rafmacalaba/datause-encoder-data huggingface dataset:
reported to be able to take these decisions, independently or jointly with the husband, in Kinshasa as compared to outside Kinshasa (60 percent vs 44 percent for health decisions; 73 percent vs. 60 percent for decisions about purchases for the household; 71 percent vs. 51 percent for visits to the family) and only 10 percent of women reported not to be able to participate in any of these three decisions in Kinshasa as compared to an average of 30 percent outside Kinshasa (DHS, 2014).

# **5. Estimation strategy and empirical results**

While the previous section documented some of the constraints that female employees and entrepreneurs face in their working lives in the DRC, here we ask what impact these have on the economic opportunity of women in the country. A priori, it is difficult to predict how restrictions and discriminatory behavior may affect women. Part of the literature suggests that the types of restrictions measured here affect women’s labor market outcomes; for example, Zveglich and Rodgers (2003) show that night-hour restrictions negatively impact female employment, whereas Islam et al. (2019) show that the same type of restrictions are associated with lower representation of women in managerial positions. However, in other contexts, reforming discriminatory laws had unanticipated consequences – research from the United Kingdom (Pike, 1985) found that equal pay legislation was associated with a decline in female employment in the manufacturing sector.

Here we consider the relationship between the restrictions measured in the survey and women’s empowerment within firms – proxied by whether or not a firm’s top manager is female; we also consider their relationship to women’s employment in firms – measured using the log of total female employment in a firm or a firm’s propensity to hire married women. We focus on the types of restrictions where the sample size is sufficiently large for meaningful estimations, and ask the following three questions: firstly, are the restrictions placed on the work-related activities that women can perform associated with worse outcomes for women? Secondly, how does allowing women to work night shifts relate to employment and career progression? And, thirdly, what is the relationship between maternity leave entitlement and the hiring of married women?

These questions are examined via the following model: 0 (1) ff ff ff SS RR ff where represents the various measures of female outcomes in firm YY = αα + ββ∙XX + γγ∙ZZ + δδ∙DD + φφ∙DD + εε . The term represents our main explanatory variables of interest, i.e., the various rights and restrictions on which we test for an impact. ff YY ff XX 14
their preferential access? Is all rent actually earned by the exporting countries? If so, how large is this rent in relation to a country’s overall exports? If it is significant, do trade preferences granted to one developing country come at the expense of another (Panagariya, 2004)?

Only recently have studies attempted to provide quantitative answers to some of these questions. Alexandraki and Lankes (2004) calculate the product level tariff margins granted by the US, EU, Canada and Japan, and then use these to derive an aggregate value of preferences for each beneficiary country. They find 18 countries for which the value of preferences exceeds 5% of the value of their exports. Their results suggest that the problem is heavily concentrated in mostly small island economies dependent upon sugar and bananas, and to a lesser extent upon textiles. However, as the authors note, these values could be overstated, since they assume full utilization of preferences, constant world prices, and full rent transfer to the beneficiary countries.

Brenton and Ikezuki (2004) provide an assessment of the scope and value of US preferences under the African Growth and Opportunity Act (AGOA) for 2002. They argue that overall, least developed AGOA beneficiary countries (LDCs) saw little expansion in the list of products eligible for duty-free access under AGOA, since they already had such access under GSP. Thus, the countries likely to benefit more from AGOA would be the non-LDCs. However, eligibility for apparel preferences significantly affects AGOA utilization rates. Among countries with such status, the LDCs have the least restrictive rules of origin, and are therefore likely to gain more than the non-LDCs. Brenton and Ikezuki’s data show wide variation in utilization, AGOA tariff preferences averaging about 6%, and only 6 AGOA countries with values of preferences (AGOA plus GSP) exceeding 5% of their total exports to the US.

> 3 Statement by the IMF Managing Director, Rodrigo de Rato, Managing Director of the IMF, to the WTO General Council  22 October 2004 <u>http://www.wto.org/english/news_e/news04_e/gc_stat_imf_22oct04_e.htm, downloaded</u> July 12, 2005.

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# **Natural Resources and Total Factor Productivity Growth in Developing Countries: Testing A New Methodology**

Kirk Hamilton, Esther Naikal, and Glenn-Marie Lange _“Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.”_ _Paul Krugman_

## **1. Introduction**

As the quote above suggests, in a world of decreasing marginal returns to factors of production, the only way to ensure increasing growth and prosperity is to increase the efficiency of factor use. Rather than focusing on a sole production factor such as labor, economists beginning with Solow (1957) have developed methods of growth accounting which derive an indirect measure of the rate of change in the efficiency of use of all factors of production, termed ‘total factor productivity’ (TFP) growth or ‘multi-factor productivity’ (MFP) growth. With the exception of recent work at the OECD (discussed below), estimates of TFP growth at the macroeconomic level have excluded natural resources as a factor of production – a serious omission when considering developing countries. Natural resources make up on average 47% of total wealth in low-income countries, 36% in Sub-Saharan Africa, 27% in lower-middle-income and 17% in upper-middle-income countries. A particular concern is that agricultural land makes up 62% of natural wealth in low-income countries. The omission of natural resources was largely owing to a lack of comparable cross-country data on natural resource stocks and flows, measured according to System of National Accounts (SNA) standards.

The publication by the World Bank of _The Changing Wealth of Nations 2018_ (Lange, Wodon and Carey 2018) has filled this gap, with data on the value of natural resource stocks and flows across 141 countries and spanning the period from 1995 to 2014. The OECD pioneered methodology to include natural resources (minerals and energy) in TFP estimations for OECDmember countries and a few other countries (Brandt et al. 2017). This paper tests the OECD approach for a broader set of natural resources and with some modifications described in section 2, for 74 middle- and low-income countries, exploiting the World Bank wealth database.

This study generalizes the preliminary ‘version 0’ estimates of TFP growth with natural resources by Hamilton (2018) in _The Changing Wealth of Nations 2018_ . It is important to emphasize at the outset that the results reported here represent ‘version 1’ estimates and future work will refine the estimates. The intent of this paper is to test the methodology for developing countries, demonstrating the extent to which including natural resources in TFP growth measures matters. We will not attempt, at this early stage, to provide in-depth analysis and policy conclusions, which will be addressed in future work.

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# **3.5. Interpretation**

Table 6 presents the results from cross country regressions with the 1971-1995 averages. Overall, only the number of telephone lines per hab. is significant, with a positive sign of 0.022. This implies that an increase in 100 lines per hab., from the average level over the period of Venezuela (63) to that of Korea (163) would add 2.2 points to the average growth rate of per capita GDP. All other infrastructure variables are insignificant and the paved roads length one is of the wrong sign. In columns 5 and 6, we add measures of quality of infrastructure, namely the number of telephone faults and electricity losses. These measures are not significant and the main indicators’ coefficients are unchanged.

When considering instead the 1984-1995 period, in Table 7, which in particular enables us to introduce indices of government stability and corruption as additional control variables, we get even less conclusive results. The number of phone lines is now only significant when quality is controlled for and its coefficient is about half of the 1971-1995 one, while the paved roads variable is now negative and significant.

In Tables 8 and 9, we address the fact that infrastructure stocks may be determined simultaneously with output. Following previous contributions in the literature, we use beginning of the period (1971) values of the indicators themselves, as well as 1971 values of the level of population, population density and the share of agriculture in GDP. Overall, the results from IV estimations are similar to simple OLS. The coefficient for the number of phone lines is now larger, between 2.8 (1971-1995) and 4.7 (1984-1995). Note however that a Wu-Hausman test does not reject exogeneity in all but one of the 12 estimations.

Next, we test regional effects by interacting the infrastructure indicators with regional dummies. Tables 9 to 13 present the results for telecom, energy, railroad and roads respectively. In each case, we first use an East Asian dummy, then dummies for low and middle income countries respectively.

As for telecom, the group of East Asian countries does not display any significantly different behavior (the coefficient is negative but not significant), while income classification indicates that telecom impact is significantly lower in low income countries, a result that may appeal to 18
total in many years. In the case of China, a 1 percent decline in the GDP of China would result in almost a 2 percent decline in DRC’s total merchandise exports to China. Since DRC’s exports to China are fully commodities, the estimated response of commodities is the same as “All Products”.

14. **A decline in the economic growth of DRC’s client countries would have an important impact on the country’s GDP growth and external accounts and macroeconomic stability.** Using the averages for 2008-2012<sup>4</sup> from Table 2, Table 3 presents the magnitude of the impact of a 1 percent reduction of the GDP of each export destination on DRC’s exports as a share of the country’s GDP, and from there, on GDP growth and external accounts. Hence, a slowdown by 1 percent in China’s GDP would entail a reduction in exports to GDP in DRC by 0.31 percentage point (ppt). Consequently, DRC growth would decline by 0.33 ppt, the current account deficit to GDP would deteriorate by 0.32 ppt, the overall reserves would be US$106.7 million lower than the baseline, and fiscal balance would deteriorate by 0.01 ppt of GDP. Hence, all other things being equal, a slowdown of China’s GDP growth from 10 to 7 percent would cut the GDP growth in DRC by 1.0 ppt, cost US$320 million in reserves, and widen the fiscal deficit by 0.019 ppt.

**Table 3. Impact on DRC of a One Percent Decline in Trade Partner’s GDP (in percentage points, unless**

**specified otherwise)**

|**Changes**|**... in the Exports**<br>**to GDP Ratio**|**... in GDP**<br>**Growth**|**... in the Current**<br>**Account Balance**|**... in Foreign**<br>**Currencies Reserves**<br>**(US$, million)**|**... in Fiscal**<br>**Balance**|
|---|---|---|---|---|---|
|United<br>States|-0.0274|-0.0296|-0.0287|-9.5|-0.0006|
|Japan|-0.0007|-0.0007|-0.0006|-0.2|-0.0000|
|EU27|-0.0613|-0.0663|-0.644|-21.3|-0.0013|
|Brazil|-0.0019|-0.0021|-0.0020|-0.7|-0.0000|
|Russian<br>Federation|-0.0003|-0.0003|-0.0003|-0.1|-0.0000|
|India|-0.0091|-0.0098|-0.0095|-3.2|-0.0002|
|China|-0.3077|-0.3322|-0.3230|-106.7|-0.0061|
|Korea,<br>Rep.|-0.0106|-0.0115|-0.0111|-3.7|-0.0002|

Source: authors’ simulations, using MFMod.

> 4 The use of the average for 2008-2012 is motivated by the fact that the destination of DRC exports had changed radically since 2008, with an increasing weight for China.

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_Ways Out of Poverty - Diffusing Best Practices and Creating Capabilities_ _22_ financial institution, new firms can compete only with difficulty. They have difficulty obtaining credit, and they tend not to enter.

When entry is possible, new firms do not enter only when demand is up, and old firms do not leave only when demand declines. Every year around 5 to 20 percent of all firms enter the market and a similar number of older firms go out of business. Most new firms are small, and some 40-60 percent go out of business within five years after entering. Data from developing countries suggest that the average new entrant is a little more productive than firms exiting the market. In most economies, the turnover of firms helps increase productivity. In part this reflects people being successful who have previously failed. In part it reflects new entrants altogether.

<!-- Start of picture text --> Figure 13: Plant turnover and market share of entrants<br>One-year old plants Five-year old plants<br>150 80.0<br>-60.0-<br>' 100 400<br>J ,U_<br>Jjm20.0<br>0.0 - Korea Taiw an United<br>Chge Cobmbia ?vbrocco States<br>E3Market share of entrants 5 Rant turover rates C Market share of entrants e lant turnover rates<br><!-- End of picture text --> Note: The plant tumover rate is the average of the entry rate and the exit rate of plants. Source: Tybout (2000).

**Opportunity for small** businesses and **farms.** Many of the key measures to help small farms and micro-enterprises are not fundamentally different from those affecting other firms. Hernando de Soto has demonstrated in writing and in his experiments with setting up new small firms that burdensome entry regulations, red tape, bureaucratic harassment, and the absence of clear property rights and contract security make it impossible for many small entrepreneurs to establish themselves, to become formal, and to grow. That spotlights the importance of programs to reduce entry regulations, reduce bureaucratic harassment, and clarify and formalize property rights (box 3).

**Box 3: Property rights and poverty reduction**

According to Hernando de Soto (2000), the total value of the fixed property held but not legally owned by the poor in developing countries is around $9.3 trillion. This is 93 times the amount of official development assistance to developing countries over the past 30 years and 20 times the stock of foreign direct investment in developing countries between 1989 and 1999. But, because the ownership of these assets is not formalized, the people in poor countries cannot use them as collateral to raise cash.
(C). Pakistan From the perspective of the models in the previous section, Pakistan's initial conditions were less than promising. Although inflation was moderate in the two years prior to the FSAL (averaging 8.4%), the private banking sector was better developed than in most of the countries in the sample (DBPC/GDP was 25.4%). The interaction term used in many of the models (DBPC/GDP * inflation) was, therefore, among the larger ones in the sample.80 In addition, Pakistan's openness to international trade was below the sample average (IMP/GDP was 19.0%). Largely because of these factors, the model predicted only a modest increase in M2/GDP (1.8 points) in the wake of the FSAL. The Bank also recognized that initial conditions had an adverse effect on the loan noting that, "without significant improvements on the stabilization front, the benefits of the reforms have been much reduced."8' Although the Bank was concerned primarily with the negative effects of inflation and the large fiscal deficit, a lack of trade openness and an already substantially developed (if highly imperfect) private banking system also probably exacted a toll on the rate of financial deepening.

According to the Bank's Project Completion Report, the saving grace of the Pakistan FSAL was the success of a number of reforms aimed at strengthening financial institutions. Prior to the FSAL, a number of foreign private banks received licenses, the so Its large DBPC/GDP figure, moreover, moved Pakistan into Group HI (high financial development, low inflation countries). The models and summary statistics indicated that Group III countries were among the worst performers in terms of postintervention financial deepening.

91 Pakistan: PCR, March 16, 1995, p. iii.

49
$1.83 if they assess the delivery to likely be risky and refer the mother to deliver at the district hospital. Hence, payments for prenatal care depend not only on the number of pregnant women coming for care and the number of times they visit, but also on the content of the care provided during those visits.

In fact, payment rates for visits are much higher if the provider supplies better content of care. As we discussed, a provider will receive $0.55 for four prenatal care visits of low quality versus $1.47 for providing high quality. If the provider detects a high-risk pregnancy and refers the woman to the hospital for delivery, payments for this high-quality care even increase to $3.30. In the case of growth monitoring, the payment to the provider is $0.18 per visit plus an additional $1.83 if the child is malnourished and she refers her to the hospital for treatment. Since 45 percent of Rwandan children under age five have moderate chronic malnutrition, and 19 percent have severe chronic malnutrition,<sup>7</sup> (Institut National de la Statistique du Rwanda and ORC Macro 2006), the expected payment for a high quality growth-monitoring visit is quite high. Overall, the incentive structure focuses not just on treating more patients, but on providing more patients with higher quality of care; this happens through both the multiplicative scaling factor _Q_ and by direct payment for content of care services in the _Ui_ ’s.

# **d. Empirical Implications**

This discussion provides us with a number of empirical predictions. First, increases in payments will be more effective for services for which the relative price increase is highest and for those that have the highest relative marginal return to effort. Second, increases in payments will not necessary increase all services. There may be no effect on services for which payment rates and the marginal return to effort is low. Third, payment rate for a service depends not only on the number of patients treated, but also the content of care provided during a visit and it is this payment rate that matters for the allocation of effort. Finally, we expect the introduction of P4P to increase quality _Q_ , the multiplicative factor in the payment formula.

> 7 Moderate (severe) chronic malnutrition corresponds to height-for-age below -2 (resp. -3) standard deviations from the median of the reference population (Institut National de la Statistique du Rwanda and ORC Macro 2006).

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# **5.  Method of Study**

57. In this paper the basic approach to estimating FDB is the residual imputation method. An agroeconomic model is employed that combines a crop simulation model, which estimates the effect of alternative irrigation water delivery scheduling options on consumptive use and crop yield, with a mathematical programming model designed to reflect farmers‘ optimal land, water use, and irrigation technology decisions given certain constraints such as available land and water supplies. Details on the crop simulation model are in Scheierling et al. (1997), and on the agroeconomic model in Scheierling et al. (2004; 2006).

58. **Study Area** . An irrigation organization in northern Colorado, the Cache La Poudre Irrigation Company, was chosen as the study area. It is one of about a dozen farmer-owned irrigation organizations located in Weld County, one of the richest agricultural counties in the United States. Its service area is located near the South Platte River which, together with its tributaries, serves Colorado‘s most important agricultural region and major urban-industrial centers. Dating back to the late 19<sup>th</sup> century, the irrigation district owns some of the most senior water right decrees for surface flows, which under Colorado‘s prior appropriation system guarantee highly secure and reliable water supplies. The water right can be transferred as long as other water rights holders are not injuriously affected (Getches, 2008). The courts usually consider the historical use of the right and require that the water consumption of the new use is not greater than the historical consumptive use. This limitation is designed to protect other water right holders dependent on the return flow regime, by aiming to ensure stream conditions present at the time of their respective appropriations.

59. Return flows are very important in the South Platte basin. Deep percolation from ditches and irrigated land reaches the river‘s unconfined shallow alluvial aquifer, where it becomes available for pumping or to replenish surface flows downstream. On the basis of comparisons of basin inflows to total surface and groundwater withdrawals, the water in Colorado‘s portion of the South Platte basin is estimated to be used and reused by a factor of about 2.5 before it reaches Nebraska. More than 80% of the withdrawals in the South Platte basin continue to be used for irrigated agriculture, but there is increasing pressure to make some of that irrigation water available for growing urban and environmental demands.

60. **Agroeconomic Model** . The main features of the crop simulation model include the modeling of water and solute movement through the soil, and of simultaneous water uptake by plants. It is formulated to capture the effects of irrigation timing as discrete-input events, and estimates water-crop production 18
i

# **TABLE OF CONTENTS**

|1. Introduction .......................................................|1|
|---|---|
|2. An overview of the process of voucher privatization in the Czech Republic ........|............ 2|
|3. Survey of Czech voucher funds .......................................................|4|
|4. Fund classification .......................................................|6|
|5. Investment strategy of voucher funds .......................................................|8|
|6. Fund performance ......................................................|15|
|7. Discount to net asset value .......................................................|17|
|8. Funds and corporate governance ......................................................|19|
|9. The relationship between banks and investment funds ..........................................|.......... 25|
|10. Legal framework for voucher funds in the Czech Republic ............................ ...|........... 30|
|1 1. Investment funds and privatization - Conclusions ..............................................|........ 34|
|12. Policy implications ......................................................|38|
|13. Literature ......................................................|42|
|14. Appendix ......................................................|45|