatera Utara|0.037|0.190|
|_D13_|<br>0-1 dummy variable, =1 if individual lives in Sumatera barat|0.028|0.165|
|_D14_|0-1 dummy variable, =1 if individual lives in Riau|0.017|0.128|
|_D15_|<br>0-1 dummy variable, =1 if individual lives in Jambi|0.011|0.103|
|_D16_|0-1 dummy variable, =1 if individual lives in Sumatera selatan|0.017|0.131|
|_D17_|<br>0-1 dummy variable, =1 if individual lives in Bengkulu|0.014|0.116|
|_D18_|0-1 dummy variable, =1 if individual lives in Lampung|0.014|0.116|
|_D19_|<br>0-1 dummy variable, =1 if individual lives in Bangka belitung|0.005|0.070|
|_D21_|<br>0-1 dummy variable, =1 if individual lives in Kepulauan Riau|0.006|0.079|
|_D31_|<br>0-1 dummy variable, =1 if individual lives in DKI Jakarta|0.228|0.419|
|_D32_|<br>0-1 dummy variable, =1 if individual lives in Jawa Barat|0.073|0.261|
|_D33_|<br>0-1 dummy variable, =1 if individual lives in Jawa Tengah|0.066|0.248|
|_D34_|0-1 dummy variable, =1 if individual lives in DI Yogyakarta|0.060|0.237|
|_D35_|<br>0-1 dummy variable, =1 if individual lives in Jawa Timur|0.113|0.317|

19
domly drawn price determines whether or not entrepreneurs are offered training. We use this random variation as an instrument for attendance. We run a two-stage least squares regression where the second stage is given by equation 1, which uses attending at least one class ( _Attendancei_ ) as endogenous variable, and the first stage is given by equation 2.

We control for WTP in a flexible way (binary categories _WTPj_ ) and to gain statistical power we employ ANCOVA regressions by including the baseline value of the outcome ( _Yi_ ,0). We then add an interaction term between ( _Attendancei_ ) and WTP and the interaction of WTP and Price as an instrument, to test whether the effect of attendance is higher for those with higher willingness to pay. We can do this only for the BDM sample, since WTP is not measured for the TIOLI sample and so cannot be interacted with attendance.

However, in practice our ability to estimate these heterogeneous treatment effects is limited for several reasons. First, the logistical difficulties implementing BDM resulted in a smaller than desired sample size, more participants assigned to treatment than control, and lower take-up of training, all of which reduce statistical power. Second, in our six to nine-month follow-up survey, attrition rates were high and strongly correlated with treatment. Only 40 percent of those who were not offered the training because of the BDM rule completed the follow-up survey, and completion rates were 32 percentage points higher for the group assigned to training. Given this large difference, we weight observations using the inverse probability of attrition weights, but still view these results as, at best, suggestive only.

Table 11 reports the impact of attending at least one class of the training on eight outcomes: three measures of intermediate channels training is intended to improve (business practices, personal initiative, and business knowledge), and five measures of firm performance (having at least one employee, having positive sales and profits, and the winsorized levels of sales and profits).<sup>25</sup> Panel A shows the main effects. We see a strong significant effect on the share of business practices adopted (an increase of 16 percentage points) and no effects on personal initiative, which is consistent with the findings in Ubfal et al. (2019). We also see significant effects on a dummy for having positive profits and on the winsorized level of profits (significant at the 10 percent level).<sup>26</sup> The impacts on knowledge, having

> 25See Appendix B for details on the construction of these variables.

> 26The only result that survives to adjusting the p-values for the fact that we are testing 8 hypotheses in this

25
contrast, it is not even significant when either the corruption index or political stability is used to instrument institutional continuity (row 11 and column 2, row 12 and column 4).

**Table 2: Regression Results for GCC and SSA Resource-Rich Countries**

||First Stage|Second Stage|First Stage|Second Stage|First Stage|Second Stage|
|---|---|---|---|---|---|---|
|**_Dependent variable_**|Corruption<br>index|log(GDP per<br>capita)|Political<br>stability|log(GDP per<br>capita)|Political risk<br>rating|<br>log(GDP per<br>capita)|
||**_Column 1_**|**_Column 2_**|**_Column 3_**|**_Column 4_**|**_Column 5_**|**_Column 6_**|
|Constant|3.065|2.681|7.977|2.382|52.167|2.207|
||(0.326)***|(0.331)***|(0.402)***|(0.33)***|(2.693)***|(0.236)***|
|log(IGPC)|0.089<br>(0.095)|0.074<br>(0.022)***|0.385<br>(0.13)***|0.052<br>(0.032)|4.445<br>(0.719)***|0.026<br>(0.031)|
|log(IPPC)|‐0.051|0.112|0.274|0.097|1.020|0.095|
||(0.079)|(0.021)***|(0.106)**|(0.023)***|(0.878)|(0.02)***|
|FDIPC|0.000||0.000||‐0.001||
||(0)*||(0)||(0.001)||
|log(M2PC)|0.179|0.362|‐0.130|0.289|‐1.349|0.289|
||(0.2)|(0.06)***|(0.281)|(0.045)***|(1.923)|(0.044)***|
|log(DOMCREPC)|0.168||0.029|0.050|3.669|0.013|
||(0.112)||(0.164)|(0.026)*|(1.078)***|(0.026)|
|NETPC|0.000|0.000|0.000|0.000|‐0.001|0.000|
||(0)***|(0)|(0)|(0)|(0.001)*|(0)*|
|CREER|0.474|‐0.179|0.606|‐0.213|0.081|‐0.194|
||(0.364)|(0.11)|(0.448)|(0.091)**|(2.158)|(0.092)**|
|CONT|0.183||0.860||4.957||
||(0.277)||(0.265)***||(1.528)***||
|log(EXPPC)|‐0.413|0.252|‐0.419|0.289|‐4.922|0.323|
||(0.115)***|(0.059)***|(0.141)***|(0.031)***|(0.921)***|(0.032)***|
|CORR-IV||0.002<br>(0.125)|||||
|POL‐IV||||0.050<br>(0.046)|||
|POLRISK_IV||||||0.011<br>(0.005)**|
|**Adjusted R2**|0.189|0.987|0.768|0.987|0.616|0.987|

Resource rich SSA countries and Gulf countries are included. The estimation technique is OLS panel regression. The standard errors given in paranthesis are heteroskedasticity corrected. *** stands for significance at 1%, ** significance at 5% and * significance at 10%. Time dummies are included but the results are not reported in the table due to space limitation. GDP per capita is in constant 2000 US$. IGPC is public investment per capita in constant 2000 US$. IPPC is private investment per capita in constant 2000 US$. FDIPC is foreign direct investment per capita in constant 2000 US$. M2PC is M2 money per capita in constant 2000 US$. DOMCREPC is domestic credit to private sector per capita in constant 2000 US$. NETPC is net interest income per capita in constant 2000 US$. CREER is change in real effective exchange rate. EXPPC is exports per capita in constant 2000 US$. CONT is the continuity dummy which is 1 for Gulf countries and Botswana and 0 otherwise. CORR‐IV is the estimated value of the corruption index from the first stage. POL‐IV is the estimated value of the political stability index from the first stage. POLRISK‐IV is the estimated value of political risk rating from the first stage.

16
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **Figure 8: World & Cote d’Ivoire Cocoa Production, World Prices,  Cocoa Land Use in Cote’ d’Ivoire, and Share of World Market**

<!-- Start of picture text --> International Cocoa Prices 1960-2003: Estimated Spot Price,<br>& Price Adusted by US Consumer Price Index (Base 1995)<br> Based on near term futures prices converted to spot equivalents,<br>Physical Cocoa Bean Production Average, London & New York price quotations, at London exchange closing<br> In the W orld a nd Cote d'Iv oire 450<br>3.5<br>Millions of Metric Tons<br>400<br>1961-2003<br>3<br>350<br>COTE D'IVOIRE<br>2.5 WORLD 300<br>250<br>2<br>200<br>1.5 150<br>1 100<br>50<br>0.5<br>0<br>0<br>Average Annual Prices, 1960 to 2003<br>Annual Output US cents/lb. Real price<br>Cote d'Ivoire: Use of Land for Cocoa Bean Production Cote d'Ivoire Share of W orld Cocoa Bean Output<br>(Share of Physical Production in Metric Tons. Source: FAO)<br>60% 45%<br>Cocoa as % of Arable and Permanent Crop Land<br>40%<br>50% Cocoa Land as % Permanent Crop Land<br>35%<br>40% 30%<br>25%<br>30%<br>20%<br>20% 15%<br>10%<br>10%<br>5%<br>0% 0%<br>Annual Market Share, 1961-2003<br>Annual 1961-2001<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Millions<br>Metric Tons US cents per pound<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Percent Land Used for Cocoa Cote d'Ivoire's Market Share<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> 10
For the specification based on planned treatment status in column (1), we do not find a statistically significant impact in the program villages in comparison to the control villages. In contrast, when basing the analysis on actual treatment status, the program is found to increase weight-for-age of children 0-36 months by one-tenth of a standard deviation on average. The age dummies (omitted for space reasons) reflect the common finding of a deterioration of the nutritional situation for the children with increasing age when compared to the children in the reference group. The other control variables mirror the findings in previous studies on the determinants of child nutrition: parent’s education and sanitary facilities in the household improve the nutritional status, while the status of being a twin reduces it significantly. The gender dummy is insignificant, as discrimination by gender is typically not observed in Africa (Svedberg, 1990). As indicated by the indicator variable ‘ _second round’,_ the villages in the sample experience an overall increase in the weight-for-age indicator of about one tenth of a standard deviation for both types of villages that is statistically significant at the 1% level. This finding confirms the general trend observed in the summary statistics in Table 1.

There are several reasons why the program impact may differ by the age at which the children were exposed to treatment (Alderman, 2007). Children who at the time of the baseline survey in April 2004 were six months old were included in the second wave in 2006 although these children likely were weaned by the time the intervention began. In contrast, a child born after April 2004 would have had the additional benefit of their mothers participating in the discussion groups and micronutrient provision for pregnant women, two important program components. There is increasing evidence that the experiences _in utero_ can have long-lasting effects (Behrman and Rosenzweig 2004; 16
The first term on the right hand side shows the output response to a change in inputs under constant technology. The remaining terms show the response of the implemented technology to a change in the state variables. This part is contained in the unexplained productivity residual in the standard productivity analysis under the assumption of constant technology. On the other hand, the elasticity with respect to the allocation error is The main message of this discussion is that to obtain a consistent estimate of the slope we need to estimate ∂ _y_ / ∂ε . Of course the allocation error is unobserved, but panel data can help us to deal with this problem.

# **II. The Statistical Model**<sup>11</sup>

To relate the discussion to the literature on empirical production functions, we start with the generic Cobb-Douglas model, and thus suppress the dependence of β on ε, which is the quadratic component of the function. The formulation is based on the micro model, but it is oriented toward macro data analysis by the introduction of additional state variables. The production function implemented under state s is: where _m0_ is an idiosyncratic term known to the firm but not to the econometrician, _u0_ is a random term whose value is unknown when the production decision is made.<sup>12</sup> _Eeu_ 0 ≡ _e_ η , and without a loss in generality, it is absorbed in Γ(s). The expectation of output, conditional on _X_ and _s_ , is _Y e_ = Γ ( _s_ ) _X_ β ( _s_ ) _em_ 0 , and the choice criterion is:

> 11 The discussion is based on Mundlak and Hoch (1965) and Mundlak (1996).

> 12 The formulation does not allow for delayed response to the transmitted error _m0_ as discussed in Chamberlain (1982).

7
# **_The Children_**

The original raw data contain information on two cohorts of children in these 310 villages: children who were one year old and those who were four years old in 2009 (when baseline data were collected). These children were followed and re-observed in 2010 when they were two and five years old, respectively. Given the project’s focus on children between birth and the age of six, analysts considered that following these children would allow us to understand the trajectory of child development in the age range of interest: 0–6.

This paper focuses on whether the package of interventions (community facilitation, block grants, teacher training and playgroups) introduced by the government had any impact on children’s development and whether it helped reduce early achievement gaps between children from richer and poorer households. Consequently, we exclude from our analysis the one-year-old children as they were too young to have attended the playgroups. Thus, we form our analysis sample consisting of children who were four years old in 2009 (baseline year) and likely to have attended playgroups. In the sample of four-year-old children in the baseline survey, we have 1,109 children in batch 1 villages, 1,184 children in batch 3 villages, and 940 children in villages that never received the project. 49.7 percent of these children are girls and have an average age of 53 months when they are first observed. Their caregivers are on average 32 years of age and on average report being the primary caregiver for four children.

Table 2 shows the descriptive statistics for enrollment and child development outcomes. We organize table into columns showing the means and standard deviations for each group of villages: batch 1, batch 3 and villages without the project (henceforth the comparison group) and by baseline and follow-up for each group to examine their similarities and differences. Our ECED enrollment variable is set to 1 if children had enrolled in any ECED service at baseline or follow-up. Otherwise, it is set to 0. We standardize most child development outcomes with mean 0 and standard deviation 1 among all children across baseline and follow-up. This allows us to interpret the progress in different child outcomes as a result of the ECED project in the same metric. The only exception is the test of executive function which required children to play a three-stage Dimensional Change Card Sorting game. This card sorting game is scored as 1 if the

> 7 This is true of all SDQ domains. Typically one domain is reverse-scored but in this paper, we have aligned all SDQ domains in the same direction for ease of interpretation. Appendix Table 1 provides details on each of these measures of development.

10