rafmacalaba/datause-encoder-data huggingface dataset:
|bined<br>l regressions<br>lso allow for<br>. Full set of<br>untry)*time<br>(6)<br>Lib<br>0.050*<br>(0.030)<br>-1.524***<br>(0.062)<br>-0.087<br>(0.164)<br>0.054<br>(0.060)<br>0.205<br>(0.131)<br>0.018<br>(0.020)<br>-0.040<br>(0.048)<br>-0.232<br>(0.166)<br>Yes<br>Yes<br>Yes<br>Yes<br>32,892|
|---|
|nnels Com<br>at time _t_0. Al<br> Estimations a<br> 4, and Table 5<br> (exporting co<br>(5)<br>All<br>0.052*<br>(0.028)<br>-1.486***<br>(0.059)<br>-0.115<br>(0.143)<br>-0.002<br>(0.047)<br>0.300**<br>(0.122)<br>0.020<br>(0.017)<br>0.007<br>(0.041)<br>-0.311*<br>(0.163)<br>Yes<br>Yes<br>Yes<br>Yes<br>37,056|
|native Cha<br>t that started <br>e fxed efects. <br> Table 3, Table <br>ors clustered at<br>pectively.<br>(4)<br>Lib<br>0.124***<br>(0.028)<br>-1.625***<br>(0.058)<br>-0.334**<br>(0.140)<br>0.137**<br>(0.064)<br>0.009<br>(0.157)<br>0.034**<br>(0.017)<br>0.005<br>(0.042)<br>-0.170<br>(0.133)<br>Yes<br>Yes<br>Yes<br>Yes<br>41,271|
|val - Alter<br>e world marke<br>untry and tim<br>are defned in <br>st standard err<br> 1% levels, res<br>(3)<br>All<br>0.132***<br>(0.027)<br>-1.169***<br>(0.051)<br>0.172*<br>(0.094)<br>0.143***<br>(0.040)<br>0.163<br>(0.118)<br>-0.014<br>(0.012)<br>-0.035<br>(0.029)<br>-0.076<br>(0.081)<br>Yes<br>Yes<br>Yes<br>Yes<br>77,986|
|port Survi<br>untry _c_ to th<br>r exporting-co<br>le. Variables <br> ratios. Robu<br> 10%, 5%, and<br>(2)<br>Lib<br>0.137***<br>(0.029)<br>-1.531***<br>(0.057)<br>0.015<br>(0.019)<br>-0.180***<br>(0.042)<br>-0.039<br>(0.030)<br>-0.553***<br>(0.054)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|g-Term Ex<br>oduct _k_ from co<br>) and control fo<br> as strata variab<br> and not hazard<br>nifcance at the <br>(1)<br>All<br>0.125***<br>(0.025)<br>-0.971***<br>(0.051)<br>0.059***<br>(0.013)<br>-0.218***<br>(0.036)<br>0.038**<br>(0.019)<br>-0.634***<br>(0.043)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|Liberalization and Lon<br> rate for an export spell of pr<br>onal Hazard Model (Cox, 1972<br>products by defning product _k_ <br> Table 3. We report coefcients <br>, and *** denote statistical sig<br> comparative advantage<br>age<br>inance Dependence<br> to comparative advantage<br>ve advantage<br>mparative advantage<br>tive advantage<br>omparative advantage|
|le 6: Stock Market <br>ndent variable is the hazard<br>ated using the Cox Proporti<br>baseline hazard rate across <br>orresponds to column (4) of <br>vel are in parentheses. *, **<br>eralization x Distance to<br>eralization<br>e to comparative advant<br>eralization x External F<br>beralization x Distance <br>beralization<br> Distance to comparati<br>arkets x Distance to co<br>arkets<br> x Distance to compara<br>penness x Distance to c<br>penness<br> of controls<br>ng country FE<br>E<br> stratifcation<br>tions|
|ab<br>epe<br>im<br>nt <br>ls c<br>) le<br>ib<br>ib<br>nc<br>ib<br> li<br> li<br>s x<br>s<br> M<br> M<br> pc<br> o<br> o<br>et <br>rti<br> F<br>uct<br>va|
|T<br>The d<br>are est<br>difere<br>contro<br>(_c ∗t_0<br>StM l<br>StM l<br>Dista<br>StM l<br>Trade<br>Trade<br>Bank<br>Bank<br>Stock<br>Stock<br>GDP <br>Trade<br>Trade<br>Full s<br>Expo<br>Time <br>Prod<br>Obser|

42
2050, labor productivity could go down by up to 80% in the hottest months globally and that up to 40% of daylight hours could be too hot to work in some tropical and subtropical latitudes. Adhvaryu, Kala, and Nyshadham (2020) estimate a negative, non-linear relationship between temperature and worker productivity in an Indian garment factory (S.-W. Lee, K. Lee, and Lim (2018) estimate similar effects for workers in the Republic of South Korea). The evidence clearly points to adverse effects on worker productivity from extreme heat.

Current evidence suggests that heat lowers labor productivity through direct impacts on worker capacity and indirect changes in time use. Masuda, Garg, Anggraeni, Wolff, et al. (2020) document direct impacts by showing that heat leads to lower cognitive performance among workers in Indonesia, echoing findings from HICs (e.g., Fogleman, Fakhrzadeh, and Bernard (2005), Hancock, Ross, and Szalma (2007), Dillender (2021)). Masuda, Garg, Anggraeni, Ebi, et al. (2021) document an indirect effect: workers need more breaks when exposed to heat, leading to lower productivity. Somanathan et al. (2021) find similar indirect effects through higher absenteeism in Indian factories on hot days, and Garg, Gibson, and F. Sun (2020) find that individuals in China allocate less time to work on days with extreme heat. LoPalo (2023) finds indirect effects among survey enumerators across LMICs. Enumerators complete 13.6% fewer surveys on hot days, becoming differentially less productive at tasks that are more difficult to monitor, pointing to another margin of worker adjustment. Such results indicate that both the quantity and quality of labor suffer on hot days.

Adverse impacts of heat on worker productivity clearly point to the need for firms to adapt through cooling. While the evidence is still limited, cooling adaptations appear to be effective. Adhvaryu, Kala, and Nyshadham (2020) show that Indian manufacturing firms who invest in LED lights that produce less ambient heat are effective at increasing worker productivity on hot days. Masuda, Castro, et al. (2019) show that rural workers in Indonesia adapt to heat by changing the timing of their work shifts and breaks, illustrating the potential for adaptation without costly investments in new technologies. While not causal, X. Chen and L. Yang (2019) find that the same high temperatures that lead to large productivity losses 8
**ii. Training**

The regression results indicate that in-service training accounted for higher wages in 1999 but not in 1993(see Table 6). In-house training is positively correlated to productivity in 1993. In 1999, in-house training had a positive and significant effect on wages and productivity in medium size firms. In 1993, only the firms benefited from inhouse training.

Table 6: In-house Trainin

|Variable||1993||||**1**|**999**||
|---|---|---|---|---|---|---|---|---|
|Teachi|Wage|P>Izl<br>Prod|uctivity|**P>Izl**|Wage|**P>lzl**<br>|Productivity|P>**zl**|
|ooled|-0.13|0.00|0.16|0.04|0.05|0.06|0.07|0.30|
|icro|-0.80|0.00|2.83|0.00|0.06|0.81|-0.09|0.87|
|mall|-0.12|0.37|-0.03|0.90|-0.09|0.20|-0.29|0.11|
|Medium|-0.02|0.85|0.03|0.84|0.09|0.03|0.44|0.00|
|Large|-0.07|0.24|0.05|0.60|0.03|0.50|-0.01|0.95|

_Source_ Author's calculations based on ENTRAM-ENESTYC.

In 1999, workers who received external training earned, on average, 26 percent higher wages (see Table 7). This result accounts for a productivity increase of only 14 percent for workers who received external training. Thus, training obtained outside a firm increases productivity. This finding suggests that employees benefit the most from external training with a 26 percent wage premium compared to 5 percent from in-house training. In 1999, we cannot reject the hypothesis of equality of coefficients for external training on the wages and productivity estimates, which suggests that both employers and employees are benefiting equally from external training.

Table 7: External Training

|Variable||19|93||||1|999||
|---|---|---|---|---|---|---|---|---|---|
|Teachx|Wage|P>Izl<br>|Prod|uctivity|P>Izl|Wage|P>Izl<br>|Productivity|**P>Izl**|
|ooled|-0.16|0.00||0.07|0.40|0.26|0.00|0.14|0.08|
|Micro|-0.87|0.00||0.44|0.41|0.33|0.01|-0.17|0.58|
|Small|0.05|0.68||0.35|0.06|0.10|0.12|-0.04|0.79|
|Medium|-0.05|0.41||0.09|0.41|0.25|0.00|0.47|0.00|
|Large|-0.25|0.00||-0.20|0.10|0.25|0.00|0.09|0.51|

_Source_ Author's calculations based on ENTRAM-ENESTYC.

12
According to the subjective assessments, 49 percent of the 1,277 units in the sample are urban in practice, and 51 percent are rural (table 2). Therefore, the sampling strategy does ensure a balanced representation of rural and urban units. At the same time, the exercise reveals substantial differences between the administrative classification of units in the sample and their subjective assessment as urban or rural. Discrepancies are greater for units deemed urban in terms of the census, many of which are reassessed as rural. A nonnegligible fraction of statutorily urban units is also reclassified as rural. But there are no substantial differences for villages, whose rural status is almost consistently confirmed by the subjective assessments.

**Table 2. Subjective assessments versus administrative classification**

|Administrative classification|Assessed urban<br>(percent)|Assessed rural<br>(percent)|Total<br>(percent)|
|---|---|---|---|
|Statutory urban units|24.9|6.8|31.7|
|Census urban unit|24.0|19.6|43.6|
|Census rural unit|0.4|24.3|24.7|
|Total|49.3|50.7|100.0|

In light of these discrepancies, a relevant question is whether the size of the sample is large enough to trust the results. In setting the sample size, we relied on the Central Limit Theorem, which implies an asymptotic normal distribution for the statistic of interest. But with a relatively small sample size, the distribution could be different. One way to address this concern is to use the bootstrap method, which generates an approximation of the distribution through a Monte Carlo simulation, in which the sampling is carried out from the fitted distribution of the observed data (Cameron and Trivedi 2005).

The bootstrap method is applied to the sample of 1,277 units, for which we know the urban status based on both the administrative classification and subjective assessment. In each replication, we randomly draw units from this sample with replacement, until arriving at the same sample size as before (1,277). The drawing is done independently for each administrative stratum. For the new sample obtained in each replication, the share classified as urban is computed on the basis of the subjective assessments. After replicating 10,000 times, we estimate the distribution of this urban share, including its mean, standard errors, and confidence intervals (figure 3).

The bootstrap analysis confirms that the sample is sufficiently large for each administrative stratum. For capital cities and municipal corporations, as well as for villages, bootstrap standard errors are below 1 percent. At 5 percent, the standard error is larger for industrial towns, reflecting the small size of the sample for this stratum. However, because the sample includes 25 of 31 units in this stratum, the sampling does not affect the accuracy of the results. Standard errors are below 3 percent for all other administrative strata.

12
Regardless, the increase in private investment and non-state-owned firms provided the basis for the deepening of the enterprise reforms.

With the development of the market economy, the volatility of firm level productivity becomes a crucial aspect of the micro performance of an economy. It reflects responses of firms to idiosyncratic shocks and frictions in product, factor and credit markets. Firm level volatility often links to their investment patterns, their access to external finance, as well as the regulation reforms. The volatility of the entire private sector reflects the joint forces of firm entry and exit and the volatility of the individual firms in the market.

# **3 Evolution of productivity volatility**

Firm level productivity volatility is an important aspect of development of the whole economy. In previous studies, volatility is measured in different ways. In Loayza and Servén (2010), macroeconomic volatility is represented by the standard deviation of the output gap, obtained as the difference between the actual and trend real GDP per capita. Trend output is estimated using the band-pass filter of Baxter and King (1999). In Hausmann and Gavin (1996), macroeconomic volatility is defined as the standard deviation of the level of GDP per capita. In Breen and GarcíaPeñalosa (2005), output volatility is defined as the standard deviation of the annual growth rate of real per capita GDP.

In this study, we follow the methodology developed in Comin and Philippon (2005). They used aggregate data from the National Income and Product Accounts (NIPAs), and firm level data from COMPUSTAT (a database of financial, statistical and market information on active and inactive global companies throughout the world) and the Center for Research in Security Prices (CRSP), which is composed of time series that vary from 1955 to 2000. For each firm _i_ at time _t_ , they computed the volatility as the standard deviation of the annual growth rate of sales during each ten-years: 6
# **5.6 Look Ahead Bias**

One final concern is ahead bias. The rules about related lending from BCL that we use to construct our index are from 1998-1999 and 2002. However, our measure of private credit growth starts in 1991, which means that we have some countries in the data set that had financial crises before 1998-1999, and that might have changed their related lending rules as a result of those crises. Since we lack systematic information about which countries changed their related lending policies in the wake of a banking crisis, we carry out two separate tests to rule out this possibility. First, we directly control for the occurrence of a banking crisis. If banking crises that occurred in the 1990s are driving the correlation between credit growth and our related lending index, our main results should disappear or weaken dramatically. The results show that our central results are robust to adding a crisis control (Table 9).<sup>22</sup> In addition, in order to be sure that our results are robust, we truncate our dataset to the period since 1998, re-estimate the regressions, and present the results in Table 10. We note that this procedure implies a quite stringent test: not only is the window of time quite short (we are measuring the rate of growth of credit over only ten years), but this particular period is known for the absence of the kinds of banking crises that are predicted to cause looting via related lending. Indeed, the period is often referred to as the great moderation.

The stringent nature of the test does affect our results, but not in a decisive way. Model 1 indicates that related lending has neither positive nor negative effects on its own, and that there is a strong, positive correlation between the rule of law and credit growth. These are the same qualitative results we obtain in our base regression (Table 3). When we interact related lending and rule of law in Model 2, we obtain results with the same signs as in Table 3, but they are not statistically significant. In order to examine robustness, we therefore recode the rule of law variable as a dummy (countries that have a rule of law score in the 66<sup>th</sup> percentile and above =1), and re-estimate the regressions. We report the results in Model 3, and find that related lending is associated with faster credit growth in countries with very strong rule of law. In order to make sure that this result is robust, we then follow the same procedure we applied in Table 8: we split the sample into thirds based on rule of law scores at the beginning of the period (1998); and then

> 22 In addition, the correlation between the related lending index and the indicator of financial crisis turns out to be weak (-0.22), suggesting that look ahead bias is unlikely to be driving our key results.

20
The remainder of the paper is organized as follows. Section 2 reviews the theoretical and empirical literature on gravity models, highlighting evidence on overland trade flows in developing countries. In Section 3, we use computer mapping to identify a network of primary roads connecting all 42 mainland Sub-Saharan capitals, along with 41 other cities with populations over 500,000.<sup>3</sup> Section 4 estimates a gravity model for SubSaharan Africa, using inter-country trade data, road transport quality indicators, actual road distances, and estimates of economic scale for trading partners. We use the results to estimate current trade flows in the inter-city network, and to simulate the impact of a major improvement in road network quality. We explore the implications of our results for trade expansion at the regional, country and city levels. In Section 5 we estimate the costs of network improvement, using a World Bank database of upgrading and maintenance costs for hundreds of African road projects. Section 6 considers the environmental implications of network upgrading, and suggests ways in which new data on African biodiversity could be integrated into network planning. Drawing on our empirical results, Section 7 considers the establishment of a consortium to finance, administer and maintain the network. The consortium would join participating states and donor institutions in a long-term collaboration for road improvement, relaxation of trade restrictions, and overland trade expansion in Sub-Saharan Africa. Section 7 also estimates the cost of operating the network-wide consortium for 15 years. Section 8 concludes the paper by summarizing our benefit and cost estimates, as well as offering some thoughts on the feasibility of continental-scale upgrading.

> 3  Our analysis excludes Madagascar, Sao Tome and Principe, the Comoros, and other island states that are considered part of Sub-Saharan African.

5
annual poverty and welfare statistics at the division level for urban and rural areas. A potential challenge is that the optimal sample for objective (i) is not necessarily identical to that needed to achieve objectives (ii) or (iii). Therefore, the design of the sample requires careful thinking on how to achieve the multiple objectives, which are not necessarily mutually consistent, within a single sample. This paper describes how the sample of the HIES 2016/17 was designed to achieve the aforementioned three objectives.

This paper also reviews the formulas that are often used by NSOs and development practitioners to calculate the sample size and the optimal allocation of the sample across strata. The HIES 2016/17 follows a stratified clustered sampling. Clustered sampling is used when the sample is selected in multiple stages. That is, in a first stage, clusters of households or individuals are selected randomly and then households or individuals are selected from the clusters in a second stage. This is less efficient than simple random sampling (selecting household or individuals in one stage directly) in terms of minimizing sampling errors, but it is more cost-effective as selected households or individuals are physically located relatively close to each other in the same cluster. This paper shows that the standard formulas used by practitioners for defining the sample size and the allocation of the sample across strata assume stratified random sampling rather than stratified clustered sampling. As a result, the sample size and the allocation of the sample can be too optimistic in terms of minimizing the sampling errors.

This paper is organized as follows. Section 2 reviews key literature on optimal sample allocation for stratified random samples and stratified clustered samples. Section 3 describes objectives of the survey, data used to design the HIES 2016/17 sample, sampling frame, stratification and domains of study, selection of target variables and accuracy statistics for sample performance evaluation, determination of the sample size, and allocation of Primary Sampling Units (PSUs) across strata. Section 4 summarizes evaluation of the proposed sampling design. Section 5 analyzes robustness of the proposed sample design. Section 6 describes survey sampling weights computation. Section 7 presents the main conclusions of the paper.

4
The premium information came from the Insurance Industry Annual Report for 2007 of the Association of Kenya Insurers.<sup>37</sup> Table 9 of their report lists premium income by company and type of insurance. We define market share of a company by the company share of total market premia. For ownership shares, we commissioned a survey from a specialist at the Association of Kenyan Insurers.<sup>38</sup> He provided the data on the ownership shares of the Kenyan companies. In the table below, we list the result of these calculations.

> 37 Available at: http://www.akinsure.com/images/aki-annual-report-2007.pdf

> 38 We thank Mr. Joseph Luvisia Jamwaka ( a fellow of the Life Management Institute of the U.S. and Associate of the Chartered institute of Insurance of the UK) for providing this information.

95
Where investment is outsourced, APRA expects a well-managed fund to take considerable care in selecting, instructing and monitoring managers. Selection should be by competitive tender. Investment mandates from trustees to managers should encompass objectives for rate of return and liquidity, allowable and non-allowable asset classes, the use of derivatives, allocation limits, portfolio duration, fees, performance benchmarks and reporting. Trustees should review the performance of managers periodically against pre-agreed objective measures that are consistent with the fund’s investment aims, and should be able to change managers without disruption to the fund.

There is no regulatory requirement for credit or investment committees but for large complex funds, or those with a component of high risk investments, that would be regarded as good practice.

Documents consulted by APRA in assessing investment risk include balance sheet data, including statistics submitted quarterly (by large funds) and annually; cash flow statements and projections; investment mandates for external managers; investment manager reports; tender and evaluation documents relating to manager selection; and regular reports to the trustees.

A fund receiving a ‘very low’ PAIRS rating on inherent balance sheet/investment risk will have well-diversified investments spread across different investment products and markets, and no exposure to volatility in returns. At the other end of the spectrum, a fund rated ‘extreme risk’ on this criterion will have a concentration of investments in one product or market, and high exposure to volatility. In between, a high-medium rating (1.6 to 2.0) is aligned with ‘some concentration’ of investments in certain products or markets, and ‘significant exposure’ to investment volatility.

The aspects of the guidance summarized above that deal with discipline in investment processes, such as limit-setting, monitoring and reporting, will feed into the ‘management and control’ rating.

The widespread availability to members of choice among a number of investment portfolios is complicating the meaning of trustee responsibility for managing investment risk beyond the need for clear strategies and properly managed execution. APRA has taken the view that trustees retain responsibility for the prudent structure of a fund in aggregate (taking into account particularly the need for diversification and liquidity) and that they need to ensure that this is not compromised by the subportfolio choices made available to or selected by members. It also requires that trustees provide appropriate advice and warnings to members about the risks to their own retirement savings from choosing only sub-portfolios that are highly concentrated or risky for other reasons.

# **4.2 Outsourcing and other operational risks**

The PAIRS guidance manual describes a superannuation fund with very low operational risks as having one or more of these features:

- a simple legal and organization structure with clear reporting lines

- no reliance on related entities for core or complementary activities

- no outsourcing of material business activities

26