rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table 4: Impacts on Access to Financial services**

||Z score on<br>Access to<br>Financial<br>Services<sup>a</sup>|Has a bank<br>account|Has a<br>business<br>bank<br>account|Used an<br>account<br>just for<br>business<br>purposes|Does not<br>save at<br>home|Does not<br>save in<br>ROSCA<br>or<br>SACCO|Saves at<br>bank|
|---|---|---|---|---|---|---|---|
|**Panel A: Full sample**||||||||
|Treatment 1: BRC|0.026|0.012|0.017**|-0.004|-0.004|0.007|0.009|
||(0.022)|(0.018)|(0.008)|(0.010)|(0.017)|(0.015)|(0.018)|
||0.240|0.505|0.031|0.715|0.792|0.656|0.603|
||_0.308_|_0.505_|_0.057_|_0.715_|_0.792_|_0.738_|_0.679_|
|Treatment 2: BRC + TPIN|0.018|0.018|0.010|-0.022*|-0.014|0.020|0.017|
||(0.028)|(0.023)|(0.011)|(0.013)|(0.022)|(0.019)|(0.023)|
||0.528|0.425|0.338|0.084|0.531|0.289|0.467|
||_0.528_|_0.478_|_0.380_|_0.095_|_0.598_|_0.371_|_0.600_|
|Treatment 3: BRC + IS + BBA|0.621***|0.181***|0.390***|0.152***|0.071***|0.033**|0.186***|
||(0.023)|(0.015)|(0.012)|(0.011)|(0.015)|(0.013)|(0.015)|
||0.000|0.000|0.000|0.000|0.000|0.012|0.000|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.017_|_0.000_|
|Control group mean|0.000|0.654|0.041|0.130|0.440|0.731|0.631|
|Sample size<sup>b</sup>|9438|9,438|10,900|10,900|9,438|9,438|9,438|
|p-value: Treat 1 = Treat 2|0.769|0.779|0.520|0.154|0.671|0.489|0.746|
|p-value: Treat 1 = Treatment 3|0.000|0.000|0.000|0.000|0.000|0.051|0.000|
|p-value: Treatment 2 = Treatment 3|0.000|0.000|0.000|0.000|0.000|0.456|0.000|
|p-value test of equality|0.000|0.000|0.000|0.000|0.000|0.053|0.000|
|p-value test of equality of treatment effects over<br>time||||||||
|Treatment 1|0.144|0.672|0.037|0.182|0.216|0.486|0.610|
|Treatment 2|0.444|0.692|0.634|0.941|0.187|0.359|0.828|
|Treatment 3|0.000|0.003|0.000|0.000|0.006|0.043|0.004|

50
**Table 4: Consumption per capita growth volatility with household credit as a share of total private non-financial credit**

|**Dep. Var:**|OLS|OLS|FE|FE|FE|FE|
|---|---|---|---|---|---|---|
|**std**|||||||
|**_**<br>**conspc**|(1)|(2)|(3)|(4)|(5)|(6)|
|hhcredit/|||||||
|pnfscredit|-0.1253***|-0.1030***|-0.0626***|-0.0753**|-0.0162*|-0.0515*|
||(0.002)|(0.005)|(0.004)|(0.012)|(0.088)|(0.057)|
|(hhcredit/|||||||
|pnfscredit)<sup>2</sup>|0.0017***|0.0011**|0.0006*|0.0007*||0.0006|
||(0.004)|(0.014)|(0.088)|(0.076)||(0.145)|
|financ.open.|-0.0000|0.0002|-0.0000|0.0001|-0.0000|-0.0001|
||(0.889)|(0.376)|(0.986)|(0.813)|(0.936)|(0.867)|
|tradeopen.|0.0048**|0.0025**|-0.0108|-0.0092|-0.0078|-0.0075|
||(0.013)|(0.041)|(0.114)|(0.104)|(0.188)|(0.203)|
|initialgdppc|-0.4602***|-0.1847|0.2503|0.4841|||
||(0.002)|(0.363)|(0.463)|(0.254)|||
|institutions||-0.1350||0.4698|||
|||(0.325)||(0.130)|||
|Constant|7.6560***|5.7346***|1.4239|-2.9863|2.5851***|3.0416***|
||(0.000)|(0.000)|(0.618)|(0.493)|(0.000)|(0.000)|
|Income Group|||||HIGH|HIGH|
|[=all, if empty]|||||INCOME|INCOME|
|# of Obs.|212|177|212|177|182|182|
|R-squared|0.279|0.267|||||
|Prob > F|0.000|0.000|0.000|0.006|0.003|0.003|
|# of countries|||40|39|30|30|

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

16
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29
- 10 -

Most people would agree that, other things being equal, increased consumption of goods and services raises individuals' levels of welfare. Much of what we observe in human behavior supports this assumption. Of course, there may be many factors other than the consumption of goods and services that affect welfare, but since these tend to be much more difficult to measure economists usually restrict themselves to that "portion" of human w^lfare which is attributable to consumption.3/ In welfare ec.'nomics, the starting point for measuring economic welfare is the utility function, which states that welfare rises as the consumption of various goods and services increases. To compare the welfare of different individuals, it is assumed thrt each individual or household possesses the same utility function. If one had data on the consumption of individuals, as distinct from the consumption of households, one could analyze the data using a utility function at the individual level.

Unfortunately, most consumption data are collected at the household level and as such require analysis using a household level utility function. Thus one assumes that household utility is a function of the consumption of goods and services and the composition of household members. The composition "adjustment" is needed to account for the fact that households with differenc compositions require different consumption levels to attain the same level of welfare (e.g. larger households need more Loods and services to attain the

> 3/ Household surveys usually collect data on income as well as consumption, and some studies of welfare focus on the income data. Yet economic theory assumes that it is consumption, not income (which may be saved or given away), that raises welfare, and in most surveys consumption data appear to be more reliable than income data.
# **Non-technical summary**

The Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs) requires a number of countries to strengthen their patent and other intellectual property rights (IPR) systems. Even though policy makers have committed to significant reforms, the implementation of this agreement remains contentious. On one side, many policymakers believe that this mandated policy change will work against their national economic interests, transferring rents to multinational corporate patent holders headquartered in the world’s most advanced countries, especially the United States. Advocates for strong IPR counter that strengthening IPR will accelerate the transfer of technology between countries, ensuring a relatively equal distribution of gains from this policy change.

This paper uses affiliate-level data on U.S. multinational firms and aggregate patent data to test if legal reforms that strengthen IPR increase the transfer of technology by multinationals to reforming countries. Throughout the 1980s and 1990s, a number of countries undertook reforms of their intellectual property systems. Analysis of data from the Bureau of Economic Analysis’s survey of U.S. multinational firms reveals evidence of an increase in technology transfer from parent firms to affiliates located in IPR-reforming countries. More precisely, royalty payments for the use or sale of intangible assets made by affiliates to parent firms increase in the wake of strengthened IPR regimes. This increase is concentrated among the affiliates of firms that make more extensive use of U.S. patents prior to the reforms. In contrast, the evidence of an increase in technology licensing to unaffiliated parties is weaker and is concentrated in those firms that did not possess affiliates in the reforming countries prior to the implementation of reforms.

The view that IPR reform results in an increase in technology transfer among U.S. multinationals is strengthened by the fact that R&D spending by affiliates – traditionally viewed as a complement to technology imports from the parent – increases after IPR reform. Examination of international trends in patent applications also suggests that at least one component of the observable increase in licensing flows is associated with the introduction of new technology in the wake of patent reform. Both the level and rate of change of nonresident patenting increase in the post-reform period. Consistent with earlier work, the results indicate no corresponding reaction in resident patent filings. Interviews with practitioners confirm that the strength of the local IPR regime is one of the critical factors reviewed by U.S.-based multinationals in determining the scope and level of local affiliate operations. Collectively, our results provide evidence that improvements in IPR result in real increases in technology transfers by multinational enterprises.
**Table 2. A** Comparison of the Vetfare Effects of Tariffs **ard Export** Smidi.-s under Different Pricing Conditions

|||CRTS||Con|testab|le3|<br>(tot|**Cournot**<br>al profi|b<br>t)|(dom|**Courrot**<br>estic pro|fit)c|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Sector|CRTS|<br>IRTS|All|||<br>||<br>|<br>||||
|||<br>||CRTS|IRTS|<br>Ail|CRTS|<br>IRTS|<br>Alt|CRTS|IRTS|**All**|
|**Ten-percent tariff**|||||||||||||
|Equivalent<br>variation<br>Sl ffii l|-7|-9|-12|-50<br>|-2<br>|-46<br>|12<br>|60<br>|78|-33|-30|-57|
|cae ecency (tota)<br>Producer goods<br>Consumer d||||-42<br>-14<br>|6<br>-8<br>|-34<br>-21<br>|19<br>9<br>|69<br>33<br>|88<br>42<br>|-27<br>-7|-22<br>-8|-47<br>-14|
|goos<br>Heavy industry<br>||||-24<br>-4|15<br>-1|-8<br>-5|8<br>2|15<br>21|23<br>23|-3<br>-3|-6<br>-6|-8<br>-8|
|Firm entry(+)/exit (-)<br>Producer goods<br>Consubner goods<br>Heavy industry<br>|||||||-5<br>-5<br>-2|-9<br>-1<br>-1|-14<br>-5<br>-12|-1<br>-1<br>0|2<br>4<br>4|1<br>4<br>4|
|Ten-percent export subsidy<br>|||||||||||||
|Equivalent<br>variation<br>Scale efficiency<br>(total)<br>Producer goods<br>Consumer oods|-6|-19|-13|-45<br>-39<br>-14<br>|52<br>74<br>38<br>|27<br>42<br>26<br>|52<br>57<br>23<br>|-168<br>-155<br>-69<br>|-108<br>-97<br>-46<br>|18<br>24<br>8|15<br>25<br>7|36<br>46<br>14|
|g<br>Heav industr||||-15<br>|22<br>|10<br>|21<br>|-48<br>|-27<br>|12|14|24|
|y y<br>||||-10|14|6|13|-38|-24|4|4|8|
|Firm entry(+)/exit<br>(-)<br>Producer goods<br>Consumer oods|||||||-8<br>|23<br>|15<br>|-4|2<br>|-1|
|g<br>Heavy industry|||||||-6<br>-9|11<br>22|6<br>13|-4<br>-5|0<br>1|-4<br>-4|

CRTS = constant returns to scale; IRTS = increasing returns to scale; Note: All figures are basis points. Figures for equivalent variation and scale efficiency are basis points of GOP (e.g., -168 is 1.68% of GOP); figures for entry/exit are basis points of initial number of firms.

a. Pricing according to equation 10. b. Pricing according to equation 11, with firm entry/exit determined by total profits, so that i = 0. c. Same as note b but with firm entry determined by profits on domestic sales, so that sy = 0.
# 3 Estimation Strategy

We collapse the individual level I2D2 data into cells based on five-year age groups, gender, survey year and country. Each cell thus contains averages or shares for each group, such as the share of women age 15-19 who were employed in Ethiopia in 1995. Given large gender differences in labor market outcomes, groups are separated by gender. We construct a panel of 83 lower, middle and upper middle income countries for which at least two waves of data exist. We also restrict the sample to countries for which the data span five or more years, in order to measure medium to longer-term changes in cohort size. We include country dummy variables in the model, to control for time-invariant country characteristics. Therefore, the results indicate the extent to which within-country changes in cohort size are correlated with changes in labor market outcomes.

We use a Dirichlet model to estimate the correlates of a set of shares, such as the categories of primary activity, employment status, and sector.<sup>18</sup> The Dirichlet distribution is a multivariate generalization of the beta distribution and is appropriate for estimating the correlates of a set of proportions that each lie between zero and one, and sum to one. Imposing these restrictions on the joint model makes the estimates more efficient than a series of separate OLS or Logit regressions. The probability density function of the Dirichlet function is given as follows: where y1 through yk indicate vectors of proportions, for example the share of 15 to 19 year old women in each of the four categories primary activity status categories (in which case k equals four). Γ is the gamma function, and α j is a parameter from which the expected value and variance of the proportion can be derived. Taking the log and summing over observations gives the log likelihood function: where N is the number of observations in the datset. We use the dirifit command in Stata, with the alternative parameterization recommended by Buis et al (2011) in the 18 In particular, we use the alternative parameterization of the dirifit command in Stata (Buis, et al, 2010). We chose the dirichlet model over the fractional logit model because we are comfortable excluding the one or two countries for which a dependent variable is equal to zero or one.

13
the 95 percent confidence intervals or even one standard error in many cases—those of actual panel data. Assuming our estimation model is valid, the standard errors on our model-based synthetic panel estimates are also found to be smaller than the sampling-based standard errors of actual panel data (an advantage derived due to the fact that sample sizes of cross-section survey data generally exceed those of the actual panel data).

This paper consists of seven sections. We start in the next section with a brief description of the method introduced in Dang et al., which is followed by our generalization of this method and estimation procedures in Section III; the Monte Carlo simulation exercise is provided in Section IV. We describe the data in Section V before applying our method to investigate poverty dynamics in Section VI, and Section VII concludes.

# **II. Bound Estimates on Poverty Mobility**

Let _xij_ be a vector of household characteristics observed in survey round _j, j_ = 1 or 2, that are also observed in the other survey round for household _i, i_ = 1,..., N. A key requirement is that these household characteristics are time variant. Subject to data availability, these household characteristics can include such clearly time-invariant variables as sex, ethnicity, religion, language, place of birth, and parental education as well as variables that can be converted into time invariant versions based, for example, on information about household heads’ age and education. The vector _xij_ can also include time-varying household characteristics if retrospective questions about the round-1 values of such characteristics are asked in the second round survey.

Let _yij_ then represent household consumption or income in survey round _j_ . The linear projection of household consumption (or income) on household characteristics for each survey round is given by 6
**Figure 2: El Alto: Average daily temperature anomaly 1/1/1995 – 12/3/2008, compared to average monthly temperatures for 1961-90.**

<!-- Start of picture text --> 6.0<br>4.0<br>2.0<br>0.0<br>‐2.0<br>‐4.0<br>‐6.0<br>‐8.0<br>Temperature anomaly (degrees C elsius) compared to the average for 1961‐1990<br>2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source:_ SENAMHI (www.senamhi.gov.bo). El Alto station. _Note:_ The green color denotes summer months (October-April), while the blue color denotes winter months (May-September). The solid black line is a 30 day moving average of daily temperature anomalies.

The colder winters in the already cold highlands could potentially have an adverse effect on the predominantly poor and indigenous population who inhabit the Bolivian highlands, since one of their main worries and limitations on agricultural productivity is frost (Gonzales Iwanciw, Cusicanqui Giles & Aparicio Effen, n.d.). This is the hypothesis that we will formally test and quantify in the following section.

We have adequate precipitation data for 19 meteorological stations in Bolivia. The precipitation anomalies for each station have been plotted in Figure A2 in the Appendix. Table 7 below shows the results of a simple trend regression for each station.

Using a confidence level of 95 percent we find that only five out of the 19 stations have experienced a significant trend in precipitation over the last six decades, and all of them experienced increases. Oruro in the highlands saw a slight increase of about 1.6 mm/decade, whereas Rurrenabaque, Santa Cruz/El Trompillo, Tarija and Trinidad saw somewhat larger increases.

**Table 7: Estimated precipitation trend (mm/decade) for 19 weather stations**

**<u>in Bolivia</u>**

|**Station**|**Trend**|**t-value**|**P-value**|**# of obs.**|
|---|---|---|---|---|
|Camiri|1.21|0.87|0.384|524|
|Charaña|0.82|1.05|0.292|428|
|Cobija|2.54|1.52|0.130|640|

12