rafmacalaba/datause-encoder-data huggingface dataset:
firm level from a sample of 82 developing economies with data from 2002-2019.

To estimate TFPR, we first estimate a log-linearized Cobb-Douglas production function (PF) following Ackerberg, Caves, and Frazer (2015). Although, the Ackerberg, Caves, and Frazer (2015) method, which builds on Olley and Pakes (1996) and Levinsohn and Petrin (2003), assumes an exogenous productivity process, we follow De Loecker (2013) and endogenize TFPR. Thus, TFPR is a function of the adoption of digital business solutions (e.g., email and website) in addition to other firm-choice variables that can also affect firm performance, such as exporting and managerial experience, which have been studied separately in the literature. We validate our data and methodology by replicating the results presented in De Loecker (2013) for the specification that only includes learning-by-exporting effects. The evidence indicates that our estimates of the production function parameters and the coefficients of the endogenous productivity process, covering 82 developing countries, are highly correlated across industries with those reported by De Loecker (2013), for Slovenia.

Assuming an exogenous TFPR would have implied that digital technologies would have no impact on efficiency or sales. This is not only unrealistic but also, from a methodological point of view, would have invalidated the moment conditions needed to identify the coefficients of the production function. In other words, if TFPR is a function of business digitization that does, in fact, affect factor demand, the estimated production-function elasticities would be biased. The sign of the bias is ambiguous, depending on whether digitization is factor-augmenting or factor-saving. If business digitization is factor-augmenting, then TFPR would be underestimated. If improvements in TFPR are factor-saving, the effect of digitization on TFPR would be overestimated.

There are good reasons to expect that firm TFPR is a function of business digitization, as well as of exporting, as in De Loecker (2013), and managerial experience, as in Bloom and Van Reenen (2007) and Bloom and Van Reenen (2010). Using email to connect with clients or suppliers or having a business website to gain online presence can affect TFPR through different channels. On the demand-side, reductions in search and transaction costs affect 3
_6_ _Mexico: The 1997 Pension Reform_ <!-- Start of picture text --> Age Age Rateper10<br>~~~~~ ~~~~~~~~~~72 I<br>71-75 i1990 1 5<br>61 65<br>25XC__-5g 51 55 -~~~~~~~~ ~~~~~~~~ ~ D~13960 ~~~~~~~~~~~~~~N ~ ~ ~ g u 0 66 ~g64 6<br>S,&, 3<br>4145 i.ij;,~ l- 62<br>31 35 60<br>21-25 5<br>56<br>11-15 5<br>1970 1975 1980 1985 1990 1993<br>2j 0-5<br>A o0% 2% 4% 6% 8% 10% 12% 14% 1c% 1o s Lif a petanc s-Frtiotyo Rate<br>-4--Mortality Rate<br>160000 16 growth o contri but ilu<br>H 140000 1 . .<br>N Lo 120000 1<br>I 3.03.5 ______________7<br>ioo100000 ¢02gg | Ratio +Elderly Dependency l |mLieExpecancyMS DisFetiibtyRatel 3.<br>. 8j 0 8 2.5<br>e60000 p6 i 2.0<br>di avng4 40000 o 1.5<br>20000 t e m 1.0<br>0.0 .<br>on hudrd Th 1990 exece 2000 avrg 2010 2020 nul 2030 aeo rwtntenmero esinr e wl 5.% o<br>15 25 35 45 55 65 75 85<br>Dependency Ratio ~~~~~Wage -IMSS Distributin]<br>C ncmiaty lieepcac3fe 6 er anrae.5 Thrfr,beeisate eieen0ilb<br><!-- End of picture text -->

Although Mexico's population pyramid continued to show an important share of young people, lower population growth and mortality rates have resulted in a rapid aging of IMSS contributors. These changes in population dynamics have lead to an increasing dependency ratio in IMSS. In 1960, there were four pensioners per one hundred contributors. By 1994 this ratio had increased to _12.5_ to one hundred. The expected average annual rate of growth in the number of pensioners will be _5.7%_ for the next 20 years, while the most optimistic forecast for growth of contributors will be around 2.6%. Concomitantly, life expectancy after _65_ years has increased. Therefore, benefits after retirement will be greater in the coming years further deteriorating IMSS' financial situation.

The positive implication of these trends is that Mexico, like many developing countries, has the distinct advantage of initiating reform at a time when its demographic profile is relatively young, making the transition more affordable:
in the VLSS data. If the value of female time increases similarly in other types of market work but not in household production, this increased value of time may be behind the shift in female labor supply away from household production.

We next consider changes in male labor supply. The bottom panel of table 2 reports the labor supply participation rates for rural males in 1993 and 1998. Before highlighting the changes across the two years, we first compare the differences in the economic activities of Vietnamese males and females. We focus mostly on data from 1993 (unless the situation has changed in 1998). A comparison of the top and bottom of table 2 reveals several interesting facts about the Vietnamese labor market. First, males and females of various age groups do not differ substantively in the overall participation rates in any kind of work with the exception of girls ages 6-15 being more likely to work than boys of the same age. Second, with the exception of work for wages for ages 6-15, men are more likely than women to work for wages and less likely to work within the household. The latter mostly reflects the lower participation rate of men in household production. Third, the participation rates in agriculture within the household do not differ much between males and females in 1993. However, women ages 16-24 and 25-64 are about 6 to 7 percentage points more likely to work in household agriculture than men in the respective age categories in 1998. These differences mostly reflect a decline in participation of men ages 16-24 in household agriculture and an increase in participation of women ages 25-64 in household agriculture. In addition, men ages 16-24 and 25-64 are more likely to work in agriculture for wages outside the household than women in the respective age groups. Overall, men ages 25-64 are more likely to participate in market work than women ages 25-64. Finally, males work fewer hours than females, mainly because females devote more time to work within the household.

19
and information exchange (KIE); (iv) fiscal incentives (FI); (v) financial products (FP); (vi) business promotion and opportunities (BPO); and (vii) other types of support (O). Because most programs have a broad set of objectives and types of support mechanisms, most programs fall in more than one category. This is important to bear in mind when discussing the evaluation strategy of this paper, and was an important factor in choosing to evaluate programs first by agency and then, when data permitted, by individual program.

**Table 12 Programs and support mechanisms**

|**Program**|**TAT**|**MITD**|**KIE**|**FI**|**FP**|**BPO**|**O**|
|---|---|---|---|---|---|---|---|
|**CIMO-PAC**|**x**|||||||
|**FIDECAP**|**x**|**x**||||**X**||
|**FAMPYME**|**x**|**x**|**x**||**x**||**X**|
|**Fondo PYME**|**x**|**x**|**x**||**x**|**X**|**X**|
|**CETRO-**<br>**CRECE**|**x**|||||**X**|**X**|
|**COMPITE**|**x**|||||||
|**PROSEC**||||**x**||||
|**PROMODE**|**x**|**x**||||**X**||
|**PAT**|**x**|||||||
|**CrediExporta**|||||**x**|||
|**PNAA**|||||||**X**|
|**Sector Funds**||**x**||||||
|**Mixed Funds**||**x**||||||
|**PMT**|**x**|**x**||||||
|**PCI**||**x**|**x**|||||
|**PAIDEC**||**x**|**x**|||||
|**Fiscal**<br>**Incentives**||**x**||**x**||||
|**CONOCER**|||||||**X**|

**Source: Authors’ compilations**

# **3.- PAST EVALUATIONS**<sup>**2**</sup>

There are a few rigorous program evaluations of SME programs in Mexico. Most evaluations have focused on client satisfaction or program outcomes, such as the number of supports given to beneficiaries. The variations in methodologies have made the comparison of results between and within programs difficult. Several external evaluations of SME programs have been undertaken, with the evaluation methodology as agreed upon by the supervising body and the external evaluators. For example, for FAMPYME and FIDECAP, the Ministry of the Economy hired the National Polytechnic Institute ( _Instituto Politécnico Nacional_ —IPN) and

> 2 World Bank (2007) includes a detailed review of evaluations of SME programs in Mexico.

12
body of literature showing effects in both directions (Desai and Jain, 1994; Kishor and Parasuraman 1998; Glick 2002).

It is ironic that in India, as the burden of <u>childbearing</u> has reduced, the burden of <u>childrearing has increased over time (table 2). There could be two main reasons for this. First,</u> intergenerational co-habitation has declined, especially in urban areas, as families have become more nuclear. Women have fewer potential helpers in child care and domestic responsibilities and hence have fewer options (Tuli and Chaudhary 2010). According to NSS data, among those married women (aged 25–55) who reported not being in the labor force in 2011, a significant majority (70 percent) reported staying in the domestic realm, due to compulsion. While the responses did not vary greatly by women’s education level, about one-third of women attending domestic duties only said they would be willing to accept work outside the home, and most of them (75 percent) wanted regular part-time jobs (World Bank 2011).

Second, it is possible that while the number of children per woman has declined, families place greater emphasis on the quality of childrearing and make higher investments in children, both as a route to social mobility and in response to pressure from schools. It is for instance, possible that especially in recent years, mothers in urban areas spend more time tutoring and mentoring their young children, as pre-schools insist on greater involvement of parents in the child’s education. Palriwala and Neetha (2011) suggest that the elite and middle classes have higher expectations of quality from child care providers, while Datta and Konantambigi (2007) argue that while many middle and upper class mothers have few alternative care options, they are pressured to provide early stimulation and education for their children. In another study, Basu and Desai (2012) find children in one-child families to be advantaged: they are more likely to be sent to private schools and English medium schools, and more likely to receive private tutoring in addition to schooling, than children from larger families. Yet women in one-child families are less likely to be employed than those in larger families.

In countries with established welfare systems, or with institutions that actively encourage women’s participation in the labor market, affordable child care is a facilitator of women’s employment, but in places that lack formal care arrangements, families and other informal arrangements are indispensable (Del Boca 2002; Del Boca 2015). Parents may choose grandparents or trusted relatives over low-quality private or public providers, to share care responsibilities (Del Boca et al. 2005; Kuhlthau and Mason 1996; World Bank 2015b). Teenage 6
and households. This is because water tariffs charged to heavy industries (e.g. mining and energy) are substantially below those charged to households and light industries.

Industrial water expenditures are reported in SA’s supply-use tables. Given the value of these expenditures and the total amount of water used by these industries (reported in StatsSA, 2000), we estimate the implied price per unit of water supplied to heavy industry. We then subtracted the cost of supplying this water via the distribution system (see Hassan and Matlanyani, 2004) in order to arrive at the residual (‘profit’) earned by water in the heavy industrial sectors. This was used as a measure of the value-added of a new water factor that used exclusively by the heavy industry water distribution sector. The demand for water by heavy industry in each region is shown in Table 6. Industrial demand is heavily concentrated within a few WMAs, particularly Upper Vaal (Johannesburg), Mvoti-Umzimkulu (Durban), and Crocodile-Marico (Pretoria).

A similar process was used to estimate the value-added of domestic and light industrial water use. Again, water expenditures for domestic and industrial use are reported in the supply-use table. More detailed information on household water expenditures (by rural/urban areas and expenditure quintiles) was taken from the 2000 Income and Expenditure Survey. These expenditures were divided by the total quantity of water demanded by these users (StatsSA, 2000) to arrive at an average price for water. Supply costs were subtracted and the residual was treated as water value-added in the domestic and light industrial water distribution sectors.

In summary, water is incorporated into the SAM and CGE model by (i) separating agriculture in irrigated and rainfed production; (ii) disaggregating all production, labor markets and households across water management areas; (iii) estimating the shadow value of irrigation water for different crops; and (iv) distinguishing between the industrial and domestic water distribution systems.

21
ig

# VII. TRANSITION TO A POST-CMEA WORLD

In addition to the combined shocks of stabilization policy, repercussions of the Gulf crisis, and the collapse of trade with the former GDR, the East European countries (the "Five" since dissolution of East Germany) face a major shock from the dismantling of the CMEA system of trade and payments. The CMEA adjustment shock has, in turn, two components: terms-of-trade adjustment and the downward adjustment of trade flows.

Terms-of-Trade Adjustments. The termination of the CMEA should result in the correction of the historically distorted price ratio between raw materials and manufactures discussed earlier. As the Five are heavily dependent on energy imports, and have averaged more than 80 percent of their energy imports from the USSR, these imports are normally considered the major factor in the Gistortion. For crude oil (indicative also for prices of oil derivatives, gas, electricity, and coal), the numbers below illustrate the situation: Table 6. Crude Oil Prices, 1988-90 (Arabian light, in $/b)

||1988|1989||1990||1991|
|---|---|---|---|---|---|---|
||_|;|__<br>July|August<br>—|Total (est.)||
|Yearaverage|13.7|16.3|14.8|25.8|23.2|--|
|Preceding five year|19.8|17.5|-|~|16.9|16.8|
|__average|__||||||

Source: World Bank.

Under CMEA’s pricing rule, the reference price would drop below the spot world market level only during the second half of 1990, in the wake of the Gulf crisis. This suggests that the oil pricing formula has not been the prime cause of Soviet terms-of-trade losses. Conversely, if manufactured exports of the Five to the USSR in exchange for crude oil had long been priced at world market levels, the USSR would not have had terms-of-trade losses prior to the Gulf crisis but, rather, a moderate gain. The primary cause of losses must therefore have been the excessively high price level for manufactures. A forthcoming study of the OECD estimates the terms-of-trade effects for the 1980s as shown in Table 7.

Up to 1989, the numbers represent realized terms-of-trade gains of the Five vis-a-vis the USSR. For 1990, however, the numbers indicate the gains that would occur if delivery volumes of 1989 would remain unchanged. But since deliveries declined in 1990, while in the second half of the year Soviet oil shipments to CMEA countries were increasingly billed in actual CC prices, the balance of payment losses of the Five from abrogation of the CMEA rules would be substantially lower than the elimination of the “implicit subsidies," shown above, suggests. Even more elusive would be an extrapolation to 1991, due to expected further contraction of CMEA trade and particularly of crude oil deliveries by the USSR. Nevertheless, the orders of magnitude leave little doubt that the combined effect of dissolving the CMEA regime and higher crude oil prices will be a major shock for the Five.
25 by the Central Statistical Office.<sup>For 1985 and 1987, It Is</sup> possible to break overall data<sup>Into three social groups (workers,</sup> farmers and pensioners); for earlier years only results<sup>for total</sup> population and pensioners' louseholds<sup>could be calculated. Number</sup> of income groups varies from<sup>8 to 12 (also ranked by per capita</sup> household Income).

I1There are no elements that would inidicate<sup>that the surveys</sup> have in general become less<sup>reliable and/or displaya</sup> systematic bias throughout the period under<sup>study. The surveys, however, do</sup> have several flaws. For example,<sup>Polish samples have a response</sup> rate of only about 70 percent.<sup>Director of the Department of</sup> Household Budgets in Polish Central<sup>Statistical Office points out</sup> that rejections are the highest<sup>among the poor and the well-off</sup> households (Mr. Kordos's personal<sup>communication to the author).</sup> Incidence of poverty (even In relative<sup>terms) may therefore be</sup> somewhat understated. In the<sup>case of Yugoslavia, surveys Include</sup> as income items those which do not<sup>belong there (e.g. revenues</sup> from the sale of assets). Since<sup>this item is shown together with</sup> Income from the lease of assets<sup>It Is Impossible to deduct It from</sup> Income. Income Is thus overestimated,<sup>and poverty Incidence</sup> biased downward. Income data<sup>in the surveys In all three</sup> countries fail to adequately<sup>reflect interest Income from</sup> financial assets. When real return<sup>on these assets (including</sup> foreign exchange) was slightly<sup>negative or nil, the bias was not</sup> very significant. In the<sup>1980's, however, the real rate of return</sup> on foreign exchange was almost continuously positive and In some years it was very high. On<sup>the other hand, real return on</sup> domestic deposits, particularly In<sup>Poland and Yugoslavia which</sup> experienced high Inflation,<sup>swung violently: In some years It was</sup> strongly negative while in others<sup>It was around zero. Due to the</sup> lack of data on the distribution<sup>of financial assets by income</sup> groups It is impossible to<sup>adjust income for this element.</sup> Consequent underreporting of Income creates an upward<sup>bias In</sup> poverty statistics (even If the<sup>bias may not be high</sup> since poor households typically have few<sup>financial assets).</sup>
# Revisiting between-group inequality measurement: An Application to the dynamics of caste inequality in two Indian villages

## Peter Lanjouw and Vijayendra Rao<sup></sup>

_Development Research Group, World Bank 1818 H. St. NW, Washington D.C., 20433_ Key words: inequality, decomposition, India, caste, village study

>  We are grateful to participants at the 2008 Conference on Rethinking Ethnicity and Ethnic Strife in Budapest, as well as to Martin Ravallion and two anonymous referees for useful comments and suggestions.  The views in this paper are those of the authors and should not be interpreted as those of the World Bank or any of its affiliates.  Contact author:  Peter Lanjouw, <u>planjouw@worldbank.org, (tel. 202-473-2724)</u>
# TABLE A4: ITT effects on attrition

||(1)<br>Child|(2)<br>Household|(3)<br>Mother|(4)<br>Father|
|---|---|---|---|---|
|Panel A|||||
|ITT|0.022<sup>_∗_</sup>|0.015|0.010|0.016|
||(0.013)|(0.012)|(0.014)|(0.015)|
|Observations|2803|2803|2803|2803|
|Mean control|0.089|0.071|0.115|0.199|
|Panel B|||||
|Nutrition & Stimulation|0.018|0.012|0.000|0.006|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Nutrition|0.039<sup>_∗∗_</sup>|0.025<sup>_∗_</sup>|0.029|0.035<sup>_∗_</sup>|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Stimulation|0.017|0.009|0.015|0.025|
||(0.017)|(0.015)|(0.018)|(0.019)|
|Observations|2803|2803|2803|2803|
|Mean control|0.089|0.071|0.115|0.199|
|**P-values Statistical Signifcance Test**|||||
|Nutrition & Stimulation = Nutrition|0.261|0.389|0.136|0.144|
|Nutrition & Stimulation = Stimulation|0.952|0.890|0.460|0.362|
|Stimulation = Nutrition|0.245|0.325|0.474|0.610|
|Panel C|||||
|ITT Leader|-0.004|-0.001|-0.010|-0.013|
||(0.017)|(0.015)|(0.021)|(0.016)|
|Observations|2332|2332|2332|2332|
|Mean Control|0.105|0.082|0.125|0.219|

_Note:_ The dependent variable measures the probability of attrition for children’s test outcomes (column 1), and information obtained from the main caregiver in the household (2), mothers (3) and fathers (4). Coefficients in panel A and B are estimated on the full sample of children less than 7 years old at baseline using equation 1 and equation 2 (including controls for the stratification variables, child age and gender, enumerator fixed effects, as well as village fixed effects). Coefficients in panel C are estimated on children less than 7 years old from non-leader households only using equation 3 (including controls for the household-level treatment, the average level of education of the leaders, as well as the level of education of the main caregiver, whether there is a male caregiver in the household, child age and gender, and test-administrator fixed effects). The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1, _∗∗_ p<0.05, _∗∗∗_ p<0.01 52
status of the heads of household. Finally, the flexibility provided in setting assumptions about changes in inequality comes at the price of an increased uncertainty in the results.

The two key inputs are country-specific unit record household-level data representing the distribution of living standards for a base year and a set of user-supplied projection parameters characterizing the paths of growth. Household-level data involve six variables that must be submitted to the simulator in the following order: (1) a household identifier, (2) monthly per capita consumption in local currency units for the base year, (3) household weight (or a population expansion factor), (4) an urban dummy, (5) household size, and (6) sector of employment of the household head.

For each year within the projection horizon, the per capita consumption for each household is computed recursively using a growth rate that is equal to the rate of per capita output. The latter is calculated as the real growth in gross domestic product (GDP) in the sector of employment of the head of household, minus the rate of population growth in that sector. For the first three sectors, the sectoral population growth rate is computed from the overall population growth rate and an adjustment factor that depends on the share of each sector in total employment and sector-specific growth rates of employment. The population in the residual sector is assumed to grow at the same rate as the overall population. Household weights are also adjusted recursively using sectoral rates of population growth.

Assuming that inequality within sectors remains constant, _PovStat_ computes the following indicators for the forecast horizon: (1) poverty measures of the Foster–Greer– Thorbecke family, (2) the number of people below the poverty line, (3) mean monthly per capita consumption, (4) Gini coefficients, (5) two inequality measures of the generalized entropy family, and (6) the variance of log consumption per person.

In general, the simulation framework offers the user the opportunity to control the simulation process by setting the following parameters: (1) the forecast horizon, (2) the poverty line, (3) the base and survey years, (4) the survey year population, (5) the country’s purchasing power parity exchange rate, (6) the base and survey year consumer price indexes (CPIs), (7) the sectoral output growth rates for each projection year, (8) the population growth rates and employment growth rates for each projection year, (9) the survey year sectoral GDP and employment shares, (10) the GDP deflator and the CPI for 14