rafmacalaba/datause-encoder-data huggingface dataset:
unemployment and vacancies are relatively high (H1), the vacancy rate is low relative to inflow (H2) and inflow is relatively sizable (H3).

In view of this background, one could provide analytical information on unemployment and its dynamics by focusing on either inflows (job destruction in firms) or outflows (matching of the unemployed and vacancies). In our analysis, we use the newly collected district-level data on individuals and vacancies to identify the extent to which the countries of Central Europe exhibit different levels of efficiency in matching. In a companion paper (Münich and Svejnar, 2007), we focus on inflows and shifts in unemployment and vacancies.

The paper is structured as follows: We start in Section 2 by presenting our conceptual framework of a matching function model and a brief survey of the literature. In Section 3 we discuss our estimating framework and explain how we overcome some of the principal problems of the existing studies. In Section 4 we describe our data and the implementation of our econometric model. In Section 5 we present basic statistics and our econometric estimates. We conclude in Section 6.

# **2. Conceptual Framework and Existing Literature**

It is useful to start by noting that most of the relevant literature focuses on the matching function – a relationship that describes the complex pairing of the unemployed _U_ and vacancies _V_ in creating outflow _O_ from unemployment into jobs: _O_ = _M(U,V)_ . (1) Theories of search and matching generally do not imply a particular functional form of the matching function and most studies use the Cobb-Douglas function, which may be written in a deterministic form of discrete observations as<sup>8</sup>

> the rise occurs only in the late 1990s rather than at the start of the transition in the early 1990s.

> 8 There are of course exceptions. Pissarides (1990) for instance shows that in his theoretical model the Cobb-Douglas

− 9 −
average. The food price-elasticity can take extreme values of -.3 or -.1, with a mid value of -.2.

The relationship between international and domestic food prices is country-specific. The transmission of high world prices to domestic prices depends on the depth of international markets for different commodities, countries’ exchange rates variations against the US dollar during the period, the degree of openness of the different economies and domestic policies in response to the shock. Domestic relative food prices are affected by inflation of non-food prices, which also varies by country.

Evidence on price transmission mechanisms is limited to few countries and commodities.<sup>6</sup> But incomplete pass-through combined with differentiated price inflation across commodities is consistent with the observation of relatively moderate food inflation in many developing countries relative to the overall consumer price index. Table 1 shows the changes in food relative prices in a few countries of different income levels. In all cases, food price inflation is significantly lower than international levels. The FAO obtains the same result with a different sample of countries over the period 2007-8.<sup>7</sup> Based on this range of observed relative price changes over the period 2005-8, the estimations reported in the next section considered three alternative scenarios for food relative price changes: 10%, 20% and 30%.

**Table 1 Hetero** **<mark>g</mark> eneit** **<mark>y</mark> in the chan** **<mark>g</mark> es in relative** **<mark>p</mark> rices** **<mark>,</mark> 2005-2008**

||CPI inflation<br>(%)|Food<br>inflation<br>(%)|Change in<br>food relative<br>prices*(%)|
|---|---|---|---|
|India|16|22|12|
|Tanzania|32|39|14|
|Colombia|13|22|18|
|Nigeria|18|17|-2|
|Bolivia|23|38|30|
|Bangladesh|17|21|8|
|Mexico|9|15|12|
|Chile|12|20|16|
|Egypt|24|38|28|
|Pakistan|33|42|18|

*The non-food price inflation is estimated assuming a weight of 50% for food in the overall consumer price index. The change in relative prices is the difference between food and non-food inflation. Source: calculations based on data from national statistical offices.

6 FAO research suggests that the pass-through of the world price of rice in US$ to domestic markets in six Asian countries currencies over the period Q4-2003 to Q4-2007 ranged between 6 and 64 percent, or onethird on average (FAO, 2008). Baffes and Gardner (2003) shows that pass-through effects are weak and they vary across countries and commodities. 7 See FAO (2008).
Figure 1 continued Attainment profiles for ages 15 to 19, by economic group: Eastern and Southern Africa <!-- Start of picture text --> Comoros 1996 Kenya 1993 Malawi 1992<br>- - -. ---------- -. - -.--- ..... --.- .----------- -------------<br>0~8 0.8 -0.8-<br>s 0~6 ;0 eg 06- 0g.6<br>024 02 4 02<br>0 20- 2 0 2<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>t.~~~~~~~ f~~~~~~~~~~~ I<br>Namlbia 1992 Rwanda 1992 Tanzania 1991-92<br>ll I~ ~ ............................................................---- .r....= '<br>0.8- 08- 0.8 -<br>O S606 \0 6<br>IC 4 0 404<br>02 - 0.2 0.2<br>O 0 0-<br>1 2 3 4 5 6 7 8 9 I 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>a. 04 -- - - 0.04- --- - :04 \<br>Tanzania 1996 Uganda 1995 Zambia 1992<br>0 8 0.84 1 0.81-<br>06 0.606<br>a. 4 0.4a. 04<br>02 -- - 02 0.2-<br>0 0 _ _ _ _ _ o__<br>I 2 3 4 5 6 7 8 9 I 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Grade Grade Grade<br>* Poorest u Middle A Richest<br><!-- End of picture text --> 15
Quite often, though, the results of such regressions are used to draw “policy implications” which, in my view, are not implications at all. Below, I offer a detailed explanation as to why. For now, though, let me just say that it is common in the literature to deal with two problems – bias in the estimated coefficients and correlation but not causation – with one solution, namely, instrumental variables estimation. Deaton (1997) has discussed the option of instrumenting for right hand side variables, presenting not only the advantages of instrumental variables methods but also their limitations. The limitations have been taken up further in Bound, Jaeger, and Baker (1995) and Stock and Staiger (1997). As Bound, Jaeger, and Baker put it so graphically in the title of the working paper version of their paper, “the cure can be worse than the disease.” That observation does not, however, stop labor economists from instrumenting at virtually every opportunity.

Notwithstanding the possible misuses of micro data regressions, it would be foolhardy in the extreme to dismiss them. Such regression coefficients are very useful information to have. My point is that we need to be suitably modest in drawing inferences from them.

3. Panel data. Panel data analysis is a relatively new and promising approach in developing countries. Panel studies follow the same individuals or families over time. Such studies enable development processes to be analyzed in ways that analysis of comparable cross-sections cannot. Two areas in which a great deal of policy-relevant knowledge has been gained are the effects of policy interventions on treated individuals and the determinants of income and earnings mobility.

Studies of policy interventions estimate such measures as the average treatment effect, the average treatment effect for those treated, and the distribution of treatment effects for different individuals. Heckman (2001) and Ravallion (2007) present comprehensive overviews of the relevant issues. See also Cunha, Heckman, and Navarro (2006).

Empirical research of this type has proved informative in the context of developing countries. One example is the study by Kugler (2005) of a policy change in Colombia. In 1990, Colombia replaced its earlier system of severance payments with a new system of severance payments savings accounts (SPSAs). Under the new system, 36
time doing practical work. About 80 percent of the teachers in VTE institutions were Hunderqualified" to teach technical subjects (Paulston 1971, World Bank 1973). Moreover, many students never intended to be technicians when they enrolled in VTE programs in the first place but, instead, did so in the (usually futile) hope of continuing their study later at a university.

Given tho deficiencies in facilities and personnel and the prevailing preferences of those enrolled, many VTE institutions had little real choice but to offer programs that resembled those given in the general educational streams, with weight given to the liberal arts and relatively little to those technical subjects linked to specific occupational outcomes. Only a small proportion of VTE graduates obtained employment in areas directly related to their areas of VTE concentration.

In response to the perceived problems surrounding VTE, teru's military government announced a program of educational reform in 1972. The reforw was designed to enhance student employment prospects by raising the quality of instruction and increasing education's relevance to the labor market.<sup>3</sup> The crux of the program was its 'diversified' curriculum, i.e., a merging of the academic and technical streams.

More specifically, the reform mandated nine years of 'basic" education, which was supposed to replace the primary and lower secondary cycles of the traditional system. Toward the end of this basic education, all pupils were to be introduced to practical work, after which all continuing students would attend new three-year schools known as Escuelas Superiores de Educacion Profesional (ESEPs). The curriculum of the ESEPs was to be highly practical, with some academic courses included but far fewer than in the traditional system of general education. Only after secondary education would students under the new system be split into separate streams. Some of the continuing students would go to university, and the rest would enter Second-Cycle (Ciclo II) ESEPs.<sup>4</sup> The reform program was implemented slowly, and it was relatively short-lived. Nonetheless, it did have an impact on Peruvian education. First-Cycle ESEPs increased in number, from just nine schools and about 9,000 students in 1975 to 72 schools and 30,000 students in 1981 (Gray 1983; World Bank 1982). At the post-secondary level, three Second-Cycle 3 Earlier, in 1961, private employers in the manufacturing sector had organized their own non-formal system to provide training for those leaving the formal education system. This system, known as the Servicio Nacional de Aprendizaie y Trabaio Industrial (SENATI), continues to this day. SENATI centers offer training programs in a variety of technical areas. During the first decade of its operation, over 100,000 industrial workers participated in SENATI courses (World Bank 1973).

4 Not part of the or.ginal reform of 1972, the Second-Cycle ESEPs were introduced in the late 1970s by the succeeding military administration. They were designed to replace the old IETSs.

4
Lucas, A. M., & Mbiti, I. M. (2012). “Access, Sorting, and Achievement: The Short-Run Effects of Free Primary Education in Kenya.” _American Economic Journal: Applied Economics_ , 4(4), 226–225.

Malamud, O., & Pop-Eleches, C. (2011). “Home Computer Use and the Development of Human Capital.” _Quarterly Journal of Economics_ , 126, 987-1027.

McEwan, P. (2012). “Improving Learning in Primary Schools of Developing Countries: A Meta-Analysis of Randomized Experiments.” _Review of Educational Research_ 20 (10): 1 –42.

Mizala, A., & Urquiola, M. (2013). “School Markets: The Impact of Information Approximating Schools’ Effectiveness.” _Journal of Development Economics_ , 103, 313-335.

Mo, D., Swinnen, J., Zhang, L., Yi, H., Qu, Q., Boswell, M., & Rozelle, S. (2012). “Can One Laptop per Child Reduce the Digital Divide and Educational Gap? Evidence from a Randomized Experiment in Migrant Schools in Bejing.” Rural Education Action Project Working Paper 233, Stanford, CA.

Mo, D., Zhang, L., Lui, R., Qu, Q., Huang, W., Wang, J., Qiao, Y., Boswell, M, & Rozelle, S. (2013). “Integrating Computer-assisted Learning into a Regular Curriculum: Evidence from a Randomized Experiment in Rural Schools in Shaanxi.” Rural Education Action Project Working Paper 248, Stanford, CA.

Muralidharan, K., & Sundararaman, V. (2011). “Teacher Performance Pay: Experimental Evidence from India.” _The Journal of Political Economy_ , 119, 39-77.

Muralidharan, K., & Sundararaman, V. (2010). "The Impact of Diagnostic Feedback to Teachers on Student Learning: Experimental Evidence from India." _The Economic Journal_ , 120, no. 546: F187-F203.

Murnane, R. J., & Ganimian, A.J. (2014). “Improving Educational Outcomes in Developing Countries: Lessons from Rigorous Evaluations.” Unpublished manuscript.

Murnane, R. J., & Ganimian, A.J. (2014a). “Improving Educational Outcomes in Developing Countries: Lessons from Rigorous Evaluations.” NBER Working Paper No. 20284, National Bureau of Economic Research, Cambridge, MA.

Nguyen, T. (2009). “Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar.” MIT Working Paper, Massachusetts Institute of Technology, Cambridge, MA.

Nwagbo, C. (2006). “Effects of Two Teaching Methods on the Achievement in and Attitude to Biology of Students of Different Levels of Scientific Literacy.” _International Journal of Educational Research_ , 45, 216– 229.

Ozier, O. (2014). “Exploiting Externalities to Estimate the Long-term Effects of Early Childhood Deworming.” World Bank Policy Research Working Paper 7052, World Bank.

Petrosino, A., Morgan, C., Fronius, T. A., Tanner-Smith, E. E., & Boruch, R. F. (2012). “Interventions in Developing Nations for Improving Primary and Secondary School Enrollment of Children: A Systematic Review.” _Campbell Systematic Reviews_ 2012:19.

22
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 4.4 Foreign investors in the SADC telecommunications sector**

||France|mbourg)|MTC|MTN|Orascom|Vodafone|Portugal||l|Note|
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Telecom|Millicom (Luxe|(Zain, Kuwait)|(South Africa)|(Egypt)|(UK)|Telecom|Other|Tota||
|Angola|||||||25%||**1**|Also has foreign investors in fixed-line<br>market|
|Botswana|54%|||53%|||||**2**||
|Congo, Dem. Rep. of||100%|99%|||51%||51%|**4**|Other = ZTE (China)|
|Lesotho||||||88%||70%|**1**|Other = Econet (Zimbabwe)|
|Madagascar|72%||100%|||||68%|**3**|Other = Distacom (Hong Kong)|
|Malawi|||100%||||||**1**||
|Mauritius|40%|50%|||||||**2**|Also has foreign investment in second<br>fixed-line operator|
|Mozambique||||||98%|||**1**||
|Namibia|||||100%||34%||**2**||
|Seychelles||||||||100%|**2**|Other(s) = Cable & Wireless (UK);<br>Bharti (India)|
|South Africa||||100%||65%||75%|**3**|Other = Oger (Saudi Arabia)|
|Swaziland||||30%|||||**1**||
|Tanzania||100%|60%|||65%||51%|**4**|Other = Etisalat (UAE)|
|Zambia|||79%|100%|||||**2**|Incumbent operator is currently<br>undergoing privatization|
|Zimbabwe|||||60%||||**1**||
|Total|**3**|**3**|**5**|**4**|**2**|**5**|**2**|**6**|||

_Source:_ Derived from Ampah and others 2009. _Note:_ UAE = United Arab Emirates.

Until recently, very few countries in the SADC region were connected to an undersea submarine cable. Connectivity to a submarine cable is critical to achieve seamless transmission of voice, text, data, and video communications traffic. Even though several transmission mediums are available, a fiber optic cable is preferred because of its ability to transmit large volumes of data rapidly, and to thus affect Internet access and speed. The main international cable in the region is the South Atlantic 3 (SAT3)/West Africa Submarine Cable (WASC)/South Africa Far East (SAFE), which extends from Malaysia to South Africa and then up the west coast of Africa to Portugal and Spain (figures 4.3). Angola, Mauritius, and South Africa have access to the SAT-3 cable. As of July 2009, Mozambique and Tanzania have connected to SEACOM (the South Africa–East Africa–South Asia fiber optic cable).<sup>6</sup> Madagascar has connected to the LION cable (Lower Indian Ocean Network). Plans to connect the remaining

> 6 <u>http://www.seacom.mu. East Africa connected to the cable first.</u>

54
of days on which luxury food items—meat, fish, rice and onions—were consumed in the week prior to the survey are reported in columns (2) and (4). Onions were a key commodity that we asked about because they are a non-essential food item largely purchased on the market during the lean season if they can be afforded. Information on how well individuals managed shocks that occurred was collected during the end line survey after harvest and these measures are examined in columns (4) and (6).

Those offered the savings consumed less well one month after the experiments than those in the insurance treatment (table 11, column 2). The difference could in part be driven by the fact that investments in insurance were lower than investments in savings which resulted in individuals in the insurance treatment taking home more unrestricted cash than individuals in other treatments. Indeed, this difference is no longer present after harvest (column 4). Individuals offered the insurance product were better able to manage shocks that had occurred (column 6), 4 percentage points more than the control group. This is consistent with the finding that these individuals produced more on average and had more savings.

**Table 11: Impact of insurance (ITT) on consumption and managing shocks**

||(1)<br>After o|(2)<br>ne month|(3)|(4)<br>Af|(5)<br>ter harvest|(6)|
|---|---|---|---|---|---|---|
||Degree of<br>food<br>insecurity|Ate meat,<br>fish, rice or<br>onions|Degree of<br>food<br>insecurity|Ate meat,<br>fish, rice or<br>onions|Days before<br>buying<br>medicine|Used household<br>liquidity to manage<br>shock|
|Insurance|0.07|1.66|-0.12|-0.11|-0.01|0.04|
||[0.09]|[0.67]**|[0.18]|[0.72]|[0.24]|[0.02]*|
|Burkina Faso|0.36|-6.68|0.17|-12.85|-1.42|0.05|
||[0.16]**|[0.89]***|[0.25]|[0.98]***|[0.20]***|[0.02]**|
|Male|-0.38|0.57|-0.28|0.68|-0.46|-0.01|
||[0.12]***|[0.78]|[0.24]|[0.95]|[0.23]*|[0.02]|
|Observations|804|796|804|791|804|794|
|R-squared|0.17|0.19|0.21|0.31|0.04|0.02|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1.

**Table 12: Impact of savings (ITT) on consumption and managing shocks**

||(1)<br>After on|(2)<br>e month|(3)|(4)<br>Af|(5)<br>ter harvest|(6)|
|---|---|---|---|---|---|---|
||Degree of<br>food<br>insecurity|Ate meat,<br>fish, rice or<br>onions|Degree of<br>food<br>insecurity|Ate meat,<br>fish, rice or<br>onions|Days before<br>buying<br>medicine|Used household<br>liquidity to<br>manage shock|
|Agricultural savings|-0.16|-1.25|-0.32|-0.59|0.13|0.00|
||[0.12]|[0.52]**|[0.21]|[0.82]|[0.26]|[0.02]|
|Low commitment|0.17|0.74|-0.01|-0.24|0.48|-0.03|
|savings|[0.10]|[0.51]|[0.25]|[0.67]|[0.69]|[0.02]|
|Low commit*|-0.05|1.27|0.05|0.97|-0.41|0.02|
|Burkina Faso|[0.20]|[1.11]|[0.36]|[1.59]|[0.68]|[0.03]|
|Burkina Faso|0.40|-7.58|0.08|-12.76|-1.34|0.04|
||[0.19]**|[0.91]***|[0.33]|[1.08]***|[0.26]***|[0.02]*|

24
mentioned here. It deals with the choice between a long growing season, X1, versus a shorter growing season, X2, with the quantity of pesticide at some predetermined level in either situation (Harper and Zilberman, 1989). If the maximum pest population, k (XO), is written as the product of an exogenous level of pest pressure, K, and a pest stimulus effect, *(X.), then the first order condition (FOC) which will determine the choice of X is as follows: If _Pefe(X,)_ ( 1- _D[K*(X 1_<sup>_)[_1 -</sup><sup>_g_</sup> _1_<sup>_(Z_</sup> _1_<sup>_)J1)_</sup> _-C(X 1 )_ (1) _> Pcf(X 2_<sup>_)_(.-1</sup><sup>_DK*(X_</sup> _2_<sup>_)[_2-</sup><sup>_g_</sup> _1_<sup>_(Z)J1])_</sup> _-C(X 2 )_ then choose X = X,, otherwise choose X2. Note that C(.) refers to the costs of cultivation, Z refers to the application of pesticide and the g(z) function gives the fraction of pests killed via application of pesticides.

The main message from this comparison is that although the potential yield is higher for the longer growing season, so is the pest population and so is the percentage of the potential yield lost through damage. As the preexisting level of pest pressure, K, increases, a threshold level of K will be reached above which the optimal growing season switches .from long to short. Myopic pest management, based on chemical control alone, will fail to perceive this.

15
grade; ADU randomly selects which specific test papers are offered in a given school. The SLQAT is transported in sealed envelopes and opened only in the presence of the school administrator and teachers. At that time, both the PEF inspection team and the school learn which grades are selected for the SLQAT. Test invigilation is coordinated by the ADU member assigned to the PEF inspection team. The completed tests are transported back to PEF headquarters and graded by ADU. The school passes the SLQAT if at least 67% of tested students score 33% or higher.

Figure 2 presents the numbers of schools which passed each step of the program entry process. Out of the 1,070 and 1,430 schools that had submitted properly filled-in applications and were inspected in phases 3 and 4, respectively, 799 (75%) and 872 (61%) schools were offered the SLQAT. Of the schools that were offered the SLQAT, 514 (64%) and 431 (49%) schools achieved the minimum pass rate and became eligible for the program.<sup>14</sup> In terms of program take-up, PEF reports that 482 (94%) and 425 (98%) of the schools that passed the SLQAT in phases 3 and 4 respectively signed the formal program participation agreement.

_Continuing benefit eligibility rules_ : The program participation agreement stipulates several conditions for maintaining benefit eligibility. The conditions that are stringently applied by PEF are (1) schooling is offered to students without charging them any fees (and displaying the freeschooling status prominently on a PEF-issued signboard outside the school gate) and (2) participation of the program school in the QAT and that at least 67% of the tested students score 40% or higher on the QAT. A one-time violation of these conditions typically results in a warning and the capping of enrollment figures for the subsidy payment until the next QAT round. A second violation results in the permanent disqualification of the school with immediate

> 14 Our own tallies based on the SLQAT data deviate slightly from the above numbers: we find that 796 and 856 schools took the SLQAT and, out of these, 511 and 432 schools achieved the minimum pass rate in phases 3 and 4, respectively.

13