rafmacalaba/datause-encoder-data huggingface dataset:
that short low-cost automated voice telephone surveys as described in Ben et al. (2015) can make randomized encouragement designs financially viable against the backdrop of high sample size requirements and oftentimes expensive traditional household surveys.<sup>6</sup> We randomly assigned 150 villages to the typical campaign component providing generic information broadcasted on national radio (GENERIC), the same generic component plus the distribution of print media that illustrate optimal lamp choices aimed to reduce information processing costs (GENERIC+CHOICE), and a control group. The medium of communication for both campaign components was determined based on the complexity of the information to be provided. While generic information tends to be easily communicable via audio messages, illustration was required for the presentation of the optimal lamp types.

For the purpose of directly comparing the impacts of both campaign components, we additionally aim to estimate the sole effect of CHOICE. In the absence of a separate treatment group, we exploit the fact that the symmetric radio encouragement creates two distinguishable populations within experimental groups: call-in contest participants and call-in contest non-participants.<sup>7</sup> The difference in the outcome variable between the experimental group that receives both campaign components and the control group among call-in contest non-participants approximates the effect of CHOICE on its own. For these non-participants, we have no reason to believe that they are subject to variation in exposure to GENERIC. In other words, these households were not exposed to the radio clips.

We find that information on optimal lamp types (i.e., CHOICE) was required to affect the extensive margin (increase the number of new adopters of solar lamps). However, providing information about the general benefits and quality (i.e., GENERIC) was sufficient to have an impact on the intensive margin (increase the number of owned solar lamps among existing customers). Our data are not suited to assess the effects of additionally exposing existing customers to CHOICE, which arguably represents a second order question given the success of the low cost generic information treatment on the extensive margin.<sup>8</sup> For the same reason, the corresponding regression for the

> 6 While cost-efficient, this methodological approach comes with some limitations. Notably, the response rates for automated telephone surveys tend to fall disproportionally with an increasing number of questions which imposes a strong focus on a few hypotheses. This rules out, for instance, a comprehensive analysis of mediating variables. On the flip side, the limited number of variables reduces the risk of multiple hypothesis testing.

> 7 Due to sample size restrictions, we were not able to include a third treatment arm consisting of CHOICE only in our experiment.

> 8 In Section VI, we also argue that the effects on the intensive margin between GENERIC and CHOICE should be more similar than on the extensive margin.

5
occurrence happened more often than would be expected by chance in either the full sample the potentially suspect project contracts.

**A low overall quality of bidding documents might be behind a low number of submitted bids for the contract, a high volume of rejected of bids, a long contract negotiation period and/or a considerable increase in final contract amounts over the BER** .  All of these indicators might suggest measures to reduce competition to award a contract to a favored firm which pads the contract to provide financing for kickbacks.

**Once more, however, joint occurrences are rarely higher than would be expected by chance.** The one exception is that limited competition in bidding does appear to be correlated with longer negotiation periods.  This may be a sign that corrupt winners are negotiating kickbacks, it may reflect the fact that firms which understand there is limited possibility of a re-bid negotiate harder on contracts or that, in good faith, it takes longer to negotiate contracts on the basis of poor bid documents.  Nonetheless, this does appear to be a case where theory may be born out by some evidence in our small sample.

**Overall, the analysis of red flag inter-relations suggests that our theorized relationships are not particularly powerful in driving outcomes.** Amongst other things, this may suggest that corruption is not the driving force behind the emergence of red flags, or that the common forms of corruption do not dictate that these particular interrelationships emerge.  Either outcome suggests the need for further analysis on the use of red flag indicators in project monitoring before they can be used to drive the oversight response to corruption.

# **A project level red flag: contractor concentration?**

**The great majority of red flags listed in the literature are at the contract level** , however there is some discussion of project-level flags.  These include officials that appear to be living beyond their means (which we cannot measure) and evidence of the same contractors consistently winning bids (which we can measure).

**The measures of degree of competition on a project level are indicative of overall project management and efficiency of procurement** , and may point up issues with corrupt or collusive contractors and officials steering contracts to a few firms. The necessary information for calculation of the project level indicators was readily available from the Client Connection system. The indicators capturing the level of competitive for a project include the following:

- Concentration ratio of four largest firms in the project;

- Herfindahl-Hirschman index (traditional and normalized); and

- Average number of contracts awarded per company.

**The** **_Herfindahl-Hirschman index_ and** **_concentration ratios_ are standard indicators measuring the level of competition in a market** . In the case of World Bank projects, we took the approach that total amount of contracts per given project represents the “market,” and each company awarded a contract(s) is considered a “market player.” Taking into

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6 behavioral evidence. The present paper does not go as far as this, but focuses on the factors shaping the opinions and what they suggest about investment climate priorities in Africa.

# 2.2 <u>Some Previous Studies</u>

Most firm surveys request qualitative views on aspects of the investment climate, but few studies provide cross-country information comparing their patterns and their relationships with “objective” constraints. Two of the few are Hellman et al (2000), and Fries, Lysenko and Polanec (2003), which use BEEPS (Business Environment and Enterprise Performance) data to assess changes in the business climate across a number of countries in Central and Eastern Europe. The former paper focuses on governance, corruption and state capture. Among other results, it concludes that there is little evidence pointing to “country perception bias” associated with the use of different benchmarks. The latter paper concludes that qualitative measures of the business environment appear to provide reasonably accurate measures of its quality and of changes over time. It also shows relationships between these qualitative measures and quantitative behavioral variables, such as firms’ investment and growth rates.

The question of whether firms’ perceptions of the severity of constraints is reflected in actual behavior is more contentious however, particularly in the area of finance. Ayyagari, Demirguc-Kunt and Maximovic (2006) assess evidence on the severity of constraints from the World Business Environment survey on firm-specific sales growth rates. They find considerable differences between the relative severity of constraints as described by firms and the factors that appear to be most correlated with differences in growth. In particular, recent growth rates appeared to be strongly influenced by whether firms considered access to finance as a serious constraint even when finance was not widely identified as a key binding constraint. Some analysts have argued the converse. Although access to credit is often flagged by firm managers as a constraint in African surveys, Teal, (1998), Swamy and Raturi (1999) and others suggest that evidence does not support the argument that limited credit is the main reason why firms are not
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**The** first step in calibration is to break down the base yeab microconsuitent data, constructed in value terms, into separate price and quantity data. For this purpose, a unit's convention is adopted (also see Mansur **and** Whalley (1984)) that defines physical units for commodities as those amounts that sell for one currency unit ($1.00 U.S.).<sup>23</sup> For factors, **baes year equilibrium** data on the price of capital, labor employment, and urban/rural earnings differentials are used to decompose capital and labor payments into separate price and quantity observations.

The share parameters for the demand and production functions can then **be** determined by calibration, dependant upon the choice of elasticity values for **the** production and utility functions in the model. In the rural sector, **the** values of the share parameter aj are taken from the average product pricing rule for labor and from the first order condition from producer cost o.limization in the urban sector.

**These ares**

<!-- Start of picture text --> . IUj-I o-1<br>Ivifll j Nj q or7<br>I 7 JPJ I q-l<br>(25) j I I ,j 1<br>ri -I o- I<br>OJ Nj q aj<br>(26) aj- 1 , j-2<br>NJ I I<br>I + v I2 E°l J<br>r IaM-1-liI<br><!-- End of picture text -->

**23/ The** 1962 and 1982 **benchmark** data on production and labor income in won are converted **Into** U.S. **dollar** using offlcial exchange rates from the Economic Planning Board (1964, 1984). **Trade data** for both years are reported in U.S. dollars.
Other limitations of the aggregate estimates are related to the partial equilibrium approach we take in aggregation. For instance, our regression estimates only measure the direct impact on connected firms and do not include any potential spillover effects from connected to unconnected firms or the general equilibrium effects on prices and wages.<sup>36</sup> Without a structural model incorporating these channels, predicting whether the general equilibrium effects will dampen or amplify the firm-level responses is challenging. Nevertheless, our simple and transparent approach provides a useful first step toward understanding the potential aggregate impact of China’s internationalization events.

# **4.5 Investor Behavior**

We exploit additional data sources to explore the behavior of investors around the internationalization events. We retrieve (1) aggregate data on foreign equity inflows from the IMF’s balance of payments statistics; (2) aggregate data on foreign equity holdings via the QFII/RQFII programs and Stock Connect program from Wind; (3) firm-level data on the share of foreign ownership from Refinitiv; (4) country-level bilateral data on foreign equity holdings from the IMF’s Coordinated Portfolio Investment Survey (CPIS).

Foreign investors entered relatively late in the internationalization process. Although foreign equity inflows increased in 2014, they experienced their fastest growth during 2018-20. Foreign equity inflows were about 15 billion U.S. dollars in 2015 and more than 80 billion in 2020 (Figure 8, Panel A). In addition, aggregate foreign equity holdings through the Stock Connect program, which channeled most of the 2018-20 expansion in foreign participation, rose from 15 billion U.S. dollars in 2015 to 151 billion in 2020 (Figure 9). Nonetheless, foreign equity holdings

> 36 Spillover effects to the unconnected firms could occur if more funds were available to them when connected firms tapped into the international markets for funding and became less reliant on domestic finance. In principle, these spillover effects could bias our difference-in-difference estimates downward, even after addressing selection issues between connected and unconnected firms. Nevertheless, evidence on the investor side (shown in Section 4.5) suggests that the supply of domestic finance increased for the connected firms during the internationalization events.

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(13) r -. (1= r)a tl* (r -g) Equation (11) then becomes (14) k * **_W_** _*_ F p * W * _G_ * A where G = (l+g) *<sup>1</sup> ) and (15) p =k * _G_ *A For a given contribution rate, the pension rate d&pends on the relative values oZ "g" and _"r"_ as well as the values of l'r" and "ma". The greater the growth rate of real wages, the higher the real rate of irterest that is requires to achieve a desired pension rate as a percentage of the final wage. However: a higher "g" also implies a higher final wage so that a lower pension rate may in fact correspond tz a higiher pension in absolute ternms.

Tables la and lb (and Figures la and lb) show the relationship between Lhe gross pension rate and the real interest rate for different growth rates of real wages, when pensions are indexed to prices. It can be seen from Table la that with k=10%, n=40 and m=20, when ge2% and r=3% the gross pension rate amounts to 33%, but when g=3% and r=2% the gross pension rate falls to 20%. A 3 percentage points difference between the real rate of intsrest and the growth rate of real wages is required for a pension rate in excess of 50%.

Table lb provides the same information for **"In"** equal to 45 and 'm" equal to 15. It can be seen that when g=2% and r=3% the gross pension rate is 47%, while when g=3% and r=2% it is 29%. These pension rates are higher than those shown in Table la. They reflect the lonqer working and shorter retirement lives and the effects of compounding. A 2 percentage points difference between "r" and 'g" is adequate to produce gross pension rates in excess of 50%. When r=5% and g=3%, the pension rate amounts to 68%.

When pensions are indexed to prices, the levels of interest rates and wage growth rates also affect the level of pension rates. Thus, as shown in Table la, when r=g=1% the pension rate amounts to 22%, but rises to 27% with r-z=3%. The corresponding pension rates in Table lb are 33% and 38%.

In considering the entries of Tables la and lb (as well as those of following tables) it should be noted that only those combinations that result in reasonable pension rates of between 50% and 70% would be compatible with the "ssumed :ontribution 8
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Baseline health status in Indonesia is much poorer than chat observed in the U.S. For example, the i mortality rate in Indonesia is 78 per 1000 live births vrsus 10.1 in the U.S. The difference is due to a multitude of factors associated widt poverty and lower income such as high population density and lower levels of nutrition, medical care and access, and to factors related to behavior and infrastructure such as occupational exposures, high exposure to passive smoke and kerosene cookers and insufficient ventlaion in the homes. For example, Achmadi" examined a cohort of children under five and found that the episodes of acute respiratory tract infection are related to the degree of indoor air quality, populaton density, and socioeconomic staus . Research in the U.S. and Britain has indicated tha frequent episodes of lower respiratory disease may be associated with chronic obstmctive pulmonary disease in adulthood. Particular problems may be expected in rural areas where an esimated 88% of the households cook with wood or charcoal, fequently indoors, in stoves without flues and in pwrly ventilated kitchens, most of the year. In urban areas like Jakarta, unvented kerosene cookers may be a significant source of respiratory disease. The 1990 Cens reports that in Jakarta, 84 percent of all households cook with kerosene, followed by gas (11.6 percent), wood or charcoal (2.3 percent), and electic (2.1 percent). In some cases, therefore, extrapolating dose-response estimutes from wesktm developed countries may undrestmae the more serious health outcomes associated with both acute and chronic exposures.

Because of all of these factors, Indonesians expericnce high rates of morbidity from acute respiratory infections, and a high prevalence of chronic respiratory disease. Although respiratory conditions in Indonesia have not been conclusively linked to indoor and outdoor air pollution, there is ample descriptive evidence of the magnitude of the problem. The 1990 Indonesian Census indicates that among chdildren under age five, acute respiratory infecions account for 14.4 percent of all mortality. It is the second leading cause of death after diartheal disease, which is responsible for 15.6 percent of all
pre-FPE.

Further, I present evidence in table 5 to show that the pre- and post-FPE cohorts do not come from different wealth groups, and even if they do, such differences would attenuate rather than overstate the FPE effect. Column (1) of the table presents the determinants of the probability of completing secondary or higher education, while column (2) presents the determinants of completed years of education. From column (1), we can see that, all else constant, the probability of completing secondary or higher education did not increase post-FPE. And the interaction of year and household wealth dummies indicates that, while household wealth increases the chances of completing secondary or higher education, the influence of household wealth did not change post-FPE. That is, pre- and post-FPE cohorts come from the same wealth groups.

From column (2), we can see that, everything held constant, 16-24 year-old children completed 0.5 more years of schooling post-FPE and those from wealthier households completed more years of schooling. However, the influence of household wealth on completed years of schooling attenuated post-FPE as indicated by the negative coefficients on year-wealth interaction dummies. The gap in completed years of education between those from rich and poor households was reduced. Consequently, if those who completed more years of education are the ones more likely to proceed to acquire higher education, the pool of students admitted into university post FPE is relatively poor compared to that admitted pre-FPE. This may have downward biased the FPE effect. Thus, taken together, the estimated effect could not have been an artefact of pre- and post-FPE differences in student composition.

# **6.3.2 Pre-FPE effect of FPE on student academic performance**

Here I estimate the placebo FPE effect using the pre-FPE data (i.e. 2010-2011), assuming that the FPE-treated first enrolled in university in 2011. Because the FPE-treated students were not at the university in those years, we do not expect to see any positive placebo FPE effect on performance because, if there is, it would imply that the observed increase in academic performance post-FPE presented in Table 4 cannot be attributed to FPE. It would imply that academic performance is simply continuing its upward trend post-FPE. The results are reported in Table 6. Looking at column (1) results, we can see that, pre-FPE, there was no statistically significant increase in academic performance. The results in column (2) indicate that, in fact, academic performance was on the decline pre-FPE, although the decline is statistically insignificant. Taken together, these results, therefore, indicate that the observed significant increase in academic performance post-FPE is indeed attributable to FPE, not some positive time trend.

# **6.3.3 Narrow age range (18-22 years old sample)**

Given the school entry age of 6 years, the 18 to 22 year olds are the age appropriate cohort for undergraduate university level. Further, narrowing the age range of the sample increases the 27
calculated at the mean value of the explanatory variables. Summary statistics of all the variables used and the correlations for the main explanatory variables are provided in Table 1.

It is important to point out that our results are based on cross-section data. Hence, it is difficult to eliminate all possible endogeneity concerns despite the number of checks performed (discussed below). So, we interpret all our results as correlations or associations, suggestive of a possible underlying causal relationship.

# 3.1 _Dependent variable_

In the Enterprise Survey, firms were asked if the top manager (main decision maker) is a woman or a man. Using responses to this question, we define our dependent variable as a dummy equal to 1 if the top manager of the firm is a woman and 0 otherwise ( _Woman manager_ ). About 19.5 percent of the firms have a top woman manager in our sample. At the low end, Afghanistan and Cameroon have less than 5 percent women (top) managers; at the high end, over 40 percent of the managers in Honduras and Lesotho are women.

# 3.2 _Main explanatory variable_

Our main explanatory variable captures the gap between women and men’s access to education across countries. To this end, for any given country, we use the average of the ratio of women’s to men’s enrollment rate in primary, secondary and tertiary education ( _Enrollment_ ). The average is taken over countries and years (described below) for which information is available on either primary, secondary or tertiary enrollment ratios. Our main results are unchanged if we restrict the sample to countries for which information is available on all the three enrollment ratios (see below).

Considering that education is a long-drawn process, it may take substantial time before acquiring education translates into becoming a manager. For this reason, we use lagged values of 12
Table **_5:_** Producer Price Trends for Cashew Nuts, 1971/72 to 1983/84

|Season|Nominal Producer<br>Price (rsh./Kg.)'|Producer Price in<br>Constant 1971/72<br>Tsh./Kg.|Producer Price as %<br>of Export Unit<br>Values<sup>2</sup>|
|---|---|---|---|
|1971/72|0.91|0.91|67.0%|
|1972/73|0.91|0.84|67.0%|
|1973/74|0.91|0.73|49.8%|
|1974/75|1.03|0.67|54.5%|
|_1975/76_|1.03|0.58|47.4|
|1976/77|1.07|_0.55_|36.5|
|1977/78|1.12|0.52|30.0%|
|1978/79|1.64|0.65|40.2%|
|1979/80|1.64|0.51|24.7%|
|1980/81|1.78.|0.44|30.9%|
|1981/82|2.80|_0.56_|72.2%|
|1982/83|4.70|0.70|65.8%|
|1983/84|6.58|0.80|48.2%|

1 Weighted value, assuming 80% Standard Grade and 20% Undergrade 2 Calculated as producer price % average unit value of all cashew export products (raw nuts, kernels, CNSL) on a raw nut equivalent basis. The methodology is similar to that used by Ellis (1979) except that the assumption here is a 20% kernal material recovery from the raw nut.

Sources: Calculated from Data provided in Marketing Development Bureau, Annual Review of Cashewnuts, various years.

By the mid-1970s, there was evidence of farmers not only neglecting their cashew shambas, but of displacing cashew nuts with short-cycle legumes, such as cowpeas, pigeon peas, and various beans. In some places, farmers began to fell cashew nut trees for use in charcoal production.' The government's response to such developments included: 1) a campaign to 'teach' fanners the importance of maintaining their shambas, 2) road blocks and the banning of petty-trading and charcoal-making in the affected areas, 3) and various types of threats against producers who did not harvest and sell their cashew nuts.' Such interventions were not effective and production and trade levels continued to decline.

**"** Havnevik (1979) reports on the situation in Rufiji District.

> 24 These efforts are discussed fiuther by Msangi et al. **(1987).**

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