rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6983

# **Abstract**

This paper assembles novel data on the Chilean wine industry to investigate the role of investments in knowledge capital on sales growth in domestic and international markets. The study uses archival data collected from the Government of Chile to compile and categorize public expenditures and programs supporting the Chilean wine industry over the period of 1990–2012 into investment in different types of knowledge capital. These spending categories are related to industry-level sales growth. The paper finds that the most important correlate is spending on research and development. The study also uses data from a new survey of Chilean wine firms to capture information on firm-specific investments in knowledge capital. The findings show that investments in collaboration capital, in particular hiring foreign consultants, as well as participation in international wine fairs are the strongest correlates of growth in export sales, while spending on aspects of branding (local advertising and brand design) are the strongest correlates of domestic market sales growth.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mdutz@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
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1047 C6te d Ivoire: Private Sector<br>Dynamics and Constralnts|Enrique Rueda-Sabater<br>Andrew Stone|<br>November 1992|P. Infante<br>37642|
|WPS1048 Targets and Indicators In World<br>Bank Population ProJects|George Baldwin|November 1992|0. Nadora<br>31091|
|WPS1049 Money Demand and Seignorage-<br>Maximizing Inflation|William Easterly<br>Paolo Mauro<br>Klaus Schmidt-Hebbel|November 1992|R. Martin<br>31448|
|WPS1050 Marginal Income Tax Rates and<br>Economic Growth in Developing<br>Countries|William Easterly<br>Sergio Rebelo|November 1992|R. Martin<br>31448|
|WPS1051 The Legal Framework for Private<br>Sector Activity In the Czech and<br>Slovak Federal Republic|Cheryl W. Gray|November 1992|M. Berg<br>36969|
|WPS1052 A Reappraisal of How Oral<br>Rehydration Therapy Affected Morta<br>in Egypt|Hoda Rashad<br>lity|November 1992|0. Nadora<br>31091|
|WPS1053Development of the Zimbabwe<br>Family Planning Program|Alex F. Zinanga|December 1992|0. Nadora<br>31091|
|WPS1054 Distributional Impact of Cash and<br>In-Kind Social Transfers in Eastern<br>Europe and Russia|Branko Milanovic|December 1992|S. Moussa<br>39019|
<!-- Start of picture text --> Percent<br>06<br>04<br>0.2<br>0.0<br>-0.2<br>04<br>“058<br>0.8<br>-1.0<br>EMDEs Advanced economies<br><!-- End of picture text --> EMDEs Advanced economies <!-- Start of picture text --> Percent<br>e<br>5 zx<br>‘<br>3<br>-e<br>5 ow5 5 S wn5 5<br>© cL S cL<br>a o<br>oOa ramoo<br>EMDEs Advanced economies<br><!-- End of picture text -->
- 34 -

their land, allow other farmers to observe the new practices, and be willing to explain them to other farmers.

o To the extent possible, come from different families as much as possible.

o Be from geographically dispersed farms (World Bank, 1984a, b).

3.44 Other contact farmer selection criteria used in Africa that are likely to discourage the participation of women farmers are land ownership, literacy, and the ability to purchase inputs. Asking the advice of village chiefs and elders in selecting contact farmers may also create a bias against female farmers.

# Cas : KENYA: SELECTING COMTACT **FARWRS**

Momer are more likety to be setected if criteris for setection eqphasize farming ability and if extension-agentsmoke the setection. Findings differed in three areas of Muranga. In 4akuyu, where the setection criteria inctude active invotvement in farming **and** avaitabitity to meet agents, more than hatf the contact farmers are wamen. In two other areas of Muranga, where tand ownership is stitt a setection criterion, about two-fîfths of the agents select women as one-fourth to one-half of contact farmers. Under the two female agents, two-thirds and 90X of the women are contact farmers. In Meru, where chiefs often setect contact farmers in local meetings, fewer women are contact farmers (World Bank 1989).

3.45 Subtler built-in biases against the selection of women contact farmers, include the previously mentioned likelihood that male extension agents feel more comfortable, at least initially, with male farmers and consider women as farmers' wives rather than as serious farmers in their own right. These attitudes have an overwhelming collective effect considering that currently 93% of extension personnel in Africa are male (FAO 1988).

3.46 Three ways to increase the number of female contact farmers in Kenya have proven effective: to encourage chiefs, ministers, and political leaders to take a public stand at local gatherings and in the media in favor of female contact farmers; to emphasize the selection of women farmers during training programs for extension agents; and to encourage extension agents to select contact farmers on the basis of merit rather than for patronage (World Bank 1989). Actual experience in working with women contact farmers often persuades male extension agents that this approach is effective. Evidence from Nigeria and Kenya, for example, indicates that some male agents prefer working with women farmers because women are more likely to follow their advice and are doing most of the farming anyway (Nnonyelu,1987, World Bank 1989).

3.47 Quotas are sometimes recommended to increase the number of female contact farmers. While quotas would highlight the need to include more women and raise the consciousness of agents about the issue, general targets and benchmarks are preferable provided they are preceded by thoughtful analysis of prevailing agricultural practices and followed by frank and open discussion
Policy Research Working Paper 6795

# **Abstract**

During the past 20 years, Morocco has implemented a wide range of macroeconomic, social, and labor market reforms that have delivered in terms of growth of gross domestic product and household welfare. Yet, these positive developments are not reflected by the main labor market indicators, a phenomenon observed elsewhere in developed and developing economies alike and labeled as “jobless growth.” For the first time for Morocco, this paper uses quarterly panel data to investigate the question of labor mobility in an effort to determine whether people have moved to better sectors and jobs. The results point to significant labor mobility between labor statuses with quite distinct features across population groups. All groups experience some form of labor market mobility every quarter and women are as mobile as men. However, the transitions that women experience are very different from the transitions than men experience and women’s performance is worse than men’s performance in almost all aspects of labor mobility.

This paper is a product of the Poverty Reduction and Economic Management Department, Middle East and North Africa Region. 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:// econ.worldbank.org. The authors may be contacted at pverme@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
technologies may have the same sophistication, the highest rank in a function _Rf ≤ Nf_ .<sup>17</sup> Combining the technology rankings with the information collected by the FAT survey on the technologies used by a firm, we construct three indices of technology at the business function level that we denote by MOST, EXT and GAP.

**MOST** The first index reflects the sophistication of the most widely used technology in a business function, and we call it MOST. The MOST index of a firm _j_ in a business function _f_ is computed as

where _rf,j_<sup>_MOST_</sup> is the sophistication rank of the technology identified by the firm as being most widely used for the business function, and _Rf_ is the maximum technology rank in the function. Therefore, the index reflects the technology sophistication of the most widely used technology relative to the most sophisticated technology available in the market to conduct a business function. To no effect, we scale the index so that it is between 1 and 5.

**EXT** The second index we construct measures the sophistication of the array of technologies used to conduct a business function, and we call it EXT (an abbreviation of extensive). In contrast with MOST, EXT does not reflect how much each technology is used but it reflects the sophistication of all the technologies used in production, rather than just the most relevant one. To measure the sophistication of the range of technologies, we must first understand the degree of substitutability between the technologies in the business function. Figure 3 illustrates four possible structures we encounter in the business functions covered by FAT and that differ in the substitutability between their technologies. Panel A depicts a quality ladder or vertical structure (Aghion and Howitt, 1992). In quality ladders there is no productivity gain from using technologies below the maximum sophistication rank employed in the firm, _rf,j_<sup>_MAX_</sup> . Therefore, the sophistication of the technologies employed in business functions with a quality ladder structure is _rf,j_<sup>_MAX_</sup> .

The technologies in other business functions may have a horizontal relationship (Romer 1990), depicted in panel B. In horizontal structures, the use of less sophisticated technologies facilitates the fulfillment of the tasks in the function even conditional on using more sophisticated technologies. For example, in marketing the use of less sophisticated technologies 17In a small number of business functions, the technologies covered are used in various subgroups of tasks. For example, in the body pressing and welding functions of the automotive sector, the survey differentiates between technologies used for pressing skin panels, pressing structural components and welding the main body. In cases like this, we construct ranks of technologies for each subgroup of tasks within the business function, and then aggregate the resulting indices by taking simple averages across the tasks groups.

10
# **Table 6 Determinants of the Return on Assets**

The dependant variable is _Return on assets_ , which is defined as profit before tax as a percentage of total assets of a bank. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding is_ the share of nondeposit short-term funding in total deposits & short-term funding. _Fee income sq_ and _non-deposit funding sq_ are the square of fee income and nondeposit funding, respectively. We get the data for these bank level variables from Bankscope. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. We obtain these macroeconomic variables from World Development Indicators (WDI) of World Bank. We estimate all regressions using country and time fixed effects and clustering at bank level. Standard errors are in parentheses. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Assets|0.000|0.002**|0.000|0.000|
||(0.000)|(0.001)|(0.000)|(0.001)|
|Equity|0.082***|0.110***|0.078***|0.073***|
||(0.017)|(0.029)|(0.020)|(0.020)|
|Assets growth|0.011***|0.011***|0.008**|0.007*|
||(0.004)|(0.004)|(0.004)|(0.004)|
|Overhead|-0.127|-0.020|-0.141|-0.152|
||(0.084)|(0.065)|(0.095)|(0.096)|
|Fee income|0.017**||0.015**|-0.021*|
||(0.008)||(0.007)|(0.011)|
|Non-deposit funding||-0.004|0.003|0.004|
|||(0.008)|(0.007)|(0.012)|
|Fee income sq||||0.040***|
|||||(0.015)|
|Non-deposit funding sq||||-0.007|
|||||(0.018)|
|Inflation|0.151***|0.126***|0.133***|0.128***|
||(0.042)|(0.042)|(0.040)|(0.040)|
|GDP growth|0.122***|0.091***|0.102***|0.101***|
||(0.035)|(0.034)|(0.033)|(0.032)|

41
determinant of earnings in all countries examined except Poland and Finland; they also find that about one-third of the variation in relative wages between skill groups across countries is explained by differences in net supply of skill groups. Green and Riddell (2003) used the measure of literacy in the IALS data set to examine the influence of observable and unobserved skills on earnings in Canada. They find that literacy skills contribute significantly to earnings and that their inclusion in earnings equations reduces the measured impact of schooling. They also find that schooling and literacy do not interact in influencing earnings. Their findings suggest that observed and unobserved skills are both productive but that having more of one skill does not enhance the other's productivity. Devroye and Freeman (2001) used the same data and found that skill inequality among advanced countries explains only about 7 percent of the cross-country differences in earnings inequality. They also find that the bulk of cross-country differences in earnings inequality occur within skill groups, not between them. Hanushek and Zhang (2006) use IALS data to control for the quality of education’s impact on the returns to education. They construct quality-adjusted measures of schooling attained at different time periods and use these along with international literacy test information to estimate returns to skills for 13 countries. Their estimated returns to quality-adjusted education are considerably higher than the traditional estimate for most countries, but these are offset to varying degrees by selection biases on ability. The combined corrections alter significantly the pattern of returns to schooling estimated from Mincer wage equations.

In this paper we use a methodology previously used by Ishikawa and Ryan (2002); it is a two-step approach which splits skill by origin to generate estimates of the return to schooling-associated and non-schooling-associated labor marker relevant skills. We apply this approach to 14 countries or 4
**Table 2: The relationship between legal gender equality and women’s labor market outcomes**

|||**Female labor f**|**orce partici**|**pation**||**Gender**|**wage gap**|||**Occupational**|**segregatio**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||**OLS**||**FHS**||**OLS**||**FHS**||**OLS**||**FHS**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|
|**WBL index (t‐1)**|0.047***|0.047***|0.053***|0.048**|‐0.088***|‐0.101***|‐0.063|‐0.260|‐0.026|‐0.056**|0.004|‐0.048|
||(0.009)|(0.009)|(0.016)|(0.020)|(0.030)|(0.034)|(0.067)|(1.012)|(0.023)|(0.024)|(0.100)|<br>(0.037)|
|**Regional non‐ag**|0.000**|0.000*|0.002|0.001|0.003**|0.003**|‐0.029|‐0.173|‐0.003***|‐0.003***|0.078|0.015|
|**VA * Oil rents**|(0.000)|(0.000)|(0.004)|(0.004)|(0.001)|(0.001)|(0.070)|(1.103)|(0.001)|(0.001)|(0.210)|<br>(0.057)|
|**GDP per capita**||0.000***||0.000***||0.000||‐0.003||0.000***||0.001**|
|**(constant 2010**||(0.000)||(0.000)||(0.000)||(0.021)||(0.000)||(0.000)|
|**US$)**|||||||||||||
|**GDP per capita**||‐0.000||‐0.000||‐0.000||0.000||‐0.000***||‐0.000***|
|**squared**||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)|
|**Fertility rate**||‐2.084***||‐2.085***||1.705**||0.263||‐1.959**||‐2.019**|
|||(0.179)||(0.179)||(0.770)||(9.640)||(0.796)||(0.919)|
|**Male working**||0.000||0.000||0.000**||0.000||‐0.000*||‐0.000|
|**age population**||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)||(0.000)|
|**Average years of**||1.258***||1.260***||5.359***||27.129||0.931||0.721|
|**education,**||(0.175)||(0.177)||(1.303)||(136.696)||(1.484)||(1.811)|
|**female ages 25**|||||||||||||
|**plus**|||||||||||||
|**_cons**|36.684***|<br>29.628***|||17.436***|‐54.180***|||33.599***|26.548*|||
||(0.722)|(1.461)|||(2.853)|(15.391)|||(2.023)|(15.819)|||
|**_N**|4,927|4,459|4,927|4,458|999|949|990|943|1,395|1,215|1,370|1,194|

**Notes: Standard errors are presented in parentheses; .01 ‐ ***; .05 ‐ **; .1 ‐ *. All regressions include country and year fixed effects**

20
effects and the time fixed effects. Standard errors will be clustered at the Boma level to allow for within-cluster correlation.<sup>6</sup> β1 is the difference-in difference estimator.

# **Household Resilience: Triple Difference**

To identify factors that make households resilient to the inflation shock, we estimate the following triple difference equation where hi is a potential resilience factor. i is a potential resilience factor. is a potential resilience factor.

triple difference equation where hi is a potential resilience factor. i is a potential resilience factor. is a potential resilience factor. i = β0 + β1 (post) + β2 (postt *inflationst) + β3 (postt *hi) + β4 (postt *inflationst *hi) + βX i + γs +δt + i _(3)_ βYY3 is the triple difference estimator. In this triple difference setting, βii 1 is the diff-in-diff estimate XX ii εε ii for the reference group (h=0). It captures average differential change in _y_ from the pre- to posttreatment period for the reference group in the treatment group _relative_ to the change in _y_ for the reference group (h=0) in the untreated group. The total treatment effect for both groups is β1 + β3.

# **Conflict indicator**

Given the ongoing conflict in South Sudan, conflict will likely be one of the confounding factors affecting household livelihoods. We control for this by including a conflict variable in the regressions. We construct an exogenous conflict variable based on conflict event data from the Armed Conflict Location & Event Data (ACLED)<sup>7</sup> for the period of our study. The data set codes the exact location of all political violence incidents that were reported during this time. We use proximity to a deadly conflict event to generate a continuous conflict exposure variable (i.e. the number of fatalities).

> 6 Default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. Failure to control for within-cluster error correlation can lead to very misleadingly small standard errors, and consequent misleadingly narrow confidence intervals, large t-statistics and low p-values (Cameron and Miller, 2015).

> 7 Information about ACLED methodology can be found at https://www.acleddata.com/.

7
Child mortality as the primary outcome variable of interest in this paper is generally defined as the number of children dying before the age of five per 1000 children born alive. This number can either be computed as a cohort measure, which means that a given birth cohort is followed over a five year period, or as a period measure, in which age-specific mortality rates from different birth cohorts are combined for a given time window. Even though cohort measures may seem more intuitive, they are problematic from an analytical perspective, since the estimated mortality rates reflect risk exposure over an extended period of time with potentially large changes in the underlying risk structure.

To minimize the time difference between observed water and sanitation infrastructure and mortality outcomes, we limit our analysis to mortality within the 12-months-window preceding the household interview. The dependent variable we construct is a binary variable capturing the probability of a child between the age of 0 and 48 months dying over the 12 months interval leading up to the interview. This mortality variable thus excludes children who died more than 12 months before the respective DHS survey, children older than 48 months one year prior to the interview, and children born less than 12 months prior to the interview.

Even though the Demographic and Health Surveys (DHS) standardize most variables in their published data, this is unfortunately not true for variables on water and sanitation infrastructure. Given the vast array of different technologies used across countries, the detail and structure of the collected information on water and sanitation infrastructure differs widely across the 38 surveys used in this paper. In total, we found about 150 different sanitation and 200 different water codes in our sample. To make the collected information comparable across countries, we developed a few simple coding rules, which map the various types of water and sanitation infrastructure into three broad categories. Since we want to explicitly differentiate between different types of water and sanitation technologies, we follow a coding similar to the water and sanitation ladder proposed by the WHO/UNICEF Joint Monitoring Program (JMP), which is slightly more complex than the improved versus unimproved water and sanitation definition originally suggested by the WHO.

In the first paper of this series (Günther and Fink, 2010) we argue that the health impact of water and sanitation is likely to critically depend on the way in which water and sanitation infrastructure are constructed and used. In the context of sanitation, is seems obvious that the health effects will be larger if excreta are not only kept out of the local water system, but also 7