rafmacalaba/datause-encoder-data huggingface dataset:
heterogeneous or asymmetric effects.<sup>9</sup> This allows us to illustrate the role of heterogeneity in the estimated linkages between consumption and extreme shocks as well as observed discrepancies in poverty outcomes. There are 5 ecological zones in total. These include urban, irrigated agriculture, rain-fed agriculture, agro-pastoral and pastoral zones. Since we estimate the implied effects simultaneously, we excluded the urban zone as our omitted category for subsequent comparison with the other zones. The effects under the pastoral group are not estimable due to data limitations. Estimation results are presented in table 1.

**<u>Table 1: Asymmetric effects of extreme shocks across eco-zones</u>**

|DV|**Expenditure, CFA (a)**|**Food–Poor (b)**|**Absolute–Poor (c)**|
|---|---|---|---|
|Extreme Shocks**X**Irrig. Agr.|-39696.2(71513.5)|0.38***(0.01)|0.42***(0.02)|
|Extreme Shocks**X**Rainf. Agr.|32901.3(72318.3)|0.61***(0.02)|0.56***(0.02)|
|Extreme Shocks**X**<br>Agropastoral|-113310.1***(14860.8)|1.50***(0.04)|1.60***(0.04)|
|joint Wald-Test, P–val|<0.00|<0.00|<0.00|
|R-/Pseudo -squared (%)|0.02|-|-|
|Observations|3,059|3,059|3,059|
|Fixed Effects|Households|Households|Households|
|Logit (Marginals)|-|Yes|Yes|

**Notes:** Table reports estimates from regressions of households per capita consumption expenditure and households poverty e.g., food versus absolute poor status on shock variables across different agro ecological zones. The baseline omitted category is the urban zone. Irrig. Agr. denotes irrigated agriculture zone, while Rainf. Agr. denotes rainfed agriculture. Extreme shocks—“Shocks”: correspond to realizations above the 80% percentile of estimated shock distribution. The columns are differentiated by outcome variables and implied regression models. All columns include household level fixed/random effects. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

In table 1, column (a) reports the results for _per capita_ consumption expenditure; while columns (b) and (c) display results for two household poverty outcomes. We conducted joint hypothesis tests under the null that extreme shock effects are symmetric across all eco-zones. The corresponding probability–values are reported in the middle row of each specification. In all cases, we strongly reject symmetric or homogenous effects in the joint tests. Consider the results for households’ expenditure: extreme shock effects are indistinguishable between urban and

> 9 We classify Nigerien households into 5 agro-ecological zones. The definitions of zones are adapted from FEWS NET —the Famine Early Warning Systems Network's classifications. FEWS NET was incorporated in 1985 by the US Agency for International Development USAID, and the US Department of State following drastic famines in major parts of Sub Saharan Africa.

‐ 11 ‐
|Part of a larger group?|||||2.165**|
|---|---|---|---|---|---|
||||||(0.989)|
|Own security system?||||0.673***||
|||||(0.157)||
|Experience a criminal attempt last||||-0.303||
|year?||||(0.235)||
|Externally audited?||||0.895***||
|||||(0.231)||
|N|1,081|216|127|419|56|
|R-squared|0.124|0.000|0.531|0.244|0.514|
|*** p<0.01, ** p<0.05, * p<0.1. Rob<br>whicha sufficientlylarge sample of|ust standard<br>innovativefi|errors in paren<br>rmsis available|theses. Showin<br>.|g results only for|countries for|

# 4. Conclusions

We study the link between innovation and productivity in major developing East Asian countries (China and ASEAN 5), highlighting the role of the complementary business environment and firm characteristics.

We investigate the determinants of R&D investments, the returns of R&D investment on innovation outcome, and the association between innovation and productivity. Our main findings are threefold. First, elements that contribute to R&D investments are using technology licensed by a foreign company, having higher human capital at work, and being associated with a large group, for example, being a subsidiary of a multinational with easier access to and absorption of new and sophisticated knowledge. Second, R&D investment can significantly boost innovation, be it product or process innovation. Among other complementary factors, FDI is the most critical determinant for overall and product innovation, though not for process innovation. Third, significant productivity gains arise from overall innovation and product innovation alone, but not from process innovation; a finding also documented for Latin America in Crespi et al. (2016) and Europe in Hall (2011).

To gain a deeper understanding of the innovation-productivity link, we rely on Hendry and Krolzig's (2004) GETS method to select significant determinants of productivity from a full range of complementary factors ranging from management capabilities to foreign ownership to availability of financial services. The results show substantial heterogeneity by innovative and non-innovative firms and across countries. Critical productivity correlates vary greatly between innovative and noninnovative firms. Moreover, the same correlates may have opposite effects on productivity across countries. Nevertheless, despite this heterogeneity, some stylized facts emerge. Having ISO quality certification is the most common determinant of higher productivity across the selected EAP countries for both innovative and non-innovative firms. We do not claim causality, rather investing in quality control may signal ability/willingness to participate in highervalue value chains that require higher standards.

Our findings suggest three policy implications. First, policies for boosting innovation should focus on human capital accumulation and factors facilitating exposure to external resources, especially foreign technology licenses and investment. Second, policies should target product and process innovation differentially, given their heterogeneous linkages with R&D investment and 17
# **IV. Patterns of Mobility**

We estimate continuous time matrices from the discrete transition data as described above. Table 2 reports the posterior probability of embeddability and suggests that the Brazilian and Mexican matrices are clearly embeddable for all different subgroups. Argentina, however, shows probabilities near unity for the overall matrix and runs into problems when the division of the sample reduces the number of observations.

Tables 3a-c present the estimated _Q_ matrices of intensities-the instantaneous probability of moving from sector _i_ to _j_ , and its two component parts, the rate of separation from the each sector, transformed into the mean duration of stay in the sector, and the matrix of propensities to move from _i_ to _j_ conditional on separation from the previous state.<sup>9</sup> The intensities, propensities, and the propensities adjusted by job openings (the T matrix) for select sectors of interest are shown in figure 1a and b, and the durations in figure 2. The _Q_ matrices suggest that the three labor markets are broadly of the same phylum, showing a high degree of commonality in most any arbitrarily chosen transition. Argentina does differ in some key aspects that seem especially related to the very high rates of unemployment as we discuss below. Hence, were we to study the markets at the same point in the business cycle, even greater commonalities may emerge.

_Transitions between formal and informal jobs._ Analysis of the transitions among sectors of work provides the clearest illustration of the relative merits of the different statistics developed above. It is also in the realm of intersectoral transitions that some of the strongest hypotheses about the functioning of the developing country labor market have been postulated.<sup>10</sup> Again, more traditional segmentation models would predict than, on average across the business cycle, workers

> 9 In the interest of space, we do not report the complete T matrices as well.  Available on request. 10 Ideally, we would have data that permit studying job to job movement including those within a sector - which evidence from the US suggests is vast (Nagypal 2004), but we do not.  However, for the purposes of identifying patterns of interaction among the informal and formal sectors, this is not a major drawback.

12
|Table 1 — Pre|-program Chara|cteristics ofStu|dents: Ba|seline Sample|||
|---|---|---|---|---|---|---|
|||All||Gris||Boys|
||Meanin<br>Control|Difference<br>Treat-Control|Meanin<br>Control|Difference Treat- <br>Control|Meanin<br>Control|Difference Treat -<br>Control|
||Q)|(2)|(3)|(4)|(3)|(6)|
|Girl|0.545|-0.016|||||
||(0.014)|(0.019)|||||
|Grade 4 at Baseline|0.332|-0.009|0.334|0.006|0.331|-0.022|
||(0.008)|(0.012)|(0.010)|(0.016)|(0.011)|(0.015)|
|Age|10.070|-0.324**|10.003|-0.260**|10.127|-O.377***|
||(0.066)|(0.095)|(0.076)|(0.105)|(0.081)|(0.110)|
|Scheduled Caste|0.305<br>|-0.014<br>|0.323<br>|-0.028<br>|0.289<br>|-0.002<br>|
||(0.030)|(0.038)|(0.034)|(0.045)|(0.029)|(0.036)|
|Scheduled Tribe|0.130<br>|0.010<br>|0.169<br>|0.029<br>|0.135<br>|-0.008<br>|
||(0.022)|(0.036)|(0.027)|(0.046)|(0.022)|(0.032)|
|Other Backward Caste|0.400<br>|-0.015<br>|0.394<br>|-0.041<br>|0.405<br>|0.008<br>|
||(0.034)|(0.052)|(0.040)|(0.059)|(0.032)|(0.050)|
|UpperPrimary School (UPS)|0.582|-0.022|0.567|-0.008|0.594|-0.035|
||(0.073)|(0.090)|(0.078)|(0.096)|(0.072)|(0.089)|
|Baseline Students Enrolled in Same Grade|23.003<br>|-1.474<br>|23.293<br>|-1.653<br>|22.762<br>|-1.331<br>|
||(1.817)|(2.310)|(1.890)|(2371)|(1.808)|(2.327)|
|Baseline Students Enrolled in AllGrades|64.985<br>|-4.913<br>|66.066<br>|-5.639<br>|64.083<br>|-4.328<br>|
||(5.318)|(6.575)|(5.633)|(6.843)|(5.174)|(6.509)|
|Electricity|0.396<br>|0.014<br>|0.386<br>|0.026<br>|0.404<br>|0.005<br>|
||(0.066)|(0.095)|(0.065)|(0.097)|(0.069)|(0.096)|
|Computer|0.117<br>|-0.023<br>|0.107<br>|-0.020<br>|0.126<br>|-0.026<br>|
||(0.041)|(0.054)|(0.040)|(0.054)|(0.044)|(0.057)|
|DrinkingWater|0.806<br>|0.022<br>|0.803<br>|0.017<br>|0.808<br>|0.027<br>|
||(0.059)|(0.076)|(0.065)|(0.082)|(0.057)|(0.074)|
|Teacher/Pupil Ratio|0.069<br>|-0.002<br>|0.070<br>|-0.004<br>|0.068<br>|0.000<br>|
||(0.007)|(0.008)|(0.007)|(0.008)|(0.007)|(0.009)|

‘Notes: Robust standard errors in parentheses, clustered by village. For differences, *** p<0.01, ** p<0.05, * p<0.1. Baseline Sample consists ofstudents enrolled in 2011 in grades 3 and 4. N=7,327 students in 230 schools.
# Table A1.1 - Sumary Basic **Figures** for Total Grain Mill Products. United States (1977 and 1975) Brazil and Mexico (1975). in national currency

-- _--------------------------------------------------------__--------.-------__------------------------

||Grore<br>Value<br>ofOutput|Value<br>Added<br>US Census<br>Concept|Value Added<br>National<br>Accounts<br>Concept|Value Added<br>Former<br>National<br>Accounts<br>Concept|<br>Number<br>of<br>Employeex|
|---|---|---|---|---|---|
|UNITED STATES,1977<br> <br> <br> <br> <br>||(mi<br>|llion US doll|ars)||
|2041 Flour<br>and Other<br>Grain<br>Mill<br>Products<br>|3,683.3<br>|824.5<br>|---|---|15,600|
|2046 WetCorn Milling|2,014.8|666.7|---|---|10.300|
|TOTAL|5,698.1|<br>1,491 .2|---||26.500|
|UNITED STATES. 19i5<br>||(mi|llion US doll|ars)||
|2041 Flour and Other Grain Mill Products|4.327.6|<br>714.9|<br>---|---|17.700|
|2046 Wet Corn Milling|2,141.7|<br>872.9||---|10,900|
|TOTAL|6.469.3|<br>1.587.8||---|28.600|
|BRAZIL<br>||(tho|usand cruzeir|os)||
|1023 Wheat mill products|5.510,563|<br>1.251.741|<br>1.051.883|1.019.496|<br>9.271|
|1029 Manufacture of maize products. excl.||||||
|<br>oil*<br>|1.546.288|<br>505.876|<br>443.920|433.880|<br>7.444|
|1035 Manufacture of various flour products||||||
|and by-products|371,291|<br>161.060|<br>134.951|130,720|<br>2,367|
||--------------------------------|--------------------------||||
|TOTAL|7.428.142|<br>1.918,677|<br>1.630.755|1.584,096|<br>19,082|
|MEXn O||(th|ousand<br>pesos)|||
|2021 Wheat flour<br>manufacturing|5.944.296|1.532,561|1.215.666|965,606|7.522|
|2022 Corn flour<br>manufacturing|2,299.211|641.220|529.702|488,202|2.884|
|<br>2029 Manufacturing<br>of other<br>flour<br>and<br>mill||||||
|products<br>based<br>on grain<br>and leguminous||||||
|<br> <br> <br>plants|879,618|418.003|235.899|226.339|2.096|
||--------------------------------|--------------------------||||
|TOTAL|9.123,125|<br>2,591.784|<br>1.981.267|1.680.147|<br>12.502|

Sources: United States (1977) and (1975) from US Census of Manufactures. Industry Series, table la. United States (1975) originally from 1975 Annual Survey of Manufactures.

Brazil (gross value of output, value added US census concept and employment) from 1975 Censo Industrial Brasil, tables 3 and 26: additional information for calculation of value added at present and former national accounts concept derived from tables 15 and 35 (see also chapter II). Mexico **(gross** value of output and amploy-meat, f. Re-;aen General, table 5: additional information for calculation of value added derived from tables 19 and 20 (see also chapter II).
22 the required firm entry -- 33.7 percent in column 6 -- results in a loss in scale efficiency, which dominates the two beneficial effects of a reduction in monopolistic price distortion and a reduction in the quota rent to foreigners.18 Because of this reduction in the rent premium, when we estimate the costs of the VER from the counterfactually created zero profit equilibrium, we find that the rent costs are significantly reduced and now amount to 67 percent of the total costs. However, despite the reduction in rent costs, the total costs exceed the corresponding CTRS estimate because of the significant loss of scale efficiency, as emphasized by Hortsman and Markusen.

Now introduce the realistic assumption of international capital mobility (row 4). With perfectly mobile capital, the rental rate is fixed in terms of the num4raire and the returns from FDI are repatriated to foreign capital owners. Clearly removing profits involves firm entry so there will be net FDI into the US with the returns of that investment accruing to foreigners. Comparing rows 4 and 3, the costs of eliminating profits are $30 million less when there is international capital mobility. This is the result of three offsetting effects: reduction of rents to foreigners, loss of scale efficiency, and the Brecher and Diaz-Alejandro effect. The dominant effect is the reduction of rents caused by the slight increase in firm entry with international capital mobility (33.9 percent vs. 33.7 percent in rows 3 and 4 of Table 4). More entry occurs because the auto sector is relatively capital intensive. (Entry without international capital mobility raises the rental rate on capital, which reduces the profitabllity of capital intensive sectors such as autos thereby retarding entry.) More entry, because of no increase in the rental rate, raises domestic supply which lowers the demand for imported
Table 2 provides Concept 2 computations for four dates in the 19th century for which Maddison provides a sufficient number of countries' GDPs per capita. We focus on years 1820 and 1870 when Maddison’s data provide an almost full coverage of the world. Between these two dates, the Gini coefficient more than doubles from about 15 to 32. This reflects the well known phenomenon of income divergence that started in the 19<sup>th</sup> century. Concept 2 inequality continued to rise throughout the beginning of the 20<sup>th</sup> century, then stabilized at a rather high plateau of 55-60 Gini points between the end of World War II and fairly recently It has been decreasing in the past 20 years thanks to high growth rates of China and India.<sup>10</sup> As the last two columns in Table 2 show, today's Concept 2 international inequality can be estimated at 60 to 63 Gini points, depending on whether we use GDPs per capita or survey means. This is some 7 Gini points higher than what we get when we use "old" PPPs. As already mentioned, this is because the new PPPs imply lower real incomes for China, India, Indonesia and a number of other Asian countries.

What is striking is not solely that Concept 2 inequality was much lower in 1820 than now but that the composition of global inequality was so much different.<sup>11</sup> Using our estimate of global inequality in the early 19<sup>th</sup> century of around Gini 43 points, we see that Concept 2 inequality represented then only 35 percent of global inequality: 15.2 Gini points out of 43 Gini points. (If we use Theil, the percentage is even smaller.) Today when global inequality is about 65-70 Gini points,<sup>12</sup> Concept 2 inequality accounts for 80 to 90 percent of global inequality measured by the Gini (see line 10 in Table 2). Using the Theil index, the share of population-weighted international inequality rises from 8 percent in 1820 to between 61 and 74 percent now (see the last line in Table 2).

> 10 This can be seen if we compare the data for 1980 and the first column for 2002 which is based on “old” PPPs (see Table 2).

> 11 It should be noted that in the 19th century, we calculate Concept 2 inequality across only 60 countries while today we do it over twice as  many.  This imparts an upward bias to the latter. 12 See Milanovic (2009). The "correct" 2002 global inequality is 70 Gini points, but I also use Gini of 65 derived from the "old" PPPs because of compatibility with Maddison's historical data based  on the "old" PPPs.

11
# **2. INTRODUCTION**

To address and meet Millennium Development Goal (MDG) 6 targets of reversing HIV/AIDS spread by 2015, a firm commitment to HIV prevention efforts must be maintained globally and nationally. Indeed, national and regional strategies call for a 50% reduction in the number of new HIV infections by 2015 (Southern African Development Community 2009; Assembly 2012) and national-level indicators and targets have been established to monitor progress towards this goal.

As we near 2015, it is important to assess progress that has been made in reaching HIV prevention goals. Reducing HIV incidence is the main goal and indicator of successful HIV prevention, and therefore, accurate monitoring and assessment of temporal HIV incidence trends at national and sub-national levels is imperative. A number of methods exist in which the rate at which new HIV infections occur can be determined, though each method has its limitations and biases. Additionally, increasing ART coverage in many countries has introduced further complications of inferring incidence levels from HIV prevalence data. Since each of the current methods is imperfect at determining HIV incidence, it is useful to triangulate and compare different incidence estimates to gain an understanding of the rate new infections occur in a population.

The next sections describe the different methods and structures in place used to monitor incidence and how ART scale-up has influenced these estimates. This is followed by a discussion of methods and data sources used in this review and description of incidence trends country by country. Sub-Saharan countries with generalized HIV epidemics were chosen for this review, including: Botswana, Burundi, Burkina Faso, Congo, Central African Republic, Ethiopia, Ghana, Kenya, Lesotho, Namibia, Nigeria, Malawi, Mozambique, Rwanda, South Africa, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe.

## **<u>2.1. Monitoring HIV incidence</u>**

National HIV prevalence trends within the overall population have been used in the past to assess the success of HIV prevention efforts, with prevalence increases clearly indicating failure to prevent new infections. However, in an era of increasing ART coverage, this method is no longer suitable to accurately understand the dynamics of the HIV epidemic. More individuals infected with HIV are living longer lives due to increased access to and improved ART drugs, thereby influencing an upward trend in HIV prevalence, as they remain longer in the HIV+ population (Zaidi et al. 2013). Indeed, Rehle et al. showed that “excess HIV prevalence,” due to longer survival on ART, added on average 2.1 percentage points to prevalence within the population aged 15-49 in South Africa’s 2008 HH Survey (Rehle et al. 2010). As such, longitudinal prevalence 11
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Policy Research Working Paper 9614

# **Abstract**

Why do more small firms in developing countries not use the market for professional business services like accounting, marketing, and human resource specialists? Two key reasons may be that firms lack information about the availability of these services, and that they struggle to distinguish the quality of good versus bad providers. A brand recognition exercise finds that most small firms are unaware of most providers in this market, and a survey of service providers reveals that they largely rely on word-of-mouth and informal reputation mechanisms for acquiring customers. This study set up a business services marketplace that contains information about the different providers present in the market and used mystery shopper visits to develop a quality ratings system. A randomized experiment with more than 1,000 firms provided access to this marketplace to the treatment group and randomized whether firms received just information or also quality ratings. The provision of quality ratings information shifts small firms’ preferences over which provider they would like to use, increasing the average quality rating of their preferred providers by 0.2 to 0.4 ratings points out of 5. However, neither the provision of information nor these quality ratings had any significant impact on the likelihood that small firms go on to hire a business service provider over the subsequent six months. The results suggest that alleviating information frictions alone is insufficient to increase usage of professional business services.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team